Add files using upload-large-folder tool

qasper-0000/instruction.md

@@ -0,0 +1,149 @@
+Name of Paper: Minimally Supervised Learning of Affective Events Using Discourse Relations
+
+Question: What is the seed lexicon?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Proposed Method",
+    "Proposed Method ::: Polarity Function",
+    "Proposed Method ::: Discourse Relation-Based Event Pairs",
+    "Proposed Method ::: Discourse Relation-Based Event Pairs ::: AL (Automatically Labeled Pairs)",
+    "Proposed Method ::: Discourse Relation-Based Event Pairs ::: CA (Cause Pairs)",
+    "Proposed Method ::: Discourse Relation-Based Event Pairs ::: CO (Concession Pairs)",
+    "Proposed Method ::: Loss Functions",
+    "Experiments",
+    "Experiments ::: Dataset",
+    "Experiments ::: Dataset ::: AL, CA, and CO",
+    "Experiments ::: Dataset ::: ACP (ACP Corpus)",
+    "Experiments ::: Model Configurations",
+    "Experiments ::: Results and Discussion",
+    "Conclusion",
+    "Acknowledgments",
+    "Appendices ::: Seed Lexicon ::: Positive Words",
+    "Appendices ::: Seed Lexicon ::: Negative Words",
+    "Appendices ::: Settings of Encoder ::: BiGRU",
+    "Appendices ::: Settings of Encoder ::: BERT"
+  ],
+  "paragraphs": [
+    [
+      "Affective events BIBREF0 are events that typically affect people in positive or negative ways. For example, getting money and playing sports are usually positive to the experiencers; catching cold and losing one's wallet are negative. Understanding affective events is important to various natural language processing (NLP) applications such as dialogue systems BIBREF1, question-answering systems BIBREF2, and humor recognition BIBREF3. In this paper, we work on recognizing the polarity of an affective event that is represented by a score ranging from $-1$ (negative) to 1 (positive).",
+      "Learning affective events is challenging because, as the examples above suggest, the polarity of an event is not necessarily predictable from its constituent words. Combined with the unbounded combinatorial nature of language, the non-compositionality of affective polarity entails the need for large amounts of world knowledge, which can hardly be learned from small annotated data.",
+      "In this paper, we propose a simple and effective method for learning affective events that only requires a very small seed lexicon and a large raw corpus. As illustrated in Figure FIGREF1, our key idea is that we can exploit discourse relations BIBREF4 to efficiently propagate polarity from seed predicates that directly report one's emotions (e.g., \u201cto be glad\u201d is positive). Suppose that events $x_1$ are $x_2$ are in the discourse relation of Cause (i.e., $x_1$ causes $x_2$). If the seed lexicon suggests $x_2$ is positive, $x_1$ is also likely to be positive because it triggers the positive emotion. The fact that $x_2$ is known to be negative indicates the negative polarity of $x_1$. Similarly, if $x_1$ and $x_2$ are in the discourse relation of Concession (i.e., $x_2$ in spite of $x_1$), the reverse of $x_2$'s polarity can be propagated to $x_1$. Even if $x_2$'s polarity is not known in advance, we can exploit the tendency of $x_1$ and $x_2$ to be of the same polarity (for Cause) or of the reverse polarity (for Concession) although the heuristic is not exempt from counterexamples. We transform this idea into objective functions and train neural network models that predict the polarity of a given event.",
+      "We trained the models using a Japanese web corpus. Given the minimum amount of supervision, they performed well. In addition, the combination of annotated and unannotated data yielded a gain over a purely supervised baseline when labeled data were small."
+    ],
+    [
+      "Learning affective events is closely related to sentiment analysis. Whereas sentiment analysis usually focuses on the polarity of what are described (e.g., movies), we work on how people are typically affected by events. In sentiment analysis, much attention has been paid to compositionality. Word-level polarity BIBREF5, BIBREF6, BIBREF7 and the roles of negation and intensification BIBREF8, BIBREF6, BIBREF9 are among the most important topics. In contrast, we are more interested in recognizing the sentiment polarity of an event that pertains to commonsense knowledge (e.g., getting money and catching cold).",
+      "Label propagation from seed instances is a common approach to inducing sentiment polarities. While BIBREF5 and BIBREF10 worked on word- and phrase-level polarities, BIBREF0 dealt with event-level polarities. BIBREF5 and BIBREF10 linked instances using co-occurrence information and/or phrase-level coordinations (e.g., \u201c$A$ and $B$\u201d and \u201c$A$ but $B$\u201d). We shift our scope to event pairs that are more complex than phrase pairs, and consequently exploit discourse connectives as event-level counterparts of phrase-level conjunctions.",
+      "BIBREF0 constructed a network of events using word embedding-derived similarities. Compared with this method, our discourse relation-based linking of events is much simpler and more intuitive.",
+      "Some previous studies made use of document structure to understand the sentiment. BIBREF11 proposed a sentiment-specific pre-training strategy using unlabeled dialog data (tweet-reply pairs). BIBREF12 proposed a method of building a polarity-tagged corpus (ACP Corpus). They automatically gathered sentences that had positive or negative opinions utilizing HTML layout structures in addition to linguistic patterns. Our method depends only on raw texts and thus has wider applicability.",
+      ""
+    ],
+    [
+      ""
+    ],
+    [
+      "",
+      "Our goal is to learn the polarity function $p(x)$, which predicts the sentiment polarity score of an event $x$. We approximate $p(x)$ by a neural network with the following form:",
+      "${\\rm Encoder}$ outputs a vector representation of the event $x$. ${\\rm Linear}$ is a fully-connected layer and transforms the representation into a scalar. ${\\rm tanh}$ is the hyperbolic tangent and transforms the scalar into a score ranging from $-1$ to 1. In Section SECREF21, we consider two specific implementations of ${\\rm Encoder}$.",
+      ""
+    ],
+    [
+      "Our method requires a very small seed lexicon and a large raw corpus. We assume that we can automatically extract discourse-tagged event pairs, $(x_{i1}, x_{i2})$ ($i=1, \\cdots $) from the raw corpus. We refer to $x_{i1}$ and $x_{i2}$ as former and latter events, respectively. As shown in Figure FIGREF1, we limit our scope to two discourse relations: Cause and Concession.",
+      "The seed lexicon consists of positive and negative predicates. If the predicate of an extracted event is in the seed lexicon and does not involve complex phenomena like negation, we assign the corresponding polarity score ($+1$ for positive events and $-1$ for negative events) to the event. We expect the model to automatically learn complex phenomena through label propagation. Based on the availability of scores and the types of discourse relations, we classify the extracted event pairs into the following three types.",
+      ""
+    ],
+    [
+      "The seed lexicon matches (1) the latter event but (2) not the former event, and (3) their discourse relation type is Cause or Concession. If the discourse relation type is Cause, the former event is given the same score as the latter. Likewise, if the discourse relation type is Concession, the former event is given the opposite of the latter's score. They are used as reference scores during training.",
+      ""
+    ],
+    [
+      "The seed lexicon matches neither the former nor the latter event, and their discourse relation type is Cause. We assume the two events have the same polarities.",
+      ""
+    ],
+    [
+      "The seed lexicon matches neither the former nor the latter event, and their discourse relation type is Concession. We assume the two events have the reversed polarities.",
+      ""
+    ],
+    [
+      "Using AL, CA, and CO data, we optimize the parameters of the polarity function $p(x)$. We define a loss function for each of the three types of event pairs and sum up the multiple loss functions.",
+      "We use mean squared error to construct loss functions. For the AL data, the loss function is defined as:",
+      "where $x_{i1}$ and $x_{i2}$ are the $i$-th pair of the AL data. $r_{i1}$ and $r_{i2}$ are the automatically-assigned scores of $x_{i1}$ and $x_{i2}$, respectively. $N_{\\rm AL}$ is the total number of AL pairs, and $\\lambda _{\\rm AL}$ is a hyperparameter.",
+      "For the CA data, the loss function is defined as:",
+      "$y_{i1}$ and $y_{i2}$ are the $i$-th pair of the CA pairs. $N_{\\rm CA}$ is the total number of CA pairs. $\\lambda _{\\rm CA}$ and $\\mu $ are hyperparameters. The first term makes the scores of the two events closer while the second term prevents the scores from shrinking to zero.",
+      "The loss function for the CO data is defined analogously:",
+      "The difference is that the first term makes the scores of the two events distant from each other.",
+      ""
+    ],
+    [
+      ""
+    ],
+    [
+      ""
+    ],
+    [
+      "As a raw corpus, we used a Japanese web corpus that was compiled through the procedures proposed by BIBREF13. To extract event pairs tagged with discourse relations, we used the Japanese dependency parser KNP and in-house postprocessing scripts BIBREF14. KNP used hand-written rules to segment each sentence into what we conventionally called clauses (mostly consecutive text chunks), each of which contained one main predicate. KNP also identified the discourse relations of event pairs if explicit discourse connectives BIBREF4 such as \u201c\u306e\u3067\u201d (because) and \u201c\u306e\u306b\u201d (in spite of) were present. We treated Cause/Reason (\u539f\u56e0\u30fb\u7406\u7531) and Condition (\u6761\u4ef6) in the original tagset BIBREF15 as Cause and Concession (\u9006\u63a5) as Concession, respectively. Here is an example of event pair extraction.",
+      ". \u91cd\u5927\u306a\u5931\u6557\u3092\u72af\u3057\u305f\u306e\u3067\u3001\u4ed5\u4e8b\u3092\u30af\u30d3\u306b\u306a\u3063\u305f\u3002",
+      "Because [I] made a serious mistake, [I] got fired.",
+      "From this sentence, we extracted the event pair of \u201c\u91cd\u5927\u306a\u5931\u6557\u3092\u72af\u3059\u201d ([I] make a serious mistake) and \u201c\u4ed5\u4e8b\u3092\u30af\u30d3\u306b\u306a\u308b\u201d ([I] get fired), and tagged it with Cause.",
+      "We constructed our seed lexicon consisting of 15 positive words and 15 negative words, as shown in Section SECREF27. From the corpus of about 100 million sentences, we obtained 1.4 millions event pairs for AL, 41 millions for CA, and 6 millions for CO. We randomly selected subsets of AL event pairs such that positive and negative latter events were equal in size. We also sampled event pairs for each of CA and CO such that it was five times larger than AL. The results are shown in Table TABREF16."
+    ],
+    [
+      "We used the latest version of the ACP Corpus BIBREF12 for evaluation. It was used for (semi-)supervised training as well. Extracted from Japanese websites using HTML layouts and linguistic patterns, the dataset covered various genres. For example, the following two sentences were labeled positive and negative, respectively:",
+      ". \u4f5c\u696d\u304c\u697d\u3060\u3002",
+      "The work is easy.",
+      ". \u99d0\u8eca\u5834\u304c\u306a\u3044\u3002",
+      "There is no parking lot.",
+      "Although the ACP corpus was originally constructed in the context of sentiment analysis, we found that it could roughly be regarded as a collection of affective events. We parsed each sentence and extracted the last clause in it. The train/dev/test split of the data is shown in Table TABREF19.",
+      "The objective function for supervised training is:",
+      "",
+      "where $v_i$ is the $i$-th event, $R_i$ is the reference score of $v_i$, and $N_{\\rm ACP}$ is the number of the events of the ACP Corpus.",
+      "To optimize the hyperparameters, we used the dev set of the ACP Corpus. For the evaluation, we used the test set of the ACP Corpus. The model output was classified as positive if $p(x) > 0$ and negative if $p(x) \\le 0$.",
+      ""
+    ],
+    [
+      "As for ${\\rm Encoder}$, we compared two types of neural networks: BiGRU and BERT. GRU BIBREF16 is a recurrent neural network sequence encoder. BiGRU reads an input sequence forward and backward and the output is the concatenation of the final forward and backward hidden states.",
+      "BERT BIBREF17 is a pre-trained multi-layer bidirectional Transformer BIBREF18 encoder. Its output is the final hidden state corresponding to the special classification tag ([CLS]). For the details of ${\\rm Encoder}$, see Sections SECREF30.",
+      "We trained the model with the following four combinations of the datasets: AL, AL+CA+CO (two proposed models), ACP (supervised), and ACP+AL+CA+CO (semi-supervised). The corresponding objective functions were: $\\mathcal {L}_{\\rm AL}$, $\\mathcal {L}_{\\rm AL} + \\mathcal {L}_{\\rm CA} + \\mathcal {L}_{\\rm CO}$, $\\mathcal {L}_{\\rm ACP}$, and $\\mathcal {L}_{\\rm ACP} + \\mathcal {L}_{\\rm AL} + \\mathcal {L}_{\\rm CA} + \\mathcal {L}_{\\rm CO}$.",
+      ""
+    ],
+    [
+      "",
+      "Table TABREF23 shows accuracy. As the Random baseline suggests, positive and negative labels were distributed evenly. The Random+Seed baseline made use of the seed lexicon and output the corresponding label (or the reverse of it for negation) if the event's predicate is in the seed lexicon. We can see that the seed lexicon itself had practically no impact on prediction.",
+      "The models in the top block performed considerably better than the random baselines. The performance gaps with their (semi-)supervised counterparts, shown in the middle block, were less than 7%. This demonstrates the effectiveness of discourse relation-based label propagation.",
+      "Comparing the model variants, we obtained the highest score with the BiGRU encoder trained with the AL+CA+CO dataset. BERT was competitive but its performance went down if CA and CO were used in addition to AL. We conjecture that BERT was more sensitive to noises found more frequently in CA and CO.",
+      "Contrary to our expectations, supervised models (ACP) outperformed semi-supervised models (ACP+AL+CA+CO). This suggests that the training set of 0.6 million events is sufficiently large for training the models. For comparison, we trained the models with a subset (6,000 events) of the ACP dataset. As the results shown in Table TABREF24 demonstrate, our method is effective when labeled data are small.",
+      "The result of hyperparameter optimization for the BiGRU encoder was as follows:",
+      "As the CA and CO pairs were equal in size (Table TABREF16), $\\lambda _{\\rm CA}$ and $\\lambda _{\\rm CO}$ were comparable values. $\\lambda _{\\rm CA}$ was about one-third of $\\lambda _{\\rm CO}$, and this indicated that the CA pairs were noisier than the CO pairs. A major type of CA pairs that violates our assumption was in the form of \u201c$\\textit {problem}_{\\text{negative}}$ causes $\\textit {solution}_{\\text{positive}}$\u201d:",
+      ". (\u60aa\u3044\u3068\u3053\u308d\u304c\u3042\u308b, \u3088\u304f\u306a\u308b\u3088\u3046\u306b\u52aa\u529b\u3059\u308b)",
+      "(there is a bad point, [I] try to improve [it])",
+      "The polarities of the two events were reversed in spite of the Cause relation, and this lowered the value of $\\lambda _{\\rm CA}$.",
+      "Some examples of model outputs are shown in Table TABREF26. The first two examples suggest that our model successfully learned negation without explicit supervision. Similarly, the next two examples differ only in voice but the model correctly recognized that they had opposite polarities. The last two examples share the predicate \u201c\u843d\u3068\u3059\" (drop) and only the objects are different. The second event \u201c\u80a9\u3092\u843d\u3068\u3059\" (lit. drop one's shoulders) is an idiom that expresses a disappointed feeling. The examples demonstrate that our model correctly learned non-compositional expressions.",
+      ""
+    ],
+    [
+      "In this paper, we proposed to use discourse relations to effectively propagate polarities of affective events from seeds. Experiments show that, even with a minimal amount of supervision, the proposed method performed well.",
+      "Although event pairs linked by discourse analysis are shown to be useful, they nevertheless contain noises. Adding linguistically-motivated filtering rules would help improve the performance."
+    ],
+    [
+      "We thank Nobuhiro Kaji for providing the ACP Corpus and Hirokazu Kiyomaru and Yudai Kishimoto for their help in extracting event pairs. This work was partially supported by Yahoo! Japan Corporation."
+    ],
+    [
+      "\u559c\u3076 (rejoice), \u5b09\u3057\u3044 (be glad), \u697d\u3057\u3044 (be pleasant), \u5e78\u305b (be happy), \u611f\u52d5 (be impressed), \u8208\u596e (be excited), \u61d0\u304b\u3057\u3044 (feel nostalgic), \u597d\u304d (like), \u5c0a\u656c (respect), \u5b89\u5fc3 (be relieved), \u611f\u5fc3 (admire), \u843d\u3061\u7740\u304f (be calm), \u6e80\u8db3 (be satisfied), \u7652\u3055\u308c\u308b (be healed), and \u30b9\u30c3\u30ad\u30ea (be refreshed)."
+    ],
+    [
+      "\u6012\u308b (get angry), \u60b2\u3057\u3044 (be sad), \u5bc2\u3057\u3044 (be lonely), \u6016\u3044 (be scared), \u4e0d\u5b89 (feel anxious), \u6065\u305a\u304b\u3057\u3044 (be embarrassed), \u5acc (hate), \u843d\u3061\u8fbc\u3080 (feel down), \u9000\u5c48 (be bored), \u7d76\u671b (feel hopeless), \u8f9b\u3044 (have a hard time), \u56f0\u308b (have trouble), \u6182\u9b31 (be depressed), \u5fc3\u914d (be worried), and \u60c5\u3051\u306a\u3044 (be sorry)."
+    ],
+    [
+      "The dimension of the embedding layer was 256. The embedding layer was initialized with the word embeddings pretrained using the Web corpus. The input sentences were segmented into words by the morphological analyzer Juman++. The vocabulary size was 100,000. The number of hidden layers was 2. The dimension of hidden units was 256. The optimizer was Momentum SGD BIBREF21. The mini-batch size was 1024. We ran 100 epochs and selected the snapshot that achieved the highest score for the dev set."
+    ],
+    [
+      "We used a Japanese BERT model pretrained with Japanese Wikipedia. The input sentences were segmented into words by Juman++, and words were broken into subwords by applying BPE BIBREF20. The vocabulary size was 32,000. The maximum length of an input sequence was 128. The number of hidden layers was 12. The dimension of hidden units was 768. The number of self-attention heads was 12. The optimizer was Adam BIBREF19. The mini-batch size was 32. We ran 1 epoch."
+    ]
+  ]
+}
+```

qasper-0038/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Stay On-Topic: Generating Context-specific Fake Restaurant Reviews
+
+Question: How many reviews in total (both generated and true) do they evaluate on Amazon Mechanical Turk?

qasper-0119/instruction.md

@@ -0,0 +1,134 @@
+Name of Paper: Diversity, Density, and Homogeneity: Quantitative Characteristic Metrics for Text Collections
+
+Question: How did they obtain human intuitions?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Proposed Characteristic Metrics",
+    "Proposed Characteristic Metrics ::: Diversity",
+    "Proposed Characteristic Metrics ::: Density",
+    "Proposed Characteristic Metrics ::: Homogeneity",
+    "Simulations",
+    "Simulations ::: Simulation Setup",
+    "Simulations ::: Simulation Results",
+    "Experiments",
+    "Experiments ::: Chosen Embedding Method",
+    "Experiments ::: Experimental Setup",
+    "Experiments ::: Experimental Results",
+    "Experiments ::: Experimental Results ::: SST-2",
+    "Experiments ::: Experimental Results ::: Snips",
+    "Analysis",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "Characteristic metrics are a set of unsupervised measures that quantitatively describe or summarize the properties of a data collection. These metrics generally do not use ground-truth labels and only measure the intrinsic characteristics of data. The most prominent example is descriptive statistics that summarizes a data collection by a group of unsupervised measures such as mean or median for central tendency, variance or minimum-maximum for dispersion, skewness for symmetry, and kurtosis for heavy-tailed analysis.",
+      "In recent years, text classification, a category of Natural Language Processing (NLP) tasks, has drawn much attention BIBREF0, BIBREF1, BIBREF2 for its wide-ranging real-world applications such as fake news detection BIBREF3, document classification BIBREF4, and spoken language understanding (SLU) BIBREF5, BIBREF6, BIBREF7, a core task of conversational assistants like Amazon Alexa or Google Assistant.",
+      "However, there are still insufficient characteristic metrics to describe a collection of texts. Unlike numeric or categorical data, simple descriptive statistics alone such as word counts and vocabulary size are difficult to capture the syntactic and semantic properties of a text collection.",
+      "In this work, we propose a set of characteristic metrics: diversity, density, and homogeneity to quantitatively summarize a collection of texts where the unit of texts could be a phrase, sentence, or paragraph. A text collection is first mapped into a high-dimensional embedding space. Our characteristic metrics are then computed to measure the dispersion, sparsity, and uniformity of the distribution. Based on the choice of embedding methods, these characteristic metrics can help understand the properties of a text collection from different linguistic perspectives, for example, lexical diversity, syntactic variation, and semantic homogeneity. Our proposed diversity, density, and homogeneity metrics extract hard-to-visualize quantitative insight for a better understanding and comparison between text collections.",
+      "To verify the effectiveness of proposed characteristic metrics, we first conduct a series of simulation experiments that cover various scenarios in two-dimensional as well as high-dimensional vector spaces. The results show that our proposed quantitative characteristic metrics exhibit several desirable and intuitive properties such as robustness and linear sensitivity of the diversity metric with respect to random down-sampling. Besides, we investigate the relationship between the characteristic metrics and the performance of a renowned model, BERT BIBREF8, on the text classification task using two public benchmark datasets. Our results demonstrate that there are high correlations between text classification model performance and the characteristic metrics, which shows the efficacy of our proposed metrics."
+    ],
+    [
+      "A building block of characteristic metrics for text collections is the language representation method. A classic way to represent a sentence or a paragraph is n-gram, with dimension equals to the size of vocabulary. More advanced methods learn a relatively low dimensional latent space that represents each word or token as a continuous semantic vector such as word2vec BIBREF9, GloVe BIBREF10, and fastText BIBREF11. These methods have been widely adopted with consistent performance improvements on many NLP tasks. Also, there has been extensive research on representing a whole sentence as a vector such as a plain or weighted average of word vectors BIBREF12, skip-thought vectors BIBREF13, and self-attentive sentence encoders BIBREF14.",
+      "More recently, there is a paradigm shift from non-contextualized word embeddings to self-supervised language model (LM) pretraining. Language encoders are pretrained on a large text corpus using a LM-based objective and then re-used for other NLP tasks in a transfer learning manner. These methods can produce contextualized word representations, which have proven to be effective for significantly improving many NLP tasks. Among the most popular approaches are ULMFiT BIBREF2, ELMo BIBREF15, OpenAI GPT BIBREF16, and BERT BIBREF8. In this work, we adopt BERT, a transformer-based technique for NLP pretraining, as the backbone to embed a sentence or a paragraph into a representation vector.",
+      "Another stream of related works is the evaluation metrics for cluster analysis. As measuring property or quality of outputs from a clustering algorithm is difficult, human judgment with cluster visualization tools BIBREF17, BIBREF18 are often used. There are unsupervised metrics to measure the quality of a clustering result such as the Calinski-Harabasz score BIBREF19, the Davies-Bouldin index BIBREF20, and the Silhouette coefficients BIBREF21. Complementary to these works that model cross-cluster similarities or relationships, our proposed diversity, density and homogeneity metrics focus on the characteristics of each single cluster, i.e., intra cluster rather than inter cluster relationships."
+    ],
+    [
+      "We introduce our proposed diversity, density, and homogeneity metrics with their detailed formulations and key intuitions.",
+      "Our first assumption is, for classification, high-quality training data entail that examples of one class are as differentiable and distinct as possible from another class. From a fine-grained and intra-class perspective, a robust text cluster should be diverse in syntax, which is captured by diversity. And each example should reflect a sufficient signature of the class to which it belongs, that is, each example is representative and contains certain salient features of the class. We define a density metric to account for this aspect. On top of that, examples should also be semantically similar and coherent among each other within a cluster, where homogeneity comes in play.",
+      "The more subtle intuition emerges from the inter-class viewpoint. When there are two or more class labels in a text collection, in an ideal scenario, we would expect the homogeneity to be monotonically decreasing. Potentially, the diversity is increasing with respect to the number of classes since text clusters should be as distinct and separate as possible from one another. If there is a significant ambiguity between classes, the behavior of the proposed metrics and a possible new metric as a inter-class confusability measurement remain for future work.",
+      "In practice, the input is a collection of texts $\\lbrace x_1, x_2, ..., x_m\\rbrace $, where $x_i$ is a sequence of tokens $x_{i1}, x_{i2}, ..., x_{il}$ denoting a phrase, a sentence, or a paragraph. An embedding method $\\mathcal {E}$ then transforms $x_i$ into a vector $\\mathcal {E}(x_i)=e_i$ and the characteristic metrics are computed with the embedding vectors. For example,",
+      "Note that these embedding vectors often lie in a high-dimensional space, e.g. commonly over 300 dimensions. This motivates our design of characteristic metrics to be sensitive to text collections of different properties while being robust to the curse of dimensionality.",
+      "We then assume a set of clusters created over the generated embedding vectors. In classification tasks, the embeddings pertaining to members of a class form a cluster, i.e., in a supervised setting. In an unsupervised setting, we may apply a clustering algorithm to the embeddings. It is worth noting that, in general, the metrics are independent of the assumed underlying grouping method."
+    ],
+    [
+      "Embedding vectors of a given group of texts $\\lbrace e_1, ..., e_m\\rbrace $ can be treated as a cluster in the high-dimensional embedding space. We propose a diversity metric to estimate the cluster's dispersion or spreadness via a generalized sense of the radius.",
+      "Specifically, if a cluster is distributed as a multi-variate Gaussian with a diagonal covariance matrix $\\Sigma $, the shape of an isocontour will be an axis-aligned ellipsoid in $\\mathbb {R}^{H}$. Such isocontours can be described as:",
+      "where $x$ are all possible points in $\\mathbb {R}^{H}$ on an isocontour, $c$ is a constant, $\\mu $ is a given mean vector with $\\mu _j$ being the value along $j$-th axis, and $\\sigma ^2_j$ is the variance of the $j$-th axis.",
+      "We leverage the geometric interpretation of this formulation and treat the square root of variance, i.e., standard deviation, $\\sqrt{\\sigma ^2_j}$ as the radius $r_j$ of the ellipsoid along the $j$-th axis. The diversity metric is then defined as the geometric mean of radii across all axes:",
+      "where $\\sigma _i$ is the standard deviation or square root of the variance along the $i$-th axis.",
+      "In practice, to compute a diversity metric, we first calculate the standard deviation of embedding vectors along each dimension and take the geometric mean of all calculated values. Note that as the geometric mean acts as a dimensionality normalization, it makes the diversity metric work well in high-dimensional embedding spaces such as BERT."
+    ],
+    [
+      "Another interesting characteristic is the sparsity of the text embedding cluster. The density metric is proposed to estimate the number of samples that falls within a unit of volume in an embedding space.",
+      "Following the assumption mentioned above, a straight-forward definition of the volume can be written as:",
+      "up to a constant factor. However, when the dimension goes higher, this formulation easily produces exploding or vanishing density values, i.e., goes to infinity or zero.",
+      "To accommodate the impact of high-dimensionality, we impose a dimension normalization. Specifically, we introduce a notion of effective axes, which assumes most variance can be explained or captured in a sub-space of a dimension $\\sqrt{H}$. We group all the axes in this sub-space together and compute the geometric mean of their radii as the effective radius. The dimension-normalized volume is then formulated as:",
+      "Given a set of embedding vectors $\\lbrace e_1, ..., e_m\\rbrace $, we define the density metric as:",
+      "In practice, the computed density metric values often follow a heavy-tailed distribution, thus sometimes its $\\log $ value is reported and denoted as $density (log\\-scale)$."
+    ],
+    [
+      "The homogeneity metric is proposed to summarize the uniformity of a cluster distribution. That is, how uniformly the embedding vectors of the samples in a group of texts are distributed in the embedding space. We propose to quantitatively describe homogeneity by building a fully-connected, edge-weighted network, which can be modeled by a Markov chain model. A Markov chain's entropy rate is calculated and normalized to be in $[0, 1]$ range by dividing by the entropy's theoretical upper bound. This output value is defined as the homogeneity metric detailed as follows:",
+      "To construct a fully-connected network from the embedding vectors $\\lbrace e_1, ..., e_m\\rbrace $, we compute their pairwise distances as edge weights, an idea similar to AttriRank BIBREF22. As the Euclidean distance is not a good metric in high-dimensions, we normalize the distance by adding a power $\\log (n\\_dim)$. We then define a Markov chain model with the weight of $edge(i, j)$ being",
+      "and the conditional probability of transition from $i$ to $j$ can be written as",
+      "All the transition probabilities $p(i \\rightarrow j)$ are from the transition matrix of a Markov chain. An entropy of this Markov chain can be calculated as",
+      "where $\\nu _i$ is the stationary distribution of the Markov chain. As self-transition probability $p(i \\rightarrow i)$ is always zero because of zero distance, there are $(m - 1)$ possible destinations and the entropy's theoretical upper bound becomes",
+      "Our proposed homogeneity metric is then normalized into $[0, 1]$ as a uniformity measure:",
+      "The intuition is that if some samples are close to each other but far from all the others, the calculated entropy decreases to reflect the unbalanced distribution. In contrast, if each sample can reach other samples within more-or-less the same distances, the calculated entropy as well as the homogeneity measure would be high as it implies the samples could be more uniformly distributed."
+    ],
+    [
+      "To verify that each proposed characteristic metric holds its desirable and intuitive properties, we conduct a series of simulation experiments in 2-dimensional as well as 768-dimensional spaces. The latter has the same dimensionality as the output of our chosen embedding method-BERT, in the following Experiments section."
+    ],
+    [
+      "The base simulation setup is a randomly generated isotropic Gaussian blob that contains $10,000$ data points with the standard deviation along each axis to be $1.0$ and is centered around the origin. All Gaussian blobs are created using make_blobs function in the scikit-learn package.",
+      "Four simulation scenarios are used to investigate the behavior of our proposed quantitative characteristic metrics:",
+      "Down-sampling: Down-sample the base cluster to be $\\lbrace 90\\%, 80\\%, ..., 10\\%\\rbrace $ of its original size. That is, create Gaussian blobs with $\\lbrace 9000, ..., 1000\\rbrace $ data points;",
+      "Varying Spread: Generate Gaussian blobs with standard deviations of each axis to be $\\lbrace 2.0, 3.0, ..., 10.0\\rbrace $;",
+      "Outliers: Add $\\lbrace 50, 100, ..., 500\\rbrace $ outlier data points, i.e., $\\lbrace 0.5\\%, ..., 5\\%\\rbrace $ of the original cluster size, randomly on the surface with a fixed norm or radius;",
+      "Multiple Sub-clusters: Along the 1th-axis, with $10,000$ data points in total, create $\\lbrace 1, 2, ..., 10\\rbrace $ clusters with equal sample sizes but at increasing distance.",
+      "For each scenario, we simulate a cluster and compute the characteristic metrics in both 2-dimensional and 768-dimensional spaces. Figure FIGREF17 visualizes each scenario by t-distributed Stochastic Neighbor Embedding (t-SNE) BIBREF23. The 768-dimensional simulations are visualized by down-projecting to 50 dimensions via Principal Component Analysis (PCA) followed by t-SNE."
+    ],
+    [
+      "Figure FIGREF24 summarizes calculated diversity metrics in the first row, density metrics in the second row, and homogeneity metrics in the third row, for all simulation scenarios.",
+      "The diversity metric is robust as its values remain almost the same to the down-sampling of an input cluster. This implies the diversity metric has a desirable property that it is insensitive to the size of inputs. On the other hand, it shows a linear relationship to varying spreads. It is another intuitive property for a diversity metric that it grows linearly with increasing dispersion or variance of input data. With more outliers or more sub-clusters, the diversity metric can also reflect the increasing dispersion of cluster distributions but is less sensitive in high-dimensional spaces.",
+      "For the density metrics, it exhibits a linear relationship to the size of inputs when down-sampling, which is desired. When increasing spreads, the trend of density metrics corresponds well with human intuition. Note that the density metrics decrease at a much faster rate in higher-dimensional space as log-scale is used in the figure. The density metrics also drop when adding outliers or having multiple distant sub-clusters. This makes sense since both scenarios should increase the dispersion of data and thus increase our notion of volume as well. In multiple sub-cluster scenario, the density metric becomes less sensitive in the higher-dimensional space. The reason could be that the sub-clusters are distributed only along one axis and thus have a smaller impact on volume in higher-dimensional spaces.",
+      "As random down-sampling or increasing variance of each axis should not affect the uniformity of a cluster distribution, we expect the homogeneity metric remains approximately the same values. And the proposed homogeneity metric indeed demonstrates these ideal properties. Interestingly, for outliers, we first saw huge drops of the homogeneity metric but the values go up again slowly when more outliers are added. This corresponds well with our intuitions that a small number of outliers break the uniformity but more outliers should mean an increase of uniformity because the distribution of added outliers themselves has a high uniformity.",
+      "For multiple sub-clusters, as more sub-clusters are presented, the homogeneity should and does decrease as the data are less and less uniformly distributed in the space.",
+      "To sum up, from all simulations, our proposed diversity, density, and homogeneity metrics indeed capture the essence or intuition of dispersion, sparsity, and uniformity in a cluster distribution."
+    ],
+    [
+      "The two real-world text classification tasks we used for experiments are sentiment analysis and Spoken Language Understanding (SLU)."
+    ],
+    [
+      "BERT is a self-supervised language model pretraining approach based on the Transformer BIBREF24, a multi-headed self-attention architecture that can produce different representation vectors for the same token in various sequences, i.e., contextual embeddings.",
+      "When pretraining, BERT concatenates two sequences as input, with special tokens $[CLS], [SEP], [EOS]$ denoting the start, separation, and end, respectively. BERT is then pretrained on a large unlabeled corpus with objective-masked language model (MLM), which randomly masks out tokens, and the model predicts the masked tokens. The other classification task is next sentence prediction (NSP). NSP is to predict whether two sequences follow each other in the original text or not.",
+      "In this work, we use the pretrained $\\text{BERT}_{\\text{BASE}}$ which has 12 layers (L), 12 self-attention heads (A), and 768 hidden dimension (H) as the language embedding to compute the proposed data metrics. The off-the-shelf pretrained BERT is obtained from GluonNLP. For each sequence $x_i = (x_{i1}, ..., x_{il})$ with length $l$, BERT takes $[CLS], x_{i1}, ..., x_{il}, [EOS]$ as input and generates embeddings $\\lbrace e_{CLS}, e_{i1}, ..., e_{il}, e_{EOS}\\rbrace $ at the token level. To obtain the sequence representation, we use a mean pooling over token embeddings:",
+      "where $e_i \\in \\mathbb {R}^{H}$. A text collection $\\lbrace x_1, ..., x_m\\rbrace $, i.e., a set of token sequences, is then transformed into a group of H-dimensional vectors $\\lbrace e_1, ..., e_m\\rbrace $.",
+      "We compute each metric as described previously, using three BERT layers L1, L6, and L12 as the embedding space, respectively. The calculated metric values are averaged over layers for each class and averaged over classes weighted by class size as the final value for a dataset."
+    ],
+    [
+      "In the first task, we use the SST-2 (Stanford Sentiment Treebank, version 2) dataset BIBREF25 to conduct sentiment analysis experiments. SST-2 is a sentence binary classification dataset with train/dev/test splits provided and two types of sentence labels, i.e., positive and negative.",
+      "The second task involves two essential problems in SLU, which are intent classification (IC) and slot labeling (SL). In IC, the model needs to detect the intention of a text input (i.e., utterance, conveys). For example, for an input of I want to book a flight to Seattle, the intention is to book a flight ticket, hence the intent class is bookFlight. In SL, the model needs to extract the semantic entities that are related to the intent. From the same example, Seattle is a slot value related to booking the flight, i.e., the destination. Here we experiment with the Snips dataset BIBREF26, which is widely used in SLU research. This dataset contains test spoken utterances (text) classified into one of 7 intents.",
+      "In both tasks, we used the open-sourced GluonNLP BERT model to perform text classification. For evaluation, sentiment analysis is measured in accuracy, whereas IC and SL are measured in accuracy and F1 score, respectively. BERT is fine-tuned on train/dev sets and evaluated on test sets.",
+      "We down-sampled SST-2 and Snips training sets from $100\\%$ to $10\\%$ with intervals being $10\\%$. BERT's performance is reported for each down-sampled setting in Table TABREF29 and Table TABREF30. We used entire test sets for all model evaluations.",
+      "To compare, we compute the proposed data metrics, i.e., diversity, density, and homogeneity, on the original and the down-sampled training sets."
+    ],
+    [
+      "We will discuss the three proposed characteristic metrics, i.e., diversity, density, and homogeneity, and model performance scores from down-sampling experiments on the two public benchmark datasets, in the following subsections:"
+    ],
+    [
+      "In Table TABREF29, the sentiment classification accuracy is $92.66\\%$ without down-sampling, which is consistent with the reported GluonNLP BERT model performance on SST-2. It also indicates SST-2 training data are differentiable between label classes, i.e., from the positive class to the negative class, which satisfies our assumption for the characteristic metrics.",
+      "Decreasing the training set size does not reduce performance until it is randomly down-sampled to only $20\\%$ of the original size. Meanwhile, density and homogeneity metrics also decrease significantly (highlighted in bold in Table TABREF29), implying a clear relationship between these metrics and model performance."
+    ],
+    [
+      "In Table TABREF30, the Snips dataset seems to be distinct between IC/SL classes since the IC accurcy and SL F1 are as high as $98.71\\%$ and $96.06\\%$ without down-sampling, respectively. Similar to SST-2, this implies that Snips training data should also support the inter-class differentiability assumption for our proposed characteristic metrics.",
+      "IC accuracy on Snips remains higher than $98\\%$ until we down-sample the training set to $20\\%$ of the original size. In contrast, SL F1 score is more sensitive to the down-sampling of the training set, as it starts decreasing when down-sampling. When the training set is only $10\\%$ left, SL F1 score drops to $87.20\\%$.",
+      "The diversity metric does not decrease immediately until the training set equals to or is less than $40\\%$ of the original set. This implies that random sampling does not impact the diversity, if the sampling rate is greater than $40\\%$. The training set is very likely to contain redundant information in terms of text diversity. This is supported by what we observed as model has consistently high IC/SL performances between $40\\%$-$100\\%$ down-sampling ratios.",
+      "Moreover, the biggest drop of density and homogeneity (highlighted in bold in Table TABREF30) highly correlates with the biggest IC/SL drop, at the point the training set size is reduced from $20\\%$ to $10\\%$. This suggests that our proposed metrics can be used as a good indicator of model performance and for characterizing text datasets."
+    ],
+    [
+      "We calculate and show in Table TABREF35 the Pearson's correlations between the three proposed characteristic metrics, i.e., diversity, density, and homogeneity, and model performance scores from down-sampling experiments in Table TABREF29 and Table TABREF30. Correlations higher than $0.5$ are highlighted in bold. As mentioned before, model performance is highly correlated with density and homogeneity, both are computed on the train set. Diversity is only correlated with Snips SL F1 score at a moderate level.",
+      "",
+      "These are consistent with our simulation results, which shows that random sampling of a dataset does not necessarily affect the diversity but can reduce the density and marginally homogeneity due to the decreasing of data points in the embedding space. However, the simultaneous huge drops of model performance, density, and homogeneity imply that there is only limited redundancy and more informative data points are being thrown away when down-sampling. Moreover, results also suggest that model performance on text classification tasks corresponds not only with data diversity but also with training data density and homogeneity as well."
+    ],
+    [
+      "In this work, we proposed several characteristic metrics to describe the diversity, density, and homogeneity of text collections without using any labels. Pre-trained language embeddings are used to efficiently characterize text datasets. Simulation and experiments showed that our intrinsic metrics are robust and highly correlated with model performance on different text classification tasks. We would like to apply the diversity, density, and homogeneity metrics for text data augmentation and selection in a semi-supervised manner as our future work."
+    ]
+  ]
+}
+```

qasper-0121/instruction.md

@@ -0,0 +1,86 @@
+Name of Paper: What Drives the International Development Agenda? An NLP Analysis of the United Nations General Debate 1970-2016
+
+Question: Is the dataset multilingual?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "The UN General Debate and international development",
+    "Estimation of topic models",
+    "Topics in the UN General Debate",
+    "Explaining the rhetoric",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Decisions made in international organisations are fundamental to international development efforts and initiatives. It is in these global governance arenas that the rules of the global economic system, which have a huge impact on development outcomes are agreed on; decisions are made about large-scale funding for development issues, such as health and infrastructure; and key development goals and targets are agreed on, as can be seen with the Millennium Development Goals (MDGs). More generally, international organisations have a profound influence on the ideas that shape international development efforts BIBREF0 .",
+      "Yet surprisingly little is known about the agenda-setting process for international development in global governance institutions. This is perhaps best demonstrated by the lack of information on how the different goals and targets of the MDGs were decided, which led to much criticism and concern about the global governance of development BIBREF1 . More generally, we know little about the types of development issues that different countries prioritise, or whether country-specific factors such as wealth or democracy make countries more likely to push for specific development issues to be put on the global political agenda.",
+      "The lack of knowledge about the agenda setting process in the global governance of development is in large part due to the absence of obvious data sources on states' preferences about international development issues. To address this gap we employ a novel approach based on the application of natural language processing (NLP) to countries' speeches in the UN. Every September, the heads of state and other high-level country representatives gather in New York at the start of a new session of the United Nations General Assembly (UNGA) and address the Assembly in the General Debate. The General Debate (GD) provides the governments of the almost two hundred UN member states with an opportunity to present their views on key issues in international politics \u2013 including international development. As such, the statements made during GD are an invaluable and, largely untapped, source of information on governments' policy preferences on international development over time.",
+      "An important feature of these annual country statements is that they are not institutionally connected to decision-making in the UN. This means that governments face few external constraints when delivering these speeches, enabling them to raise the issues that they consider the most important. Therefore, the General Debate acts \u201cas a barometer of international opinion on important issues, even those not on the agenda for that particular session\u201d BIBREF2 . In fact, the GD is usually the first item for each new session of the UNGA, and as such it provides a forum for governments to identify like-minded members, and to put on the record the issues they feel the UNGA should address. Therefore, the GD can be viewed as a key forum for governments to put different policy issues on international agenda.",
+      "We use a new dataset of GD statements from 1970 to 2016, the UN General Debate Corpus (UNGDC), to examine the international development agenda in the UN BIBREF3 . Our application of NLP to these statements focuses in particular on structural topic models (STMs) BIBREF4 . The paper makes two contributions using this approach: (1) It sheds light on the main international development issues that governments prioritise in the UN; and (2) It identifies the key country-specific factors associated with governments discussing development issues in their GD statements."
+    ],
+    [
+      "In the analysis we consider the nature of international development issues raised in the UN General Debates, and the effect of structural covariates on the level of developmental rhetoric in the GD statements. To do this, we first implement a structural topic model BIBREF4 . This enables us to identify the key international development topics discussed in the GD. We model topic prevalence in the context of the structural covariates. In addition, we control for region fixed effects and time trend. The aim is to allow the observed metadata to affect the frequency with which a topic is discussed in General Debate speeches. This allows us to test the degree of association between covariates (and region/time effects) and the average proportion of a document discussing a topic."
+    ],
+    [
+      "We assess the optimal number of topics that need to be specified for the STM analysis. We follow the recommendations of the original STM paper and focus on exclusivity and semantic coherence measures. BIBREF5 propose semantic coherence measure, which is closely related to point-wise mutual information measure posited by BIBREF6 to evaluate topic quality. BIBREF5 show that semantic coherence corresponds to expert judgments and more general human judgments in Amazon's Mechanical Turk experiments.",
+      "Exclusivity scores for each topic follows BIBREF7 . Highly frequent words in a given topic that do not appear very often in other topics are viewed as making that topic exclusive. Cohesive and exclusive topics are more semantically useful. Following BIBREF8 we generate a set of candidate models ranging between 3 and 50 topics. We then plot the exclusivity and semantic coherence (numbers closer to 0 indicate higher coherence), with a linear regression overlaid (Figure FIGREF3 ). Models above the regression line have a \u201cbetter\u201d exclusivity-semantic coherence trade off. We select the 16-topic model, which has the largest positive residual in the regression fit, and provides higher exclusivity at the same level of semantic coherence. The topic quality is usually evaluated by highest probability words, which is presented in Figure FIGREF4 ."
+    ],
+    [
+      "Figure FIGREF4 provides a list of the main topics (and the highest probability words associated these topics) that emerge from the STM of UN General Debate statements. In addition to the highest probability words, we use several other measures of key words (not presented here) to interpret the dimensions. This includes the FREX metric (which combines exclusivity and word frequency), the lift (which gives weight to words that appear less frequently in other topics), and the score (which divides the log frequency of the word in the topic by the log frequency of the word in other topics). We provide a brief description of each of the 16 topics here.",
+      "Topic 1 - Security and cooperation in Europe.",
+      "The first topic is related to issues of security and cooperation, with a focus on Central and Eastern Europe.",
+      "Topic 2 - Economic development and the global system.",
+      "This topic is related to economic development, particularly around the global economic system. The focus on `trade', `growth', `econom-', `product', `growth', `financ-', and etc. suggests that Topic 2 represent a more traditional view of international development in that the emphasis is specifically on economic processes and relations.",
+      "Topic 3 - Nuclear disarmament.",
+      "This topic picks up the issue of nuclear weapons, which has been a major issue in the UN since its founding.",
+      "Topic 4 - Post-conflict development.",
+      "This topic relates to post-conflict development. The countries that feature in the key words (e.g. Rwanda, Liberia, Bosnia) have experienced devastating civil wars, and the emphasis on words such as `develop', `peace', `hope', and `democrac-' suggest that this topic relates to how these countries recover and move forward.",
+      "Topic 5 - African independence / decolonisation.",
+      "This topic picks up the issue of African decolonisation and independence. It includes the issue of apartheid in South Africa, as well as racism and imperialism more broadly.",
+      "Topic 6 - Africa.",
+      "While the previous topic focused explicitly on issues of African independence and decolonisation, this topic more generally picks up issues linked to Africa, including peace, governance, security, and development.",
+      "Topic 7 - Sustainable development.",
+      "This topic centres on sustainable development, picking up various issues linked to development and climate change. In contrast to Topic 2, this topic includes some of the newer issues that have emerged in the international development agenda, such as sustainability, gender, education, work and the MDGs.",
+      "Topic 8 - Functional topic.",
+      "This topic appears to be comprised of functional or process-oriented words e.g. `problem', `solution', `effort', `general', etc.",
+      "Topic 9 - War.",
+      "This topic directly relates to issues of war. The key words appear to be linked to discussions around ongoing wars.",
+      "Topic 10 - Conflict in the Middle East.",
+      "This topic clearly picks up issues related to the Middle East \u2013 particularly around peace and conflict in the Middle East.",
+      "Topic 11 - Latin America.",
+      "This is another topic with a regional focus, picking up on issues related to Latin America.",
+      "Topic 12 - Commonwealth.",
+      "This is another of the less obvious topics to emerge from the STM in that the key words cover a wide range of issues. However, the places listed (e.g. Australia, Sri Lanka, Papua New Guinea) suggest the topic is related to the Commonwealth (or former British colonies).",
+      "Topic 13 - International security.",
+      "This topic broadly captures international security issues (e.g. terrorism, conflict, peace) and in particularly the international response to security threats, such as the deployment of peacekeepers.",
+      "Topic 14 - International law.",
+      "This topic picks up issues related to international law, particularly connected to territorial disputes.",
+      "Topic 15 - Decolonisation.",
+      "This topic relates more broadly to decolonisation. As well as specific mention of decolonisation, the key words include a range of issues and places linked to the decolonisation process.",
+      "Topic 16 - Cold War.",
+      "This is another of the less tightly defined topics. The topics appears to pick up issues that are broadly related to the Cold War. There is specific mention of the Soviet Union, and detente, as well as issues such as nuclear weapons, and the Helsinki Accords.",
+      "Based on these topics, we examine Topic 2 and Topic 7 as the principal \u201cinternational development\u201d topics. While a number of other topics \u2013 for example post-conflict development, Africa, Latin America, etc. \u2013 are related to development issues, Topic 2 and Topic 7 most directly capture aspects of international development. We consider these two topics more closely by contrasting the main words linked to these two topics. In Figure FIGREF6 , the word clouds show the 50 words most likely to mentioned in relation to each of the topics.",
+      "The word clouds provide further support for Topic 2 representing a more traditional view of international development focusing on economic processes. In addition to a strong emphasis on 'econom-', other key words, such as `trade', `debt', `market', `growth', `industri-', `financi-', `technolog-', `product', and `argicultur-', demonstrate the narrower economic focus on international development captured by Topic 2. In contrast, Topic 7 provides a much broader focus on development, with key words including `climat-', `sustain', `environ-', `educ-', `health', `women', `work', `mdgs', `peac-', `govern-', and `right'. Therefore, Topic 7 captures many of the issues that feature in the recent Sustainable Development Goals (SDGs) agenda BIBREF9 .",
+      "Figure FIGREF7 calculates the difference in probability of a word for the two topics, normalized by the maximum difference in probability of any word between the two topics. The figure demonstrates that while there is a much high probability of words, such as `econom-', `trade', and even `develop-' being used to discuss Topic 2; words such as `climat-', `govern-', `sustain', `goal', and `support' being used in association with Topic 7. This provides further support for the Topic 2 representing a more economistic view of international development, while Topic 7 relating to a broader sustainable development agenda.",
+      "We also assess the relationship between topics in the STM framework, which allows correlations between topics to be examined. This is shown in the network of topics in Figure FIGREF8 . The figure shows that Topic 2 and Topic 7 are closely related, which we would expect as they both deal with international development (and share key words on development, such as `develop-', `povert-', etc.). It is also worth noting that while Topic 2 is more closely correlated with the Latin America topic (Topic 11), Topic 7 is more directly correlated with the Africa topic (Topic 6)."
+    ],
+    [
+      "We next look at the relationship between topic proportions and structural factors. The data for these structural covariates is taken from the World Bank's World Development Indicators (WDI) unless otherwise stated. Confidence intervals produced by the method of composition in STM allow us to pick up statistical uncertainty in the linear regression model.",
+      "Figure FIGREF9 demonstrates the effect of wealth (GDP per capita) on the the extent to which states discuss the two international development topics in their GD statements. The figure shows that the relationship between wealth and the topic proportions linked to international development differs across Topic 2 and Topic 7. Discussion of Topic 2 (economic development) remains far more constant across different levels of wealth than Topic 7. The poorest states tend to discuss both topics more than other developing nations. However, this effect is larger for Topic 7. There is a decline in the proportion of both topics as countries become wealthier until around $30,000 when there is an increase in discussion of Topic 7. There is a further pronounced increase in the extent countries discuss Topic 7 at around $60,000 per capita. However, there is a decline in expected topic proportions for both Topic 2 and Topic 7 for the very wealthiest countries.",
+      "Figure FIGREF10 shows the expected topic proportions for Topic 2 and Topic 7 associated with different population sizes. The figure shows a slight surge in the discussion of both development topics for countries with the very smallest populations. This reflects the significant amount of discussion of development issues, particularly sustainable development (Topic 7) by the small island developing states (SIDs). The discussion of Topic 2 remains relatively constant across different population sizes, with a slight increase in the expected topic proportion for the countries with the very largest populations. However, with Topic 7 there is an increase in expected topic proportion until countries have a population of around 300 million, after which there is a decline in discussion of Topic 7. For countries with populations larger than 500 million there is no effect of population on discussion of Topic 7. It is only with the very largest populations that we see a positive effect on discussion of Topic 7.",
+      "We would also expect the extent to which states discuss international development in their GD statements to be impacted by the amount of aid or official development assistance (ODA) they receive. Figure FIGREF11 plots the expected topic proportion according to the amount of ODA countries receive. Broadly-speaking the discussion of development topics remains largely constant across different levels of ODA received. There is, however, a slight increase in the expected topic proportions of Topic 7 according to the amount of ODA received. It is also worth noting the spikes in discussion of Topic 2 and Topic 7 for countries that receive negative levels of ODA. These are countries that are effectively repaying more in loans to lenders than they are receiving in ODA. These countries appear to raise development issues far more in their GD statements, which is perhaps not altogether surprising.",
+      "We also consider the effects of democracy on the expected topic proportions of both development topics using the Polity IV measure of democracy BIBREF10 . Figure FIGREF12 shows the extent to which states discuss the international development topics according to their level of democracy. Discussion of Topic 2 is fairly constant across different levels of democracy (although there are some slight fluctuations). However, the extent to which states discuss Topic 7 (sustainable development) varies considerably across different levels of democracy. Somewhat surprisingly the most autocratic states tend to discuss Topic 7 more than the slightly less autocratic states. This may be because highly autocratic governments choose to discuss development and environmental issues to avoid a focus on democracy and human rights. There is then an increase in the expected topic proportion for Topic 7 as levels of democracy increase reaching a peak at around 5 on the Polity scale, after this there is a gradual decline in discussion of Topic 7. This would suggest that democratizing or semi-democratic countries (which are more likely to be developing countries with democratic institutions) discuss sustainable development more than established democracies (that are more likely to be developed countries).",
+      "We also plot the results of the analysis as the difference in topic proportions for two different values of the effect of conflict. Our measure of whether a country is experiencing a civil conflict comes from the UCDP/PRIO Armed Conflict Dataset BIBREF11 . Point estimates and 95% confidence intervals are plotted in Figure FIGREF13 . The figure shows that conflict affects only Topic 7 and not Topic 2. Countries experiencing conflict are less likely to discuss Topic 7 (sustainable development) than countries not experiencing conflict. The most likely explanation is that these countries are more likely to devote a greater proportion of their annual statements to discussing issues around conflict and security than development. The fact that there is no effect of conflict on Topic 2 is interesting in this regard.",
+      "Finally, we consider regional effects in Figure FIGREF14 . We use the World Bank's classifications of regions: Latin America and the Caribbean (LCN), South Asia (SAS), Sub-Saharan Africa (SSA), Europe and Central Asia (ECS), Middle East and North Africa (MEA), East Asia and the Pacific (EAS), North America (NAC). The figure shows that states in South Asia, and Latin America and the Caribbean are likely to discuss Topic 2 the most. States in South Asia and East Asia and the Pacific discuss Topic 7 the most. The figure shows that countries in North America are likely to speak about Topic 7 least.",
+      "The analysis of discussion of international development in annual UN General Debate statements therefore uncovers two principle development topics: economic development and sustainable development. We find that discussion of Topic 2 is not significantly impacted by country-specific factors, such as wealth, population, democracy, levels of ODA, and conflict (although there are regional effects). However, we find that the extent to which countries discuss sustainable development (Topic 7) in their annual GD statements varies considerably according to these different structural factors. The results suggest that broadly-speaking we do not observe linear trends in the relationship between these country-specific factors and discussion of Topic 7. Instead, we find that there are significant fluctuations in the relationship between factors such as wealth, democracy, etc., and the extent to which these states discuss sustainable development in their GD statements. These relationships require further analysis and exploration."
+    ],
+    [
+      "Despite decisions taken in international organisations having a huge impact on development initiatives and outcomes, we know relatively little about the agenda-setting process around the global governance of development. Using a novel approach that applies NLP methods to a new dataset of speeches in the UN General Debate, this paper has uncovered the main development topics discussed by governments in the UN, and the structural factors that influence the degree to which governments discuss international development. In doing so, the paper has shed some light on state preferences regarding the international development agenda in the UN. The paper more broadly demonstrates how text analytic approaches can help us to better understand different aspects of global governance."
+    ]
+  ]
+}
+```

qasper-0126/instruction.md

@@ -0,0 +1,72 @@
+Name of Paper: A simple discriminative training method for machine translation with large-scale features
+
+Question: How they measure robustness in experiments?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Plackett-Luce Model",
+    "Plackett-Luce Model in Statistical Machine Translation",
+    "Plackett-Luce Model in Statistical Machine Translation ::: N-best Hypotheses Resample",
+    "Evaluation",
+    "Evaluation ::: Plackett-Luce Model for SMT Tuning",
+    "Evaluation ::: Plackett-Luce Model for SMT Reranking"
+  ],
+  "paragraphs": [
+    [
+      "Since Och BIBREF0 proposed minimum error rate training (MERT) to exactly optimize objective evaluation measures, MERT has become a standard model tuning technique in statistical machine translation (SMT). Though MERT performs better by improving its searching algorithm BIBREF1, BIBREF2, BIBREF3, BIBREF4, it does not work reasonably when there are lots of features. As a result, margin infused relaxed algorithms (MIRA) dominate in this case BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10.",
+      "In SMT, MIRAs consider margin losses related to sentence-level BLEUs. However, since the BLEU is not decomposable into each sentence, these MIRA algorithms use some heuristics to compute the exact losses, e.g., pseudo-document BIBREF8, and document-level loss BIBREF9.",
+      "Recently, another successful work in large-scale feature tuning include force decoding basedBIBREF11, classification based BIBREF12.",
+      "We aim to provide a simpler tuning method for large-scale features than MIRAs. Out motivation derives from an observation on MERT. As MERT considers the quality of only top1 hypothesis set, there might have more-than-one set of parameters, which have similar top1 performances in tuning, but have very different topN hypotheses. Empirically, we expect an ideal model to benefit the total N-best list. That is, better hypotheses should be assigned with higher ranks, and this might decrease the error risk of top1 result on unseen data.",
+      "PlackettBIBREF13 offered an easy-to-understand theory of modeling a permutation. An N-best list is assumedly generated by sampling without replacement. The $i$th hypothesis to sample relies on those ranked after it, instead of on the whole list. This model also supports a partial permutation which accounts for top $k$ positions in a list, regardless of the remaining. When taking $k$ as 1, this model reduces to a standard conditional probabilistic training, whose dual problem is actual the maximum entropy based BIBREF14. Although Och BIBREF0 substituted direct error optimization for a maximum entropy based training, probabilistic models correlate with BLEU well when features are rich enough. The similar claim also appears in BIBREF15. This also make the new method be applicable in large-scale features."
+    ],
+    [
+      "Plackett-Luce was firstly proposed to predict ranks of horses in gambling BIBREF13. Let $\\mathbf {r}=(r_{1},r_{2}\\ldots r_{N})$ be $N$ horses with a probability distribution $\\mathcal {P}$ on their abilities to win a game, and a rank $\\mathbf {\\pi }=(\\pi (1),\\pi (2)\\ldots \\pi (|\\mathbf {\\pi }|))$ of horses can be understood as a generative procedure, where $\\pi (j)$ denotes the index of the horse in the $j$th position.",
+      "In the 1st position, there are $N$ horses as candidates, each of which $r_{j}$ has a probability $p(r_{j})$ to be selected. Regarding the rank $\\pi $, the probability of generating the champion is $p(r_{\\pi (1)})$. Then the horse $r_{\\pi (1)}$ is removed from the candidate pool.",
+      "In the 2nd position, there are only $N-1$ horses, and their probabilities to be selected become $p(r_{j})/Z_{2}$, where $Z_{2}=1-p(r_{\\pi (1)})$ is the normalization. Then the runner-up in the rank $\\pi $, the $\\pi (2)$th horse, is chosen at the probability $p(r_{\\pi (2)})/Z_{2}$. We use a consistent terminology $Z_{1}$ in selecting the champion, though $Z_{1}$ equals 1 trivially.",
+      "This procedure iterates to the last rank in $\\pi $. The key idea for the Plackett-Luce model is the choice in the $i$th position in a rank $\\mathbf {\\pi }$ only depends on the candidates not chosen at previous stages. The probability of generating a rank $\\pi $ is given as follows",
+      "where $Z_{j}=1-\\sum _{t=1}^{j-1}p(r_{\\pi (t)})$.",
+      "We offer a toy example (Table TABREF3) to demonstrate this procedure.",
+      "Theorem 1 The permutation probabilities $p(\\mathbf {\\pi })$ form a probability distribution over a set of permutations $\\Omega _{\\pi }$. For example, for each $\\mathbf {\\pi }\\in \\Omega _{\\pi }$, we have $p(\\mathbf {\\pi })>0$, and $\\sum _{\\pi \\in \\Omega _{\\pi }}p(\\mathbf {\\pi })=1$.",
+      "We have to note that, $\\Omega _{\\pi }$ is not necessarily required to be completely ranked permutations in theory and in practice, since gamblers might be interested in only the champion and runner-up, and thus $|\\mathbf {\\pi }|\\le N$. In experiments, we would examine the effects on different length of permutations, systems being termed $PL(|\\pi |)$.",
+      "Theorem 2 Given any two permutations $\\mathbf {\\pi }$ and $\\mathbf {\\pi }\\prime $, and they are different only in two positions $p$ and $q$, $p<q$, with $\\pi (p)=\\mathbf {\\pi }\\prime (q)$ and $\\pi (q)=\\mathbf {\\pi }\\prime (p)$. If $p(\\pi (p))>p(\\pi (q))$, then $p(\\pi )>p(\\pi \\prime )$.",
+      "In other words, exchanging two positions in a permutation where the horse more likely to win is not ranked before the other would lead to an increase of the permutation probability.",
+      "This suggests the ground-truth permutation, ranked decreasingly by their probabilities, owns the maximum permutation probability on a given distribution. In SMT, we are motivated to optimize parameters to maximize the likelihood of ground-truth permutation of an N-best hypotheses.",
+      "Due to the limitation of space, see BIBREF13, BIBREF16 for the proofs of the theorems."
+    ],
+    [
+      "In SMT, let $\\mathbf {f}=(f_{1},f_{2}\\ldots )$ denote source sentences, and $\\mathbf {e}=(\\lbrace e_{1,1},\\ldots \\rbrace ,\\lbrace e_{2,1},\\ldots \\rbrace \\ldots )$ denote target hypotheses. A set of features are defined on both source and target side. We refer to $h(e_{i,*})$ as a feature vector of a hypothesis from the $i$th source sentence, and its score from a ranking function is defined as the inner product $h(e_{i,*})^{T}w$ of the weight vector $w$ and the feature vector.",
+      "We first follow the popular exponential style to define a parameterized probability distribution over a list of hypotheses.",
+      "The ground-truth permutation of an $n$best list is simply obtained after ranking by their sentence-level BLEUs. Here we only concentrate on their relative ranks which are straightforward to compute in practice, e.g. add 1 smoothing. Let $\\pi _{i}^{*}$ be the ground-truth permutation of hypotheses from the $i$th source sentences, and our optimization objective is maximizing the log-likelihood of the ground-truth permutations and penalized using a zero-mean and unit-variance Gaussian prior. This results in the following objective and gradient:",
+      "where $Z_{i,j}$ is defined as the $Z_{j}$ in Formula (1) of the $i$th source sentence.",
+      "The log-likelihood function is smooth, differentiable, and concave with the weight vector $w$, and its local maximal solution is also a global maximum. Iteratively selecting one parameter in $\\alpha $ for tuning in a line search style (or MERT style) could also converge into the global global maximum BIBREF17. In practice, we use more fast limited-memory BFGS (L-BFGS) algorithm BIBREF18."
+    ],
+    [
+      "The log-likelihood of a Plackett-Luce model is not a strict upper bound of the BLEU score, however, it correlates with BLEU well in the case of rich features. The concept of \u201crich\u201d is actually qualitative, and obscure to define in different applications. We empirically provide a formula to measure the richness in the scenario of machine translation.",
+      "The greater, the richer. In practice, we find a rough threshold of r is 5.",
+      "In engineering, the size of an N-best list with unique hypotheses is usually less than several thousands. This suggests that, if features are up to thousands or more, the Plackett-Luce model is quite suitable here. Otherwise, we could reduce the size of N-best lists by sampling to make $r$ beyond the threshold.",
+      "Their may be other efficient sampling methods, and here we adopt a simple one. If we want to $m$ samples from a list of hypotheses $\\mathbf {e}$, first, the $\\frac{m}{3}$ best hypotheses and the $\\frac{m}{3}$ worst hypotheses are taken by their sentence-level BLEUs. Second, we sample the remaining hypotheses on distribution $p(e_{i})\\propto \\exp (h(e_{i})^{T}w)$, where $\\mathbf {w}$ is an initial weight from last iteration."
+    ],
+    [
+      "We compare our method with MERT and MIRA in two tasks, iterative training, and N-best list rerank. We do not list PRO BIBREF12 as our baseline, as Cherry et al.BIBREF10 have compared PRO with MIRA and MERT massively.",
+      "In the first task, we align the FBIS data (about 230K sentence pairs) with GIZA++, and train a 4-gram language model on the Xinhua portion of Gigaword corpus. A hierarchical phrase-based (HPB) model (Chiang, 2007) is tuned on NIST MT 2002, and tested on MT 2004 and 2005. All features are eight basic ones BIBREF20 and extra 220 group features. We design such feature templates to group grammars by the length of source side and target side, (feat-type,a$\\le $src-side$\\le $b,c$\\le $tgt-side$\\le $d), where the feat-type denotes any of the relative frequency, reversed relative frequency, lexical probability and reversed lexical probability, and [a, b], [c, d] enumerate all possible subranges of [1, 10], as the maximum length on both sides of a hierarchical grammar is limited to 10. There are 4 $\\times $ 55 extra group features.",
+      "In the second task, we rerank an N-best list from a HPB system with 7491 features from a third party. The system uses six million parallel sentence pairs available to the DARPA BOLT Chinese-English task. This system includes 51 dense features (translation probabilities, provenance features, etc.) and up to 7440 sparse features (mostly lexical and fertility-based). The language model is a 6-gram model trained on a 10 billion words, including the English side of our parallel corpora plus other corpora such as Gigaword (LDC2011T07) and Google News. For the tuning and test sets, we use 1275 and 1239 sentences respectively from the LDC2010E30 corpus."
+    ],
+    [
+      "We conduct a full training of machine translation models. By default, a decoder is invoked for at most 40 times, and each time it outputs 200 hypotheses to be combined with those from previous iterations and sent into tuning algorithms.",
+      "In getting the ground-truth permutations, there are many ties with the same sentence-level BLEU, and we just take one randomly. In this section, all systems have only around two hundred features, hence in Plackett-Luce based training, we sample 30 hypotheses in an accumulative $n$best list in each round of training.",
+      "All results are shown in Table TABREF10, we can see that all PL($k$) systems does not perform well as MERT or MIRA in the development data, this maybe due to that PL($k$) systems do not optimize BLEU and the features here are relatively not enough compared to the size of N-best lists (empirical Formula DISPLAY_FORM9). However, PL($k$) systems are better than MERT in testing. PL($k$) systems consider the quality of hypotheses from the 2th to the $k$th, which is guessed to act the role of the margin like SVM in classification . Interestingly, MIRA wins first in training, and still performs quite well in testing.",
+      "The PL(1) system is equivalent to a max-entropy based algorithm BIBREF14 whose dual problem is actually maximizing the conditional probability of one oracle hypothesis. When we increase the $k$, the performances improve at first. After reaching a maximum around $k=5$, they decrease slowly. We explain this phenomenon as this, when features are rich enough, higher BLEU scores could be easily fitted, then longer ground-truth permutations include more useful information."
+    ],
+    [
+      "After being de-duplicated, the N-best list has an average size of around 300, and with 7491 features. Refer to Formula DISPLAY_FORM9, this is ideal to use the Plackett-Luce model. Results are shown in Figure FIGREF12. We observe some interesting phenomena.",
+      "First, the Plackett-Luce models boost the training BLEU very greatly, even up to 2.5 points higher than MIRA. This verifies our assumption, richer features benefit BLEU, though they are optimized towards a different objective.",
+      "Second, the over-fitting problem of the Plackett-Luce models PL($k$) is alleviated with moderately large $k$. In PL(1), the over-fitting is quite obvious, the portion in which the curve overpasses MIRA is the smallest compared to other $k$, and its convergent performance is below the baseline. When $k$ is not smaller than 5, the curves are almost above the MIRA line. After 500 L-BFGS iterations, their performances are no less than the baseline, though only by a small margin.",
+      "This experiment displays, in large-scale features, the Plackett-Luce model correlates with BLEU score very well, and alleviates overfitting in some degree."
+    ]
+  ]
+}
+```

qasper-0145/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: InScript: Narrative texts annotated with script information
+
+Question: Did the annotators agreed and how much?

qasper-0172/instruction.md

@@ -0,0 +1,112 @@
+Name of Paper: Towards Multimodal Emotion Recognition in German Speech Events in Cars using Transfer Learning
+
+Question: Does the paper evaluate any adjustment to improve the predicion accuracy of face and audio features?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work ::: Facial Expressions",
+    "Related Work ::: Acoustic",
+    "Related Work ::: Text",
+    "Data set Collection",
+    "Data set Collection ::: Study Setup and Design",
+    "Data set Collection ::: Procedure",
+    "Data set Collection ::: Data Analysis",
+    "Methods ::: Emotion Recognition from Facial Expressions",
+    "Methods ::: Emotion Recognition from Audio Signal",
+    "Methods ::: Emotion Recognition from Transcribed Utterances",
+    "Results ::: Facial Expressions and Audio",
+    "Results ::: Text from Transcribed Utterances",
+    "Results ::: Text from Transcribed Utterances ::: Experiment 1: In-Domain application",
+    "Results ::: Text from Transcribed Utterances ::: Experiment 2: Simple Out-Of-Domain application",
+    "Results ::: Text from Transcribed Utterances ::: Experiment 3: Transfer Learning application",
+    "Summary & Future Work",
+    "Acknowledgment"
+  ],
+  "paragraphs": [
+    [
+      "Automatic emotion recognition is commonly understood as the task of assigning an emotion to a predefined instance, for example an utterance (as audio signal), an image (for instance with a depicted face), or a textual unit (e.g., a transcribed utterance, a sentence, or a Tweet). The set of emotions is often following the original definition by Ekman Ekman1992, which includes anger, fear, disgust, sadness, joy, and surprise, or the extension by Plutchik Plutchik1980 who adds trust and anticipation.",
+      "Most work in emotion detection is limited to one modality. Exceptions include Busso2004 and Sebe2005, who investigate multimodal approaches combining speech with facial information. Emotion recognition in speech can utilize semantic features as well BIBREF0. Note that the term \u201cmultimodal\u201d is also used beyond the combination of vision, audio, and text. For example, Soleymani2012 use it to refer to the combination of electroencephalogram, pupillary response and gaze distance.",
+      "In this paper, we deal with the specific situation of car environments as a testbed for multimodal emotion recognition. This is an interesting environment since it is, to some degree, a controlled environment: Dialogue partners are limited in movement, the degrees of freedom for occurring events are limited, and several sensors which are useful for emotion recognition are already integrated in this setting. More specifically, we focus on emotion recognition from speech events in a dialogue with a human partner and with an intelligent agent.",
+      "Also from the application point of view, the domain is a relevant choice: Past research has shown that emotional intelligence is beneficial for human computer interaction. Properly processing emotions in interactions increases the engagement of users and can improve performance when a specific task is to be fulfilled BIBREF1, BIBREF2, BIBREF3, BIBREF4. This is mostly based on the aspect that machines communicating with humans appear to be more trustworthy when they show empathy and are perceived as being natural BIBREF3, BIBREF5, BIBREF4.",
+      "Virtual agents play an increasingly important role in the automotive context and the speech modality is increasingly being used in cars due to its potential to limit distraction. It has been shown that adapting the in-car speech interaction system according to the drivers' emotional state can help to enhance security, performance as well as the overall driving experience BIBREF6, BIBREF7.",
+      "With this paper, we investigate how each of the three considered modalitites, namely facial expressions, utterances of a driver as an audio signal, and transcribed text contributes to the task of emotion recognition in in-car speech interactions. We focus on the five emotions of joy, insecurity, annoyance, relaxation, and boredom since terms corresponding to so-called fundamental emotions like fear have been shown to be associated to too strong emotional states than being appropriate for the in-car context BIBREF8. Our first contribution is the description of the experimental setup for our data collection. Aiming to provoke specific emotions with situations which can occur in real-world driving scenarios and to induce speech interactions, the study was conducted in a driving simulator. Based on the collected data, we provide baseline predictions with off-the-shelf tools for face and speech emotion recognition and compare them to a neural network-based approach for emotion recognition from text. Our second contribution is the introduction of transfer learning to adapt models trained on established out-of-domain corpora to our use case. We work on German language, therefore the transfer consists of a domain and a language transfer."
+    ],
+    [
+      "A common approach to encode emotions for facial expressions is the facial action coding system FACS BIBREF9, BIBREF10, BIBREF11. As the reliability and reproducability of findings with this method have been critically discussed BIBREF12, the trend has increasingly shifted to perform the recognition directly on images and videos, especially with deep learning. For instance, jung2015joint developed a model which considers temporal geometry features and temporal appearance features from image sequences. kim2016hierarchical propose an ensemble of convolutional neural networks which outperforms isolated networks.",
+      "In the automotive domain, FACS is still popular. Ma2017 use support vector machines to distinguish happy, bothered, confused, and concentrated based on data from a natural driving environment. They found that bothered and confused are difficult to distinguish, while happy and concentrated are well identified. Aiming to reduce computational cost, Tews2011 apply a simple feature extraction using four dots in the face defining three facial areas. They analyze the variance of the three facial areas for the recognition of happy, anger and neutral. Ihme2018 aim at detecting frustration in a simulator environment. They induce the emotion with specific scenarios and a demanding secondary task and are able to associate specific face movements according to FACS. Paschero2012 use OpenCV (https://opencv.org/) to detect the eyes and the mouth region and track facial movements. They simulate different lightning conditions and apply a multilayer perceptron for the classification task of Ekman's set of fundamental emotions.",
+      "Overall, we found that studies using facial features usually focus on continuous driver monitoring, often in driver-only scenarios. In contrast, our work investigates the potential of emotion recognition during speech interactions."
+    ],
+    [
+      "Past research on emotion recognition from acoustics mainly concentrates on either feature selection or the development of appropriate classifiers. rao2013emotion as well as ververidis2004automatic compare local and global features in support vector machines. Next to such discriminative approaches, hidden Markov models are well-studied, however, there is no agreement on which feature-based classifier is most suitable BIBREF13. Similar to the facial expression modality, recent efforts on applying deep learning have been increased for acoustic speech processing. For instance, lee2015high use a recurrent neural network and palaz2015analysis apply a convolutional neural network to the raw speech signal. Neumann2017 as well as Trigeorgis2016 analyze the importance of features in the context of deep learning-based emotion recognition.",
+      "In the automotive sector, Boril2011 approach the detection of negative emotional states within interactions between driver and co-driver as well as in calls of the driver towards the automated spoken dialogue system. Using real-world driving data, they find that the combination of acoustic features and their respective Gaussian mixture model scores performs best. Schuller2006 collects 2,000 dialog turns directed towards an automotive user interface and investigate the classification of anger, confusion, and neutral. They show that automatic feature generation and feature selection boost the performance of an SVM-based classifier. Further, they analyze the performance under systematically added noise and develop methods to mitigate negative effects. For more details, we refer the reader to the survey by Schuller2018. In this work, we explore the straight-forward application of domain independent software to an in-car scenario without domain-specific adaptations."
+    ],
+    [
+      "Previous work on emotion analysis in natural language processing focuses either on resource creation or on emotion classification for a specific task and domain. On the side of resource creation, the early and influential work of Pennebaker2015 is a dictionary of words being associated with different psychologically relevant categories, including a subset of emotions. Another popular resource is the NRC dictionary by Mohammad2012b. It contains more than 10000 words for a set of discrete emotion classes. Other resources include WordNet Affect BIBREF14 which distinguishes particular word classes. Further, annotated corpora have been created for a set of different domains, for instance fairy tales BIBREF15, Blogs BIBREF16, Twitter BIBREF17, BIBREF18, BIBREF19, BIBREF20, BIBREF21, Facebook BIBREF22, news headlines BIBREF23, dialogues BIBREF24, literature BIBREF25, or self reports on emotion events BIBREF26 (see BIBREF27 for an overview).",
+      "To automatically assign emotions to textual units, the application of dictionaries has been a popular approach and still is, particularly in domains without annotated corpora. Another approach to overcome the lack of huge amounts of annotated training data in a particular domain or for a specific topic is to exploit distant supervision: use the signal of occurrences of emoticons or specific hashtags or words to automatically label the data. This is sometimes referred to as self-labeling BIBREF21, BIBREF28, BIBREF29, BIBREF30.",
+      "A variety of classification approaches have been tested, including SNoW BIBREF15, support vector machines BIBREF16, maximum entropy classification, long short-term memory network, and convolutional neural network models BIBREF18. More recently, the state of the art is the use of transfer learning from noisy annotations to more specific predictions BIBREF29. Still, it has been shown that transferring from one domain to another is challenging, as the way emotions are expressed varies between areas BIBREF27. The approach by Felbo2017 is different to our work as they use a huge noisy data set for pretraining the model while we use small high quality data sets instead.",
+      "Recently, the state of the art has also been pushed forward with a set of shared tasks, in which the participants with top results mostly exploit deep learning methods for prediction based on pretrained structures like embeddings or language models BIBREF21, BIBREF31, BIBREF20.",
+      "Our work follows this approach and builds up on embeddings with deep learning. Furthermore, we approach the application and adaption of text-based classifiers to the automotive domain with transfer learning."
+    ],
+    [
+      "The first contribution of this paper is the construction of the AMMER data set which we describe in the following. We focus on the drivers' interactions with both a virtual agent as well as a co-driver. To collect the data in a safe and controlled environment and to be able to consider a variety of predefined driving situations, the study was conducted in a driving simulator."
+    ],
+    [
+      "The study environment consists of a fixed-base driving simulator running Vires's VTD (Virtual Test Drive, v2.2.0) simulation software (https://vires.com/vtd-vires-virtual-test-drive/). The vehicle has an automatic transmission, a steering wheel and gas and brake pedals. We collect data from video, speech and biosignals (Empatica E4 to record heart rate, electrodermal activity, skin temperature, not further used in this paper) and questionnaires. Two RGB cameras are fixed in the vehicle to capture the drivers face, one at the sun shield above the drivers seat and one in the middle of the dashboard. A microphone is placed on the center console. One experimenter sits next to the driver, the other behind the simulator. The virtual agent accompanying the drive is realized as Wizard-of-Oz prototype which enables the experimenter to manually trigger prerecorded voice samples playing trough the in-car speakers and to bring new content to the center screen. Figure FIGREF4 shows the driving simulator.",
+      "The experimental setting is comparable to an everyday driving task. Participants are told that the goal of the study is to evaluate and to improve an intelligent driving assistant. To increase the probability of emotions to arise, participants are instructed to reach the destination of the route as fast as possible while following traffic rules and speed limits. They are informed that the time needed for the task would be compared to other participants. The route comprises highways, rural roads, and city streets. A navigation system with voice commands and information on the screen keeps the participants on the predefined track.",
+      "To trigger emotion changes in the participant, we use the following events: (i) a car on the right lane cutting off to the left lane when participants try to overtake followed by trucks blocking both lanes with a slow overtaking maneuver (ii) a skateboarder who appears unexpectedly on the street and (iii) participants are praised for reaching the destination unexpectedly quickly in comparison to previous participants.",
+      "Based on these events, we trigger three interactions (Table TABREF6 provides examples) with the intelligent agent (Driver-Agent Interactions, D\u2013A). Pretending to be aware of the current situation, e. g., to recognize unusual driving behavior such as strong braking, the agent asks the driver to explain his subjective perception of these events in detail. Additionally, we trigger two more interactions with the intelligent agent at the beginning and at the end of the drive, where participants are asked to describe their mood and thoughts regarding the (upcoming) drive. This results in five interactions between the driver and the virtual agent.",
+      "Furthermore, the co-driver asks three different questions during sessions with light traffic and low cognitive demand (Driver-Co-Driver Interactions, D\u2013Co). These questions are more general and non-traffic-related and aim at triggering the participants' memory and fantasy. Participants are asked to describe their last vacation, their dream house and their idea of the perfect job. In sum, there are eight interactions per participant (5 D\u2013A, 3 D\u2013Co)."
+    ],
+    [
+      "At the beginning of the study, participants were welcomed and the upcoming study procedure was explained. Subsequently, participants signed a consent form and completed a questionnaire to provide demographic information. After that, the co-driving experimenter started with the instruction in the simulator which was followed by a familiarization drive consisting of highway and city driving and covering different driving maneuvers such as tight corners, lane changing and strong braking. Subsequently, participants started with the main driving task. The drive had a duration of 20 minutes containing the eight previously mentioned speech interactions. After the completion of the drive, the actual goal of improving automatic emotional recognition was revealed and a standard emotional intelligence questionnaire, namely the TEIQue-SF BIBREF32, was handed to the participants. Finally, a retrospective interview was conducted, in which participants were played recordings of their in-car interactions and asked to give discrete (annoyance, insecurity, joy, relaxation, boredom, none, following BIBREF8) was well as dimensional (valence, arousal, dominance BIBREF33 on a 11-point scale) emotion ratings for the interactions and the according situations. We only use the discrete class annotations in this paper."
+    ],
+    [
+      "Overall, 36 participants aged 18 to 64 years ($\\mu $=28.89, $\\sigma $=12.58) completed the experiment. This leads to 288 interactions, 180 between driver and the agent and 108 between driver and co-driver. The emotion self-ratings from the participants yielded 90 utterances labeled with joy, 26 with annoyance, 49 with insecurity, 9 with boredom, 111 with relaxation and 3 with no emotion. One example interaction per interaction type and emotion is shown in Table TABREF7. For further experiments, we only use joy, annoyance/anger, and insecurity/fear due to the small sample size for boredom and no emotion and under the assumption that relaxation brings little expressivity."
+    ],
+    [
+      "We preprocess the visual data by extracting the sequence of images for each interaction from the point where the agent's or the co-driver's question was completely uttered until the driver's response stops. The average length is 16.3 seconds, with the minimum at 2.2s and the maximum at 54.7s. We apply an off-the-shelf tool for emotion recognition (the manufacturer cannot be disclosed due to licensing restrictions). It delivers frame-by-frame scores ($\\in [0;100]$) for discrete emotional states of joy, anger and fear. While joy corresponds directly to our annotation, we map anger to our label annoyance and fear to our label insecurity. The maximal average score across all frames constitutes the overall classification for the video sequence. Frames where the software is not able to detect the face are ignored."
+    ],
+    [
+      "We extract the audio signal for the same sequence as described for facial expressions and apply an off-the-shelf tool for emotion recognition. The software delivers single classification scores for a set of 24 discrete emotions for the entire utterance. We consider the outputs for the states of joy, anger, and fear, mapping analogously to our classes as for facial expressions. Low-confidence predictions are interpreted as \u201cno emotion\u201d. We accept the emotion with the highest score as the discrete prediction otherwise."
+    ],
+    [
+      "For the emotion recognition from text, we manually transcribe all utterances of our AMMER study. To exploit existing and available data sets which are larger than the AMMER data set, we develop a transfer learning approach. We use a neural network with an embedding layer (frozen weights, pre-trained on Common Crawl and Wikipedia BIBREF36), a bidirectional LSTM BIBREF37, and two dense layers followed by a soft max output layer. This setup is inspired by BIBREF38. We use a dropout rate of 0.3 in all layers and optimize with Adam BIBREF39 with a learning rate of $10^{-5}$ (These parameters are the same for all further experiments). We build on top of the Keras library with the TensorFlow backend. We consider this setup our baseline model.",
+      "We train models on a variety of corpora, namely the common format published by BIBREF27 of the FigureEight (formally known as Crowdflower) data set of social media, the ISEAR data BIBREF40 (self-reported emotional events), and, the Twitter Emotion Corpus (TEC, weakly annotated Tweets with #anger, #disgust, #fear, #happy, #sadness, and #surprise, Mohammad2012). From all corpora, we use instances with labels fear, anger, or joy. These corpora are English, however, we do predictions on German utterances. Therefore, each corpus is preprocessed to German with Google Translate. We remove URLs, user tags (\u201c@Username\u201d), punctuation and hash signs. The distributions of the data sets are shown in Table TABREF12.",
+      "To adapt models trained on these data, we apply transfer learning as follows: The model is first trained until convergence on one out-of-domain corpus (only on classes fear, joy, anger for compatibility reasons). Then, the parameters of the bi-LSTM layer are frozen and the remaining layers are further trained on AMMER. This procedure is illustrated in Figure FIGREF13"
+    ],
+    [
+      "Table TABREF16 shows the confusion matrices for facial and audio emotion recognition on our complete AMMER data set and Table TABREF17 shows the results per class for each method, including facial and audio data and micro and macro averages. The classification from facial expressions yields a macro-averaged $\\text{F}_1$ score of 33 % across the three emotions joy, insecurity, and annoyance (P=0.31, R=0.35). While the classification results for joy are promising (R=43 %, P=57 %), the distinction of insecurity and annoyance from the other classes appears to be more challenging.",
+      "Regarding the audio signal, we observe a macro $\\text{F}_1$ score of 29 % (P=42 %, R=22 %). There is a bias towards negative emotions, which results in a small number of detected joy predictions (R=4 %). Insecurity and annoyance are frequently confused."
+    ],
+    [
+      "The experimental setting for the evaluation of emotion recognition from text is as follows: We evaluate the BiLSTM model in three different experiments: (1) in-domain, (2) out-of-domain and (3) transfer learning. For all experiments we train on the classes anger/annoyance, fear/insecurity and joy. Table TABREF19 shows all results for the comparison of these experimental settings."
+    ],
+    [
+      "We first set a baseline by validating our models on established corpora. We train the baseline model on 60 % of each data set listed in Table TABREF12 and evaluate that model with 40 % of the data from the same domain (results shown in the column \u201cIn-Domain\u201d in Table TABREF19). Excluding AMMER, we achieve an average micro $\\text{F}_1$ of 68 %, with best results of F$_1$=73 % on TEC. The model trained on our AMMER corpus achieves an F1 score of 57%. This is most probably due to the small size of this data set and the class bias towards joy, which makes up more than half of the data set. These results are mostly in line with Bostan2018."
+    ],
+    [
+      "Now we analyze how well the models trained in Experiment 1 perform when applied to our data set. The results are shown in column \u201cSimple\u201d in Table TABREF19. We observe a clear drop in performance, with an average of F$_1$=48 %. The best performing model is again the one trained on TEC, en par with the one trained on the Figure8 data. The model trained on ISEAR performs second best in Experiment 1, it performs worst in Experiment 2."
+    ],
+    [
+      "To adapt models trained on previously existing data sets to our particular application, the AMMER corpus, we apply transfer learning. Here, we perform leave-one-out cross validation. As pre-trained models we use each model from Experiment 1 and further optimize with the training subset of each crossvalidation iteration of AMMER. The results are shown in the column \u201cTransfer L.\u201d in Table TABREF19. The confusion matrix is also depicted in Table TABREF16.",
+      "With this procedure we achieve an average performance of F$_1$=75 %, being better than the results from the in-domain Experiment 1. The best performance of F$_1$=76 % is achieved with the model pre-trained on each data set, except for ISEAR. All transfer learning models clearly outperform their simple out-of-domain counterpart.",
+      "To ensure that this performance increase is not only due to the larger data set, we compare these results to training the model without transfer on a corpus consisting of each corpus together with AMMER (again, in leave-one-out crossvalidation). These results are depicted in column \u201cJoint C.\u201d. Thus, both settings, \u201ctransfer learning\u201d and \u201cjoint corpus\u201d have access to the same information.",
+      "The results show an increase in performance in contrast to not using AMMER for training, however, the transfer approach based on partial retraining the model shows a clear improvement for all models (by 7pp for Figure8, 10pp for EmoInt, 8pp for TEC, 13pp for ISEAR) compared to the \u201dJoint\u201d setup."
+    ],
+    [
+      "We described the creation of the multimodal AMMER data with emotional speech interactions between a driver and both a virtual agent and a co-driver. We analyzed the modalities of facial expressions, acoustics, and transcribed utterances regarding their potential for emotion recognition during in-car speech interactions. We applied off-the-shelf emotion recognition tools for facial expressions and acoustics. For transcribed text, we developed a neural network-based classifier with transfer learning exploiting existing annotated corpora. We find that analyzing transcribed utterances is most promising for classification of the three emotional states of joy, annoyance and insecurity.",
+      "Our results for facial expressions indicate that there is potential for the classification of joy, however, the states of annoyance and insecurity are not well recognized. Future work needs to investigate more sophisticated approaches to map frame predictions to sequence predictions. Furthermore, movements of the mouth region during speech interactions might negatively influence the classification from facial expressions. Therefore, the question remains how facial expressions can best contribute to multimodal detection in speech interactions.",
+      "Regarding the classification from the acoustic signal, the application of off-the-shelf classifiers without further adjustments seems to be challenging. We find a strong bias towards negative emotional states for our experimental setting. For instance, the personalization of the recognition algorithm (e. g., mean and standard deviation normalization) could help to adapt the classification for specific speakers and thus to reduce this bias. Further, the acoustic environment in the vehicle interior has special properties and the recognition software might need further adaptations.",
+      "Our transfer learning-based text classifier shows considerably better results. This is a substantial result in its own, as only one previous method for transfer learning in emotion recognition has been proposed, in which a sentiment/emotion specific source for labels in pre-training has been used, to the best of our knowledge BIBREF29. Other applications of transfer learning from general language models include BIBREF41, BIBREF42. Our approach is substantially different, not being trained on a huge amount of noisy data, but on smaller out-of-domain sets of higher quality. This result suggests that emotion classification systems which work across domains can be developed with reasonable effort.",
+      "For a productive application of emotion detection in the context of speech events we conclude that a deployed system might perform best with a speech-to-text module followed by an analysis of the text. Further, in this work, we did not explore an ensemble model or the interaction of different modalities. Thus, future work should investigate the fusion of multiple modalities in a single classifier."
+    ],
+    [
+      "We thank Laura-Ana-Maria Bostan for discussions and data set preparations. This research has partially been funded by the German Research Council (DFG), project SEAT (KL 2869/1-1)."
+    ]
+  ]
+}
+```

qasper-0186/instruction.md

@@ -0,0 +1,107 @@
+Name of Paper: "Hinglish"Language -- Modeling a Messy Code-Mixed Language
+
+Question: How big is the dataset?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Introduction ::: Modeling challenges",
+    "Related Work ::: Transfer learning based approaches",
+    "Related Work ::: Hybrid models",
+    "Dataset and Features",
+    "Dataset and Features ::: Challenges",
+    "Model Architecture",
+    "Model Architecture ::: Loss function",
+    "Model Architecture ::: Models",
+    "Model Architecture ::: Hyper parameters",
+    "Results",
+    "Conclusion and Future work",
+    "References"
+  ],
+  "paragraphs": [
+    [
+      "Hinglish is a linguistic blend of Hindi (very widely spoken language in India) and English (an associate language of urban areas) and is spoken by upwards of 350 million people in India. While the name is based on the Hindi language, it does not refer exclusively to Hindi, but is used in India, with English words blending with Punjabi, Gujarati, Marathi and Hindi. Sometimes, though rarely, Hinglish is used to refer to Hindi written in English script and mixing with English words or phrases. This makes analyzing the language very interesting. Its rampant usage in social media like Twitter, Facebook, Online blogs and reviews has also led to its usage in delivering hate and abuses in similar platforms. We aim to find such content in the social media focusing on the tweets. Hypothetically, if we can classify such tweets, we might be able to detect them and isolate them for further analysis before it reaches public. This will a great application of AI to the social cause and thus is motivating. An example of a simple, non offensive message written in Hinglish could be:",
+      "\"Why do you waste your time with <redacted content>. Aapna ghar sambhalta nahi(<redacted content>). Chale dusro ko basane..!!\"",
+      "The second part of the above sentence is written in Hindi while the first part is in English. Second part calls for an action to a person to bring order to his/her home before trying to settle others."
+    ],
+    [
+      "From the modeling perspective there are couple of challenges introduced by the language and the labelled dataset. Generally, Hinglish follows largely fuzzy set of rules which evolves and is dependent upon the users preference. It doesn't have any formal definitions and thus the rules of usage are ambiguous. Thus, when used by different users the text produced may differ. Overall the challenges posed by this problem are:",
+      "Geographical variation: Depending upon the geography of origination, the content may be be highly influenced by the underlying region.",
+      "Language and phonetics variation: Based on a census in 2001, India has 122 major languages and 1599 other languages. The use of Hindi and English in a code switched setting is highly influenced by these language.",
+      "No grammar rules: Hinglish has no fixed set of grammar rules. The rules are inspired from both Hindi and English and when mixed with slur and slang produce large variation.",
+      "Spelling variation: There is no agreement on the spellings of the words which are mixed with English. For example to express love, a code mixed spelling, specially when used social platforms might be pyaar, pyar or pyr.",
+      "Dataset: Based on some earlier work, only available labelled dataset had 3189 rows of text messages of average length of 116 words and with a range of 1, 1295. Prior work addresses this concern by using Transfer Learning on an architecture learnt on about 14,500 messages with an accuracy of 83.90. We addressed this concern using data augmentation techniques applied on text data."
+    ],
+    [
+      "Mathur et al. in their paper for detecting offensive tweets proposed a Ternary Trans-CNN model where they train a model architecture comprising of 3 layers of Convolution 1D having filter sizes of 15, 12 and 10 and kernel size of 3 followed by 2 dense fully connected layer of size 64 and 3. The first dense FC layer has ReLU activation while the last Dense layer had Softmax activation. They were able to train this network on a parallel English dataset provided by Davidson et al. The authors were able to achieve Accuracy of 83.9%, Precision of 80.2%, Recall of 69.8%.",
+      "The approach looked promising given that the dataset was merely 3189 sentences divided into three categories and thus we replicated the experiment but failed to replicate the results. The results were poor than what the original authors achieved. But, most of the model hyper-parameter choices where inspired from this work."
+    ],
+    [
+      "In another localized setting of Vietnamese language, Nguyen et al. in 2017 proposed a Hybrid multi-channel CNN and LSTM model where they build feature maps for Vietnamese language using CNN to capture shorterm dependencies and LSTM to capture long term dependencies and concatenate both these feature sets to learn a unified set of features on the messages. These concatenated feature vectors are then sent to a few fully connected layers. They achieved an accuracy rate of 87.3% with this architecture."
+    ],
+    [
+      "We used dataset, HEOT obtained from one of the past studies done by Mathur et al. where they annotated a set of cleaned tweets obtained from twitter for the conversations happening in Indian subcontinent. A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al. This dataset was important to employ Transfer Learning to our task since the number of labeled dataset was very small. Basic summary and examples of the data from the dataset are below:"
+    ],
+    [
+      "The obtained data set had many challenges and thus a data preparation task was employed to clean the data and make it ready for the deep learning pipeline. The challenges and processes that were applied are stated below:",
+      "Messy text messages: The tweets had urls, punctuations, username mentions, hastags, emoticons, numbers and lots of special characters. These were all cleaned up in a preprocessing cycle to clean the data.",
+      "Stop words: Stop words corpus obtained from NLTK was used to eliminate most unproductive words which provide little information about individual tweets.",
+      "Transliteration: Followed by above two processes, we translated Hinglish tweets into English words using a two phase process",
+      "Transliteration: In phase I, we used translation API's provided by Google translation services and exposed via a SDK, to transliteration the Hinglish messages to English messages.",
+      "Translation: After transliteration, words that were specific to Hinglish were translated to English using an Hinglish-English dictionary. By doing this we converted the Hinglish message to and assortment of isolated words being presented in the message in a sequence that can also be represented using word to vector representation.",
+      "Data augmentation: Given the data set was very small with a high degree of imbalance in the labelled messages for three different classes, we employed a data augmentation technique to boost the learning of the deep network. Following techniques from the paper by Jason et al. was utilized in this setting that really helped during the training phase.Thsi techniques wasnt used in previous studies. The techniques were:",
+      "Synonym Replacement (SR):Randomly choose n words from the sentence that are not stop words. Replace each of these words with one of its synonyms chosen at random.",
+      "Random Insertion (RI):Find a random synonym of a random word in the sentence that is not a stop word. Insert that synonym into a random position in the sentence. Do this n times.",
+      "Random Swap (RS):Randomly choose two words in the sentence and swap their positions. Do this n times.",
+      "Random Deletion (RD):For each word in the sentence, randomly remove it with probability p.",
+      "Word Representation: We used word embedding representations by Glove for creating word embedding layers and to obtain the word sequence vector representations of the processed tweets. The pre-trained embedding dimension were one of the hyperparamaters for model. Further more, we introduced another bit flag hyperparameter that determined if to freeze these learnt embedding.",
+      "Train-test split: The labelled dataset that was available for this task was very limited in number of examples and thus as noted above few data augmentation techniques were applied to boost the learning of the network. Before applying augmentation, a train-test split of 78%-22% was done from the original, cleansed data set. Thus, 700 tweets/messages were held out for testing. All model evaluation were done in on the test set that got generated by this process. The results presented in this report are based on the performance of the model on the test set. The training set of 2489 messages were however sent to an offline pipeline for augmenting the data. The resulting training dataset was thus 7934 messages. the final distribution of messages for training and test was thus below:"
+    ],
+    [
+      "We tested the performance of various model architectures by running our experiment over 100 times on a CPU based compute which later as migrated to GPU based compute to overcome the slow learning progress. Our universal metric for minimizing was the validation loss and we employed various operational techniques for optimizing on the learning process. These processes and its implementation details will be discussed later but they were learning rate decay, early stopping, model checkpointing and reducing learning rate on plateau."
+    ],
+    [
+      "For the loss function we chose categorical cross entropy loss in finding the most optimal weights/parameters of the model. Formally this loss function for the model is defined as below:",
+      "The double sum is over the number of observations and the categories respectively. While the model probability is the probability that the observation i belongs to category c."
+    ],
+    [
+      "Among the model architectures we experimented with and without data augmentation were:",
+      "Fully Connected dense networks: Model hyperparameters were inspired from the previous work done by Vo et al and Mathur et al. This was also used as a baseline model but we did not get appreciable performance on such architecture due to FC networks not being able to capture local and long term dependencies.",
+      "Convolution based architectures: Architecture and hyperparameter choices were chosen from the past study Deon on the subject. We were able to boost the performance as compared to only FC based network but we noticed better performance from architectures that are suitable to sequences such as text messages or any timeseries data.",
+      "Sequence models: We used SimpleRNN, LSTM, GRU, Bidirectional LSTM model architecture to capture long term dependencies of the messages in determining the class the message or the tweet belonged to.",
+      "Based on all the experiments we conducted below model had best performance related to metrics - Recall rate, F1 score and Overall accuracy."
+    ],
+    [
+      "Choice of model parameters were in the above models were inspired from previous work done but then were tuned to the best performance of the Test dataset. Following parameters were considered for tuning.",
+      "Learning rate: Based on grid search the best performance was achieved when learning rate was set to 0.01. This value was arrived by a grid search on lr parameter.",
+      "Number of Bidirectional LSTM units: A set of 32, 64, 128 hidden activation units were considered for tuning the model. 128 was a choice made by Vo et al in modeling for Vietnamese language but with our experiments and with a small dataset to avoid overfitting to train dataset, a smaller unit sizes were considered.",
+      "Embedding dimension: 50, 100 and 200 dimension word representation from Glove word embedding were considered and the best results were obtained with 100d representation, consistent with choices made in the previous work.",
+      "Transfer learning on Embedding; Another bit flag for training the embedding on the train data or freezing the embedding from Glove was used. It was determined that set of pre-trained weights from Glove was best when it was fine tuned with Hinglish data. It provides evidence that a separate word or sentence level embedding when learnt for Hinglish text analysis will be very useful.",
+      "Number of dense FC layers.",
+      "Maximum length of the sequence to be considered: The max length of tweets/message in the dataset was 1265 while average was 116. We determined that choosing 200 resulted in the best performance."
+    ],
+    [
+      "During our experimentation, it was evident that this is a hard problem especially detecting the hate speech, text in a code- mixed language. The best recall rate of 77 % for hate speech was obtained by a Bidirectional LSTM with 32 units with a recurrent drop out rate of 0.2. Precision wise GRU type of RNN sequence model faired better than other kinds for hate speech detection. On the other hand for detecting offensive and non offensive tweets, fairly satisfactory results were obtained. For offensive tweets, 92 % precision was and recall rate of 88% was obtained with GRU versus BiLSTM based models. Comparatively, Recall of 85 % and precision of 76 % was obtained by again GRU and BiLSTM based models as shown and marked in the results."
+    ],
+    [
+      "The results of the experiments are encouraging on detective offensive vs non offensive tweets and messages written in Hinglish in social media. The utilization of data augmentation technique in this classification task was one of the vital contributions which led us to surpass results obtained by previous state of the art Hybrid CNN-LSTM based models. However, the results of the model for predicting hateful tweets on the contrary brings forth some shortcomings of the model. The biggest shortcoming on the model based on error analysis indicates less than generalized examples presented by the dataset. We also note that the embedding learnt from the Hinglish data set may be lacking and require extensive training to have competent word representations of Hinglish text. Given this learning's, we identify that creating word embeddings on much larger Hinglish corpora may have significant results. We also hypothesize that considering alternate methods than translation and transliteration may prove beneficial."
+    ],
+    [
+      "[1] Mathur, Puneet and Sawhney, Ramit and Ayyar, Meghna and Shah, Rajiv, Did you offend me? classification of offensive tweets in hinglish language, Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)",
+      "[2] Mathur, Puneet and Shah, Rajiv and Sawhney, Ramit and Mahata, Debanjan Detecting offensive tweets in hindi-english code-switched language Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media",
+      "[3] Vo, Quan-Hoang and Nguyen, Huy-Tien and Le, Bac and Nguyen, Minh-Le Multi-channel LSTM-CNN model for Vietnamese sentiment analysis 2017 9th international conference on knowledge and systems engineering (KSE)",
+      "[4] Hochreiter, Sepp and Schmidhuber, J\u00fcrgen Long short-term memory Neural computation 1997",
+      "[5] Sinha, R Mahesh K and Thakur, Anil Multi-channel LSTM-CNN model for Vietnamese sentiment analysis 2017 9th international conference on knowledge and systems engineering (KSE)",
+      "[6] Pennington, Jeffrey and Socher, Richard and Manning, Christopher Glove: Global vectors for word representation Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)",
+      "[7] Zhang, Lei and Wang, Shuai and Liu, Bing Deep learning for sentiment analysis: A survey Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery",
+      "[8] Caruana, Rich and Lawrence, Steve and Giles, C Lee Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping Advances in neural information processing systems",
+      "[9] Beale, Mark Hudson and Hagan, Martin T and Demuth, Howard B Neural network toolbox user\u2019s guide The MathWorks Incs",
+      "[10] Chollet, Fran\u00e7ois and others Keras: The python deep learning library Astrophysics Source Code Library",
+      "[11] Wei, Jason and Zou, Kai EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)"
+    ]
+  ]
+}
+```

qasper-0189/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: "Hinglish"Language -- Modeling a Messy Code-Mixed Language
+
+Question: What models do previous work use?

qasper-0232/instruction.md

@@ -0,0 +1,160 @@
+Name of Paper: Gating Mechanisms for Combining Character and Word-level Word Representations: An Empirical Study
+
+Question: Where do they employ feature-wise sigmoid gating?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background",
+    "Mapping Characters to Character-level Word Representations",
+    "Combining Character and Word-level Representations",
+    "Obtaining Sentence Representations",
+    "Experimental Setup",
+    "Datasets",
+    "Word Similarity",
+    "Word Frequencies and Gating Values",
+    "Sentence-level Evaluation",
+    "Relationship Between Word- and Sentence-level Evaluation Tasks",
+    "Gating Mechanisms for Combining Characters and Word Representations",
+    "Sentence Representation Learning",
+    "General Feature-wise Transformations",
+    "Conclusions",
+    "Acknowledgements",
+    "Hyperparameters",
+    "Sentence Evaluation Datasets"
+  ],
+  "paragraphs": [
+    [
+      "Incorporating sub-word structures like substrings, morphemes and characters to the creation of word representations significantly increases their quality as reflected both by intrinsic metrics and performance in a wide range of downstream tasks BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 .",
+      "The reason for this improvement is related to sub-word structures containing information that is usually ignored by standard word-level models. Indeed, when representing words as vectors extracted from a lookup table, semantically related words resulting from inflectional processes such as surf, surfing, and surfed, are treated as being independent from one another. Further, word-level embeddings do not account for derivational processes resulting in syntactically-similar words with different meanings such as break, breakable, and unbreakable. This causes derived words, which are usually less frequent, to have lower-quality (or no) vector representations.",
+      "Previous works have successfully combined character-level and word-level word representations, obtaining overall better results than using only word-level representations. For example BIBREF1 achieved state-of-the-art results in a machine translation task by representing unknown words as a composition of their characters. BIBREF4 created word representations by adding the vector representations of the words' surface forms and their morphemes ( INLINEFORM0 ), obtaining significant improvements on intrinsic evaluation tasks, word similarity and machine translation. BIBREF5 concatenated character-level and word-level representations for creating word representations, and then used them as input to their models for obtaining state-of-the-art results in Named Entity Recognition on several languages.",
+      "What these works have in common is that the models they describe first learn how to represent subword information, at character BIBREF1 , morpheme BIBREF4 , or substring BIBREF0 levels, and then combine these learned representations at the word level. The incorporation of information at a finer-grained hierarchy results in higher-quality modeling of rare words, morphological processes, and semantics BIBREF6 .",
+      "There is no consensus, however, on which combination method works better in which case, or how the choice of a combination method affects downstream performance, either measured intrinsically at the word level, or extrinsically at the sentence level.",
+      "In this paper we aim to provide some intuitions about how the choice of mechanism for combining character-level with word-level representations influences the quality of the final word representations, and the subsequent effect these have in the performance of downstream tasks. Our contributions are as follows:"
+    ],
+    [
+      "We are interested in studying different ways of combining word representations, obtained from different hierarchies, into a single word representation. Specifically, we want to study how combining word representations (1) taken directly from a word embedding lookup table, and (2) obtained from a function over the characters composing them, affects the quality of the final word representations.",
+      "Let INLINEFORM0 be a set, or vocabulary, of words with INLINEFORM1 elements, and INLINEFORM2 a vocabulary of characters with INLINEFORM3 elements. Further, let INLINEFORM4 be a sequence of words, and INLINEFORM5 be the sequence of characters composing INLINEFORM6 . Each token INLINEFORM7 can be represented as a vector INLINEFORM8 extracted directly from an embedding lookup table INLINEFORM9 , pre-trained or otherwise, and as a vector INLINEFORM10 built from the characters that compose it; in other words, INLINEFORM11 , where INLINEFORM12 is a function that maps a sequence of characters to a vector.",
+      "The methods for combining word and character-level representations we study, are of the form INLINEFORM0 where INLINEFORM1 is the final word representation."
+    ],
+    [
+      "The function INLINEFORM0 is composed of an embedding layer, an optional context function, and an aggregation function.",
+      "The embedding layer transforms each character INLINEFORM0 into a vector INLINEFORM1 of dimension INLINEFORM2 , by directly taking it from a trainable embedding lookup table INLINEFORM3 . We define the matrix representation of word INLINEFORM4 as INLINEFORM5 .",
+      "The context function takes INLINEFORM0 as input and returns a context-enriched matrix representation INLINEFORM1 , in which each INLINEFORM2 contains a measure of information about its context, and interactions with its neighbors. In particular, we chose to do this by feeding INLINEFORM3 to a BiLSTM BIBREF7 , BIBREF8 .",
+      "Informally, we can think of LSTM BIBREF10 as a function INLINEFORM0 that takes a matrix INLINEFORM1 as input and returns a context-enriched matrix representation INLINEFORM2 , where each INLINEFORM3 encodes information about the previous elements INLINEFORM4 .",
+      "A BiLSTM is simply composed of 2 LSTM, one that reads the input from left to right (forward), and another that does so from right to left (backward). The output of the forward and backward LSTM are INLINEFORM0 and INLINEFORM1 respectively. In the backward case the LSTM reads INLINEFORM2 first and INLINEFORM3 last, therefore INLINEFORM4 will encode the context from INLINEFORM5 .",
+      "The aggregation function takes the context-enriched matrix representation of word INLINEFORM0 for both directions, INLINEFORM1 and INLINEFORM2 , and returns a single vector INLINEFORM3 . To do so we followed BIBREF11 , and defined the character-level representation INLINEFORM4 of word INLINEFORM5 as the linear combination of the forward and backward last hidden states returned by the context function: DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 are trainable parameters, and INLINEFORM2 represents the concatenation operation between two vectors."
+    ],
+    [
+      "We tested three different methods for combining INLINEFORM0 with INLINEFORM1 : simple concatenation, a learned scalar gate BIBREF11 , and a learned vector gate (also referred to as feature-wise sigmoidal gate). Additionally, we compared these methods to two baselines: using pre-trained word vectors only, and using character-only features for representing words. See fig:methods for a visual description of the proposed methods.",
+      "word-only (w) considers only INLINEFORM0 and ignores INLINEFORM1 : DISPLAYFORM0 ",
+      "char-only (c) considers only INLINEFORM0 and ignores INLINEFORM1 : DISPLAYFORM0 ",
+      "concat (cat) concatenates both word and character-level representations: DISPLAYFORM0 ",
+      "scalar gate (sg) implements the scalar gating mechanism described by BIBREF11 : DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 are trainable parameters, INLINEFORM2 , and INLINEFORM3 is the sigmoid function.",
+      "vector gate (vg): DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 are trainable parameters, INLINEFORM2 , INLINEFORM3 is the element-wise sigmoid function, INLINEFORM4 is the element-wise product for vectors, and INLINEFORM5 is a vector of ones.",
+      "The vector gate is inspired by BIBREF11 and BIBREF12 , but is different to the former in that the gating mechanism acts upon each dimension of the word and character-level vectors, and different to the latter in that it does not rely on external sources of information for calculating the gating mechanism.",
+      "Finally, note that word only and char only are special cases of both gating mechanisms: INLINEFORM0 (scalar gate) and INLINEFORM1 (vector gate) correspond to word only; INLINEFORM2 and INLINEFORM3 correspond to char only."
+    ],
+    [
+      "To enable sentence-level classification we need to obtain a sentence representation from the word vectors INLINEFORM0 . We achieved this by using a BiLSTM with max pooling, which was shown to be a good universal sentence encoding mechanism BIBREF13 .",
+      "Let INLINEFORM0 , be an input sentence and INLINEFORM1 its matrix representation, where each INLINEFORM2 was obtained by one of the methods described in subsec:methods. INLINEFORM3 is the context-enriched matrix representation of INLINEFORM4 obtained by feeding INLINEFORM5 to a BiLSTM of output dimension INLINEFORM6 . Lastly, INLINEFORM11 is the final sentence representation of INLINEFORM12 obtained by max-pooling INLINEFORM13 along the sequence dimension.",
+      "Finally, we initialized the word representations INLINEFORM0 using GloVe embeddings BIBREF14 , and fine-tuned them during training. Refer to app:hyperparams for details on the other hyperparameters we used."
+    ],
+    [
+      "We trained our models for solving the Natural Language Inference (NLI) task in two datasets, SNLI BIBREF15 and MultiNLI BIBREF16 , and validated them in each corresponding development set (including the matched and mismatched development sets of MultiNLI).",
+      "For each dataset-method combination we trained 7 models initialized with different random seeds, and saved each when it reached its best validation accuracy. We then evaluated the quality of each trained model's word representations INLINEFORM0 in 10 word similarity tasks, using the system created by BIBREF17 .",
+      "Finally, we fed these obtained word vectors to a BiLSTM with max-pooling and evaluated the final sentence representations in 11 downstream transfer tasks BIBREF13 , BIBREF18 ."
+    ],
+    [
+      "Word-level Semantic Similarity A desirable property of vector representations of words is that semantically similar words should have similar vector representations. Assessing whether a set of word representations possesses this quality is referred to as the semantic similarity task. This is the most widely-used evaluation method for evaluating word representations, despite its shortcomings BIBREF20 .",
+      "This task consists of comparing the similarity between word vectors measured by a distance metric (usually cosine distance), with a similarity score obtained from human judgements. High correlation between these similarities is an indicator of good performance.",
+      "A problem with this formulation though, is that the definition of \u201csimilarity\u201d often confounds the meaning of both similarity and relatedness. For example, cup and tea are related but dissimilar words, and this type of distinction is not always clear BIBREF21 , BIBREF22 .",
+      "To face the previous problem, we tested our methods in a wide variety of datasets, including some that explicitly model relatedness (WS353R), some that explicitly consider similarity (WS353S, SimLex999, SimVerb3500), and some where the distinction is not clear (MEN, MTurk287, MTurk771, RG, WS353). We also included the RareWords (RW) dataset for evaluating the quality of rare word representations. See appendix:datasets for a more complete description of the datasets we used.",
+      "Sentence-level Evaluation Tasks Unlike word-level representations, there is no consensus on the desirable properties sentence representations should have. In response to this, BIBREF13 created SentEval, a sentence representation evaluation benchmark designed for assessing how well sentence representations perform in various downstream tasks BIBREF23 .",
+      "Some of the datasets included in SentEval correspond to sentiment classification (CR, MPQA, MR, SST2, and SST5), subjectivity classification (SUBJ), question-type classification (TREC), recognizing textual entailment (SICK E), estimating semantic relatedness (SICK R), and measuring textual semantic similarity (STS16, STSB). The datasets are described by BIBREF13 , and we provide pointers to their original sources in the appendix table:sentence-eval-datasets.",
+      "To evaluate these sentence representations SentEval trained a linear model on top of them, and evaluated their performance in the validation sets accompanying each dataset. The only exception was the STS16 task, in which our representations were evaluated directly."
+    ],
+    [
+      "table:wordlevelresults shows the quality of word representations in terms of the correlation between word similarity scores obtained by the proposed models and word similarity scores defined by humans.",
+      "First, we can see that for each task, character only models had significantly worse performance than every other model trained on the same dataset. The most likely explanation for this is that these models are the only ones that need to learn word representations from scratch, since they have no access to the global semantic knowledge encoded by the GloVe embeddings.",
+      "Further, bold results show the overall trend that vector gates outperformed the other methods regardless of training dataset. This implies that learning how to combine character and word-level representations at the dimension level produces word vector representations that capture a notion of word similarity and relatedness that is closer to that of humans.",
+      "Additionally, results from the MNLI row in general, and underlined results in particular, show that training on MultiNLI produces word representations better at capturing word similarity. This is probably due to MultiNLI data being richer than that of SNLI. Indeed, MultiNLI data was gathered from various sources (novels, reports, letters, and telephone conversations, among others), rather than the single image captions dataset from which SNLI was created.",
+      "Exceptions to the previous rule are models evaluated in MEN and RW. The former case can be explained by the MEN dataset containing only words that appear as image labels in the ESP-Game and MIRFLICKR-1M image datasets BIBREF24 , and therefore having data that is more closely distributed to SNLI than to MultiNLI.",
+      "More notably, in the RareWords dataset BIBREF25 , the word only, concat, and scalar gate methods performed equally, despite having been trained in different datasets ( INLINEFORM0 ), and the char only method performed significantly worse when trained in MultiNLI. The vector gate, however, performed significantly better than its counterpart trained in SNLI. These facts provide evidence that this method is capable of capturing linguistic phenomena that the other methods are unable to model.",
+      "table:word-similarity-dataset lists the word-similarity datasets and their corresponding reference. As mentioned in subsec:datasets, all the word-similarity datasets contain pairs of words annotated with similarity or relatedness scores, although this difference is not always explicit. Below we provide some details for each.",
+      "MEN contains 3000 annotated word pairs with integer scores ranging from 0 to 50. Words correspond to image labels appearing in the ESP-Game and MIRFLICKR-1M image datasets.",
+      "MTurk287 contains 287 annotated pairs with scores ranging from 1.0 to 5.0. It was created from words appearing in both DBpedia and in news articles from The New York Times.",
+      "MTurk771 contains 771 annotated pairs with scores ranging from 1.0 to 5.0, with words having synonymy, holonymy or meronymy relationships sampled from WordNet BIBREF56 .",
+      "RG contains 65 annotated pairs with scores ranging from 0.0 to 4.0 representing \u201csimilarity of meaning\u201d.",
+      "RW contains 2034 pairs of words annotated with similarity scores in a scale from 0 to 10. The words included in this dataset were obtained from Wikipedia based on their frequency, and later filtered depending on their WordNet synsets, including synonymy, hyperonymy, hyponymy, holonymy and meronymy. This dataset was created with the purpose of testing how well models can represent rare and complex words.",
+      "SimLex999 contains 999 word pairs annotated with similarity scores ranging from 0 to 10. In this case the authors explicitly considered similarity and not relatedness, addressing the shortcomings of datasets that do not, such as MEN and WS353. Words include nouns, adjectives and verbs.",
+      "SimVerb3500 contains 3500 verb pairs annotated with similarity scores ranging from 0 to 10. Verbs were obtained from the USF free association database BIBREF66 , and VerbNet BIBREF63 . This dataset was created to address the lack of representativity of verbs in SimLex999, and the fact that, at the time of creation, the best performing models had already surpassed inter-annotator agreement in verb similarity evaluation resources. Like SimLex999, this dataset also explicitly considers similarity as opposed to relatedness.",
+      "WS353 contains 353 word pairs annotated with similarity scores from 0 to 10.",
+      "WS353R is a subset of WS353 containing 252 word pairs annotated with relatedness scores. This dataset was created by asking humans to classify each WS353 word pair into one of the following classes: synonyms, antonyms, identical, hyperonym-hyponym, hyponym-hyperonym, holonym-meronym, meronym-holonym, and none-of-the-above. These annotations were later used to group the pairs into: similar pairs (synonyms, antonyms, identical, hyperonym-hyponym, and hyponym-hyperonym), related pairs (holonym-meronym, meronym-holonym, and none-of-the-above with a human similarity score greater than 5), and unrelated pairs (classified as none-of-the-above with a similarity score less than or equal to 5). This dataset is composed by the union of related and unrelated pairs.",
+      "WS353S is another subset of WS353 containing 203 word pairs annotated with similarity scores. This dataset is composed by the union of similar and unrelated pairs, as described previously."
+    ],
+    [
+      "fig:gatingviz shows that for more common words the vector gate mechanism tends to favor only a few dimensions while keeping a low average gating value across dimensions. On the other hand, values are greater and more homogeneous across dimensions in rarer words. Further, fig:freqvsgatevalue shows this mechanism assigns, on average, a greater gating value to less frequent words, confirming the findings by BIBREF11 , and BIBREF12 .",
+      "In other words, the less frequent the word, the more this mechanism allows the character-level representation to influence the final word representation, as shown by eq:vg. A possible interpretation of this result is that exploiting character information becomes increasingly necessary as word-level representations' quality decrease.",
+      "Another observable trend in both figures is that gating values tend to be low on average. Indeed, it is possible to see in fig:freqvsgatevalue that the average gating values range from INLINEFORM0 to INLINEFORM1 . This result corroborates the findings by BIBREF11 , stating that setting INLINEFORM2 in eq:scalar-gate, was better than setting it to higher values.",
+      "In summary, the gating mechanisms learn how to compensate the lack of expressivity of underrepresented words by selectively combining their representations with those of characters."
+    ],
+    [
+      "table:sentlevelresults shows the impact that different methods for combining character and word-level word representations have in the quality of the sentence representations produced by our models.",
+      "We can observe the same trend mentioned in subsec:word-similarity-eval, and highlighted by the difference between bold values, that models trained in MultiNLI performed better than those trained in SNLI at a statistically significant level, confirming the findings of BIBREF13 . In other words, training sentence encoders on MultiNLI yields more general sentence representations than doing so on SNLI.",
+      "The two exceptions to the previous trend, SICKE and SICKR, benefited more from models trained on SNLI. We hypothesize this is again due to both SNLI and SICK BIBREF26 having similar data distributions.",
+      "Additionally, there was no method that significantly outperformed the word only baseline in classification tasks. This means that the added expressivity offered by explicitly modeling characters, be it through concatenation or gating, was not significantly better than simply fine-tuning the pre-trained GloVe embeddings for this type of task. We hypothesize this is due to the conflation of two effects. First, the fact that morphological processes might not encode important information for solving these tasks; and second, that SNLI and MultiNLI belong to domains that are too dissimilar to the domains in which the sentence representations are being tested.",
+      "On the other hand, the vector gate significantly outperformed every other method in the STSB task when trained in both datasets, and in the STS16 task when trained in SNLI. This again hints at this method being capable of modeling phenomena at the word level, resulting in improved semantic representations at the sentence level."
+    ],
+    [
+      "It is clear that the better performance the vector gate had in word similarity tasks did not translate into overall better performance in downstream tasks. This confirms previous findings indicating that intrinsic word evaluation metrics are not good predictors of downstream performance BIBREF29 , BIBREF30 , BIBREF20 , BIBREF31 .",
+      "subfig:mnli-correlations shows that the word representations created by the vector gate trained in MultiNLI had positively-correlated results within several word-similarity tasks. This hints at the generality of the word representations created by this method when modeling similarity and relatedness.",
+      "However, the same cannot be said about sentence-level evaluation performance; there is no clear correlation between word similarity tasks and sentence-evaluation tasks. This is clearly illustrated by performance in the STSBenchmark, the only in which the vector gate was significantly superior, not being correlated with performance in any word-similarity dataset. This can be interpreted simply as word-level representations capturing word-similarity not being a sufficient condition for good performance in sentence-level tasks.",
+      "In general, fig:correlations shows that there are no general correlation effects spanning both training datasets and combination mechanisms. For example, subfig:snli-correlations shows that, for both word-only and concat models trained in SNLI, performance in word similarity tasks correlates positively with performance in most sentence evaluation tasks, however, this does not happen as clearly for the same models trained in MultiNLI (subfig:mnli-correlations)."
+    ],
+    [
+      "To the best of our knowledge, there are only two recent works that specifically study how to combine word and subword-level vector representations.",
+      " BIBREF11 propose to use a trainable scalar gating mechanism capable of learning a weighting scheme for combining character-level and word-level representations. They compared their proposed method to manually weighting both levels; using characters only; words only; or their concatenation. They found that in some datasets a specific manual weighting scheme performed better, while in others the learned scalar gate did.",
+      " BIBREF12 further expand the gating concept by making the mechanism work at a finer-grained level, learning how to weight each vector's dimensions independently, conditioned on external word-level features such as part-of-speech and named-entity tags. Similarly, they compared their proposed mechanism to using words only, characters only, and a concatenation of both, with and without external features. They found that their vector gate performed better than the other methods in all the reported tasks, and beat the state of the art in two reading comprehension tasks.",
+      "Both works showed that the gating mechanisms assigned greater importance to character-level representations in rare words, and to word-level representations in common ones, reaffirming the previous findings that subword structures in general, and characters in particular, are beneficial for modeling uncommon words."
+    ],
+    [
+      "The problem of representing sentences as fixed-length vectors has been widely studied.",
+      " BIBREF32 suggested a self-adaptive hierarchical model that gradually composes words into intermediate phrase representations, and adaptively selects specific hierarchical levels for specific tasks. BIBREF33 proposed an encoder-decoder model trained by attempting to reconstruct the surrounding sentences of an encoded passage, in a fashion similar to Skip-gram BIBREF34 . BIBREF35 overcame the previous model's need for ordered training sentences by using autoencoders for creating the sentence representations. BIBREF36 implemented a model simpler and faster to train than the previous two, while having competitive performance. Similar to BIBREF33 , BIBREF37 suggested predicting future sentences with a hierarchical CNN-LSTM encoder.",
+      " BIBREF13 trained several sentence encoding architectures on a combination of the SNLI and MultiNLI datasets, and showed that a BiLSTM with max-pooling was the best at producing highly transferable sentence representations. More recently, BIBREF18 empirically showed that sentence representations created in a multi-task setting BIBREF38 , performed increasingly better the more tasks they were trained in. BIBREF39 proposed using an autoencoder that relies on multi-head self-attention over the concatenation of the max and mean pooled encoder outputs for producing sentence representations. Finally, BIBREF40 show that modern sentence embedding methods are not vastly superior to random methods.",
+      "The works mentioned so far usually evaluate the quality of the produced sentence representations in sentence-level downstream tasks. Common benchmarks grouping these kind of tasks include SentEval BIBREF23 , and GLUE BIBREF41 . Another trend, however, is to probe sentence representations to understand what linguistic phenomena they encode BIBREF42 , BIBREF43 , BIBREF44 , BIBREF45 , BIBREF46 ."
+    ],
+    [
+      " BIBREF47 provide a review on feature-wise transformation methods, of which the mechanisms presented in this paper form a part of. In a few words, the INLINEFORM0 parameter, in both scalar gate and vector gate mechanisms, can be understood as a scaling parameter limited to the INLINEFORM1 range and conditioned on word representations, whereas adding the scaled INLINEFORM2 and INLINEFORM3 representations can be seen as biasing word representations conditioned on character representations.",
+      "The previous review extends the work by BIBREF48 , which describes the Feature-wise Linear Modulation (FiLM) framework as a generalization of Conditional Normalization methods, and apply it in visual reasoning tasks. Some of the reported findings are that, in general, scaling has greater impact than biasing, and that in a setting similar to the scalar gate, limiting the scaling parameter to INLINEFORM0 hurt performance. Future decisions involving the design of mechanisms for combining character and word-level representations should be informed by these insights."
+    ],
+    [
+      "We presented an empirical study showing the effect that different ways of combining character and word representations has in word-level and sentence-level evaluation tasks.",
+      "We showed that a vector gate performed consistently better across a variety of word similarity and relatedness tasks. Additionally, despite showing inconsistent results in sentence evaluation tasks, it performed significantly better than the other methods in semantic similarity tasks.",
+      "We further showed through this mechanism, that learning character-level representations is always beneficial, and becomes increasingly so with less common words.",
+      "In the future it would be interesting to study how the choice of mechanism for combining subword and word representations affects the more recent language-model-based pretraining methods such as ELMo BIBREF49 , GPT BIBREF50 , BIBREF51 and BERT BIBREF52 ."
+    ],
+    [
+      "Thanks to Edison Marrese-Taylor and Pablo Loyola for their feedback on early versions of this manuscript. We also gratefully acknowledge the support of the NVIDIA Corporation with the donation of one of the GPUs used for this research. Jorge A. Balazs is partially supported by the Japanese Government MEXT Scholarship."
+    ],
+    [
+      "We only considered words that appear at least twice, for each dataset. Those that appeared only once were considered UNK. We used the Treebank Word Tokenizer as implemented in NLTK for tokenizing the training and development datasets.",
+      "In the same fashion as conneau2017supervised, we used a batch size of 64, an SGD optmizer with an initial learning rate of INLINEFORM0 , and at each epoch divided the learning rate by 5 if the validation accuracy decreased. We also used gradient clipping when gradients where INLINEFORM1 .",
+      "We defined character vector representations as 50-dimensional vectors randomly initialized by sampling from the uniform distribution in the INLINEFORM0 range.",
+      "The output dimension of the character-level BiLSTM was 300 per direction, and remained of such size after combining forward and backward representations as depicted in eq. EQREF9 .",
+      "Word vector representations where initialized from the 300-dimensional GloVe vectors BIBREF14 , trained in 840B tokens from the Common Crawl, and finetuned during training. Words not present in the GloVe vocabulary where randomly initialized by sampling from the uniform distribution in the INLINEFORM0 range.",
+      "The input size of the word-level LSTM was 300 for every method except concat in which it was 600, and its output was always 2048 per direction, resulting in a 4096-dimensional sentence representation."
+    ],
+    [
+      "table:sentence-eval-datasets lists the sentence-level evaluation datasets used in this paper. The provided URLs correspond to the original sources, and not necessarily to the URLs where SentEval got the data from.",
+      "The version of the CR, MPQA, MR, and SUBJ datasets used in this paper were the ones preprocessed by BIBREF75 . Both SST2 and SST5 correspond to preprocessed versions of the SST dataset by BIBREF74 . SST2 corresponds to a subset of SST used by BIBREF54 containing flat representations of sentences annotated with binary sentiment labels, and SST5 to another subset annotated with more fine-grained sentiment labels (very negative, negative, neutral, positive, very positive)."
+    ]
+  ]
+}
+```

qasper-0235/instruction.md

@@ -0,0 +1,160 @@
+Name of Paper: Gating Mechanisms for Combining Character and Word-level Word Representations: An Empirical Study
+
+Question: Which similarity datasets do they use?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background",
+    "Mapping Characters to Character-level Word Representations",
+    "Combining Character and Word-level Representations",
+    "Obtaining Sentence Representations",
+    "Experimental Setup",
+    "Datasets",
+    "Word Similarity",
+    "Word Frequencies and Gating Values",
+    "Sentence-level Evaluation",
+    "Relationship Between Word- and Sentence-level Evaluation Tasks",
+    "Gating Mechanisms for Combining Characters and Word Representations",
+    "Sentence Representation Learning",
+    "General Feature-wise Transformations",
+    "Conclusions",
+    "Acknowledgements",
+    "Hyperparameters",
+    "Sentence Evaluation Datasets"
+  ],
+  "paragraphs": [
+    [
+      "Incorporating sub-word structures like substrings, morphemes and characters to the creation of word representations significantly increases their quality as reflected both by intrinsic metrics and performance in a wide range of downstream tasks BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 .",
+      "The reason for this improvement is related to sub-word structures containing information that is usually ignored by standard word-level models. Indeed, when representing words as vectors extracted from a lookup table, semantically related words resulting from inflectional processes such as surf, surfing, and surfed, are treated as being independent from one another. Further, word-level embeddings do not account for derivational processes resulting in syntactically-similar words with different meanings such as break, breakable, and unbreakable. This causes derived words, which are usually less frequent, to have lower-quality (or no) vector representations.",
+      "Previous works have successfully combined character-level and word-level word representations, obtaining overall better results than using only word-level representations. For example BIBREF1 achieved state-of-the-art results in a machine translation task by representing unknown words as a composition of their characters. BIBREF4 created word representations by adding the vector representations of the words' surface forms and their morphemes ( INLINEFORM0 ), obtaining significant improvements on intrinsic evaluation tasks, word similarity and machine translation. BIBREF5 concatenated character-level and word-level representations for creating word representations, and then used them as input to their models for obtaining state-of-the-art results in Named Entity Recognition on several languages.",
+      "What these works have in common is that the models they describe first learn how to represent subword information, at character BIBREF1 , morpheme BIBREF4 , or substring BIBREF0 levels, and then combine these learned representations at the word level. The incorporation of information at a finer-grained hierarchy results in higher-quality modeling of rare words, morphological processes, and semantics BIBREF6 .",
+      "There is no consensus, however, on which combination method works better in which case, or how the choice of a combination method affects downstream performance, either measured intrinsically at the word level, or extrinsically at the sentence level.",
+      "In this paper we aim to provide some intuitions about how the choice of mechanism for combining character-level with word-level representations influences the quality of the final word representations, and the subsequent effect these have in the performance of downstream tasks. Our contributions are as follows:"
+    ],
+    [
+      "We are interested in studying different ways of combining word representations, obtained from different hierarchies, into a single word representation. Specifically, we want to study how combining word representations (1) taken directly from a word embedding lookup table, and (2) obtained from a function over the characters composing them, affects the quality of the final word representations.",
+      "Let INLINEFORM0 be a set, or vocabulary, of words with INLINEFORM1 elements, and INLINEFORM2 a vocabulary of characters with INLINEFORM3 elements. Further, let INLINEFORM4 be a sequence of words, and INLINEFORM5 be the sequence of characters composing INLINEFORM6 . Each token INLINEFORM7 can be represented as a vector INLINEFORM8 extracted directly from an embedding lookup table INLINEFORM9 , pre-trained or otherwise, and as a vector INLINEFORM10 built from the characters that compose it; in other words, INLINEFORM11 , where INLINEFORM12 is a function that maps a sequence of characters to a vector.",
+      "The methods for combining word and character-level representations we study, are of the form INLINEFORM0 where INLINEFORM1 is the final word representation."
+    ],
+    [
+      "The function INLINEFORM0 is composed of an embedding layer, an optional context function, and an aggregation function.",
+      "The embedding layer transforms each character INLINEFORM0 into a vector INLINEFORM1 of dimension INLINEFORM2 , by directly taking it from a trainable embedding lookup table INLINEFORM3 . We define the matrix representation of word INLINEFORM4 as INLINEFORM5 .",
+      "The context function takes INLINEFORM0 as input and returns a context-enriched matrix representation INLINEFORM1 , in which each INLINEFORM2 contains a measure of information about its context, and interactions with its neighbors. In particular, we chose to do this by feeding INLINEFORM3 to a BiLSTM BIBREF7 , BIBREF8 .",
+      "Informally, we can think of LSTM BIBREF10 as a function INLINEFORM0 that takes a matrix INLINEFORM1 as input and returns a context-enriched matrix representation INLINEFORM2 , where each INLINEFORM3 encodes information about the previous elements INLINEFORM4 .",
+      "A BiLSTM is simply composed of 2 LSTM, one that reads the input from left to right (forward), and another that does so from right to left (backward). The output of the forward and backward LSTM are INLINEFORM0 and INLINEFORM1 respectively. In the backward case the LSTM reads INLINEFORM2 first and INLINEFORM3 last, therefore INLINEFORM4 will encode the context from INLINEFORM5 .",
+      "The aggregation function takes the context-enriched matrix representation of word INLINEFORM0 for both directions, INLINEFORM1 and INLINEFORM2 , and returns a single vector INLINEFORM3 . To do so we followed BIBREF11 , and defined the character-level representation INLINEFORM4 of word INLINEFORM5 as the linear combination of the forward and backward last hidden states returned by the context function: DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 are trainable parameters, and INLINEFORM2 represents the concatenation operation between two vectors."
+    ],
+    [
+      "We tested three different methods for combining INLINEFORM0 with INLINEFORM1 : simple concatenation, a learned scalar gate BIBREF11 , and a learned vector gate (also referred to as feature-wise sigmoidal gate). Additionally, we compared these methods to two baselines: using pre-trained word vectors only, and using character-only features for representing words. See fig:methods for a visual description of the proposed methods.",
+      "word-only (w) considers only INLINEFORM0 and ignores INLINEFORM1 : DISPLAYFORM0 ",
+      "char-only (c) considers only INLINEFORM0 and ignores INLINEFORM1 : DISPLAYFORM0 ",
+      "concat (cat) concatenates both word and character-level representations: DISPLAYFORM0 ",
+      "scalar gate (sg) implements the scalar gating mechanism described by BIBREF11 : DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 are trainable parameters, INLINEFORM2 , and INLINEFORM3 is the sigmoid function.",
+      "vector gate (vg): DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 are trainable parameters, INLINEFORM2 , INLINEFORM3 is the element-wise sigmoid function, INLINEFORM4 is the element-wise product for vectors, and INLINEFORM5 is a vector of ones.",
+      "The vector gate is inspired by BIBREF11 and BIBREF12 , but is different to the former in that the gating mechanism acts upon each dimension of the word and character-level vectors, and different to the latter in that it does not rely on external sources of information for calculating the gating mechanism.",
+      "Finally, note that word only and char only are special cases of both gating mechanisms: INLINEFORM0 (scalar gate) and INLINEFORM1 (vector gate) correspond to word only; INLINEFORM2 and INLINEFORM3 correspond to char only."
+    ],
+    [
+      "To enable sentence-level classification we need to obtain a sentence representation from the word vectors INLINEFORM0 . We achieved this by using a BiLSTM with max pooling, which was shown to be a good universal sentence encoding mechanism BIBREF13 .",
+      "Let INLINEFORM0 , be an input sentence and INLINEFORM1 its matrix representation, where each INLINEFORM2 was obtained by one of the methods described in subsec:methods. INLINEFORM3 is the context-enriched matrix representation of INLINEFORM4 obtained by feeding INLINEFORM5 to a BiLSTM of output dimension INLINEFORM6 . Lastly, INLINEFORM11 is the final sentence representation of INLINEFORM12 obtained by max-pooling INLINEFORM13 along the sequence dimension.",
+      "Finally, we initialized the word representations INLINEFORM0 using GloVe embeddings BIBREF14 , and fine-tuned them during training. Refer to app:hyperparams for details on the other hyperparameters we used."
+    ],
+    [
+      "We trained our models for solving the Natural Language Inference (NLI) task in two datasets, SNLI BIBREF15 and MultiNLI BIBREF16 , and validated them in each corresponding development set (including the matched and mismatched development sets of MultiNLI).",
+      "For each dataset-method combination we trained 7 models initialized with different random seeds, and saved each when it reached its best validation accuracy. We then evaluated the quality of each trained model's word representations INLINEFORM0 in 10 word similarity tasks, using the system created by BIBREF17 .",
+      "Finally, we fed these obtained word vectors to a BiLSTM with max-pooling and evaluated the final sentence representations in 11 downstream transfer tasks BIBREF13 , BIBREF18 ."
+    ],
+    [
+      "Word-level Semantic Similarity A desirable property of vector representations of words is that semantically similar words should have similar vector representations. Assessing whether a set of word representations possesses this quality is referred to as the semantic similarity task. This is the most widely-used evaluation method for evaluating word representations, despite its shortcomings BIBREF20 .",
+      "This task consists of comparing the similarity between word vectors measured by a distance metric (usually cosine distance), with a similarity score obtained from human judgements. High correlation between these similarities is an indicator of good performance.",
+      "A problem with this formulation though, is that the definition of \u201csimilarity\u201d often confounds the meaning of both similarity and relatedness. For example, cup and tea are related but dissimilar words, and this type of distinction is not always clear BIBREF21 , BIBREF22 .",
+      "To face the previous problem, we tested our methods in a wide variety of datasets, including some that explicitly model relatedness (WS353R), some that explicitly consider similarity (WS353S, SimLex999, SimVerb3500), and some where the distinction is not clear (MEN, MTurk287, MTurk771, RG, WS353). We also included the RareWords (RW) dataset for evaluating the quality of rare word representations. See appendix:datasets for a more complete description of the datasets we used.",
+      "Sentence-level Evaluation Tasks Unlike word-level representations, there is no consensus on the desirable properties sentence representations should have. In response to this, BIBREF13 created SentEval, a sentence representation evaluation benchmark designed for assessing how well sentence representations perform in various downstream tasks BIBREF23 .",
+      "Some of the datasets included in SentEval correspond to sentiment classification (CR, MPQA, MR, SST2, and SST5), subjectivity classification (SUBJ), question-type classification (TREC), recognizing textual entailment (SICK E), estimating semantic relatedness (SICK R), and measuring textual semantic similarity (STS16, STSB). The datasets are described by BIBREF13 , and we provide pointers to their original sources in the appendix table:sentence-eval-datasets.",
+      "To evaluate these sentence representations SentEval trained a linear model on top of them, and evaluated their performance in the validation sets accompanying each dataset. The only exception was the STS16 task, in which our representations were evaluated directly."
+    ],
+    [
+      "table:wordlevelresults shows the quality of word representations in terms of the correlation between word similarity scores obtained by the proposed models and word similarity scores defined by humans.",
+      "First, we can see that for each task, character only models had significantly worse performance than every other model trained on the same dataset. The most likely explanation for this is that these models are the only ones that need to learn word representations from scratch, since they have no access to the global semantic knowledge encoded by the GloVe embeddings.",
+      "Further, bold results show the overall trend that vector gates outperformed the other methods regardless of training dataset. This implies that learning how to combine character and word-level representations at the dimension level produces word vector representations that capture a notion of word similarity and relatedness that is closer to that of humans.",
+      "Additionally, results from the MNLI row in general, and underlined results in particular, show that training on MultiNLI produces word representations better at capturing word similarity. This is probably due to MultiNLI data being richer than that of SNLI. Indeed, MultiNLI data was gathered from various sources (novels, reports, letters, and telephone conversations, among others), rather than the single image captions dataset from which SNLI was created.",
+      "Exceptions to the previous rule are models evaluated in MEN and RW. The former case can be explained by the MEN dataset containing only words that appear as image labels in the ESP-Game and MIRFLICKR-1M image datasets BIBREF24 , and therefore having data that is more closely distributed to SNLI than to MultiNLI.",
+      "More notably, in the RareWords dataset BIBREF25 , the word only, concat, and scalar gate methods performed equally, despite having been trained in different datasets ( INLINEFORM0 ), and the char only method performed significantly worse when trained in MultiNLI. The vector gate, however, performed significantly better than its counterpart trained in SNLI. These facts provide evidence that this method is capable of capturing linguistic phenomena that the other methods are unable to model.",
+      "table:word-similarity-dataset lists the word-similarity datasets and their corresponding reference. As mentioned in subsec:datasets, all the word-similarity datasets contain pairs of words annotated with similarity or relatedness scores, although this difference is not always explicit. Below we provide some details for each.",
+      "MEN contains 3000 annotated word pairs with integer scores ranging from 0 to 50. Words correspond to image labels appearing in the ESP-Game and MIRFLICKR-1M image datasets.",
+      "MTurk287 contains 287 annotated pairs with scores ranging from 1.0 to 5.0. It was created from words appearing in both DBpedia and in news articles from The New York Times.",
+      "MTurk771 contains 771 annotated pairs with scores ranging from 1.0 to 5.0, with words having synonymy, holonymy or meronymy relationships sampled from WordNet BIBREF56 .",
+      "RG contains 65 annotated pairs with scores ranging from 0.0 to 4.0 representing \u201csimilarity of meaning\u201d.",
+      "RW contains 2034 pairs of words annotated with similarity scores in a scale from 0 to 10. The words included in this dataset were obtained from Wikipedia based on their frequency, and later filtered depending on their WordNet synsets, including synonymy, hyperonymy, hyponymy, holonymy and meronymy. This dataset was created with the purpose of testing how well models can represent rare and complex words.",
+      "SimLex999 contains 999 word pairs annotated with similarity scores ranging from 0 to 10. In this case the authors explicitly considered similarity and not relatedness, addressing the shortcomings of datasets that do not, such as MEN and WS353. Words include nouns, adjectives and verbs.",
+      "SimVerb3500 contains 3500 verb pairs annotated with similarity scores ranging from 0 to 10. Verbs were obtained from the USF free association database BIBREF66 , and VerbNet BIBREF63 . This dataset was created to address the lack of representativity of verbs in SimLex999, and the fact that, at the time of creation, the best performing models had already surpassed inter-annotator agreement in verb similarity evaluation resources. Like SimLex999, this dataset also explicitly considers similarity as opposed to relatedness.",
+      "WS353 contains 353 word pairs annotated with similarity scores from 0 to 10.",
+      "WS353R is a subset of WS353 containing 252 word pairs annotated with relatedness scores. This dataset was created by asking humans to classify each WS353 word pair into one of the following classes: synonyms, antonyms, identical, hyperonym-hyponym, hyponym-hyperonym, holonym-meronym, meronym-holonym, and none-of-the-above. These annotations were later used to group the pairs into: similar pairs (synonyms, antonyms, identical, hyperonym-hyponym, and hyponym-hyperonym), related pairs (holonym-meronym, meronym-holonym, and none-of-the-above with a human similarity score greater than 5), and unrelated pairs (classified as none-of-the-above with a similarity score less than or equal to 5). This dataset is composed by the union of related and unrelated pairs.",
+      "WS353S is another subset of WS353 containing 203 word pairs annotated with similarity scores. This dataset is composed by the union of similar and unrelated pairs, as described previously."
+    ],
+    [
+      "fig:gatingviz shows that for more common words the vector gate mechanism tends to favor only a few dimensions while keeping a low average gating value across dimensions. On the other hand, values are greater and more homogeneous across dimensions in rarer words. Further, fig:freqvsgatevalue shows this mechanism assigns, on average, a greater gating value to less frequent words, confirming the findings by BIBREF11 , and BIBREF12 .",
+      "In other words, the less frequent the word, the more this mechanism allows the character-level representation to influence the final word representation, as shown by eq:vg. A possible interpretation of this result is that exploiting character information becomes increasingly necessary as word-level representations' quality decrease.",
+      "Another observable trend in both figures is that gating values tend to be low on average. Indeed, it is possible to see in fig:freqvsgatevalue that the average gating values range from INLINEFORM0 to INLINEFORM1 . This result corroborates the findings by BIBREF11 , stating that setting INLINEFORM2 in eq:scalar-gate, was better than setting it to higher values.",
+      "In summary, the gating mechanisms learn how to compensate the lack of expressivity of underrepresented words by selectively combining their representations with those of characters."
+    ],
+    [
+      "table:sentlevelresults shows the impact that different methods for combining character and word-level word representations have in the quality of the sentence representations produced by our models.",
+      "We can observe the same trend mentioned in subsec:word-similarity-eval, and highlighted by the difference between bold values, that models trained in MultiNLI performed better than those trained in SNLI at a statistically significant level, confirming the findings of BIBREF13 . In other words, training sentence encoders on MultiNLI yields more general sentence representations than doing so on SNLI.",
+      "The two exceptions to the previous trend, SICKE and SICKR, benefited more from models trained on SNLI. We hypothesize this is again due to both SNLI and SICK BIBREF26 having similar data distributions.",
+      "Additionally, there was no method that significantly outperformed the word only baseline in classification tasks. This means that the added expressivity offered by explicitly modeling characters, be it through concatenation or gating, was not significantly better than simply fine-tuning the pre-trained GloVe embeddings for this type of task. We hypothesize this is due to the conflation of two effects. First, the fact that morphological processes might not encode important information for solving these tasks; and second, that SNLI and MultiNLI belong to domains that are too dissimilar to the domains in which the sentence representations are being tested.",
+      "On the other hand, the vector gate significantly outperformed every other method in the STSB task when trained in both datasets, and in the STS16 task when trained in SNLI. This again hints at this method being capable of modeling phenomena at the word level, resulting in improved semantic representations at the sentence level."
+    ],
+    [
+      "It is clear that the better performance the vector gate had in word similarity tasks did not translate into overall better performance in downstream tasks. This confirms previous findings indicating that intrinsic word evaluation metrics are not good predictors of downstream performance BIBREF29 , BIBREF30 , BIBREF20 , BIBREF31 .",
+      "subfig:mnli-correlations shows that the word representations created by the vector gate trained in MultiNLI had positively-correlated results within several word-similarity tasks. This hints at the generality of the word representations created by this method when modeling similarity and relatedness.",
+      "However, the same cannot be said about sentence-level evaluation performance; there is no clear correlation between word similarity tasks and sentence-evaluation tasks. This is clearly illustrated by performance in the STSBenchmark, the only in which the vector gate was significantly superior, not being correlated with performance in any word-similarity dataset. This can be interpreted simply as word-level representations capturing word-similarity not being a sufficient condition for good performance in sentence-level tasks.",
+      "In general, fig:correlations shows that there are no general correlation effects spanning both training datasets and combination mechanisms. For example, subfig:snli-correlations shows that, for both word-only and concat models trained in SNLI, performance in word similarity tasks correlates positively with performance in most sentence evaluation tasks, however, this does not happen as clearly for the same models trained in MultiNLI (subfig:mnli-correlations)."
+    ],
+    [
+      "To the best of our knowledge, there are only two recent works that specifically study how to combine word and subword-level vector representations.",
+      " BIBREF11 propose to use a trainable scalar gating mechanism capable of learning a weighting scheme for combining character-level and word-level representations. They compared their proposed method to manually weighting both levels; using characters only; words only; or their concatenation. They found that in some datasets a specific manual weighting scheme performed better, while in others the learned scalar gate did.",
+      " BIBREF12 further expand the gating concept by making the mechanism work at a finer-grained level, learning how to weight each vector's dimensions independently, conditioned on external word-level features such as part-of-speech and named-entity tags. Similarly, they compared their proposed mechanism to using words only, characters only, and a concatenation of both, with and without external features. They found that their vector gate performed better than the other methods in all the reported tasks, and beat the state of the art in two reading comprehension tasks.",
+      "Both works showed that the gating mechanisms assigned greater importance to character-level representations in rare words, and to word-level representations in common ones, reaffirming the previous findings that subword structures in general, and characters in particular, are beneficial for modeling uncommon words."
+    ],
+    [
+      "The problem of representing sentences as fixed-length vectors has been widely studied.",
+      " BIBREF32 suggested a self-adaptive hierarchical model that gradually composes words into intermediate phrase representations, and adaptively selects specific hierarchical levels for specific tasks. BIBREF33 proposed an encoder-decoder model trained by attempting to reconstruct the surrounding sentences of an encoded passage, in a fashion similar to Skip-gram BIBREF34 . BIBREF35 overcame the previous model's need for ordered training sentences by using autoencoders for creating the sentence representations. BIBREF36 implemented a model simpler and faster to train than the previous two, while having competitive performance. Similar to BIBREF33 , BIBREF37 suggested predicting future sentences with a hierarchical CNN-LSTM encoder.",
+      " BIBREF13 trained several sentence encoding architectures on a combination of the SNLI and MultiNLI datasets, and showed that a BiLSTM with max-pooling was the best at producing highly transferable sentence representations. More recently, BIBREF18 empirically showed that sentence representations created in a multi-task setting BIBREF38 , performed increasingly better the more tasks they were trained in. BIBREF39 proposed using an autoencoder that relies on multi-head self-attention over the concatenation of the max and mean pooled encoder outputs for producing sentence representations. Finally, BIBREF40 show that modern sentence embedding methods are not vastly superior to random methods.",
+      "The works mentioned so far usually evaluate the quality of the produced sentence representations in sentence-level downstream tasks. Common benchmarks grouping these kind of tasks include SentEval BIBREF23 , and GLUE BIBREF41 . Another trend, however, is to probe sentence representations to understand what linguistic phenomena they encode BIBREF42 , BIBREF43 , BIBREF44 , BIBREF45 , BIBREF46 ."
+    ],
+    [
+      " BIBREF47 provide a review on feature-wise transformation methods, of which the mechanisms presented in this paper form a part of. In a few words, the INLINEFORM0 parameter, in both scalar gate and vector gate mechanisms, can be understood as a scaling parameter limited to the INLINEFORM1 range and conditioned on word representations, whereas adding the scaled INLINEFORM2 and INLINEFORM3 representations can be seen as biasing word representations conditioned on character representations.",
+      "The previous review extends the work by BIBREF48 , which describes the Feature-wise Linear Modulation (FiLM) framework as a generalization of Conditional Normalization methods, and apply it in visual reasoning tasks. Some of the reported findings are that, in general, scaling has greater impact than biasing, and that in a setting similar to the scalar gate, limiting the scaling parameter to INLINEFORM0 hurt performance. Future decisions involving the design of mechanisms for combining character and word-level representations should be informed by these insights."
+    ],
+    [
+      "We presented an empirical study showing the effect that different ways of combining character and word representations has in word-level and sentence-level evaluation tasks.",
+      "We showed that a vector gate performed consistently better across a variety of word similarity and relatedness tasks. Additionally, despite showing inconsistent results in sentence evaluation tasks, it performed significantly better than the other methods in semantic similarity tasks.",
+      "We further showed through this mechanism, that learning character-level representations is always beneficial, and becomes increasingly so with less common words.",
+      "In the future it would be interesting to study how the choice of mechanism for combining subword and word representations affects the more recent language-model-based pretraining methods such as ELMo BIBREF49 , GPT BIBREF50 , BIBREF51 and BERT BIBREF52 ."
+    ],
+    [
+      "Thanks to Edison Marrese-Taylor and Pablo Loyola for their feedback on early versions of this manuscript. We also gratefully acknowledge the support of the NVIDIA Corporation with the donation of one of the GPUs used for this research. Jorge A. Balazs is partially supported by the Japanese Government MEXT Scholarship."
+    ],
+    [
+      "We only considered words that appear at least twice, for each dataset. Those that appeared only once were considered UNK. We used the Treebank Word Tokenizer as implemented in NLTK for tokenizing the training and development datasets.",
+      "In the same fashion as conneau2017supervised, we used a batch size of 64, an SGD optmizer with an initial learning rate of INLINEFORM0 , and at each epoch divided the learning rate by 5 if the validation accuracy decreased. We also used gradient clipping when gradients where INLINEFORM1 .",
+      "We defined character vector representations as 50-dimensional vectors randomly initialized by sampling from the uniform distribution in the INLINEFORM0 range.",
+      "The output dimension of the character-level BiLSTM was 300 per direction, and remained of such size after combining forward and backward representations as depicted in eq. EQREF9 .",
+      "Word vector representations where initialized from the 300-dimensional GloVe vectors BIBREF14 , trained in 840B tokens from the Common Crawl, and finetuned during training. Words not present in the GloVe vocabulary where randomly initialized by sampling from the uniform distribution in the INLINEFORM0 range.",
+      "The input size of the word-level LSTM was 300 for every method except concat in which it was 600, and its output was always 2048 per direction, resulting in a 4096-dimensional sentence representation."
+    ],
+    [
+      "table:sentence-eval-datasets lists the sentence-level evaluation datasets used in this paper. The provided URLs correspond to the original sources, and not necessarily to the URLs where SentEval got the data from.",
+      "The version of the CR, MPQA, MR, and SUBJ datasets used in this paper were the ones preprocessed by BIBREF75 . Both SST2 and SST5 correspond to preprocessed versions of the SST dataset by BIBREF74 . SST2 corresponds to a subset of SST used by BIBREF54 containing flat representations of sentences annotated with binary sentiment labels, and SST5 to another subset annotated with more fine-grained sentiment labels (very negative, negative, neutral, positive, very positive)."
+    ]
+  ]
+}
+```

qasper-0313/instruction.md

@@ -0,0 +1,84 @@
+Name of Paper: Multitask Learning with CTC and Segmental CRF for Speech Recognition
+
+Question: Can SCRF be used to pretrain the model?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Segmental Conditional Random Fields",
+    "Feature Function and Acoustic Embedding",
+    "Loss Function",
+    "Connectionist Temporal Classification ",
+    "Joint Training Loss",
+    "Experiments",
+    "Baseline Results",
+    "Multitask Learning Results",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "State-of-the-art speech recognition accuracy has significantly improved over the past few years since the application of deep neural networks BIBREF0 , BIBREF1 . Recently, it has been shown that with the application of both neural network acoustic model and language model, an automatic speech recognizer can approach human-level accuracy on the Switchboard conversational speech recognition benchmark using around 2,000 hours of transcribed data BIBREF2 . While progress is mainly driven by well engineered neural network architectures and a large amount of training data, the hidden Markov model (HMM) that has been the backbone for speech recognition for decades is still playing a central role. Though tremendously successful for the problem of speech recognition, the HMM-based pipeline factorizes the whole system into several components, and building these components separately may be less computationally efficient when developing a large-scale system from thousands to hundred of thousands of examples BIBREF3 .",
+      "Recently, along with hybrid HMM/NN frameworks for speech recognition, there has been increasing interest in end-to-end training approaches. The key idea is to directly map the input acoustic frames to output characters or words without the intermediate alignment to context-dependent phones used by HMMs. In particular, three architectures have been proposed for the goal of end-to-end learning: connectionist temporal classification (CTC) BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , sequence-to-sequence with attention model BIBREF8 , BIBREF9 , BIBREF10 , and neural network segmental conditional random field (SCRF) BIBREF11 , BIBREF12 . These end-to-end models simplify the pipeline of speech recognition significantly. They do not require intermediate alignment or segmentation like HMMs, instead, the alignment or segmentation is marginalized out during training for CTC and SCRF or inferred by the attention mechanism. In terms of the recognition accuracy, however, the end-to-end models usually lag behind their HMM-based counterparts. Though CTC has been shown to outperform HMM systems BIBREF13 , the improvement is based on the use of context-dependent phone targets and a very large amount of training data. Therefore, it has almost the same system complexity as HMM acoustic models. When the training data is less abundant, it has been shown that the accuracy of CTC systems degrades significantly BIBREF14 .",
+      "However, end-to-end models have the flexibility to be combined to mitigate their individual weaknesses. For instance, multitask learning with attention models has been investigated for machine translation BIBREF15 , and Mandarin speech recognition using joint Character-Pinyin training BIBREF16 . In BIBREF17 , Kim et al. proposed a multitask learning approach to train a joint attention model and a CTC model using a shared encoder. They showed that the CTC auxiliary task can help the attention model to overcome the misalignment problem in the initial few epochs, and speed up the convergence of the attention model. Another nice property of the multitask learning approach is that the joint model can still be trained end-to-end. Inspired by this work, we study end-to-end training of a joint CTC and SCRF model using an interpolated loss function. The key difference of our study from BIBREF17 is that the two loss functions of the CTC and attention models are locally normalized for each output token, and they are both trained using the cross entropy criterion. However, the SCRF loss function is normalized at the sequence-level, which is similar to the sequence discriminative training objective function for HMMs. From this perspective, the interpolation of CTC and SCRF loss functions is analogous to the sequence discriminative training of HMMs with CE regularization to overcome overfitting, where a sequence-level loss is also interpolated with a frame-level loss, e.g., BIBREF18 . Similar to the observations in BIBREF17 , we demonstrate that the joint training approach improves the recognition accuracies of both CTC and SCRF acoustic models. Further, we also show that CTC can be used to pretrain the neural network feature extractor to speed up the convergence of the joint model. Experiments were performed on the TIMIT database."
+    ],
+    [
+      "SCRF is a variant of the linear-chain CRF model where each output token corresponds to a segment of input tokens instead of a single input instance. In the context of speech recognition, given a sequence of input vectors of $T$ frames ${X} = ( {x}_1, \\cdots , {x}_T )$ and its corresponding sequence of output labels ${y} = ( y_1, \\cdots , y_J)$ , the zero-order linear-chain CRF defines the sequence-level conditional probability as P(y X) = 1Z(X) t=1T f ( yt, xt ), where $Z({X})$ denotes the normalization term, and $T=J$ . Extension to higher order models is straightforward, but it is usually computationally much more expensive. The model defined in Eq. ( \"Segmental Conditional Random Fields\" ) requires the length of ${X}$ and ${y}$ to be equal, which makes it inappropriate for speech recognition because the lengths of the input and output sequences are not equal. For the case where $T\\ge J$ as in speech recognition, SCRF defines the sequence-level conditional probability with the auxiliary segment labels ${E} = ({e}_1, \\cdots , {e}_J) $ as P(y, E X) = 1Z(X) j=1J f ( yj, ej, xj ), where $\\mathbf {e}_j = \\langle s_{j}, n_{j} \\rangle $ is a tuple of the beginning ( ${X} = ( {x}_1, \\cdots , {x}_T )$0 ) and the end ( ${X} = ( {x}_1, \\cdots , {x}_T )$1 ) time tag for the segment of ${X} = ( {x}_1, \\cdots , {x}_T )$2 , and ${X} = ( {x}_1, \\cdots , {x}_T )$3 while ${X} = ( {x}_1, \\cdots , {x}_T )$4 ; ${X} = ( {x}_1, \\cdots , {x}_T )$5 and ${X} = ( {x}_1, \\cdots , {x}_T )$6 denotes the vocabulary set; ${X} = ( {x}_1, \\cdots , {x}_T )$7 is the embedding vector of the segment corresponding to the token ${X} = ( {x}_1, \\cdots , {x}_T )$8 . In this case, ${X} = ( {x}_1, \\cdots , {x}_T )$9 sums over all the possible ${y} = ( y_1, \\cdots , y_J)$0 pairs, i.e., ",
+      "$$Z({X}) = \\sum _{y,E} \\prod _{j=1}^J \\exp f \\left( y_j, {e}_j, \\bar{x}_j \\right).$$   (Eq. 1) ",
+      "Similar to other CRFs, the function $f(\\cdot )$ is defined as ",
+      "$$f \\left( y_j, {e}_j, \\bar{x}_t \\right) = \\mathbf {w}^\\top \\Phi (y_j, {e}_j, \\bar{x}_j),$$   (Eq. 2) ",
+      "where $\\Phi (\\cdot )$ denotes the feature function, and $\\mathbf {w}$ is the weight vector. Most of conventional approaches for SCRF-based acoustic models use a manually defined feature function $\\Phi (\\cdot )$ , where the features and segment boundary information are provided by an auxiliary system BIBREF19 , BIBREF20 . In BIBREF21 , BIBREF12 , we proposed an end-to-end training approach for SCRFs, where $\\Phi (\\cdot )$ was defined with neural networks, and the segmental level features were learned by RNNs. The model was referred to as the segmental RNN (SRNN), and it will be used as the implementation of the SCRF acoustic model for multitask learning in this study."
+    ],
+    [
+      "SRNN uses an RNN to learn segmental level acoustic embeddings. Given the input sequence ${X} = ({x}_1, \\cdots , {x}_T)$ , and we need to compute the embedding vector $\\bar{x}_j$ in Eq. ( 2 ) corresponding to the segment ${e}_j = \\langle s_j, n_j\\rangle $ . Since the segment boundaries are known, it is straightforward to employ an RNN to map the segment into a vector as [ l hsj",
+      "hsj+1",
+      " $\\vdots $ ",
+      "hnj ] = [ l RNN(h0, xsj)",
+      "RNN(hsj, xsj+1)",
+      " $\\vdots $ ",
+      "RNN(hnj-1, xnj) ] where ${h}_0$ denotes the initial hidden state, which is initialized to be zero. RNN( $\\cdot $ ) denotes the nonlinear recurrence operation used in an RNN, which takes the previous hidden state and the feature vector at the current timestep as inputs, and produce an updated hidden state vector. Given the recurrent hidden states, the embedding vector can be simply defined as $\\bar{x}_j= {h}_{n_j}$ as in our previous work BIBREF12 . However, the drawback of this implementation is the large memory cost, as we need to store the array of hidden states $({h}_{s_j}, \\cdots , {h}_{n_j})$ for all the possible segments $\\langle s_j, n_j\\rangle $ . If we denote $H$ as the dimension of an RNN hidden state, the memory cost will be on the order of $O(T^2H)$ , where $T$ is the length of $X$ . It is especially problematic for the joint model as the CTC model requires additional memory space. In this work, we adopt another approach that requires much less memory. In this approach, we use an RNN to read the whole input sequence as [ c h1",
+      "h2",
+      " $\\vdots $ ",
+      "hT ] = [ l RNN(h0, x1)",
+      "RNN(h1, x2)",
+      " $\\vdots $ ",
+      "RNN(hT-1, xT) ] and we define the embedding vector for segment ${e} = \\langle k, t\\rangle $ as xj = [ c hsj",
+      "hnj ] In this case, we only provide the context information for the feature function $\\Phi (\\cdot )$ to extract segmental features. We refer this approach as context-aware embedding. Since we only need to read the input sequence once, the memory requirement is on the order of $O(TH)$ , which is much smaller. The cost, however, is the slightly degradation of the recognition accuracy. This model is illustrated by Figure 1 .",
+      "The feature function $\\Phi (\\cdot )$ also requires a vector representation of the label $y_j$ . This embedding vector can be obtained using a linear embedding matrix, following common practice for RNN language models. More specifically, $y_j$ is first represented as a one-hot vector ${v}_j$ , and it is then mapped into a continuous space by a linear embedding matrix ${M}$ as ",
+      "$${u}_j = {M v}_j$$   (Eq. 4) ",
+      "Given the acoustic embedding $\\bar{x}_j$ and label embedding $u_j$ , the feature function $\\Phi (\\cdot )$ can be represented as (yj, ej, xj) = (W1uj + W2xj + b), where $\\sigma $ denotes a non-linear activation function (e.g., sigmoid or tanh); $W_1, W_2$ and $b$ are weight matrices and a bias vector. Eq. ( \"Connectionist Temporal Classification \" ) corresponds to one layer of non-linear transformation. In fact, it is straightforward to stack multiple nonlinear layers in this feature function."
+    ],
+    [
+      "For speech recognition, the segmentation labels ${E}$ are usually unknown in the training set. In this case, we cannot train the model directly by maximizing the conditional probability in Eq. ( \"Segmental Conditional Random Fields\" ). However, the problem can be addressed by marginalizing out the segmentation variable as Lscrf = - P(y X)",
+      "= - E P(y, E X)",
+      "= - E j f ( yj, ej, xj ) Z(X, y) + Z(X), where $Z({X}, {y})$ denotes the summation over all the possible segmentations when only ${y}$ is observed. To simplify notation, the objective function $\\mathcal {L}_{\\mathit {scrf}}$ is defined here with only one training utterance.",
+      "However, the number of possible segmentations is exponential in the length of ${X}$ , which makes the na\u00efve computation of both $Z({X}, {y})$ and $Z({X})$ impractical. To address this problem, a dynamic programming algorithm can be applied, which can reduce the computational complexity to $O(T^2\\cdot |\\mathcal {Y}|)$ BIBREF22 . The computational cost can be further reduced by limiting the maximum length of all the possible segments. The reader is referred to BIBREF12 for further details including the decoding algorithm."
+    ],
+    [
+      "CTC also directly computes the conditional probability $P(y \\mid X)$ , with the key difference from SCRF in that it normalizes the probabilistic distribution at the frame level. To address the problem of length mismatch between the input and output sequences, CTC allows repetitions of output labels and introduces a special blank token ( $-$ ), which represents the probability of not emitting any label at a particular time step. The conditional probability is then obtained by summing over all the probabilities of all the paths that corresponding to $y$ after merging the repeated labels and removing the blank tokens, i.e., P(y X) = (y) P(X), where $\\Psi (y)$ denotes the set of all possible paths that correspond to $y$ after repetitions of labels and insertions of the blank token. Now the length of $\\pi $ is the same as $X$ , the probability $P(\\pi \\mid X)$ is then approximated by the independence assumption as P(X) t=1T P(t xt), where $\\pi _t $ ranges over $\\mathcal {Y}\\cup \\lbrace -\\rbrace $ , and $-$0 can be computed using the softmax function. The training criterion for CTC is to maximize the conditional probability of the ground truth labels, which is equivalent to minimizing the negative log likelihood: Lctc = -P(y X), which can be reformulated as the CE criterion. More details regarding the computation of the loss and the backpropagation algorithm to train CTC models can be found in BIBREF23 ."
+    ],
+    [
+      "Training the two models jointly is trivial. We can simply interpolate the CTC and SCRF loss functions as L = Lctc + (1-)Lscrf, where $\\lambda \\in [0, 1]$ is the interpolation weight. The two models share the same neural network for feature extraction. In this work, we focus on the RNN with long short-term memory (LSTM) BIBREF24 units for feature extraction. Other types of neural architecture, e.g., convolutional neural network (CNN) or combinations of CNN and RNN, may be considered in future work."
+    ],
+    [
+      "Our experiments were performed on the TIMIT database, and both the SRNN and CTC models were implemented using the DyNet toolkit BIBREF25 . We followed the standard protocol of the TIMIT dataset, and our experiments were based on the Kaldi recipe BIBREF26 . We used the core test set as our evaluation set, which has 192 utterances. Our models were trained with 48 phonemes, and their predictions were converted to 39 phonemes before scoring. The dimension of $\\mathbf {u}_j$ was fixed to be 64, and the dimension of $\\mathbf {w}$ in Eq. ( 2 ) is also 64. We set the initial SGD learning rate to be 0.1, and we exponentially decay the learning rate by 0.75 when the validation error stopped decreasing. We also subsampled the acoustic sequence by a factor of 4 using the hierarchical RNN as in BIBREF12 . Our models were trained with dropout regularization BIBREF27 , using a specific implementation for recurrent networks BIBREF28 . The dropout rate was 0.2 unless specified otherwise. Our models were randomly initialized with the same random seed."
+    ],
+    [
+      "Table 1 shows the baseline results of SRNN and CTC models using two different kinds of features. The FBANK features are 120-dimensional with delta and delta-delta coefficients, and the fMLLR features are 40-dimensional, which were obtained from a Kaldi baseline system. We used a 3-layer bidirectional LSTMs for feature extraction, and we used the greedy best path decoding algorithm for both models. Our SRNN and CTC achieved comparable phone error rate (PER) for both kinds of features. However, for the CTC system, Graves et al. BIBREF29 obtained a better result, using about the same size of neural network (3 hidden layers with 250 hidden units of bidirectional LSTMs), compared to ours (18.6% vs. 19.9%). Apart from the implementation difference of using different code bases, Graves et al. BIBREF29 applied the prefix decoding with beam search, which may have lower search error than our best path decoding algorithm."
+    ],
+    [
+      "Table 2 shows results of multitask learning for CTC and SRNN using the interpolated loss in Eq. ( \"Joint Training Loss\" ). We only show results of using LSTMs with 250 dimensional hidden states. The interpolation weight was set to be 0.5. In our experiments, tuning the interpolation weight did not further improve the recognition accuracy. From Table 2 , we can see that multitask learning improves recognition accuracies of both SRNN and CTC acoustic models, which may due to the regularization effect of the joint training loss. The improvement for FBANK features is much larger than fMLLR features. In particular, with multitask learning, the recognition accuracy of our CTC system with best path decoding is comparable to the results obtained by Graves et al. BIBREF29 with beam search decoding.",
+      "One of the major drawbacks of SCRF models is their high computational cost. In our experiments, the CTC model is around 3\u20134 times faster than the SRNN model that uses the same RNN encoder. The joint model by multitask learning is slightly more expensive than the stand-alone SRNN model. To cut down the computational cost, we investigated if CTC can be used to pretrain the RNN encoder to speed up the training of the joint model. This is analogous to sequence training of HMM acoustic models, where the network is usually pretrained by the frame-level CE criterion. Figure 2 shows the convergence curves of the joint model with and without CTC pretraining, and we see pretraining indeed improves the convergence speed of the joint model."
+    ],
+    [
+      "We investigated multitask learning with CTC and SCRF for speech recognition in this paper. Using an RNN encoder for feature extraction, both CTC and SCRF can be trained end-to-end, and the two models can be trained together by interpolating the two loss functions. From experiments on the TIMIT dataset, the multitask learning approach improved the recognition accuracies of both CTC and SCRF acoustic models. We also showed that CTC can be used to pretrain the RNN encoder, speeding up the training of the joint model. In the future, we will study the multitask learning approach for larger-scale speech recognition tasks, where the CTC pretraining approach may be more helpful to overcome the problem of high computational cost."
+    ],
+    [
+      "We thank the NVIDIA Corporation for the donation of a Titan X GPU."
+    ]
+  ]
+}
+```

qasper-0314/instruction.md

@@ -0,0 +1,64 @@
+Name of Paper: Filling Gender&Number Gaps in Neural Machine Translation with Black-box Context Injection
+
+Question: What conclusions are drawn from the syntactic analysis?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Morphological Ambiguity in Translation",
+    "Black-Box Knowledge Injection",
+    "Experiments & Results",
+    "Quantitative Results",
+    "Qualitative Results",
+    "Comparison to vanmassenhove-hardmeier-way:2018:EMNLP",
+    "Other Languages",
+    "Related Work",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "A common way for marking information about gender, number, and case in language is morphology, or the structure of a given word in the language. However, different languages mark such information in different ways \u2013 for example, in some languages gender may be marked on the head word of a syntactic dependency relation, while in other languages it is marked on the dependent, on both, or on none of them BIBREF0 . This morphological diversity creates a challenge for machine translation, as there are ambiguous cases where more than one correct translation exists for the same source sentence. For example, while the English sentence \u201cI love language\u201d is ambiguous with respect to the gender of the speaker, Hebrew marks verbs for the gender of their subject and does not allow gender-neutral translation. This allows two possible Hebrew translations \u2013 one in a masculine and the other in a feminine form. As a consequence, a sentence-level translator (either human or machine) must commit to the gender of the speaker, adding information that is not present in the source. Without additional context, this choice must be done arbitrarily by relying on language conventions, world knowledge or statistical (stereotypical) knowledge.",
+      "Indeed, the English sentence \u201cI work as a doctor\u201d is translated into Hebrew by Google Translate using the masculine verb form oved, indicating a male speaker, while \u201cI work as a nurse\u201d is translated with the feminine form ovedet, indicating a female speaker (verified on March 2019). While this is still an issue, there have been recent efforts to reduce it for specific language pairs.",
+      "We present a simple black-box method to influence the interpretation chosen by an NMT system in these ambiguous cases. More concretely, we construct pre-defined textual hints about the gender and number of the speaker and the audience (the interlocutors), which we concatenate to a given input sentence that we would like to translate accordingly. We then show that a black-box NMT system makes the desired morphological decisions according to the given hint, even when no other evidence is available on the source side. While adding those hints results in additional text on the target side, we show that it is simple to remove, leaving only the desired translation.",
+      "Our method is appealing as it only requires simple pre-and-post processing of the inputs and outputs, without considering the system internals, or requiring specific annotated data and training procedure as in previous work BIBREF1 . We show that in spite of its simplicity, it is effective in resolving many of the ambiguities and improves the translation quality in up to 2.3 BLEU when given the correct hints, which may be inferred from text metadata or other sources. Finally, we perform a fine-grained syntactic analysis of the translations generated using our method which shows its effectiveness."
+    ],
+    [
+      "Different languages use different morphological features marking different properties on different elements. For example, English marks for number, case, aspect, tense, person, and degree of comparison. However, English does not mark gender on nouns and verbs. Even when a certain property is marked, languages differ in the form and location of the marking BIBREF0 . For example, marking can occur on the head of a syntactic dependency construction, on its argument, on both (requiring agreement), or on none of them. Translation systems must generate correct target-language morphology as part of the translation process. This requires knowledge of both the source-side and target-side morphology. Current state-of-the-art translation systems do capture many aspects of natural language, including morphology, when a relevant context is available BIBREF2 , BIBREF3 , but resort to \u201cguessing\u201d based on the training-data statistics when it is not. Complications arise when different languages convey different kinds of information in their morphological systems. In such cases, a translation system may be required to remove information available in the source sentence, or to add information not available in it, where the latter can be especially tricky."
+    ],
+    [
+      "Our goal is to supply an NMT system with knowledge regarding the speaker and interlocutor of first-person sentences, in order to produce the desired target-side morphology when the information is not available in the source sentence. The approach we take in the current work is that of black-box injection, in which we attempt to inject knowledge to the input in order to influence the output of a trained NMT system, without having access to its internals or its training procedure as proposed by vanmassenhove-hardmeier-way:2018:EMNLP.",
+      "We are motivated by recent work by BIBREF4 who showed that NMT systems learn to track coreference chains when presented with sufficient discourse context. We conjecture that there are enough sentence-internal pronominal coreference chains appearing in the training data of large-scale NMT systems, such that state-of-the-art NMT systems can and do track sentence-internal coreference. We devise a wrapper method to make use of this coreference tracking ability by introducing artificial antecedents that unambiguously convey the desired gender and number properties of the speaker and audience.",
+      "More concretely, a sentence such as \u201cI love you\u201d is ambiguous with respect to the gender of the speaker and the gender and number of the audience. However, sentences such as \u201cI love you, she told him\u201d are unambiguous given the coreference groups {I, she} and {you, him} which determine I to be feminine singular and you to be masculine singular. We can thus inject the desired information by prefixing a sentence with short generic sentence fragment such as \u201cShe told him:\u201d or \u201cShe told them that\u201d, relying on the NMT system's coreference tracking abilities to trigger the correctly marked translation, and then remove the redundant translated prefix from the generated target sentence. We observed that using a parataxis construction (i.e. \u201cshe said to him:\u201d) almost exclusively results in target-side parataxis as well (in 99.8% of our examples), making it easy to identify and strip the translated version from the target side. Moreover, because the parataxis construction is grammatically isolated from the rest of the sentence, it can be stripped without requiring additional changes or modification to the rest of the sentence, ensuring grammaticality."
+    ],
+    [
+      "To demonstrate our method in a black-box setting, we focus our experiments on Google's machine translation system (GMT), accessed through its Cloud API. To test the method on real-world sentences, we consider a monologue from the stand-up comedy show \u201cSarah Silverman: A Speck of Dust\u201d. The monologue consists of 1,244 English sentences, all by a female speaker conveyed to a plural, gender-neutral audience. Our parallel corpora consists of the 1,244 English sentences from the transcript, and their corresponding Hebrew translations based on the Hebrew subtitles. We translate the monologue one sentence at a time through the Google Cloud API. Eyeballing the results suggest that most of the translations use the incorrect, but default, masculine and singular forms for the speaker and the audience, respectively. We expect that by adding the relevant condition of \u201cfemale speaking to an audience\u201d we will get better translations, affecting both the gender of the speaker and the number of the audience.",
+      "To verify this, we experiment with translating the sentences with the following variations: No Prefix\u2014The baseline translation as returned by the GMT system. \u201cHe said:\u201d\u2014Signaling a male speaker. We expect to further skew the system towards masculine forms. \u201cShe said:\u201d\u2014Signaling a female speaker and unknown audience. As this matches the actual speaker's gender, we expect an improvement in translation of first-person pronouns and verbs with first-person pronouns as subjects. \u201cI said to them:\u201d\u2014Signaling an unknown speaker and plural audience. \u201cHe said to them:\u201d\u2014Masculine speaker and plural audience. \u201cShe said to them:\u201d\u2014Female speaker and plural audience\u2014the complete, correct condition. We expect the best translation accuracy on this setup. \u201cHe/she said to him/her\u201d\u2014Here we set an (incorrect) singular gender-marked audience, to investigate our ability to control the audience morphology."
+    ],
+    [
+      "We compare the different conditions by comparing BLEU BIBREF5 with respect to the reference Hebrew translations. We use the multi-bleu.perl script from the Moses toolkit BIBREF6 . Table shows BLEU scores for the different prefixes. The numbers match our expectations: Generally, providing an incorrect speaker and/or audience information decreases the BLEU scores, while providing the correct information substantially improves it - we see an increase of up to 2.3 BLEU over the baseline. We note the BLEU score improves in all cases, even when given the wrong gender of either the speaker or the audience. We hypothesise this improvement stems from the addition of the word \u201csaid\u201d which hints the model to generate a more \u201cspoken\u201d language which matches the tested scenario. Providing correct information for both speaker and audience usually helps more than providing correct information to either one of them individually. The one outlier is providing \u201cShe\u201d for the speaker and \u201cher\u201d for the audience. While this is not the correct scenario, we hypothesise it gives an improvement in BLEU as it further reinforces the female gender in the sentence."
+    ],
+    [
+      "The BLEU score is an indication of how close the automated translation is to the reference translation, but does not tell us what exactly changed concerning the gender and number properties we attempt to control. We perform a finer-grained analysis focusing on the relation between the injected speaker and audience information, and the morphological realizations of the corresponding elements. We parse the translations and the references using a Hebrew dependency parser. In addition to the parse structure, the parser also performs morphological analysis and tagging of the individual tokens. We then perform the following analysis.",
+      "Speaker's Gender Effects: We search for first-person singular pronouns with subject case (ani, unmarked for gender, corresponding to the English I), and consider the gender of its governing verb (or adjectives in copular constructions such as `I am nice'). The possible genders are `masculine', `feminine' and `both', where the latter indicates a case where the none-diacriticized written form admits both a masculine and a feminine reading. We expect the gender to match the ones requested in the prefix.",
+      "Interlocutors' Gender and Number Effects: We search for second-person pronouns and consider their gender and number. For pronouns in subject position, we also consider the gender and number of their governing verbs (or adjectives in copular constructions). For a singular audience, we expect the gender and number to match the requested ones. For a plural audience, we expect the masculine-plural forms.",
+      "Results: Speaker. Figure FIGREF3 shows the result for controlling the morphological properties of the speaker ({he, she, I} said). It shows the proportion of gender-inflected verbs for the various conditions and the reference. We see that the baseline system severely under-predicts the feminine form of verbs as compared to the reference. The \u201cHe said\u201d conditions further decreases the number of feminine verbs, while the \u201cI said\u201d conditions bring it back to the baseline level. Finally, the \u201cShe said\u201d prefixes substantially increase the number of feminine-marked verbs, bringing the proportion much closer to that of the reference (though still under-predicting some of the feminine cases).",
+      "Results: Audience. The chart in Figure FIGREF3 shows the results for controlling the number of the audience (...to them vs nothing). It shows the proportion of singular vs. plural second-person pronouns on the various conditions. It shows a similar trend: the baseline system severely under-predicts the plural forms with respect to the reference translation, while adding the \u201cto them\u201d condition brings the proportion much closer to that of the reference."
+    ],
+    [
+      "Closely related to our work, vanmassenhove-hardmeier-way:2018:EMNLP proposed a method and an English-French test set to evaluate gender-aware translation, based on the Europarl corpus BIBREF7 . We evaluate our method (using Google Translate and the given prefixes) on their test set to see whether it is applicable to another language pair and domain. Table shows the results of our approach vs. their published results and the Google Translate baseline. As may be expected, Google Translate outperforms their system as it is trained on a different corpus and may use more complex machine translation models. Using our method improves the BLEU score even further."
+    ],
+    [
+      "To test our method\u2019s outputs on multiple languages, we run our pre-and post-processing steps with Google Translate using examples we sourced from native speakers of different languages. For every example we have an English sentence and two translations in the corresponding language, one in masculine and one in feminine form. Not all examples are using the same source English sentence as different languages mark different information. Table shows that for these specific examples our method worked on INLINEFORM0 of the languages we had examples for, while for INLINEFORM1 languages both translations are masculine, and for 1 language both are feminine."
+    ],
+    [
+      "E17-1101 showed that given input with author traits like gender, it is possible to retain those traits in Statistical Machine Translation (SMT) models. W17-4727 showed that incorporating morphological analysis in the decoder improves NMT performance for morphologically rich languages. burlot:hal-01618387 presented a new protocol for evaluating the morphological competence of MT systems, indicating that current translation systems only manage to capture some morphological phenomena correctly. Regarding the application of constraints in NMT, N16-1005 presented a method for controlling the politeness level in the generated output. DBLP:journals/corr/FiclerG17aa showed how to guide a neural text generation system towards style and content parameters like the level of professionalism, subjective/objective, sentiment and others. W17-4811 showed that incorporating more context when translating subtitles can improve the coherence of the generated translations. Most closely to our work, vanmassenhove-hardmeier-way:2018:EMNLP also addressed the missing gender information by training proprietary models with a gender-indicating-prefix. We differ from this work by treating the problem in a black-box manner, and by addressing additional information like the number of the speaker and the gender and number of the audience."
+    ],
+    [
+      "We highlight the problem of translating between languages with different morphological systems, in which the target translation must contain gender and number information that is not available in the source. We propose a method for injecting such information into a pre-trained NMT model in a black-box setting. We demonstrate the effectiveness of this method by showing an improvement of 2.3 BLEU in an English-to-Hebrew translation setting where the speaker and audience gender can be inferred. We also perform a fine-grained syntactic analysis that shows how our method enables to control the morphological realization of first and second-person pronouns, together with verbs and adjectives related to them. In future work we would like to explore automatic generation of the injected context, or the use of cross-sentence context to infer the injected information."
+    ]
+  ]
+}
+```

qasper-0340/instruction.md

@@ -0,0 +1,106 @@
+Name of Paper: Exploring Hate Speech Detection in Multimodal Publications
+
+Question: Are all tweets in English?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work ::: Hate Speech Detection",
+    "Related Work ::: Visual and Textual Data Fusion",
+    "The MMHS150K dataset",
+    "The MMHS150K dataset ::: Tweets Gathering",
+    "The MMHS150K dataset ::: Textual Image Filtering",
+    "The MMHS150K dataset ::: Annotation",
+    "Methodology ::: Unimodal Treatment ::: Images.",
+    "Methodology ::: Unimodal Treatment ::: Tweet Text.",
+    "Methodology ::: Unimodal Treatment ::: Image Text.",
+    "Methodology ::: Multimodal Architectures",
+    "Methodology ::: Multimodal Architectures ::: Feature Concatenation Model (FCM)",
+    "Methodology ::: Multimodal Architectures ::: Spatial Concatenation Model (SCM)",
+    "Methodology ::: Multimodal Architectures ::: Textual Kernels Model (TKM)",
+    "Methodology ::: Multimodal Architectures ::: Training",
+    "Results",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "Social Media platforms such as Facebook, Twitter or Reddit have empowered individuals' voices and facilitated freedom of expression. However they have also been a breeding ground for hate speech and other types of online harassment. Hate speech is defined in legal literature as speech (or any form of expression) that expresses (or seeks to promote, or has the capacity to increase) hatred against a person or a group of people because of a characteristic they share, or a group to which they belong BIBREF0. Twitter develops this definition in its hateful conduct policy as violence against or directly attack or threaten other people on the basis of race, ethnicity, national origin, sexual orientation, gender, gender identity, religious affiliation, age, disability, or serious disease.",
+      "In this work we focus on hate speech detection. Due to the inherent complexity of this task, it is important to distinguish hate speech from other types of online harassment. In particular, although it might be offensive to many people, the sole presence of insulting terms does not itself signify or convey hate speech. And, the other way around, hate speech may denigrate or threaten an individual or a group of people without the use of any profanities. People from the african-american community, for example, often use the term nigga online, in everyday language, without malicious intentions to refer to folks within their community, and the word cunt is often used in non hate speech publications and without any sexist purpose. The goal of this work is not to discuss if racial slur, such as nigga, should be pursued. The goal is to distinguish between publications using offensive terms and publications attacking communities, which we call hate speech.",
+      "Modern social media content usually include images and text. Some of these multimodal publications are only hate speech because of the combination of the text with a certain image. That is because, as we have stated, the presence of offensive terms does not itself signify hate speech, and the presence of hate speech is often determined by the context of a publication. Moreover, users authoring hate speech tend to intentionally construct publications where the text is not enough to determine they are hate speech. This happens especially in Twitter, where multimodal tweets are formed by an image and a short text, which in many cases is not enough to judge them. In those cases, the image might give extra context to make a proper judgement. Fig. FIGREF5 shows some of such examples in MMHS150K.",
+      "The contributions of this work are as follows:",
+      "[noitemsep,leftmargin=*]",
+      "We propose the novel task of hate speech detection in multimodal publications, collect, annotate and publish a large scale dataset.",
+      "We evaluate state of the art multimodal models on this specific task and compare their performance with unimodal detection. Even though images are proved to be useful for hate speech detection, the proposed multimodal models do not outperform unimodal textual models.",
+      "We study the challenges of the proposed task, and open the field for future research."
+    ],
+    [
+      "The literature on detecting hate speech on online textual publications is extensive. Schmidt and Wiegand BIBREF1 recently provided a good survey of it, where they review the terminology used over time, the features used, the existing datasets and the different approaches. However, the field lacks a consistent dataset and evaluation protocol to compare proposed methods. Saleem et al. BIBREF2 compare different classification methods detecting hate speech in Reddit and other forums. Wassem and Hovy BIBREF3 worked on hate speech detection on twitter, published a manually annotated dataset and studied its hate distribution. Later Wassem BIBREF4 extended the previous published dataset and compared amateur and expert annotations, concluding that amateur annotators are more likely than expert annotators to label items as hate speech. Park and Fung BIBREF5 worked on Wassem datasets and proposed a classification method using a CNN over Word2Vec BIBREF6 word embeddings, showing also classification results on racism and sexism hate sub-classes. Davidson et al. BIBREF7 also worked on hate speech detection on twitter, publishing another manually annotated dataset. They test different classifiers such as SVMs and decision trees and provide a performance comparison. Malmasi and Zampieri BIBREF8 worked on Davidson's dataset improving his results using more elaborated features. ElSherief et al. BIBREF9 studied hate speech on twitter and selected the most frequent terms in hate tweets based on Hatebase, a hate expression repository. They propose a big hate dataset but it lacks manual annotations, and all the tweets containing certain hate expressions are considered hate speech. Zhang et al. BIBREF10 recently proposed a more sophisticated approach for hate speech detection, using a CNN and a GRU BIBREF11 over Word2Vec BIBREF6 word embeddings. They show experiments in different datasets outperforming previous methods. Next, we summarize existing hate speech datasets:",
+      "[noitemsep,leftmargin=*]",
+      "RM BIBREF10: Formed by $2,435$ tweets discussing Refugees and Muslims, annotated as hate or non-hate.",
+      "DT BIBREF7: Formed by $24,783$ tweets annotated as hate, offensive language or neither. In our work, offensive language tweets are considered as non-hate.",
+      "WZ-LS BIBREF5: A combination of Wassem datasets BIBREF4, BIBREF3 labeled as racism, sexism, neither or both that make a total of $18,624$ tweets.",
+      "Semi-Supervised BIBREF9: Contains $27,330$ general hate speech Twitter tweets crawled in a semi-supervised manner.",
+      "Although often modern social media publications include images, not too many contributions exist that exploit visual information. Zhong et al. BIBREF12 worked on classifying Instagram images as potential cyberbullying targets, exploiting both the image content, the image caption and the comments. However, their visual information processing is limited to the use of features extracted by a pre-trained CNN, the use of which does not achieve any improvement. Hosseinmardi et al. BIBREF13 also address the problem of detecting cyberbullying incidents on Instagram exploiting both textual and image content. But, again, their visual information processing is limited to use the features of a pre-trained CNN, and the improvement when using visual features on cyberbullying classification is only of 0.01%."
+    ],
+    [
+      "A typical task in multimodal visual and textual analysis is to learn an alignment between feature spaces. To do that, usually a CNN and a RNN are trained jointly to learn a joint embedding space from aligned multimodal data. This approach is applied in tasks such as image captioning BIBREF14, BIBREF15 and multimodal image retrieval BIBREF16, BIBREF17. On the other hand, instead of explicitly learning an alignment between two spaces, the goal of Visual Question Answering (VQA) is to merge both data modalities in order to decide which answer is correct. This problem requires modeling very precise correlations between the image and the question representations. The VQA task requirements are similar to our hate speech detection problem in multimodal publications, where we have a visual and a textual input and we need to combine both sources of information to understand the global context and make a decision. We thus take inspiration from the VQA literature for the tested models. Early VQA methods BIBREF18 fuse textual and visual information by feature concatenation. Later methods, such as Multimodal Compact Bilinear pooling BIBREF19, utilize bilinear pooling to learn multimodal features. An important limitation of these methods is that the multimodal features are fused in the latter model stage, so the textual and visual relationships are modeled only in the last layers. Another limitation is that the visual features are obtained by representing the output of the CNN as a one dimensional vector, which losses the spatial information of the input images. In a recent work, Gao et al. BIBREF20 propose a feature fusion scheme to overcome these limitations. They learn convolution kernels from the textual information \u2013which they call question-guided kernels\u2013 and convolve them with the visual information in an earlier stage to get the multimodal features. Margffoy-Tuay et al. BIBREF21 use a similar approach to combine visual and textual information, but they address a different task: instance segmentation guided by natural language queries. We inspire in these latest feature fusion works to build the models for hate speech detection."
+    ],
+    [
+      "Existing hate speech datasets contain only textual data. Moreover, a reference benchmark does not exists. Most of the published datasets are crawled from Twitter and distributed as tweet IDs but, since Twitter removes reported user accounts, an important amount of their hate tweets is no longer accessible. We create a new manually annotated multimodal hate speech dataset formed by $150,000$ tweets, each one of them containing text and an image. We call the dataset MMHS150K, and made it available online . In this section, we explain the dataset creation steps."
+    ],
+    [
+      "We used the Twitter API to gather real-time tweets from September 2018 until February 2019, selecting the ones containing any of the 51 Hatebase terms that are more common in hate speech tweets, as studied in BIBREF9. We filtered out retweets, tweets containing less than three words and tweets containing porn related terms. From that selection, we kept the ones that included images and downloaded them. Twitter applies hate speech filters and other kinds of content control based on its policy, although the supervision is based on users' reports. Therefore, as we are gathering tweets from real-time posting, the content we get has not yet passed any filter."
+    ],
+    [
+      "We aim to create a multimodal hate speech database where all the instances contain visual and textual information that we can later process to determine if a tweet is hate speech or not. But a considerable amount of the images of the selected tweets contain only textual information, such as screenshots of other tweets. To ensure that all the dataset instances contain both visual and textual information, we remove those tweets. To do that, we use TextFCN BIBREF22, BIBREF23 , a Fully Convolutional Network that produces a pixel wise text probability map of an image. We set empirical thresholds to discard images that have a substantial total text probability, filtering out $23\\%$ of the collected tweets."
+    ],
+    [
+      "We annotate the gathered tweets using the crowdsourcing platform Amazon Mechanical Turk. There, we give the workers the definition of hate speech and show some examples to make the task clearer. We then show the tweet text and image and we ask them to classify it in one of 6 categories: No attacks to any community, racist, sexist, homophobic, religion based attacks or attacks to other communities. Each one of the $150,000$ tweets is labeled by 3 different workers to palliate discrepancies among workers.",
+      "We received a lot of valuable feedback from the annotators. Most of them had understood the task correctly, but they were worried because of its subjectivity. This is indeed a subjective task, highly dependent on the annotator convictions and sensitivity. However, we expect to get cleaner annotations the more strong the attack is, which are the publications we are more interested on detecting. We also detected that several users annotate tweets for hate speech just by spotting slur. As already said previously, just the use of particular words can be offensive to many people, but this is not the task we aim to solve. We have not included in our experiments those hits that were made in less than 3 seconds, understanding that it takes more time to grasp the multimodal context and make a decision.",
+      "We do a majority voting between the three annotations to get the tweets category. At the end, we obtain $112,845$ not hate tweets and $36,978$ hate tweets. The latest are divided in $11,925$ racist, $3,495$ sexist, $3,870$ homophobic, 163 religion-based hate and $5,811$ other hate tweets (Fig. FIGREF17). In this work, we do not use hate sub-categories, and stick to the hate / not hate split. We separate balanced validation ($5,000$) and test ($10,000$) sets. The remaining tweets are used for training.",
+      "We also experimented using hate scores for each tweet computed given the different votes by the three annotators instead of binary labels. The results did not present significant differences to those shown in the experimental part of this work, but the raw annotations will be published nonetheless for further research.",
+      "As far as we know, this dataset is the biggest hate speech dataset to date, and the first multimodal hate speech dataset. One of its challenges is to distinguish between tweets using the same key offensive words that constitute or not an attack to a community (hate speech). Fig. FIGREF18 shows the percentage of hate and not hate tweets of the top keywords."
+    ],
+    [
+      "All images are resized such that their shortest size has 500 pixels. During training, online data augmentation is applied as random cropping of $299\\times 299$ patches and mirroring. We use a CNN as the image features extractor which is an Imagenet BIBREF24 pre-trained Google Inception v3 architecture BIBREF25. The fine-tuning process of the Inception v3 layers aims to modify its weights to extract the features that, combined with the textual information, are optimal for hate speech detection."
+    ],
+    [
+      "We train a single layer LSTM with a 150-dimensional hidden state for hate / not hate classification. The input dimensionality is set to 100 and GloVe BIBREF26 embeddings are used as word input representations. Since our dataset is not big enough to train a GloVe word embedding model, we used a pre-trained model that has been trained in two billion tweets. This ensures that the model will be able to produce word embeddings for slang and other words typically used in Twitter. To process the tweets text before generating the word embeddings, we use the same pipeline as the model authors, which includes generating symbols to encode Twitter special interactions such as user mentions (@user) or hashtags (#hashtag). To encode the tweet text and input it later to multimodal models, we use the LSTM hidden state after processing the last tweet word. Since the LSTM has been trained for hate speech classification, it extracts the most useful information for this task from the text, which is encoded in the hidden state after inputting the last tweet word."
+    ],
+    [
+      "The text in the image can also contain important information to decide if a publication is hate speech or not, so we extract it and also input it to our model. To do so, we use Google Vision API Text Detection module BIBREF27. We input the tweet text and the text from the image separately to the multimodal models, so it might learn different relations between them and between them and the image. For instance, the model could learn to relate the image text with the area in the image where the text appears, so it could learn to interpret the text in a different way depending on the location where it is written in the image. The image text is also encoded by the LSTM as the hidden state after processing its last word."
+    ],
+    [
+      "The objective of this work is to build a hate speech detector that leverages both textual and visual data and detects hate speech publications based on the context given by both data modalities. To study how the multimodal context can boost the performance compared to an unimodal context we evaluate different models: a Feature Concatenation Model (FCM), a Spatial Concatenation Model (SCM) and a Textual Kernels Model (TKM). All of them are CNN+RNN models with three inputs: the tweet image, the tweet text and the text appearing in the image (if any)."
+    ],
+    [
+      "The image is fed to the Inception v3 architecture and the 2048 dimensional feature vector after the last average pooling layer is used as the visual representation. This vector is then concatenated with the 150 dimension vectors of the LSTM last word hidden states of the image text and the tweet text, resulting in a 2348 feature vector. This vector is then processed by three fully connected layers of decreasing dimensionality $(2348, 1024, 512)$ with following corresponding batch normalization and ReLu layers until the dimensions are reduced to two, the number of classes, in the last classification layer. The FCM architecture is illustrated in Fig. FIGREF26."
+    ],
+    [
+      "Instead of using the latest feature vector before classification of the Inception v3 as the visual representation, in the SCM we use the $8\\times 8\\times 2048$ feature map after the last Inception module. Then we concatenate the 150 dimension vectors encoding the tweet text and the tweet image text at each spatial location of that feature map. The resulting multimodal feature map is processed by two Inception-E blocks BIBREF28. After that, dropout and average pooling are applied and, as in the FCM model, three fully connected layers are used to reduce the dimensionality until the classification layer."
+    ],
+    [
+      "The TKM design, inspired by BIBREF20 and BIBREF21, aims to capture interactions between the two modalities more expressively than concatenation models. As in SCM we use the $8\\times 8\\times 2048$ feature map after the last Inception module as the visual representation. From the 150 dimension vector encoding the tweet text, we learn $K_t$ text dependent kernels using independent fully connected layers that are trained together with the rest of the model. The resulting $K_t$ text dependent kernels will have dimensionality of $1\\times 1\\times 2048$. We do the same with the feature vector encoding the image text, learning $K_{it}$ kernels. The textual kernels are convolved with the visual feature map in the channel dimension at each spatial location, resulting in a $8\\times 8\\times (K_i+K_{it})$ multimodal feature map, and batch normalization is applied. Then, as in the SCM, the 150 dimension vectors encoding the tweet text and the tweet image text are concatenated at each spatial dimension. The rest of the architecture is the same as in SCM: two Inception-E blocks, dropout, average pooling and three fully connected layers until the classification layer. The number of tweet textual kernels $K_t$ and tweet image textual kernels $K_it$ is set to $K_t = 10$ and $K_it = 5$. The TKM architecture is illustrated in Fig. FIGREF29."
+    ],
+    [
+      "We train the multimodal models with a Cross-Entropy loss with Softmax activations and an ADAM optimizer with an initial learning rate of $1e-4$. Our dataset suffers from a high class imbalance, so we weight the contribution to the loss of the samples to totally compensate for it. One of the goals of this work is to explore how every one of the inputs contributes to the classification and to prove that the proposed model can learn concurrences between visual and textual data useful to improve the hate speech classification results on multimodal data. To do that we train different models where all or only some inputs are available. When an input is not available, we set it to zeros, and we do the same when an image has no text."
+    ],
+    [
+      "Table TABREF31 shows the F-score, the Area Under the ROC Curve (AUC) and the mean accuracy (ACC) of the proposed models when different inputs are available. $TT$ refers to the tweet text, $IT$ to the image text and $I$ to the image. It also shows results for the LSTM, for the Davison method proposed in BIBREF7 trained with MMHS150K, and for random scores. Fig. FIGREF32 shows the Precision vs Recall plot and the ROC curve (which plots the True Positive Rate vs the False Positive Rate) of the different models.",
+      "First, notice that given the subjectivity of the task and the discrepancies between annotators, getting optimal scores in the evaluation metrics is virtually impossible. However, a system with relatively low metric scores can still be very useful for hate speech detection in a real application: it will fire on publications for which most annotators agree they are hate, which are often the stronger attacks. The proposed LSTM to detect hate speech when only text is available, gets similar results as the method presented in BIBREF7, which we trained with MMHS150K and the same splits. However, more than substantially advancing the state of the art on hate speech detection in textual publications, our key purpose in this work is to introduce and work on its detection on multimodal publications. We use LSTM because it provides a strong representation of the tweet texts.",
+      "The FCM trained only with images gets decent results, considering that in many publications the images might not give any useful information for the task. Fig. FIGREF33 shows some representative examples of the top hate and not hate scored images of this model. Many hate tweets are accompanied by demeaning nudity images, being sexist or homophobic. Other racist tweets are accompanied by images caricaturing black people. Finally, MEMES are also typically used in hate speech publications. The top scored images for not hate are portraits of people belonging to minorities. This is due to the use of slur inside these communities without an offensive intention, such as the word nigga inside the afro-american community or the word dyke inside the lesbian community. These results show that images can be effectively used to discriminate between offensive and non-offensive uses of those words.",
+      "Despite the model trained only with images proves that they are useful for hate speech detection, the proposed multimodal models are not able to improve the detection compared to the textual models. Besides the different architectures, we have tried different training strategies, such as initializing the CNN weights with a model already trained solely with MMHS150K images or using dropout to force the multimodal models to use the visual information. Eventually, though, these models end up using almost only the text input for the prediction and producing very similar results to those of the textual models. The proposed multimodal models, such as TKM, have shown good performance in other tasks, such as VQA. Next, we analyze why they do not perform well in this task and with this data:",
+      "[noitemsep,leftmargin=*]",
+      "Noisy data. A major challenge of this task is the discrepancy between annotations due to subjective judgement. Although this affects also detection using only text, its repercussion is bigger in more complex tasks, such as detection using images or multimodal detection.",
+      "Complexity and diversity of multimodal relations. Hate speech multimodal publications employ a lot of background knowledge which makes the relations between visual and textual elements they use very complex and diverse, and therefore difficult to learn by a neural network.",
+      "Small set of multimodal examples. Fig. FIGREF5 shows some of the challenging multimodal hate examples that we aimed to detect. But although we have collected a big dataset of $150K$ tweets, the subset of multimodal hate there is still too small to learn the complex multimodal relations needed to identify multimodal hate."
+    ],
+    [
+      "In this work we have explored the task of hate speech detection on multimodal publications. We have created MMHS150K, to our knowledge the biggest available hate speech dataset, and the first one composed of multimodal data, namely tweets formed by image and text. We have trained different textual, visual and multimodal models with that data, and found out that, despite the fact that images are useful for hate speech detection, the multimodal models do not outperform the textual models. Finally, we have analyzed the challenges of the proposed task and dataset. Given that most of the content in Social Media nowadays is multimodal, we truly believe on the importance of pushing forward this research. The code used in this work is available in ."
+    ]
+  ]
+}
+```

qasper-0348/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Exploring Hate Speech Detection in Multimodal Publications
+
+Question: What different models for multimodal detection were proposed?

qasper-0377/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Vocabulary-based Method for Quantifying Controversy in Social Media
+
+Question: How many languages do they experiment with?

qasper-0378/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Semantic Sentiment Analysis of Twitter Data
+
+Question: What is the current SOTA for sentiment analysis on Twitter at the time of writing?

qasper-0383/instruction.md

@@ -0,0 +1,100 @@
+Name of Paper: COSTRA 1.0: A Dataset of Complex Sentence Transformations
+
+Question: How are possible sentence transformations represented in dataset, as new sentences?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background",
+    "Annotation",
+    "Annotation ::: First Round: Collecting Ideas",
+    "Annotation ::: Second Round: Collecting Data ::: Sentence Transformations",
+    "Annotation ::: Second Round: Collecting Data ::: Seed Data",
+    "Annotation ::: Second Round: Collecting Data ::: Spell-Checking",
+    "Dataset Description",
+    "Dataset Description ::: First Observations",
+    "Conclusion and Future Work"
+  ],
+  "paragraphs": [
+    [
+      "Vector representations are becoming truly essential in majority of natural language processing tasks. Word embeddings became widely popular with the introduction of word2vec BIBREF0 and GloVe BIBREF1 and their properties have been analyzed in length from various aspects.",
+      "Studies of word embeddings range from word similarity BIBREF2, BIBREF3, over the ability to capture derivational relations BIBREF4, linear superposition of multiple senses BIBREF5, the ability to predict semantic hierarchies BIBREF6 or POS tags BIBREF7 up to data efficiency BIBREF8.",
+      "Several studies BIBREF9, BIBREF10, BIBREF11, BIBREF12 show that word vector representations are capable of capturing meaningful syntactic and semantic regularities. These include, for example, male/female relation demonstrated by the pairs \u201cman:woman\u201d, \u201cking:queen\u201d and the country/capital relation (\u201cRussia:Moscow\u201d, \u201cJapan:Tokyo\u201d). These regularities correspond to simple arithmetic operations in the vector space.",
+      "Sentence embeddings are becoming equally ubiquitous in NLP, with novel representations appearing almost every other week. With an overwhelming number of methods to compute sentence vector representations, the study of their general properties becomes difficult. Furthermore, it is not so clear in which way the embeddings should be evaluated.",
+      "In an attempt to bring together more traditional representations of sentence meanings and the emerging vector representations, bojar:etal:jnle:representations:2019 introduce a number of aspects or desirable properties of sentence embeddings. One of them is denoted as \u201crelatability\u201d, which highlights the correspondence between meaningful differences between sentences and geometrical relations between their respective embeddings in the highly dimensional continuous vector space. If such a correspondence could be found, we could use geometrical operations in the space to induce meaningful changes in sentences.",
+      "In this work, we present COSTRA, a new dataset of COmplex Sentence TRAnsformations. In its first version, the dataset is limited to sample sentences in Czech. The goal is to support studies of semantic and syntactic relations between sentences in the continuous space. Our dataset is the prerequisite for one of possible ways of exploring sentence meaning relatability: we envision that the continuous space of sentences induced by an ideal embedding method would exhibit topological similarity to the graph of sentence variations. For instance, one could argue that a subset of sentences could be organized along a linear scale reflecting the formalness of the language used. Another set of sentences could form a partially ordered set of gradually less and less concrete statements. And yet another set, intersecting both of the previous ones in multiple sentences could be partially or linearly ordered according to the strength of the speakers confidence in the claim.",
+      "Our long term goal is to search for an embedding method which exhibits this behaviour, i.e. that the topological map of the embedding space corresponds to meaningful operations or changes in the set of sentences of a language (or more languages at once). We prefer this behaviour to emerge, as it happened for word vector operations, but regardless if the behaviour is emergent or trained, we need a dataset of sentences illustrating these patterns. If large enough, such a dataset could serve for training. If it will be smaller, it will provide a test set. In either case, these sentences could provide a \u201cskeleton\u201d to the continuous space of sentence embeddings.",
+      "The paper is structured as follows: related summarizes existing methods of sentence embeddings evaluation and related work. annotation describes our methodology for constructing our dataset. data details the obtained dataset and some first observations. We conclude and provide the link to the dataset in conclusion"
+    ],
+    [
+      "As hinted above, there are many methods of converting a sequence of words into a vector in a highly dimensional space. To name a few: BiLSTM with the max-pooling trained for natural language inference BIBREF13, masked language modeling and next sentence prediction using bidirectional Transformer BIBREF14, max-pooling last states of neural machine translation among many languages BIBREF15 or the encoder final state in attentionless neural machine translation BIBREF16.",
+      "The most common way of evaluating methods of sentence embeddings is extrinsic, using so called `transfer tasks', i.e. comparing embeddings via the performance in downstream tasks such as paraphrasing, entailment, sentence sentiment analysis, natural language inference and other assignments. However, even simple bag-of-words (BOW) approaches achieve often competitive results on such tasks BIBREF17.",
+      "Adi16 introduce intrinsic evaluation by measuring the ability of models to encode basic linguistic properties of a sentence such as its length, word order, and word occurrences. These so called `probing tasks' are further extended by a depth of the syntactic tree, top constituent or verb tense by DBLP:journals/corr/abs-1805-01070.",
+      "Both transfer and probing tasks are integrated in SentEval BIBREF18 framework for sentence vector representations. Later, Perone2018 applied SentEval to eleven different encoding methods revealing that there is no consistently well performing method across all tasks. SentEval was further criticized for pitfalls such as comparing different embedding sizes or correlation between tasks BIBREF19, BIBREF20.",
+      "shi-etal-2016-string show that NMT encoder is able to capture syntactic information about the source sentence. DBLP:journals/corr/BelinkovDDSG17 examine the ability of NMT to learn morphology through POS and morphological tagging.",
+      "Still, very little is known about semantic properties of sentence embeddings. Interestingly, cifka:bojar:meanings:2018 observe that the better self-attention embeddings serve in NMT, the worse they perform in most of SentEval tasks.",
+      "zhu-etal-2018-exploring generate automatically sentence variations such as:",
+      "Original sentence: A rooster pecked grain.",
+      "Synonym Substitution: A cock pecked grain.",
+      "Not-Negation: A rooster didn't peck grain.",
+      "Quantifier-Negation: There was no rooster pecking grain.",
+      "and compare their triplets by examining distances between their embeddings, i.e. distance between (1) and (2) should be smaller than distances between (1) and (3), (2) and (3), similarly, (3) and (4) should be closer together than (1)\u2013(3) or (1)\u2013(4).",
+      "In our previous study BIBREF21, we examined the effect of small sentence alternations in sentence vector spaces. We used sentence pairs automatically extracted from datasets for natural language inference BIBREF22, BIBREF23 and observed, that the simple vector difference, familiar from word embeddings, serves reasonably well also in sentence embedding spaces. The examined relations were however very simple: a change of gender, number, addition of an adjective, etc. The structure of the sentence and its wording remained almost identical.",
+      "We would like to move to more interesting non-trivial sentence comparison, beyond those in zhu-etal-2018-exploring or BaBo2019, such as change of style of a sentence, the introduction of a small modification that drastically changes the meaning of a sentence or reshuffling of words in a sentence that alters its meaning.",
+      "Unfortunately, such a dataset cannot be generated automatically and it is not available to our best knowledge. We try to start filling this gap with COSTRA 1.0."
+    ],
+    [
+      "We acquired the data in two rounds of annotation. In the first one, we were looking for original and uncommon sentence change suggestions. In the second one, we collected sentence alternations using ideas from the first round. The first and second rounds of annotation could be broadly called as collecting ideas and collecting data, respectively."
+    ],
+    [
+      "We manually selected 15 newspaper headlines. Eleven annotators were asked to modify each headline up to 20 times and describe the modification with a short name. They were given an example sentence and several of its possible alternations, see tab:firstroundexamples.",
+      "Unfortunately, these examples turned out to be highly influential on the annotators' decisions and they correspond to almost two thirds of all of modifications gathered in the first round. Other very common transformations include change of a word order or transformation into a interrogative/imperative sentence.",
+      "Other interesting modification were also proposed such as change into a fairy-tale style, excessive use of diminutives/vulgarisms or dadaism\u2014a swap of roles in the sentence so that the resulting sentence is grammatically correct but nonsensical in our world. Of these suggestions, we selected only the dadaistic swap of roles for the current exploration (see nonsense in Table TABREF7).",
+      "In total, we collected 984 sentences with 269 described unique changes. We use them as an inspiration for second round of annotation."
+    ],
+    [
+      "We selected 15 modifications types to collect COSTRA 1.0. They are presented in annotationinstructions.",
+      "We asked for two distinct paraphrases of each sentence because we believe that a good sentence embedding should put paraphrases close together in vector space.",
+      "Several modification types were specifically selected to constitute a thorough test of embeddings. In different meaning, the annotators should create a sentence with some other meaning using the same words as the original sentence. Other transformations which should be difficult for embeddings include minimal change, in which the sentence meaning should be significantly changed by using only very small modification, or nonsense, in which words of the source sentence should be shuffled so that it is grammatically correct, but without any sense."
+    ],
+    [
+      "The source sentences for annotations were selected from Czech data of Global Voices BIBREF24 and OpenSubtitles BIBREF25. We used two sources in order to have different styles of seed sentences, both journalistic and common spoken language. We considered only sentences with more than 5 and less than 15 words and we manually selected 150 of them for further annotation. This step was necessary to remove sentences that are:",
+      "too unreal, out of this world, such as:",
+      "Jedno fotonov\u00fd torp\u00e9do a je z tebe vesm\u00edrn\u00e1 topinka.",
+      "\u201cOne photon torpedo and you're a space toast.\u201d",
+      "photo captions (i.e. incomplete sentences), e.g.:",
+      "Zvl\u00e1\u0161tn\u00ed ekv\u00e1dorsk\u00fd p\u0159\u00edpad Correa vs. Crudo",
+      "\u201cSpecific Ecuadorian case Correa vs. Crudo\u201d",
+      "too vague, overly dependent on the context:",
+      "B\u011b\u017e tam a mluv na ni.",
+      "\u201cGo there and speak to her.\u201d",
+      "Many of the intended sentence transformations would be impossible to apply to such sentences and annotators' time would be wasted. Even after such filtering, it was still quite possible that a desired sentence modification could not be achieved for a sentence. For such a case, we gave the annotators the option to enter the keyword IMPOSSIBLE instead of the particular (impossible) modification.",
+      "This option allowed to explicitly state that no such transformation is possible. At the same time most of the transformations are likely to lead to a large number possible outcomes. As documented in scratching2013, Czech sentence might have hundreds of thousand of paraphrases. To support some minimal exploration of this possible diversity, most of sentences were assigned to several annotators."
+    ],
+    [
+      "The annotation is a challenging task and the annotators naturally make mistakes. Unfortunately, a single typo can significantly influence the resulting embedding BIBREF26. After collecting all the sentence variations, we applied the statistical spellchecker and grammar checker Korektor BIBREF27 in order to minimize influence of typos to performance of embedding methods. We manually inspected 519 errors identified by Korektor and fixed 129, which were identified correctly."
+    ],
+    [
+      "In the second round, we collected 293 annotations from 12 annotators. After Korektor, there are 4262 unique sentences (including 150 seed sentences) that form the COSTRA 1.0 dataset. Statistics of individual annotators are available in tab:statistics.",
+      "The time needed to carry out one piece of annotation (i.e. to provide one seed sentence with all 15 transformations) was on average almost 20 minutes but some annotators easily needed even half an hour. Out of the 4262 distinct sentences, only 188 was recorded more than once. In other words, the chance of two annotators producing the same output string is quite low. The most repeated transformations are by far past, future and ban. The least repeated is paraphrase with only single one repeated.",
+      "multiple-annots documents this in another way. The 293 annotations are split into groups depending on how many annotators saw the same input sentence: 30 annotations were annotated by one person only, 30 annotations by two different persons etc. The last column shows the number of unique outputs obtained in that group. Across all cases, 96.8% of produced strings were unique.",
+      "In line with instructions, the annotators were using the IMPOSSIBLE option scarcely (95 times, i.e. only 2%). It was also a case of 7 annotators only; the remaining 5 annotators were capable of producing all requested transformations. The top three transformations considered unfeasible were different meaning (using the same set of words), past (esp. for sentences already in the past tense) and simple sentence."
+    ],
+    [
+      "We embedded COSTRA sentences with LASER BIBREF15, the method that performed very well in revealing linear relations in BaBo2019. Having browsed a number of 2D visualizations (PCA and t-SNE) of the space, we have to conclude that visually, LASER space does not seem to exhibit any of the desired topological properties discussed above, see fig:pca for one example.",
+      "The lack of semantic relations in the LASER space is also reflected in vector similarities, summarized in similarities. The minimal change operation substantially changed the meaning of the sentence, and yet the embedding of the transformation lies very closely to the original sentence (average similarity of 0.930). Tense changes and some form of negation or banning also keep the vectors very similar.",
+      "The lowest average similarity was observed for generalization (0.739) and simplification (0.781), which is not any bad sign. However the fact that paraphrases have much smaller similarity (0.826) than opposite meaning (0.902) documents that the vector space lacks in terms of \u201crelatability\u201d."
+    ],
+    [
+      "We presented COSTRA 1.0, a small corpus of complex transformations of Czech sentences.",
+      "We plan to use this corpus to analyze a wide spectrum sentence embeddings methods to see to what extent the continuous space they induce reflects semantic relations between sentences in our corpus. The very first analysis using LASER embeddings indicates lack of \u201cmeaning relatability\u201d, i.e. the ability to move along a trajectory in the space in order to reach desired sentence transformations. Actually, not even paraphrases are found in close neighbourhoods of embedded sentences. More \u201csemantic\u201d sentence embeddings methods are thus to be sought for.",
+      "The corpus is freely available at the following link:",
+      "http://hdl.handle.net/11234/1-3123",
+      "Aside from extending the corpus in Czech and adding other language variants, we are also considering to wrap COSTRA 1.0 into an API such as SentEval, so that it is very easy for researchers to evaluate their sentence embeddings in terms of \u201crelatability\u201d."
+    ]
+  ]
+}
+```

qasper-0384/instruction.md

@@ -0,0 +1,100 @@
+Name of Paper: COSTRA 1.0: A Dataset of Complex Sentence Transformations
+
+Question: What are all 15 types of modifications ilustrated in the dataset?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background",
+    "Annotation",
+    "Annotation ::: First Round: Collecting Ideas",
+    "Annotation ::: Second Round: Collecting Data ::: Sentence Transformations",
+    "Annotation ::: Second Round: Collecting Data ::: Seed Data",
+    "Annotation ::: Second Round: Collecting Data ::: Spell-Checking",
+    "Dataset Description",
+    "Dataset Description ::: First Observations",
+    "Conclusion and Future Work"
+  ],
+  "paragraphs": [
+    [
+      "Vector representations are becoming truly essential in majority of natural language processing tasks. Word embeddings became widely popular with the introduction of word2vec BIBREF0 and GloVe BIBREF1 and their properties have been analyzed in length from various aspects.",
+      "Studies of word embeddings range from word similarity BIBREF2, BIBREF3, over the ability to capture derivational relations BIBREF4, linear superposition of multiple senses BIBREF5, the ability to predict semantic hierarchies BIBREF6 or POS tags BIBREF7 up to data efficiency BIBREF8.",
+      "Several studies BIBREF9, BIBREF10, BIBREF11, BIBREF12 show that word vector representations are capable of capturing meaningful syntactic and semantic regularities. These include, for example, male/female relation demonstrated by the pairs \u201cman:woman\u201d, \u201cking:queen\u201d and the country/capital relation (\u201cRussia:Moscow\u201d, \u201cJapan:Tokyo\u201d). These regularities correspond to simple arithmetic operations in the vector space.",
+      "Sentence embeddings are becoming equally ubiquitous in NLP, with novel representations appearing almost every other week. With an overwhelming number of methods to compute sentence vector representations, the study of their general properties becomes difficult. Furthermore, it is not so clear in which way the embeddings should be evaluated.",
+      "In an attempt to bring together more traditional representations of sentence meanings and the emerging vector representations, bojar:etal:jnle:representations:2019 introduce a number of aspects or desirable properties of sentence embeddings. One of them is denoted as \u201crelatability\u201d, which highlights the correspondence between meaningful differences between sentences and geometrical relations between their respective embeddings in the highly dimensional continuous vector space. If such a correspondence could be found, we could use geometrical operations in the space to induce meaningful changes in sentences.",
+      "In this work, we present COSTRA, a new dataset of COmplex Sentence TRAnsformations. In its first version, the dataset is limited to sample sentences in Czech. The goal is to support studies of semantic and syntactic relations between sentences in the continuous space. Our dataset is the prerequisite for one of possible ways of exploring sentence meaning relatability: we envision that the continuous space of sentences induced by an ideal embedding method would exhibit topological similarity to the graph of sentence variations. For instance, one could argue that a subset of sentences could be organized along a linear scale reflecting the formalness of the language used. Another set of sentences could form a partially ordered set of gradually less and less concrete statements. And yet another set, intersecting both of the previous ones in multiple sentences could be partially or linearly ordered according to the strength of the speakers confidence in the claim.",
+      "Our long term goal is to search for an embedding method which exhibits this behaviour, i.e. that the topological map of the embedding space corresponds to meaningful operations or changes in the set of sentences of a language (or more languages at once). We prefer this behaviour to emerge, as it happened for word vector operations, but regardless if the behaviour is emergent or trained, we need a dataset of sentences illustrating these patterns. If large enough, such a dataset could serve for training. If it will be smaller, it will provide a test set. In either case, these sentences could provide a \u201cskeleton\u201d to the continuous space of sentence embeddings.",
+      "The paper is structured as follows: related summarizes existing methods of sentence embeddings evaluation and related work. annotation describes our methodology for constructing our dataset. data details the obtained dataset and some first observations. We conclude and provide the link to the dataset in conclusion"
+    ],
+    [
+      "As hinted above, there are many methods of converting a sequence of words into a vector in a highly dimensional space. To name a few: BiLSTM with the max-pooling trained for natural language inference BIBREF13, masked language modeling and next sentence prediction using bidirectional Transformer BIBREF14, max-pooling last states of neural machine translation among many languages BIBREF15 or the encoder final state in attentionless neural machine translation BIBREF16.",
+      "The most common way of evaluating methods of sentence embeddings is extrinsic, using so called `transfer tasks', i.e. comparing embeddings via the performance in downstream tasks such as paraphrasing, entailment, sentence sentiment analysis, natural language inference and other assignments. However, even simple bag-of-words (BOW) approaches achieve often competitive results on such tasks BIBREF17.",
+      "Adi16 introduce intrinsic evaluation by measuring the ability of models to encode basic linguistic properties of a sentence such as its length, word order, and word occurrences. These so called `probing tasks' are further extended by a depth of the syntactic tree, top constituent or verb tense by DBLP:journals/corr/abs-1805-01070.",
+      "Both transfer and probing tasks are integrated in SentEval BIBREF18 framework for sentence vector representations. Later, Perone2018 applied SentEval to eleven different encoding methods revealing that there is no consistently well performing method across all tasks. SentEval was further criticized for pitfalls such as comparing different embedding sizes or correlation between tasks BIBREF19, BIBREF20.",
+      "shi-etal-2016-string show that NMT encoder is able to capture syntactic information about the source sentence. DBLP:journals/corr/BelinkovDDSG17 examine the ability of NMT to learn morphology through POS and morphological tagging.",
+      "Still, very little is known about semantic properties of sentence embeddings. Interestingly, cifka:bojar:meanings:2018 observe that the better self-attention embeddings serve in NMT, the worse they perform in most of SentEval tasks.",
+      "zhu-etal-2018-exploring generate automatically sentence variations such as:",
+      "Original sentence: A rooster pecked grain.",
+      "Synonym Substitution: A cock pecked grain.",
+      "Not-Negation: A rooster didn't peck grain.",
+      "Quantifier-Negation: There was no rooster pecking grain.",
+      "and compare their triplets by examining distances between their embeddings, i.e. distance between (1) and (2) should be smaller than distances between (1) and (3), (2) and (3), similarly, (3) and (4) should be closer together than (1)\u2013(3) or (1)\u2013(4).",
+      "In our previous study BIBREF21, we examined the effect of small sentence alternations in sentence vector spaces. We used sentence pairs automatically extracted from datasets for natural language inference BIBREF22, BIBREF23 and observed, that the simple vector difference, familiar from word embeddings, serves reasonably well also in sentence embedding spaces. The examined relations were however very simple: a change of gender, number, addition of an adjective, etc. The structure of the sentence and its wording remained almost identical.",
+      "We would like to move to more interesting non-trivial sentence comparison, beyond those in zhu-etal-2018-exploring or BaBo2019, such as change of style of a sentence, the introduction of a small modification that drastically changes the meaning of a sentence or reshuffling of words in a sentence that alters its meaning.",
+      "Unfortunately, such a dataset cannot be generated automatically and it is not available to our best knowledge. We try to start filling this gap with COSTRA 1.0."
+    ],
+    [
+      "We acquired the data in two rounds of annotation. In the first one, we were looking for original and uncommon sentence change suggestions. In the second one, we collected sentence alternations using ideas from the first round. The first and second rounds of annotation could be broadly called as collecting ideas and collecting data, respectively."
+    ],
+    [
+      "We manually selected 15 newspaper headlines. Eleven annotators were asked to modify each headline up to 20 times and describe the modification with a short name. They were given an example sentence and several of its possible alternations, see tab:firstroundexamples.",
+      "Unfortunately, these examples turned out to be highly influential on the annotators' decisions and they correspond to almost two thirds of all of modifications gathered in the first round. Other very common transformations include change of a word order or transformation into a interrogative/imperative sentence.",
+      "Other interesting modification were also proposed such as change into a fairy-tale style, excessive use of diminutives/vulgarisms or dadaism\u2014a swap of roles in the sentence so that the resulting sentence is grammatically correct but nonsensical in our world. Of these suggestions, we selected only the dadaistic swap of roles for the current exploration (see nonsense in Table TABREF7).",
+      "In total, we collected 984 sentences with 269 described unique changes. We use them as an inspiration for second round of annotation."
+    ],
+    [
+      "We selected 15 modifications types to collect COSTRA 1.0. They are presented in annotationinstructions.",
+      "We asked for two distinct paraphrases of each sentence because we believe that a good sentence embedding should put paraphrases close together in vector space.",
+      "Several modification types were specifically selected to constitute a thorough test of embeddings. In different meaning, the annotators should create a sentence with some other meaning using the same words as the original sentence. Other transformations which should be difficult for embeddings include minimal change, in which the sentence meaning should be significantly changed by using only very small modification, or nonsense, in which words of the source sentence should be shuffled so that it is grammatically correct, but without any sense."
+    ],
+    [
+      "The source sentences for annotations were selected from Czech data of Global Voices BIBREF24 and OpenSubtitles BIBREF25. We used two sources in order to have different styles of seed sentences, both journalistic and common spoken language. We considered only sentences with more than 5 and less than 15 words and we manually selected 150 of them for further annotation. This step was necessary to remove sentences that are:",
+      "too unreal, out of this world, such as:",
+      "Jedno fotonov\u00fd torp\u00e9do a je z tebe vesm\u00edrn\u00e1 topinka.",
+      "\u201cOne photon torpedo and you're a space toast.\u201d",
+      "photo captions (i.e. incomplete sentences), e.g.:",
+      "Zvl\u00e1\u0161tn\u00ed ekv\u00e1dorsk\u00fd p\u0159\u00edpad Correa vs. Crudo",
+      "\u201cSpecific Ecuadorian case Correa vs. Crudo\u201d",
+      "too vague, overly dependent on the context:",
+      "B\u011b\u017e tam a mluv na ni.",
+      "\u201cGo there and speak to her.\u201d",
+      "Many of the intended sentence transformations would be impossible to apply to such sentences and annotators' time would be wasted. Even after such filtering, it was still quite possible that a desired sentence modification could not be achieved for a sentence. For such a case, we gave the annotators the option to enter the keyword IMPOSSIBLE instead of the particular (impossible) modification.",
+      "This option allowed to explicitly state that no such transformation is possible. At the same time most of the transformations are likely to lead to a large number possible outcomes. As documented in scratching2013, Czech sentence might have hundreds of thousand of paraphrases. To support some minimal exploration of this possible diversity, most of sentences were assigned to several annotators."
+    ],
+    [
+      "The annotation is a challenging task and the annotators naturally make mistakes. Unfortunately, a single typo can significantly influence the resulting embedding BIBREF26. After collecting all the sentence variations, we applied the statistical spellchecker and grammar checker Korektor BIBREF27 in order to minimize influence of typos to performance of embedding methods. We manually inspected 519 errors identified by Korektor and fixed 129, which were identified correctly."
+    ],
+    [
+      "In the second round, we collected 293 annotations from 12 annotators. After Korektor, there are 4262 unique sentences (including 150 seed sentences) that form the COSTRA 1.0 dataset. Statistics of individual annotators are available in tab:statistics.",
+      "The time needed to carry out one piece of annotation (i.e. to provide one seed sentence with all 15 transformations) was on average almost 20 minutes but some annotators easily needed even half an hour. Out of the 4262 distinct sentences, only 188 was recorded more than once. In other words, the chance of two annotators producing the same output string is quite low. The most repeated transformations are by far past, future and ban. The least repeated is paraphrase with only single one repeated.",
+      "multiple-annots documents this in another way. The 293 annotations are split into groups depending on how many annotators saw the same input sentence: 30 annotations were annotated by one person only, 30 annotations by two different persons etc. The last column shows the number of unique outputs obtained in that group. Across all cases, 96.8% of produced strings were unique.",
+      "In line with instructions, the annotators were using the IMPOSSIBLE option scarcely (95 times, i.e. only 2%). It was also a case of 7 annotators only; the remaining 5 annotators were capable of producing all requested transformations. The top three transformations considered unfeasible were different meaning (using the same set of words), past (esp. for sentences already in the past tense) and simple sentence."
+    ],
+    [
+      "We embedded COSTRA sentences with LASER BIBREF15, the method that performed very well in revealing linear relations in BaBo2019. Having browsed a number of 2D visualizations (PCA and t-SNE) of the space, we have to conclude that visually, LASER space does not seem to exhibit any of the desired topological properties discussed above, see fig:pca for one example.",
+      "The lack of semantic relations in the LASER space is also reflected in vector similarities, summarized in similarities. The minimal change operation substantially changed the meaning of the sentence, and yet the embedding of the transformation lies very closely to the original sentence (average similarity of 0.930). Tense changes and some form of negation or banning also keep the vectors very similar.",
+      "The lowest average similarity was observed for generalization (0.739) and simplification (0.781), which is not any bad sign. However the fact that paraphrases have much smaller similarity (0.826) than opposite meaning (0.902) documents that the vector space lacks in terms of \u201crelatability\u201d."
+    ],
+    [
+      "We presented COSTRA 1.0, a small corpus of complex transformations of Czech sentences.",
+      "We plan to use this corpus to analyze a wide spectrum sentence embeddings methods to see to what extent the continuous space they induce reflects semantic relations between sentences in our corpus. The very first analysis using LASER embeddings indicates lack of \u201cmeaning relatability\u201d, i.e. the ability to move along a trajectory in the space in order to reach desired sentence transformations. Actually, not even paraphrases are found in close neighbourhoods of embedded sentences. More \u201csemantic\u201d sentence embeddings methods are thus to be sought for.",
+      "The corpus is freely available at the following link:",
+      "http://hdl.handle.net/11234/1-3123",
+      "Aside from extending the corpus in Czech and adding other language variants, we are also considering to wrap COSTRA 1.0 into an API such as SentEval, so that it is very easy for researchers to evaluate their sentence embeddings in terms of \u201crelatability\u201d."
+    ]
+  ]
+}
+```

qasper-0464/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Natural Language State Representation for Reinforcement Learning
+
+Question: How is state to learn and complete tasks represented via natural language?

qasper-0490/instruction.md

@@ -0,0 +1,93 @@
+Name of Paper: Civique: Using Social Media to Detect Urban Emergencies
+
+Question: What was the baseline?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Motivation and Challenges",
+    "Our Approach",
+    "Pre-Processing Modules",
+    "Emergency Classification",
+    "Type Classification",
+    "Location Visualizer",
+    "Evaluation",
+    "Dataset Creation",
+    "Classifier Evaluation",
+    "Demostration Description",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "With the surge in the use of social media, micro-blogging sites like Twitter, Facebook, and Foursquare have become household words. Growing ubiquity of mobile phones in highly populated developing nations has spurred an exponential rise in social media usage. The heavy volume of social media posts tagged with users' location information on micro-blogging website Twitter presents a unique opportunity to scan these posts. These Short texts (e.g. \"tweets\") on social media contain information about various events happening around the globe, as people post about events and incidents alike. Conventional web outlets provide emergency phone numbers (i.e. 100, 911), etc., and are fast and accurate. Our system, on the other hand, connects its users through a relatively newer platform i.e. social media, and provides an alternative to these conventional methods. In case of their failure or when such means are busy/occupied, an alternative could prove to be life saving.",
+      "These real life events are reported on Twitter with different perspectives, opinions, and sentiment. Every day, people discuss events thousands of times across social media sites. We would like to detect such events in case of an emergency. Some previous studies BIBREF0 investigate the use of features such as keywords in the tweet, number of words, and context to devise a classifier for event detection. BIBREF1 discusses various techniques researchers have used previously to detect events from Twitter. BIBREF2 describe a system to automatically detect events about known entities from Twitter. This work is highly specific to detection of events only related to known entities. BIBREF3 discuss a system that returns a ranked list of relevant events given a user query.",
+      "Several research efforts have focused on identifying events in real time( BIBREF4 BIBREF5 BIBREF6 BIBREF0 ). These include systems to detect emergent topics from Twitter in real time ( BIBREF4 BIBREF7 ), an online clustering technique for identifying tweets in real time BIBREF5 , a system to detect localized events and also track evolution of such events over a period of time BIBREF6 . Our focus is on detecting urban emergencies as events from Twitter messages. We classify events ranging from natural disasters to fire break outs, and accidents. Our system detects whether a tweet, which contains a keyword from a pre-decided list, is related to an actual emergency or not. It also classifies the event into its appropriate category, and visualizes the possible location of the emergency event on the map. We also support notifications to our users, containing the contacts of specifically concerned authorities, as per the category of their tweet.",
+      "The rest of the paper is as follows: Section SECREF2 provides the motivation for our work, and the challenges in building such a system. Section SECREF3 describes the step by step details of our work, and its results. We evaluate our system and present the results in Section SECREF4 . Section SECREF5 showcases our demonstrations in detail, and Section SECREF6 concludes the paper by briefly describing the overall contribution, implementation and demonstration."
+    ],
+    [
+      "In 2015, INLINEFORM0 of all unnatural deaths in India were caused by accidents, and INLINEFORM1 by accidental fires. Moreover, the Indian subcontinent suffered seven earthquakes in 2015, with the recent Nepal earthquake alone killing more than 9000 people and injuring INLINEFORM2 . We believe we can harness the current social media activity on the web to minimize losses by quickly connecting affected people and the concerned authorities. Our work is motivated by the following factors, (a) Social media is very accessible in the current scenario. (The \u201cDigital India\u201d initiative by the Government of India promotes internet activity, and thus a pro-active social media.) (b) As per the Internet trends reported in 2014, about 117 million Indians are connected to the Internet through mobile devices. (c) A system such as ours can point out or visualize the affected areas precisely and help inform the authorities in a timely fashion. (d) Such a system can be used on a global scale to reduce the effect of natural calamities and prevent loss of life.",
+      "There are several challenges in building such an application: (a) Such a system expects a tweet to be location tagged. Otherwise, event detection techniques to extract the spatio-temporal data from the tweet can be vague, and lead to false alarms. (b) Such a system should also be able to verify the user's credibility as pranksters may raise false alarms. (c) Tweets are usually written in a very informal language, which requires a sophisticated language processing component to sanitize the tweet input before event detection. (d) A channel with the concerned authorities should be established for them to take serious action, on alarms raised by such a system. (e) An urban emergency such as a natural disaster could affect communications severely, in case of an earthquake or a cyclone, communications channels like Internet connectivity may get disrupted easily. In such cases, our system may not be of help, as it requires the user to be connected to the internet. We address the above challenges and present our approach in the next section."
+    ],
+    [
+      "We propose a software architecture for Emergency detection and visualization as shown in figure FIGREF9 . We collect data using Twitter API, and perform language pre-processing before applying a classification model. Tweets are labelled manually with <emergency>and <non-emergency>labels, and later classified manually to provide labels according to the type of emergency they indicate. We use the manually labeled data for training our classifiers.",
+      "We use traditional classification techniques such as Support Vector Machines(SVM), and Naive Bayes(NB) for training, and perform 10-fold cross validation to obtain f-scores. Later, in real time, our system uses the Twitter streaming APIs to get data, pre-processes it using the same modules, and detects emergencies using the classifiers built above. The tweets related to emergencies are displayed on the web interface along with the location and information for the concerned authorities. The pre-processing of Twitter data obtained is needed as it usually contains ad-hoc abbreviations, phonetic substitutions, URLs, hashtags, and a lot of misspelled words. We use the following language processing modules for such corrections."
+    ],
+    [
+      "We implement a cleaning module to automate the cleaning of tweets obtained from the Twitter API. We remove URLs, special symbols like @ along with the user mentions, Hashtags and any associated text. We also replace special symbols by blank spaces, and inculcate the module as shown in figure FIGREF9 .",
+      "An example of such a sample tweet cleaning is shown in table TABREF10 .",
+      "While tweeting, users often express their emotions by stressing over a few characters in the word. For example, usage of words like hellpppp, fiiiiiireeee, ruuuuunnnnn, druuuuuunnnkkk, soooooooo actually corresponds to help, fire, run, drunk, so etc. We use the compression module implemented by BIBREF8 for converting terms like \u201cpleeeeeeeaaaaaassseeee\u201d to \u201cplease\u201d.",
+      "It is unlikely for an English word to contain the same character consecutively for three or more times. We, hence, compress all the repeated windows of character length greater than two, to two characters. For example \u201cpleeeeeaaaassee\u201d is converted to \u201cpleeaassee\u201d. Each window now contains two characters of the same alphabet in cases of repetition. Let n be the number of windows, obtained from the previous step. We, then, apply brute force search over INLINEFORM0 possibilities to select a valid dictionary word.",
+      "Table TABREF13 contains sanitized sample output from our compression module for further processing.",
+      "Text Normalization is the process of translating ad-hoc abbreviations, typographical errors, phonetic substitution and ungrammatical structures used in text messaging (Tweets and SMS) to plain English. Use of such language (often referred as Chatting Language) induces noise which poses additional processing challenges.",
+      "We use the normalization module implemented by BIBREF8 for text normalization. Training process requires a Language Model of the target language and a parallel corpora containing aligned un-normalized and normalized word pairs. Our language model consists of 15000 English words taken from various sources on the web.",
+      "Parallel corpora was collected from the following sources:",
+      "Stanford Normalization Corpora which consists of 9122 pairs of un-normalized and normalized words / phrases.",
+      "The above corpora, however, lacked acronyms and short hand texts like 2mrw, l8r, b4, hlp, flor which are frequently used in chatting. We collected 215 pairs un-normalized to normalized word/phrase mappings via crowd-sourcing.",
+      "Table TABREF16 contains input and normalized output from our module.",
+      "Users often make spelling mistakes while tweeting. A spell checker makes sure that a valid English word is sent to the classification system. We take this problem into account by introducing a spell checker as a pre-processing module by using the JAVA API of Jazzy spell checker for handling spelling mistakes.",
+      "An example of correction provided by the Spell Checker module is given below:-",
+      "Input: building INLINEFORM0 flor, help",
+      "Output: building INLINEFORM0 floor, help",
+      "Please note that, our current system performs compression, normalization and spell-checking if the language used is English. The classifier training and detection process are described below."
+    ],
+    [
+      "The first classifier model acts as a filter for the second stage of classification. We use both SVM and NB to compare the results and choose SVM later for stage one classification model, owing to a better F-score. The training is performed on tweets labeled with classes <emergency>, and <non-emergency> based on unigrams as features. We create word vectors of strings in the tweet using a filter available in the WEKA API BIBREF9 , and perform cross validation using standard classification techniques."
+    ],
+    [
+      "We employ a multi-class Naive Bayes classifier as the second stage classification mechanism, for categorizing tweets appropriately, depending on the type of emergencies they indicate. This multi-class classifier is trained on data manually labeled with classes. We tokenize the training data using \u201cNgramTokenizer\u201d and then, apply a filter to create word vectors of strings before training. We use \u201ctrigrams\u201d as features to build a model which, later, classifies tweets into appropriate categories, in real time. We then perform cross validation using standard techniques to calculate the results, which are shown under the label \u201cStage 2\u201d, in table TABREF20 ."
+    ],
+    [
+      "We use Google Maps Geocoding API to display the possible location of the tweet origin based on longitude and latitude. Our visualizer presents the user with a map and pinpoints the location with custom icons for earthquake, cyclone, fire accident etc. Since we currently collect tweets with a location filter for the city of \"Mumbai\", we display its map location on the interface. The possible occurrences of such incidents are displayed on the map as soon as our system is able to detect it.",
+      "We also display the same on an Android device using the WebView functionality available to developers, thus solving the issue of portability. Our system displays visualization of the various emergencies detected on both web browsers and mobile devices."
+    ],
+    [
+      "We evaluate our system using automated, and manual evaluation techniques. We perform 10-fold cross validation to obtain the F-scores for our classification systems. We use the following technique for dataset creation. We test the system in realtime environments, and tweet about fires at random locations in our city, using test accounts. Our system was able to detect such tweets and detect them with locations shown on the map."
+    ],
+    [
+      "We collect data by using the Twitter API for saved data, available for public use. For our experiments we collect 3200 tweets filtered by keywords like \u201cfire\u201d, \u201cearthquake\u201d, \u201ctheft\u201d, \u201crobbery\u201d, \u201cdrunk driving\u201d, \u201cdrunk driving accident\u201d etc. Later, we manually label tweets with <emergency>and <non-emergency>labels for classification as stage one. Our dataset contains 1313 tweet with positive label <emergency>and 1887 tweets with a negative label <non-emergency>. We create another dataset with the positively labeled tweets and provide them with category labels like \u201cfire\u201d, \u201caccident\u201d, \u201cearthquake\u201d etc.",
+      ""
+    ],
+    [
+      "The results of 10-fold cross-validation performed for stage one are shown in table TABREF20 , under the label \u201cStage 1\u201d. In table TABREF20 , For \u201cStage 1\u201d of classification, F-score obtained using SVM classifier is INLINEFORM0 as shown in row 2, column 2. We also provide the system with sample tweets in real time and assess its ability to detect the emergency, and classify it accordingly. The classification training for Stage 1 was performed using two traditional classification techniques SVM and NB. SVM outperformed NB by around INLINEFORM1 and became the choice of classification technique for stage one.",
+      "Some false positives obtained during manual evaluation are, \u201cI am sooooo so drunk right nowwwwwwww\u201d and \u201cfire in my office , the boss is angry\u201d. These occurrences show the need of more labeled gold data for our classifiers, and some other features, like Part-of-Speech tags, Named Entity recognition, Bigrams, Trigrams etc. to perform better.",
+      "The results of 10-fold cross-validation performed for stage two classfication model are also shown in table TABREF20 , under the label \u201cStage 2\u201d. The training for stage two was also performed using both SVM and NB, but NB outperformed SVM by around INLINEFORM0 to become a choice for stage two classification model.",
+      "We also perform attribute evaluation for the classification model, and create a word cloud based on the output values, shown in figure FIGREF24 . It shows that our classifier model is trained on appropriate words, which are very close to the emergency situations viz. \u201cfire\u201d, \u201cearthquake\u201d, \u201caccident\u201d, \u201cbreak\u201d (Unigram representation here, but possibly occurs in a bigram phrase with \u201cfire\u201d) etc. In figure FIGREF24 , the word cloud represents the word \u201crespond\u201d as the most frequently occurring word as people need urgent help, and quick response from the assistance teams."
+    ],
+    [
+      "Users interact with Civique through its Web-based user interface and Android based application interface. The features underlying Civique are demonstrated through the following two show cases:",
+      "Show case 1: Tweet Detection and Classification",
+      "This showcase aims at detecting related tweets, and classifying them into appropriate categories. For this, we have created a list of filter words, which are used to filter tweets from the Twitter streaming API. These set of words help us filter the tweets related to any incident. We will tweet, and users are able to see how our system captures such tweets and classifies them. Users should be able to see the tweet emerge as an incident on the web-interface, as shown in figure FIGREF26 and the on the android application, as shown in figure FIGREF27 . Figure FIGREF27 demonstrates how a notification is generated when our system detects an emergency tweet. When a user clicks the emerged spot, the system should be able to display the sanitized version / extracted spatio-temporal data from the tweet. We test the system in a realtime environment, and validate our experiments. We also report the false positives generated during the process in section SECREF25 above.",
+      "Show case 2: User Notification and Contact Info.",
+      "Civique includes a set of local contacts for civic authorities who are to be / who can be contacted in case of various emergencies. Users can see how Civique detects an emergency and classifies it. They can also watch how the system generates a notification on the web interface and the Android interface, requesting them to contact the authorities for emergencies. Users can change their preferences on the mobile device anytime and can also opt not to receive notifications. Users should be able to contact the authorities online using the application, but in case the online contact is not responsive, or in case of a sudden loss of connectivity, we provide the user with the offline contact information of the concerned civic authorities along with the notifications."
+    ],
+    [
+      "Civique is a system which detects urban emergencies like earthquakes, cyclones, fire break out, accidents etc. and visualizes them on both on a browsable web interface and an Android application. We collect data from the popular micro-blogging site Twitter and use language processing modules to sanitize the input. We use this data as input to train a two step classification system, which indicates whether a tweet is related to an emergency or not, and if it is, then what category of emergency it belongs to. We display such positively classified tweets along with their type and location on a Google map, and notify our users to inform the concerned authorities, and possibly evacuate the area, if his location matches the affected area. We believe such a system can help the disaster management machinery, and government bodies like Fire department, Police department, etc., to act swiftly, thus minimizing the loss of life.",
+      "Twitter users use slang, profanity, misspellings and neologisms. We, use standard cleaning methods, and combine NLP with Machine Learning (ML) to further our cause of tweet classification. At the current stage, we also have an Android application ready for our system, which shows the improvised, mobile-viewable web interface.",
+      "In the future, we aim to develop detection of emergency categories on the fly, obscure emergencies like \u201cairplane hijacking\u201d should also be detected by our system. We plan to analyze the temporal sequence of the tweet set from a single location to determine whether multiple problems on the same location are the result of a single event, or relate to multiple events."
+    ]
+  ]
+}
+```

qasper-0516/instruction.md

@@ -0,0 +1,50 @@
+Name of Paper: Visual Natural Language Query Auto-Completion for Estimating Instance Probabilities
+
+Question: How big is data provided by this research?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Methods",
+    "Methods ::: Modifying FactorCell LSTM for Image Query Auto-Completion",
+    "Methods ::: Fine Tuning BERT for Instance Probability Estimation",
+    "Methods ::: Data and Training Details",
+    "Results",
+    "Results ::: Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "This work focuses on the problem of finding objects in an image based on natural language descriptions. Existing solutions take into account both the image and the query BIBREF0, BIBREF1, BIBREF2. In our problem formulation, rather than having the entire text, we are given only a prefix of the text which requires completing the text based on a language model and the image, and finding a relevant object in the image. We decompose the problem into three components: (i) completing the query from text prefix and an image; (ii) estimating probabilities of objects based on the completed text, and (iii) segmenting and classifying all instances in the image. We combine, extend, and modify state of the art components: (i) we extend a FactorCell LSTM BIBREF3, BIBREF4 which conditionally completes text to complete a query from both a text prefix and an image; (ii) we fine tune a BERT embedding to compute instance probabilities from a complete sentence, and (iii) we use Mask-RCNN BIBREF5 for instance segmentation.",
+      "Recent natural language embeddings BIBREF6 have been trained with the objectives of predicting masked words and determining whether sentences follow each other, and are efficiently used across a dozen of natural language processing tasks. Sequence models have been conditioned to complete text from a prefix and index BIBREF3, however have not been extended to take into account an image. Deep neural networks have been trained to segment all instances in an image at very high quality BIBREF5, BIBREF7. We propose a novel method of natural language query auto-completion for estimating instance probabilities conditioned on the image and a user query prefix. Our system combines and modifies state of the art components used in query completion, language embedding, and masked instance segmentation. Estimating a broad set of instance probabilities enables selection which is agnostic to the segmentation procedure."
+    ],
+    [
+      "Figure FIGREF2 shows the architecture of our approach. First, we extract image features with a pre-trained CNN. We incorporate the image features into a modified FactorCell LSTM language model along with the user query prefix to complete the query. The completed query is then fed into a fine-tuned BERT embedding to estimate instance probabilities, which in turn are used for instance selection.",
+      "We denote a set of objects $o_k \\in O$ where O is the entire set of recognizable object classes. The user inputs a prefix, $p$, an incomplete query on an image, $I$. Given $p$, we auto-complete the intended query $q$. We define the auto-completion query problem in equation DISPLAY_FORM3 as the maximization of the probability of a query conditioned on an image where $w_i \\in A$ is the word in position $i$.",
+      "",
+      "We pose our instance probability estimation problem given an auto-completed query $\\mathbf {q^*}$ as a multilabel problem where each class can independently exist. Let $O_{q*}$ be the set of instances referred to in $\\mathbf {q^*}$. Given $\\hat{p}_k$ is our estimate of $P(o_k \\in O_{q*})$ and $y_k = \\mathbb {1}[o_k \\in O_{q*}]$, the instance selection model minimizes the sigmoid cross-entropy loss function:"
+    ],
+    [
+      "We utilize the FactorCell (FC) adaptation of an LSTM with coupled input and forget gates BIBREF4 to autocomplete queries. The FactorCell is an LSTM with a context-dependent weight matrix $\\mathbf {W^{\\prime }} = \\mathbf {W} + \\mathbf {A}$ in place of $\\mathbf {W}$. Given a character embedding $w_t \\in \\mathbb {R}^e$, a previous hidden state $h_{t-1} \\in \\mathbb {R}^h$ , the adaptation matrix, $\\mathbf {A}$, is formed by taking the product of the context, c, with two basis tensors $\\mathbf {Z_L} \\in \\mathbb {R}^{m\\times (e+h)\\times r}$ and $\\mathbf {Z_R} \\in \\mathbb {R}^{r\\times h \\times m}$.",
+      "To adapt the FactorCell BIBREF4 for our purposes, we replace user embeddings with a low-dimensional image representation. Thus, we are able to modify each query completion to be personalized to a specific image representation. We extract features from an input image using a CNN pretrained on ImageNet, retraining only the last two fully connected layers. The image feature vector is fed into the FactorCell through the adaptation matrix. We perform beam search over the sequence of predicted characters to chose the optimal completion for the given prefix."
+    ],
+    [
+      "We fine tune a pre-trained BERT embedding to perform transfer learning for our instance selection task. We use a 12-layer implementation which has been shown to generalize and perform well when fine-tuned for new tasks such as question answering, text classification, and named entity recognition. To apply the model to our task, we add an additional dense layer to the BERT architecture with 10% dropout, mapping the last pooled layer to the object classes in our data."
+    ],
+    [
+      "We use the Visual Genome (VG) BIBREF8 and ReferIt BIBREF9 datasets which are suitable for our purposes. The VG data contains images, region descriptions, relationships, question-answers, attributes, and object instances. The region descriptions provide a replacement for queries since they mention various objects in different regions of each image. However, while some region descriptions are referring phrases, some are more similar to descriptions (see examples in Table TABREF10). The large number of examples makes the Visual Genome dataset particularly useful for our task. The smaller ReferIt dataset consists of referring expressions attached to images which more closely resemble potential user queries of images. We train separate models using both datasets.",
+      "For training, we aggregated (query, image) pairs using the region descriptions from the VG dataset and referring expressions from the ReferIt dataset. Our VG training set consists of 85% of the data: 16k images and 740k corresponding region descriptions. The Referit training data consists of 9k images and 54k referring expressions.",
+      "The query completion models are trained using a 128 dimensional image representation, a rank $r=64$ personalized matrix, 24 dimensional character embeddings, 512 dimensional LSTM hidden units, and a max length of 50 characters per query, with Adam at a 5e-4 learning rate, and a batch size of 32 for 80K iterations. The instance selection model is trained using (region description, object set) pairs from the VG dataset resulting in a training set of approximately 1.73M samples. The remaining 300K samples are split into validation and testing. Our training procedure for the instance selection model fine tunes all 12 layers of BERT with 32 sample batch sizes for 250K iterations, using Adam and performing learning rate warm-up for the first 10% of iterations with a target 5e-5 learning rate. The entire training processes takes around a day on an NVIDIA Tesla P100 GPU."
+    ],
+    [
+      "Figure 3 shows example results. We evaluate query completion by language perplexity and mean reciprocal rank (MRR) and evaluate instance selection by F1-score. We compare the perplexity on both sets of test queries using corresponding images vs. random noise as context. Table TABREF11 shows perplexity on the VG and ReferIt test queries with both corresponding images and random noise. The VG and ReferIt datasets have character vocabulary sizes of 89 and 77 respectively.",
+      "Given the matching index $t$ of the true query in the top 10 completions we compute the MRR as $\\sum _{n}{\\frac{1}{t}}$ where we replace the reciprocal rank with 0 if the true query does not appear in the top ten completions. We evaluate the VG and ReferIt test queries with varying prefix sizes and compare performance with the corresponding image and random noise as context. MRR is influenced by the length of the query, as longer queries are more difficult to match. Therefore, as expected we observe better performance on the ReferIt dataset for all prefix lengths. Finally, our instance selection achieves an F1-score of 0.7618 over all 2,909 instance classes."
+    ],
+    [
+      "Our results demonstrate that auto-completion based on both language and vision performs better than by using only language, and that fine tuning a BERT embedding allows to efficiently rank instances in the image. In future work we would like to extract referring expressions using simple grammatical rules to differentiate between referring and non-referring region descriptions. We would also like to combine the VG and ReferIt datasets to train a single model and scale up our datasets to improve query completions."
+    ]
+  ]
+}
+```

qasper-0519/instruction.md

@@ -0,0 +1,130 @@
+Name of Paper: Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation
+
+Question: By how much did their model outperform the baseline?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related work",
+    "Problem Formulation",
+    "The Behavioral Graph: A Knowledge Base For Navigation",
+    "Approach",
+    "Dataset",
+    "Experiments",
+    "Evaluation Metrics",
+    "Models Used in the Evaluation",
+    "Implementation Details",
+    "Quantitative Evaluation",
+    "Qualitative Evaluation",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Enabling robots to follow navigation instructions in natural language can facilitate human-robot interaction across a variety of applications. For instance, within the service robotics domain, robots can follow navigation instructions to help with mobile manipulation BIBREF0 and delivery tasks BIBREF1 .",
+      "Interpreting navigation instructions in natural language is difficult due to the high variability in the way people describe routes BIBREF2 . For example, there are a variety of ways to describe the route in Fig. FIGREF4 (a):",
+      "Each fragment of a sentence within these instructions can be mapped to one or more than one navigation behaviors. For instance, assume that a robot counts with a number of primitive, navigation behaviors, such as \u201center the room on the left (or on right)\u201d , \u201cfollow the corridor\u201d, \u201ccross the intersection\u201d, etc. Then, the fragment \u201cadvance forward\u201d in a navigation instruction could be interpreted as a \u201cfollow the corridor\u201d behavior, or as a sequence of \u201cfollow the corridor\u201d interspersed with \u201ccross the intersection\u201d behaviors depending on the topology of the environment. Resolving such ambiguities often requires reasoning about \u201ccommon-sense\u201d concepts, as well as interpreting spatial information and landmarks, e.g., in sentences such as \u201cthe room on the left right before the end of the corridor\u201d and \u201cthe room which is in the middle of two vases\u201d.",
+      "In this work, we pose the problem of interpreting navigation instructions as finding a mapping (or grounding) of the commands into an executable navigation plan. While the plan is typically modeled as a formal specification of low-level motions BIBREF2 or a grammar BIBREF3 , BIBREF4 , we focus specifically on translating instructions to a high-level navigation plan based on a topological representation of the environment. This representation is a behavioral navigation graph, as recently proposed by BIBREF5 , designed to take advantage of the semantic structure typical of human environments. The nodes of the graph correspond to semantically meaningful locations for the navigation task, such as kitchens or entrances to rooms in corridors. The edges are parameterized, visuo-motor behaviors that allow a robot to navigate between neighboring nodes, as illustrated in Fig. FIGREF4 (b). Under this framework, complex navigation routes can be achieved by sequencing behaviors without an explicit metric representation of the world.",
+      "We formulate the problem of following instructions under the framework of BIBREF5 as finding a path in the behavioral navigation graph that follows the desired route, given a known starting location. The edges (behaviors) along this path serve to reach the \u2013 sometimes implicit \u2013 destination requested by the user. As in BIBREF6 , our focus is on the problem of interpreting navigation directions. We assume that a robot can realize valid navigation plans according to the graph.",
+      "We contribute a new end-to-end model for following directions in natural language under the behavioral navigation framework. Inspired by the information retrieval and question answering literature BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , we propose to leverage the behavioral graph as a knowledge base to facilitate the interpretation of navigation commands. More specifically, the proposed model takes as input user directions in text form, the behavioral graph of the environment encoded as INLINEFORM0 node; edge; node INLINEFORM1 triplets, and the initial location of the robot in the graph. The model then predicts a set of behaviors to reach the desired destination according to the instructions and the map (Fig. FIGREF4 (c)). Our main insight is that using attention mechanisms to correlate navigation instructions with the topological map of the environment can facilitate predicting correct navigation plans.",
+      "This work also contributes a new dataset of INLINEFORM0 pairs of free-form natural language instructions and high-level navigation plans. This dataset was collected through Mechanical Turk using 100 simulated environments with a corresponding topological map and, to the best of our knowledge, it is the first of its kind for behavioral navigation. The dataset opens up opportunities to explore data-driven methods for grounding navigation commands into high-level motion plans.",
+      "We conduct extensive experiments to study the generalization capabilities of the proposed model for following natural language instructions. We investigate both generalization to new instructions in known and in new environments. We conclude this paper by discussing the benefits of the proposed approach as well as opportunities for future research based on our findings."
+    ],
+    [
+      "This section reviews relevant prior work on following navigation instructions. Readers interested in an in-depth review of methods to interpret spatial natural language for robotics are encouraged to refer to BIBREF11 .",
+      "Typical approaches to follow navigation commands deal with the complexity of natural language by manually parsing commands, constraining language descriptions, or using statistical machine translation methods. While manually parsing commands is often impractical, the first type of approaches are foundational: they showed that it is possible to leverage the compositionality of semantic units to interpret spatial language BIBREF12 , BIBREF13 .",
+      "Constraining language descriptions can reduce the size of the input space to facilitate the interpretation of user commands. For example, BIBREF14 explored using structured, symbolic language phrases for navigation. As in this earlier work, we are also interested in navigation with a topological map of the environment. However, we do not process symbolic phrases. Our aim is to translate free-form natural language instructions to a navigation plan using information from a high-level representation of the environment. This translation problem requires dealing with missing actions in navigation instructions and actions with preconditions, such as \u201cat the end of the corridor, turn right\u201d BIBREF15 .",
+      "Statistical machine translation BIBREF16 is at the core of recent approaches to enable robots to follow navigation instructions. These methods aim to automatically discover translation rules from a corpus of data, and often leverage the fact that navigation directions are composed of sequential commands. For instance, BIBREF17 , BIBREF4 , BIBREF2 used statistical machine translation to map instructions to a formal language defined by a grammar. Likewise, BIBREF18 , BIBREF0 mapped commands to spatial description clauses based on the hierarchical structure of language in the navigation problem. Our approach to machine translation builds on insights from these prior efforts. In particular, we focus on end-to-end learning for statistical machine translation due to the recent success of Neural Networks in Natural Language Processing BIBREF19 .",
+      "Our work is inspired by methods that reduce the task of interpreting user commands to a sequential prediction problem BIBREF20 , BIBREF21 , BIBREF22 . Similar to BIBREF21 and BIBREF22 , we use a sequence-to-sequence model to enable a mobile agent to follow routes. But instead leveraging visual information to output low-level navigation commands, we focus on using a topological map of the environment to output a high-level navigation plan. This plan is a sequence of behaviors that can be executed by a robot to reach a desired destination BIBREF5 , BIBREF6 .",
+      "We explore machine translation from the perspective of automatic question answering. Following BIBREF8 , BIBREF9 , our approach uses attention mechanisms to learn alignments between different input modalities. In our case, the inputs to our model are navigation instructions, a topological environment map, and the start location of the robot (Fig. FIGREF4 (c)). Our results show that the map can serve as an effective source of contextual information for the translation task. Additionally, it is possible to leverage this kind of information in an end-to-end fashion."
+    ],
+    [
+      "Our goal is to translate navigation instructions in text form into a sequence of behaviors that a robot can execute to reach a desired destination from a known start location. We frame this problem under a behavioral approach to indoor autonomous navigation BIBREF5 and assume that prior knowledge about the environment is available for the translation task. This prior knowledge is a topological map, in the form of a behavioral navigation graph (Fig. FIGREF4 (b)). The nodes of the graph correspond to semantically-meaningful locations for the navigation task, and its directed edges are visuo-motor behaviors that a robot can use to move between nodes. This formulation takes advantage of the rich semantic structure behind man-made environments, resulting in a compact route representation for robot navigation.",
+      "Fig. FIGREF4 (c) provides a schematic view of the problem setting. The inputs are: (1) a navigation graph INLINEFORM0 , (2) the starting node INLINEFORM1 of the robot in INLINEFORM2 , and (3) a set of free-form navigation instructions INLINEFORM3 in natural language. The instructions describe a path in the graph to reach from INLINEFORM4 to a \u2013 potentially implicit \u2013 destination node INLINEFORM5 . Using this information, the objective is to predict a suitable sequence of robot behaviors INLINEFORM6 to navigate from INLINEFORM7 to INLINEFORM8 according to INLINEFORM9 . From a supervised learning perspective, the goal is then to estimate: DISPLAYFORM0 ",
+      "based on a dataset of input-target pairs INLINEFORM0 , where INLINEFORM1 and INLINEFORM2 , respectively. The sequential execution of the behaviors INLINEFORM3 should replicate the route intended by the instructions INLINEFORM4 . We assume no prior linguistic knowledge. Thus, translation approaches have to cope with the semantics and syntax of the language by discovering corresponding patterns in the data."
+    ],
+    [
+      "We view the behavioral graph INLINEFORM0 as a knowledge base that encodes a set of navigational rules as triplets INLINEFORM1 , where INLINEFORM2 and INLINEFORM3 are adjacent nodes in the graph, and the edge INLINEFORM4 is an executable behavior to navigate from INLINEFORM5 to INLINEFORM6 . In general, each behaviors includes a list of relevant navigational attributes INLINEFORM7 that the robot might encounter when moving between nodes.",
+      "We consider 7 types of semantic locations, 11 types of behaviors, and 20 different types of landmarks. A location in the navigation graph can be a room, a lab, an office, a kitchen, a hall, a corridor, or a bathroom. These places are labeled with unique tags, such as \"room-1\" or \"lab-2\", except for bathrooms and kitchens which people do not typically refer to by unique names when describing navigation routes.",
+      "Table TABREF7 lists the navigation behaviors that we consider in this work. These behaviors can be described in reference to visual landmarks or objects, such as paintings, book shelfs, tables, etc. As in Fig. FIGREF4 , maps might contain multiple landmarks of the same type. Please see the supplementary material (Appendix A) for more details."
+    ],
+    [
+      "We leverage recent advances in deep learning to translate natural language instructions to a sequence of navigation behaviors in an end-to-end fashion. Our proposed model builds on the sequence-to-sequence translation model of BIBREF23 , which computes a soft-alignment between a source sequence (natural language instructions in our case) and the corresponding target sequence (navigation behaviors).",
+      "As one of our main contributions, we augment the neural machine translation approach of BIBREF23 to take as input not only natural language instructions, but also the corresponding behavioral navigation graph INLINEFORM0 of the environment where navigation should take place. Specifically, at each step, the graph INLINEFORM1 operates as a knowledge base that the model can access to obtain information about path connectivity, facilitating the grounding of navigation commands.",
+      "Figure FIGREF8 shows the structure of the proposed model for interpreting navigation instructions. The model consists of six layers:",
+      "Embed layer: The model first encodes each word and symbol in the input sequences INLINEFORM0 and INLINEFORM1 into fixed-length representations. The instructions INLINEFORM2 are embedded into a 100-dimensional pre-trained GloVe vector BIBREF24 . Each of the triplet components, INLINEFORM3 , INLINEFORM4 , and INLINEFORM5 of the graph INLINEFORM6 , are one-hot encoded into vectors of dimensionality INLINEFORM7 , where INLINEFORM8 and INLINEFORM9 are the number of nodes and edges in INLINEFORM10 , respectively.",
+      "Encoder layer: The model then uses two bidirectional Gated Recurrent Units (GRUs) BIBREF25 to independently process the information from INLINEFORM0 and INLINEFORM1 , and incorporate contextual cues from the surrounding embeddings in each sequence. The outputs of the encoder layer are the matrix INLINEFORM2 for the navigational commands and the matrix INLINEFORM3 for the behavioral graph, where INLINEFORM4 is the hidden size of each GRU, INLINEFORM5 is the number of words in the instruction INLINEFORM6 , and INLINEFORM7 is the number of triplets in the graph INLINEFORM8 .",
+      "Attention layer: Matrices INLINEFORM0 and INLINEFORM1 generated by the encoder layer are combined using an attention mechanism. We use one-way attention because the graph contains information about the whole environment, while the instruction has (potentially incomplete) local information about the route of interest. The use of attention provides our model with a two-step strategy to interpret commands. This resembles the way people find paths on a map: first, relevant parts on the map are selected according to their affinity to each of the words in the input instruction (attention layer); second, the selected parts are connected to assemble a valid path (decoder layer). More formally, let INLINEFORM2 ( INLINEFORM3 ) be the INLINEFORM4 -th row of INLINEFORM5 , and INLINEFORM6 ( INLINEFORM7 ) the INLINEFORM8 -th row of INLINEFORM9 . We use each encoded triplet INLINEFORM10 in INLINEFORM11 to calculate its associated attention distribution INLINEFORM12 over all the atomic instructions INLINEFORM13 : DISPLAYFORM0 ",
+      "where the matrix INLINEFORM0 serves to combine the different sources of information INLINEFORM1 and INLINEFORM2 . Each component INLINEFORM3 of the attention distributions INLINEFORM4 quantifies the affinity between the INLINEFORM5 -th triplet in INLINEFORM6 and the INLINEFORM7 -th word in the corresponding input INLINEFORM8 .",
+      "The model then uses each attention distribution INLINEFORM0 to obtain a weighted sum of the encodings of the words in INLINEFORM1 , according to their relevance to the corresponding triplet INLINEFORM2 . This results in L attention vectors INLINEFORM3 , INLINEFORM4 .",
+      "The final step in the attention layer concatenates each INLINEFORM0 with INLINEFORM1 to generate the outputs INLINEFORM2 , INLINEFORM3 . Following BIBREF8 , we include the encoded triplet INLINEFORM4 in the output tensor INLINEFORM5 of this layer to prevent early summaries of relevant map information.",
+      "FC layer: The model reduces the dimensionality of each individual vector INLINEFORM0 from INLINEFORM1 to INLINEFORM2 with a fully-connected (FC) layer. The resulting L vectors are output to the next layer as columns of a context matrix INLINEFORM3 .",
+      "Decoder layer: After the FC layer, the model predicts likelihoods over the sequence of behaviors that correspond to the input instructions with a GRU network. Without loss of generality, consider the INLINEFORM0 -th recurrent cell in the GRU network. This cell takes two inputs: a hidden state vector INLINEFORM1 from the prior cell, and a one-hot embedding of the previous behavior INLINEFORM2 that was predicted by the model. Based on these inputs, the GRU cell outputs a new hidden state INLINEFORM3 to compute likelihoods for the next behavior. These likelihoods are estimated by combining the output state INLINEFORM4 with relevant information from the context INLINEFORM5 : DISPLAYFORM0 ",
+      " where INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 are trainable parameters. The attention vector INLINEFORM3 in Eq. () quantifies the affinity of INLINEFORM4 with respect to each of the columns INLINEFORM5 of INLINEFORM6 , where INLINEFORM7 . The attention vector also helps to estimate a dynamic contextual vector INLINEFORM8 that the INLINEFORM9 -th GRU cell uses to compute logits for the next behavior: DISPLAYFORM0 ",
+      "with INLINEFORM0 trainable parameters. Note that INLINEFORM1 includes a value for each of the pre-defined behaviors in the graph INLINEFORM2 , as well as for a special \u201cstop\u201d symbol to identify the end of the output sequence.",
+      "Output layer: The final layer of the model searches for a valid sequence of robot behaviors based on the robot's initial node, the connectivity of the graph INLINEFORM0 , and the output logits from the previous decoder layer. Again, without loss of generality, consider the INLINEFORM1 -th behavior INLINEFORM2 that is finally predicted by the model. The search for this behavior is implemented as: DISPLAYFORM0 ",
+      "with INLINEFORM0 a masking function that takes as input the graph INLINEFORM1 and the node INLINEFORM2 that the robot reaches after following the sequence of behaviors INLINEFORM3 previously predicted by the model. The INLINEFORM4 function returns a vector of the same dimensionality as the logits INLINEFORM5 , but with zeros for the valid behaviors after the last location INLINEFORM6 and for the special stop symbol, and INLINEFORM7 for any invalid predictions according to the connectivity of the behavioral navigation graph."
+    ],
+    [
+      "We created a new dataset for the problem of following navigation instructions under the behavioral navigation framework of BIBREF5 . This dataset was created using Amazon Mechanical Turk and 100 maps of simulated indoor environments, each with 6 to 65 rooms. To the best of our knowledge, this is the first benchmark for comparing translation models in the context of behavioral robot navigation.",
+      "As shown in Table TABREF16 , the dataset consists of 8066 pairs of free-form natural language instructions and navigation plans for training. This training data was collected from 88 unique simulated environments, totaling 6064 distinct navigation plans (2002 plans have two different navigation instructions each; the rest has one). The dataset contains two test set variants:",
+      "While the dataset was collected with simulated environments, no structure was imposed on the navigation instructions while crowd-sourcing data. Thus, many instructions in our dataset are ambiguous. Moreover, the order of the behaviors in the instructions is not always the same. For instance, a person said \u201cturn right and advance\u201d to describe part of a route, while another person said \u201cgo straight after turning right\u201d in a similar situation. The high variability present in the natural language descriptions of our dataset makes the problem of decoding instructions into behaviors not trivial. See Appendix A of the supplementary material for additional details on our data collection effort."
+    ],
+    [
+      "This section describes our evaluation of the proposed approach for interpreting navigation commands in natural language. We provide both quantitative and qualitative results."
+    ],
+    [
+      "While computing evaluation metrics, we only consider the behaviors present in the route because they are sufficient to recover the high-level navigation plan from the graph. Our metrics treat each behavior as a single token. For example, the sample plan \u201cR-1 oor C-1 cf C-1 lt C-0 cf C-0 iol O-3\" is considered to have 5 tokens, each corresponding to one of its behaviors (\u201coor\", \u201ccf\", \u201clt\", \u201ccf\", \u201ciol\"). In this plan, \u201cR-1\",\u201cC-1\", \u201cC-0\", and \u201cO-3\" are symbols for locations (nodes) in the graph.",
+      "We compare the performance of translation approaches based on four metrics:",
+      "[align=left,leftmargin=0em,labelsep=0.4em,font=]",
+      "As in BIBREF20 , EM is 1 if a predicted plan matches exactly the ground truth; otherwise it is 0.",
+      "The harmonic average of the precision and recall over all the test set BIBREF26 .",
+      "The minimum number of insertions, deletions or swap operations required to transform a predicted sequence of behaviors into the ground truth sequence BIBREF27 .",
+      "GM is 1 if a predicted plan reaches the ground truth destination (even if the full sequence of behaviors does not match exactly the ground truth). Otherwise, GM is 0."
+    ],
+    [
+      "We compare the proposed approach for translating natural language instructions into a navigation plan against alternative deep-learning models:",
+      "[align=left,leftmargin=0em,labelsep=0.4em,font=]",
+      "The baseline approach is based on BIBREF20 . It divides the task of interpreting commands for behavioral navigation into two steps: path generation, and path verification. For path generation, this baseline uses a standard sequence-to-sequence model augmented with an attention mechanism, similar to BIBREF23 , BIBREF6 . For path verification, the baseline uses depth-first search to find a route in the graph that matches the sequence of predicted behaviors. If no route matches perfectly, the baseline changes up to three behaviors in the predicted sequence to try to turn it into a valid path.",
+      "To test the impact of using the behavioral graphs as an extra input to our translation model, we implemented a version of our approach that only takes natural language instructions as input. In this ablation model, the output of the bidirectional GRU that encodes the input instruction INLINEFORM0 is directly fed to the decoder layer. This model does not have the attention and FC layers described in Sec. SECREF4 , nor uses the masking function in the output layer.",
+      "This model is the same as the previous Ablation model, but with the masking function in the output layer."
+    ],
+    [
+      "We pre-processed the inputs to the various models that are considered in our experiment. In particular, we lowercased, tokenized, spell-checked and lemmatized the input instructions in text-form using WordNet BIBREF28 . We also truncated the graphs to a maximum of 300 triplets, and the navigational instructions to a maximum of 150 words. Only 6.4% (5.4%) of the unique graphs in the training (validation) set had more than 300 triplets, and less than 0.15% of the natural language instructions in these sets had more than 150 tokens.",
+      "The dimensionality of the hidden state of the GRU networks was set to 128 in all the experiments. In general, we used 12.5% of the training set as validation for choosing models' hyper-parameters. In particular, we used dropout after the encoder and the fully-connected layers of the proposed model to reduce overfitting. Best performance was achieved with a dropout rate of 0.5 and batch size equal to 256. We also used scheduled sampling BIBREF29 at training time for all models except the baseline.",
+      "We input the triplets from the graph to our proposed model in alphabetical order, and consider a modification where the triplets that surround the start location of the robot are provided first in the input graph sequence. We hypothesized that such rearrangement would help identify the starting location (node) of the robot in the graph. In turn, this could facilitate the prediction of correct output sequences. In the remaining of the paper, we refer to models that were provided a rearranged graph, beginning with the starting location of the robot, as models with \u201cOrdered Triplets\u201d."
+    ],
+    [
+      "Table TABREF28 shows the performance of the models considered in our evaluation on both test sets. The next two sections discuss the results in detail.",
+      "First, we can observe that the final model \u201cOurs with Mask and Ordered Triplets\u201d outperforms the Baseline and Ablation models on all metrics in previously seen environments. The difference in performance is particularly evident for the Exact Match and Goal Match metrics, with our model increasing accuracy by 35% and 25% in comparison to the Baseline and Ablation models, respectively. These results suggest that providing the behavioral navigation graph to the model and allowing it to process this information as a knowledge base in an end-to-end fashion is beneficial.",
+      "We can also observe from Table TABREF28 that the masking function of Eq. ( EQREF12 ) tends to increase performance in the Test-Repeated Set by constraining the output sequence to a valid set of navigation behaviors. For the Ablation model, using the masking function leads to about INLINEFORM0 increase in EM and GM accuracy. For the proposed model (with or without reordering the graph triplets), the increase in accuracy is around INLINEFORM1 . Note that the impact of the masking function is less evident in terms of the F1 score because this metric considers if a predicted behavior exists in the ground truth navigation plan, irrespective of its specific position in the output sequence.",
+      "The results in the last four rows of Table TABREF28 suggest that ordering the graph triplets can facilitate predicting correct navigation plans in previously seen environments. Providing the triplets that surround the starting location of the robot first to the model leads to a boost of INLINEFORM0 in EM and GM performance. The rearrangement of the graph triplets also helps to reduce ED and increase F1.",
+      "Lastly, it is worth noting that our proposed model (last row of Table TABREF28 ) outperforms all other models in previously seen environments. In particular, we obtain over INLINEFORM0 increase in EM and GM between our model and the next best two models.",
+      "The previous section evaluated model performance on new instructions (and corresponding navigation plans) for environments that were previously seen at training time. Here, we examine whether the trained models succeed on environments that are completely new.",
+      "The evaluation on the Test-New Set helps understand the generalization capabilities of the models under consideration. This experiment is more challenging than the one in the previous section, as can be seen in performance drops in Table TABREF28 for the new environments. Nonetheless, the insights from the previous section still hold: masking in the output layer and reordering the graph triplets tend to increase performance.",
+      "Even though the results in Table TABREF28 suggest that there is room for future work on decoding natural language instructions, our model still outperforms the baselines by a clear margin in new environments. For instance, the difference between our model and the second best model in the Test-New set is about INLINEFORM0 EM and GM. Note that the average number of actions in the ground truth output sequences is 7.07 for the Test-New set. Our model's predictions are just INLINEFORM1 edits off on average from the correct navigation plans."
+    ],
+    [
+      "This section discusses qualitative results to better understand how the proposed model uses the navigation graph.",
+      "We analyze the evolution of the attention weights INLINEFORM0 in Eq. () to assess if the decoder layer of the proposed model is attending to the correct parts of the behavioral graph when making predictions. Fig FIGREF33 (b) shows an example of the resulting attention map for the case of a correct prediction. In the Figure, the attention map is depicted as a scaled and normalized 2D array of color codes. Each column in the array shows the attention distribution INLINEFORM1 used to generate the predicted output at step INLINEFORM2 . Consequently, each row in the array represents a triplet in the corresponding behavioral graph. This graph consists of 72 triplets for Fig FIGREF33 (b).",
+      "We observe a locality effect associated to the attention coefficients corresponding to high values (bright areas) in each column of Fig FIGREF33 (b). This suggests that the decoder is paying attention to graph triplets associated to particular neighborhoods of the environment in each prediction step. We include additional attention visualizations in the supplementary Appendix, including cases where the dynamics of the attention distribution are harder to interpret.",
+      "All the routes in our dataset are the shortest paths from a start location to a given destination. Thus, we collected a few additional natural language instructions to check if our model was able to follow navigation instructions describing sub-optimal paths. One such example is shown in Fig. FIGREF37 , where the blue route (shortest path) and the red route (alternative path) are described by:",
+      "[leftmargin=*, labelsep=0.2em, itemsep=0em]",
+      "\u201cGo out the office and make a left. Turn right at the corner and go down the hall. Make a right at the next corner and enter the kitchen in front of table.\u201d",
+      "\u201cExit the room 0 and turn right, go to the end of the corridor and turn left, go straight to the end of the corridor and turn left again. After passing bookshelf on your left and table on your right, Enter the kitchen on your right.\u201d",
+      "For both routes, the proposed model was able to predict the correct sequence of navigation behaviors. This result suggests that the model is indeed using the input instructions and is not just approximating shortest paths in the behavioral graph. Other examples on the prediction of sub-obtimal paths are described in the Appendix."
+    ],
+    [
+      "This work introduced behavioral navigation through free-form natural language instructions as a challenging and a novel task that falls at the intersection of natural language processing and robotics. This problem has a range of interesting cross-domain applications, including information retrieval.",
+      "We proposed an end-to-end system to translate user instructions to a high-level navigation plan. Our model utilized an attention mechanism to merge relevant information from the navigation instructions with a behavioral graph of the environment. The model then used a decoder to predict a sequence of navigation behaviors that matched the input commands.",
+      "As part of this effort, we contributed a new dataset of 11,051 pairs of user instructions and navigation plans from 100 different environments. Our model achieved the best performance in this dataset in comparison to a two-step baseline approach for interpreting navigation instructions, and a sequence-to-sequence model that does not consider the behavioral graph. Our quantitative and qualitative results suggest that attention mechanisms can help leverage the behavioral graph as a relevant knowledge base to facilitate the translation of free-form navigation instructions. Overall, our approach demonstrated practical form of learning for a complex and useful task. In future work, we are interested in investigating mechanisms to improve generalization to new environments. For example, pointer and graph networks BIBREF30 , BIBREF31 are a promising direction to help supervise translation models and predict motion behaviors."
+    ],
+    [
+      "The Toyota Research Institute (TRI) provided funds to assist with this research, but this paper solely reflects the opinions and conclusions of its authors and not TRI or any other Toyota entity. This work is also partially funded by Fondecyt grant 1181739, Conicyt, Chile. The authors would also like to thank Gabriel Sep\u00falveda for his assistance with parts of this project."
+    ]
+  ]
+}
+```

qasper-0521/instruction.md

@@ -0,0 +1,130 @@
+Name of Paper: Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation
+
+Question: What was the performance of their model?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related work",
+    "Problem Formulation",
+    "The Behavioral Graph: A Knowledge Base For Navigation",
+    "Approach",
+    "Dataset",
+    "Experiments",
+    "Evaluation Metrics",
+    "Models Used in the Evaluation",
+    "Implementation Details",
+    "Quantitative Evaluation",
+    "Qualitative Evaluation",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Enabling robots to follow navigation instructions in natural language can facilitate human-robot interaction across a variety of applications. For instance, within the service robotics domain, robots can follow navigation instructions to help with mobile manipulation BIBREF0 and delivery tasks BIBREF1 .",
+      "Interpreting navigation instructions in natural language is difficult due to the high variability in the way people describe routes BIBREF2 . For example, there are a variety of ways to describe the route in Fig. FIGREF4 (a):",
+      "Each fragment of a sentence within these instructions can be mapped to one or more than one navigation behaviors. For instance, assume that a robot counts with a number of primitive, navigation behaviors, such as \u201center the room on the left (or on right)\u201d , \u201cfollow the corridor\u201d, \u201ccross the intersection\u201d, etc. Then, the fragment \u201cadvance forward\u201d in a navigation instruction could be interpreted as a \u201cfollow the corridor\u201d behavior, or as a sequence of \u201cfollow the corridor\u201d interspersed with \u201ccross the intersection\u201d behaviors depending on the topology of the environment. Resolving such ambiguities often requires reasoning about \u201ccommon-sense\u201d concepts, as well as interpreting spatial information and landmarks, e.g., in sentences such as \u201cthe room on the left right before the end of the corridor\u201d and \u201cthe room which is in the middle of two vases\u201d.",
+      "In this work, we pose the problem of interpreting navigation instructions as finding a mapping (or grounding) of the commands into an executable navigation plan. While the plan is typically modeled as a formal specification of low-level motions BIBREF2 or a grammar BIBREF3 , BIBREF4 , we focus specifically on translating instructions to a high-level navigation plan based on a topological representation of the environment. This representation is a behavioral navigation graph, as recently proposed by BIBREF5 , designed to take advantage of the semantic structure typical of human environments. The nodes of the graph correspond to semantically meaningful locations for the navigation task, such as kitchens or entrances to rooms in corridors. The edges are parameterized, visuo-motor behaviors that allow a robot to navigate between neighboring nodes, as illustrated in Fig. FIGREF4 (b). Under this framework, complex navigation routes can be achieved by sequencing behaviors without an explicit metric representation of the world.",
+      "We formulate the problem of following instructions under the framework of BIBREF5 as finding a path in the behavioral navigation graph that follows the desired route, given a known starting location. The edges (behaviors) along this path serve to reach the \u2013 sometimes implicit \u2013 destination requested by the user. As in BIBREF6 , our focus is on the problem of interpreting navigation directions. We assume that a robot can realize valid navigation plans according to the graph.",
+      "We contribute a new end-to-end model for following directions in natural language under the behavioral navigation framework. Inspired by the information retrieval and question answering literature BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , we propose to leverage the behavioral graph as a knowledge base to facilitate the interpretation of navigation commands. More specifically, the proposed model takes as input user directions in text form, the behavioral graph of the environment encoded as INLINEFORM0 node; edge; node INLINEFORM1 triplets, and the initial location of the robot in the graph. The model then predicts a set of behaviors to reach the desired destination according to the instructions and the map (Fig. FIGREF4 (c)). Our main insight is that using attention mechanisms to correlate navigation instructions with the topological map of the environment can facilitate predicting correct navigation plans.",
+      "This work also contributes a new dataset of INLINEFORM0 pairs of free-form natural language instructions and high-level navigation plans. This dataset was collected through Mechanical Turk using 100 simulated environments with a corresponding topological map and, to the best of our knowledge, it is the first of its kind for behavioral navigation. The dataset opens up opportunities to explore data-driven methods for grounding navigation commands into high-level motion plans.",
+      "We conduct extensive experiments to study the generalization capabilities of the proposed model for following natural language instructions. We investigate both generalization to new instructions in known and in new environments. We conclude this paper by discussing the benefits of the proposed approach as well as opportunities for future research based on our findings."
+    ],
+    [
+      "This section reviews relevant prior work on following navigation instructions. Readers interested in an in-depth review of methods to interpret spatial natural language for robotics are encouraged to refer to BIBREF11 .",
+      "Typical approaches to follow navigation commands deal with the complexity of natural language by manually parsing commands, constraining language descriptions, or using statistical machine translation methods. While manually parsing commands is often impractical, the first type of approaches are foundational: they showed that it is possible to leverage the compositionality of semantic units to interpret spatial language BIBREF12 , BIBREF13 .",
+      "Constraining language descriptions can reduce the size of the input space to facilitate the interpretation of user commands. For example, BIBREF14 explored using structured, symbolic language phrases for navigation. As in this earlier work, we are also interested in navigation with a topological map of the environment. However, we do not process symbolic phrases. Our aim is to translate free-form natural language instructions to a navigation plan using information from a high-level representation of the environment. This translation problem requires dealing with missing actions in navigation instructions and actions with preconditions, such as \u201cat the end of the corridor, turn right\u201d BIBREF15 .",
+      "Statistical machine translation BIBREF16 is at the core of recent approaches to enable robots to follow navigation instructions. These methods aim to automatically discover translation rules from a corpus of data, and often leverage the fact that navigation directions are composed of sequential commands. For instance, BIBREF17 , BIBREF4 , BIBREF2 used statistical machine translation to map instructions to a formal language defined by a grammar. Likewise, BIBREF18 , BIBREF0 mapped commands to spatial description clauses based on the hierarchical structure of language in the navigation problem. Our approach to machine translation builds on insights from these prior efforts. In particular, we focus on end-to-end learning for statistical machine translation due to the recent success of Neural Networks in Natural Language Processing BIBREF19 .",
+      "Our work is inspired by methods that reduce the task of interpreting user commands to a sequential prediction problem BIBREF20 , BIBREF21 , BIBREF22 . Similar to BIBREF21 and BIBREF22 , we use a sequence-to-sequence model to enable a mobile agent to follow routes. But instead leveraging visual information to output low-level navigation commands, we focus on using a topological map of the environment to output a high-level navigation plan. This plan is a sequence of behaviors that can be executed by a robot to reach a desired destination BIBREF5 , BIBREF6 .",
+      "We explore machine translation from the perspective of automatic question answering. Following BIBREF8 , BIBREF9 , our approach uses attention mechanisms to learn alignments between different input modalities. In our case, the inputs to our model are navigation instructions, a topological environment map, and the start location of the robot (Fig. FIGREF4 (c)). Our results show that the map can serve as an effective source of contextual information for the translation task. Additionally, it is possible to leverage this kind of information in an end-to-end fashion."
+    ],
+    [
+      "Our goal is to translate navigation instructions in text form into a sequence of behaviors that a robot can execute to reach a desired destination from a known start location. We frame this problem under a behavioral approach to indoor autonomous navigation BIBREF5 and assume that prior knowledge about the environment is available for the translation task. This prior knowledge is a topological map, in the form of a behavioral navigation graph (Fig. FIGREF4 (b)). The nodes of the graph correspond to semantically-meaningful locations for the navigation task, and its directed edges are visuo-motor behaviors that a robot can use to move between nodes. This formulation takes advantage of the rich semantic structure behind man-made environments, resulting in a compact route representation for robot navigation.",
+      "Fig. FIGREF4 (c) provides a schematic view of the problem setting. The inputs are: (1) a navigation graph INLINEFORM0 , (2) the starting node INLINEFORM1 of the robot in INLINEFORM2 , and (3) a set of free-form navigation instructions INLINEFORM3 in natural language. The instructions describe a path in the graph to reach from INLINEFORM4 to a \u2013 potentially implicit \u2013 destination node INLINEFORM5 . Using this information, the objective is to predict a suitable sequence of robot behaviors INLINEFORM6 to navigate from INLINEFORM7 to INLINEFORM8 according to INLINEFORM9 . From a supervised learning perspective, the goal is then to estimate: DISPLAYFORM0 ",
+      "based on a dataset of input-target pairs INLINEFORM0 , where INLINEFORM1 and INLINEFORM2 , respectively. The sequential execution of the behaviors INLINEFORM3 should replicate the route intended by the instructions INLINEFORM4 . We assume no prior linguistic knowledge. Thus, translation approaches have to cope with the semantics and syntax of the language by discovering corresponding patterns in the data."
+    ],
+    [
+      "We view the behavioral graph INLINEFORM0 as a knowledge base that encodes a set of navigational rules as triplets INLINEFORM1 , where INLINEFORM2 and INLINEFORM3 are adjacent nodes in the graph, and the edge INLINEFORM4 is an executable behavior to navigate from INLINEFORM5 to INLINEFORM6 . In general, each behaviors includes a list of relevant navigational attributes INLINEFORM7 that the robot might encounter when moving between nodes.",
+      "We consider 7 types of semantic locations, 11 types of behaviors, and 20 different types of landmarks. A location in the navigation graph can be a room, a lab, an office, a kitchen, a hall, a corridor, or a bathroom. These places are labeled with unique tags, such as \"room-1\" or \"lab-2\", except for bathrooms and kitchens which people do not typically refer to by unique names when describing navigation routes.",
+      "Table TABREF7 lists the navigation behaviors that we consider in this work. These behaviors can be described in reference to visual landmarks or objects, such as paintings, book shelfs, tables, etc. As in Fig. FIGREF4 , maps might contain multiple landmarks of the same type. Please see the supplementary material (Appendix A) for more details."
+    ],
+    [
+      "We leverage recent advances in deep learning to translate natural language instructions to a sequence of navigation behaviors in an end-to-end fashion. Our proposed model builds on the sequence-to-sequence translation model of BIBREF23 , which computes a soft-alignment between a source sequence (natural language instructions in our case) and the corresponding target sequence (navigation behaviors).",
+      "As one of our main contributions, we augment the neural machine translation approach of BIBREF23 to take as input not only natural language instructions, but also the corresponding behavioral navigation graph INLINEFORM0 of the environment where navigation should take place. Specifically, at each step, the graph INLINEFORM1 operates as a knowledge base that the model can access to obtain information about path connectivity, facilitating the grounding of navigation commands.",
+      "Figure FIGREF8 shows the structure of the proposed model for interpreting navigation instructions. The model consists of six layers:",
+      "Embed layer: The model first encodes each word and symbol in the input sequences INLINEFORM0 and INLINEFORM1 into fixed-length representations. The instructions INLINEFORM2 are embedded into a 100-dimensional pre-trained GloVe vector BIBREF24 . Each of the triplet components, INLINEFORM3 , INLINEFORM4 , and INLINEFORM5 of the graph INLINEFORM6 , are one-hot encoded into vectors of dimensionality INLINEFORM7 , where INLINEFORM8 and INLINEFORM9 are the number of nodes and edges in INLINEFORM10 , respectively.",
+      "Encoder layer: The model then uses two bidirectional Gated Recurrent Units (GRUs) BIBREF25 to independently process the information from INLINEFORM0 and INLINEFORM1 , and incorporate contextual cues from the surrounding embeddings in each sequence. The outputs of the encoder layer are the matrix INLINEFORM2 for the navigational commands and the matrix INLINEFORM3 for the behavioral graph, where INLINEFORM4 is the hidden size of each GRU, INLINEFORM5 is the number of words in the instruction INLINEFORM6 , and INLINEFORM7 is the number of triplets in the graph INLINEFORM8 .",
+      "Attention layer: Matrices INLINEFORM0 and INLINEFORM1 generated by the encoder layer are combined using an attention mechanism. We use one-way attention because the graph contains information about the whole environment, while the instruction has (potentially incomplete) local information about the route of interest. The use of attention provides our model with a two-step strategy to interpret commands. This resembles the way people find paths on a map: first, relevant parts on the map are selected according to their affinity to each of the words in the input instruction (attention layer); second, the selected parts are connected to assemble a valid path (decoder layer). More formally, let INLINEFORM2 ( INLINEFORM3 ) be the INLINEFORM4 -th row of INLINEFORM5 , and INLINEFORM6 ( INLINEFORM7 ) the INLINEFORM8 -th row of INLINEFORM9 . We use each encoded triplet INLINEFORM10 in INLINEFORM11 to calculate its associated attention distribution INLINEFORM12 over all the atomic instructions INLINEFORM13 : DISPLAYFORM0 ",
+      "where the matrix INLINEFORM0 serves to combine the different sources of information INLINEFORM1 and INLINEFORM2 . Each component INLINEFORM3 of the attention distributions INLINEFORM4 quantifies the affinity between the INLINEFORM5 -th triplet in INLINEFORM6 and the INLINEFORM7 -th word in the corresponding input INLINEFORM8 .",
+      "The model then uses each attention distribution INLINEFORM0 to obtain a weighted sum of the encodings of the words in INLINEFORM1 , according to their relevance to the corresponding triplet INLINEFORM2 . This results in L attention vectors INLINEFORM3 , INLINEFORM4 .",
+      "The final step in the attention layer concatenates each INLINEFORM0 with INLINEFORM1 to generate the outputs INLINEFORM2 , INLINEFORM3 . Following BIBREF8 , we include the encoded triplet INLINEFORM4 in the output tensor INLINEFORM5 of this layer to prevent early summaries of relevant map information.",
+      "FC layer: The model reduces the dimensionality of each individual vector INLINEFORM0 from INLINEFORM1 to INLINEFORM2 with a fully-connected (FC) layer. The resulting L vectors are output to the next layer as columns of a context matrix INLINEFORM3 .",
+      "Decoder layer: After the FC layer, the model predicts likelihoods over the sequence of behaviors that correspond to the input instructions with a GRU network. Without loss of generality, consider the INLINEFORM0 -th recurrent cell in the GRU network. This cell takes two inputs: a hidden state vector INLINEFORM1 from the prior cell, and a one-hot embedding of the previous behavior INLINEFORM2 that was predicted by the model. Based on these inputs, the GRU cell outputs a new hidden state INLINEFORM3 to compute likelihoods for the next behavior. These likelihoods are estimated by combining the output state INLINEFORM4 with relevant information from the context INLINEFORM5 : DISPLAYFORM0 ",
+      " where INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 are trainable parameters. The attention vector INLINEFORM3 in Eq. () quantifies the affinity of INLINEFORM4 with respect to each of the columns INLINEFORM5 of INLINEFORM6 , where INLINEFORM7 . The attention vector also helps to estimate a dynamic contextual vector INLINEFORM8 that the INLINEFORM9 -th GRU cell uses to compute logits for the next behavior: DISPLAYFORM0 ",
+      "with INLINEFORM0 trainable parameters. Note that INLINEFORM1 includes a value for each of the pre-defined behaviors in the graph INLINEFORM2 , as well as for a special \u201cstop\u201d symbol to identify the end of the output sequence.",
+      "Output layer: The final layer of the model searches for a valid sequence of robot behaviors based on the robot's initial node, the connectivity of the graph INLINEFORM0 , and the output logits from the previous decoder layer. Again, without loss of generality, consider the INLINEFORM1 -th behavior INLINEFORM2 that is finally predicted by the model. The search for this behavior is implemented as: DISPLAYFORM0 ",
+      "with INLINEFORM0 a masking function that takes as input the graph INLINEFORM1 and the node INLINEFORM2 that the robot reaches after following the sequence of behaviors INLINEFORM3 previously predicted by the model. The INLINEFORM4 function returns a vector of the same dimensionality as the logits INLINEFORM5 , but with zeros for the valid behaviors after the last location INLINEFORM6 and for the special stop symbol, and INLINEFORM7 for any invalid predictions according to the connectivity of the behavioral navigation graph."
+    ],
+    [
+      "We created a new dataset for the problem of following navigation instructions under the behavioral navigation framework of BIBREF5 . This dataset was created using Amazon Mechanical Turk and 100 maps of simulated indoor environments, each with 6 to 65 rooms. To the best of our knowledge, this is the first benchmark for comparing translation models in the context of behavioral robot navigation.",
+      "As shown in Table TABREF16 , the dataset consists of 8066 pairs of free-form natural language instructions and navigation plans for training. This training data was collected from 88 unique simulated environments, totaling 6064 distinct navigation plans (2002 plans have two different navigation instructions each; the rest has one). The dataset contains two test set variants:",
+      "While the dataset was collected with simulated environments, no structure was imposed on the navigation instructions while crowd-sourcing data. Thus, many instructions in our dataset are ambiguous. Moreover, the order of the behaviors in the instructions is not always the same. For instance, a person said \u201cturn right and advance\u201d to describe part of a route, while another person said \u201cgo straight after turning right\u201d in a similar situation. The high variability present in the natural language descriptions of our dataset makes the problem of decoding instructions into behaviors not trivial. See Appendix A of the supplementary material for additional details on our data collection effort."
+    ],
+    [
+      "This section describes our evaluation of the proposed approach for interpreting navigation commands in natural language. We provide both quantitative and qualitative results."
+    ],
+    [
+      "While computing evaluation metrics, we only consider the behaviors present in the route because they are sufficient to recover the high-level navigation plan from the graph. Our metrics treat each behavior as a single token. For example, the sample plan \u201cR-1 oor C-1 cf C-1 lt C-0 cf C-0 iol O-3\" is considered to have 5 tokens, each corresponding to one of its behaviors (\u201coor\", \u201ccf\", \u201clt\", \u201ccf\", \u201ciol\"). In this plan, \u201cR-1\",\u201cC-1\", \u201cC-0\", and \u201cO-3\" are symbols for locations (nodes) in the graph.",
+      "We compare the performance of translation approaches based on four metrics:",
+      "[align=left,leftmargin=0em,labelsep=0.4em,font=]",
+      "As in BIBREF20 , EM is 1 if a predicted plan matches exactly the ground truth; otherwise it is 0.",
+      "The harmonic average of the precision and recall over all the test set BIBREF26 .",
+      "The minimum number of insertions, deletions or swap operations required to transform a predicted sequence of behaviors into the ground truth sequence BIBREF27 .",
+      "GM is 1 if a predicted plan reaches the ground truth destination (even if the full sequence of behaviors does not match exactly the ground truth). Otherwise, GM is 0."
+    ],
+    [
+      "We compare the proposed approach for translating natural language instructions into a navigation plan against alternative deep-learning models:",
+      "[align=left,leftmargin=0em,labelsep=0.4em,font=]",
+      "The baseline approach is based on BIBREF20 . It divides the task of interpreting commands for behavioral navigation into two steps: path generation, and path verification. For path generation, this baseline uses a standard sequence-to-sequence model augmented with an attention mechanism, similar to BIBREF23 , BIBREF6 . For path verification, the baseline uses depth-first search to find a route in the graph that matches the sequence of predicted behaviors. If no route matches perfectly, the baseline changes up to three behaviors in the predicted sequence to try to turn it into a valid path.",
+      "To test the impact of using the behavioral graphs as an extra input to our translation model, we implemented a version of our approach that only takes natural language instructions as input. In this ablation model, the output of the bidirectional GRU that encodes the input instruction INLINEFORM0 is directly fed to the decoder layer. This model does not have the attention and FC layers described in Sec. SECREF4 , nor uses the masking function in the output layer.",
+      "This model is the same as the previous Ablation model, but with the masking function in the output layer."
+    ],
+    [
+      "We pre-processed the inputs to the various models that are considered in our experiment. In particular, we lowercased, tokenized, spell-checked and lemmatized the input instructions in text-form using WordNet BIBREF28 . We also truncated the graphs to a maximum of 300 triplets, and the navigational instructions to a maximum of 150 words. Only 6.4% (5.4%) of the unique graphs in the training (validation) set had more than 300 triplets, and less than 0.15% of the natural language instructions in these sets had more than 150 tokens.",
+      "The dimensionality of the hidden state of the GRU networks was set to 128 in all the experiments. In general, we used 12.5% of the training set as validation for choosing models' hyper-parameters. In particular, we used dropout after the encoder and the fully-connected layers of the proposed model to reduce overfitting. Best performance was achieved with a dropout rate of 0.5 and batch size equal to 256. We also used scheduled sampling BIBREF29 at training time for all models except the baseline.",
+      "We input the triplets from the graph to our proposed model in alphabetical order, and consider a modification where the triplets that surround the start location of the robot are provided first in the input graph sequence. We hypothesized that such rearrangement would help identify the starting location (node) of the robot in the graph. In turn, this could facilitate the prediction of correct output sequences. In the remaining of the paper, we refer to models that were provided a rearranged graph, beginning with the starting location of the robot, as models with \u201cOrdered Triplets\u201d."
+    ],
+    [
+      "Table TABREF28 shows the performance of the models considered in our evaluation on both test sets. The next two sections discuss the results in detail.",
+      "First, we can observe that the final model \u201cOurs with Mask and Ordered Triplets\u201d outperforms the Baseline and Ablation models on all metrics in previously seen environments. The difference in performance is particularly evident for the Exact Match and Goal Match metrics, with our model increasing accuracy by 35% and 25% in comparison to the Baseline and Ablation models, respectively. These results suggest that providing the behavioral navigation graph to the model and allowing it to process this information as a knowledge base in an end-to-end fashion is beneficial.",
+      "We can also observe from Table TABREF28 that the masking function of Eq. ( EQREF12 ) tends to increase performance in the Test-Repeated Set by constraining the output sequence to a valid set of navigation behaviors. For the Ablation model, using the masking function leads to about INLINEFORM0 increase in EM and GM accuracy. For the proposed model (with or without reordering the graph triplets), the increase in accuracy is around INLINEFORM1 . Note that the impact of the masking function is less evident in terms of the F1 score because this metric considers if a predicted behavior exists in the ground truth navigation plan, irrespective of its specific position in the output sequence.",
+      "The results in the last four rows of Table TABREF28 suggest that ordering the graph triplets can facilitate predicting correct navigation plans in previously seen environments. Providing the triplets that surround the starting location of the robot first to the model leads to a boost of INLINEFORM0 in EM and GM performance. The rearrangement of the graph triplets also helps to reduce ED and increase F1.",
+      "Lastly, it is worth noting that our proposed model (last row of Table TABREF28 ) outperforms all other models in previously seen environments. In particular, we obtain over INLINEFORM0 increase in EM and GM between our model and the next best two models.",
+      "The previous section evaluated model performance on new instructions (and corresponding navigation plans) for environments that were previously seen at training time. Here, we examine whether the trained models succeed on environments that are completely new.",
+      "The evaluation on the Test-New Set helps understand the generalization capabilities of the models under consideration. This experiment is more challenging than the one in the previous section, as can be seen in performance drops in Table TABREF28 for the new environments. Nonetheless, the insights from the previous section still hold: masking in the output layer and reordering the graph triplets tend to increase performance.",
+      "Even though the results in Table TABREF28 suggest that there is room for future work on decoding natural language instructions, our model still outperforms the baselines by a clear margin in new environments. For instance, the difference between our model and the second best model in the Test-New set is about INLINEFORM0 EM and GM. Note that the average number of actions in the ground truth output sequences is 7.07 for the Test-New set. Our model's predictions are just INLINEFORM1 edits off on average from the correct navigation plans."
+    ],
+    [
+      "This section discusses qualitative results to better understand how the proposed model uses the navigation graph.",
+      "We analyze the evolution of the attention weights INLINEFORM0 in Eq. () to assess if the decoder layer of the proposed model is attending to the correct parts of the behavioral graph when making predictions. Fig FIGREF33 (b) shows an example of the resulting attention map for the case of a correct prediction. In the Figure, the attention map is depicted as a scaled and normalized 2D array of color codes. Each column in the array shows the attention distribution INLINEFORM1 used to generate the predicted output at step INLINEFORM2 . Consequently, each row in the array represents a triplet in the corresponding behavioral graph. This graph consists of 72 triplets for Fig FIGREF33 (b).",
+      "We observe a locality effect associated to the attention coefficients corresponding to high values (bright areas) in each column of Fig FIGREF33 (b). This suggests that the decoder is paying attention to graph triplets associated to particular neighborhoods of the environment in each prediction step. We include additional attention visualizations in the supplementary Appendix, including cases where the dynamics of the attention distribution are harder to interpret.",
+      "All the routes in our dataset are the shortest paths from a start location to a given destination. Thus, we collected a few additional natural language instructions to check if our model was able to follow navigation instructions describing sub-optimal paths. One such example is shown in Fig. FIGREF37 , where the blue route (shortest path) and the red route (alternative path) are described by:",
+      "[leftmargin=*, labelsep=0.2em, itemsep=0em]",
+      "\u201cGo out the office and make a left. Turn right at the corner and go down the hall. Make a right at the next corner and enter the kitchen in front of table.\u201d",
+      "\u201cExit the room 0 and turn right, go to the end of the corridor and turn left, go straight to the end of the corridor and turn left again. After passing bookshelf on your left and table on your right, Enter the kitchen on your right.\u201d",
+      "For both routes, the proposed model was able to predict the correct sequence of navigation behaviors. This result suggests that the model is indeed using the input instructions and is not just approximating shortest paths in the behavioral graph. Other examples on the prediction of sub-obtimal paths are described in the Appendix."
+    ],
+    [
+      "This work introduced behavioral navigation through free-form natural language instructions as a challenging and a novel task that falls at the intersection of natural language processing and robotics. This problem has a range of interesting cross-domain applications, including information retrieval.",
+      "We proposed an end-to-end system to translate user instructions to a high-level navigation plan. Our model utilized an attention mechanism to merge relevant information from the navigation instructions with a behavioral graph of the environment. The model then used a decoder to predict a sequence of navigation behaviors that matched the input commands.",
+      "As part of this effort, we contributed a new dataset of 11,051 pairs of user instructions and navigation plans from 100 different environments. Our model achieved the best performance in this dataset in comparison to a two-step baseline approach for interpreting navigation instructions, and a sequence-to-sequence model that does not consider the behavioral graph. Our quantitative and qualitative results suggest that attention mechanisms can help leverage the behavioral graph as a relevant knowledge base to facilitate the translation of free-form navigation instructions. Overall, our approach demonstrated practical form of learning for a complex and useful task. In future work, we are interested in investigating mechanisms to improve generalization to new environments. For example, pointer and graph networks BIBREF30 , BIBREF31 are a promising direction to help supervise translation models and predict motion behaviors."
+    ],
+    [
+      "The Toyota Research Institute (TRI) provided funds to assist with this research, but this paper solely reflects the opinions and conclusions of its authors and not TRI or any other Toyota entity. This work is also partially funded by Fondecyt grant 1181739, Conicyt, Chile. The authors would also like to thank Gabriel Sep\u00falveda for his assistance with parts of this project."
+    ]
+  ]
+}
+```

qasper-0529/instruction.md

@@ -0,0 +1,104 @@
+Name of Paper: Morphological Word Segmentation on Agglutinative Languages for Neural Machine Translation
+
+Question: How many linguistic and semantic features are learned?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Approach",
+    "Approach ::: Morpheme Segmentation",
+    "Approach ::: Morpheme Segmentation ::: Stem with Combined Suffix",
+    "Approach ::: Morpheme Segmentation ::: Stem with Singular Suffix",
+    "Approach ::: Byte Pair Encoding (BPE)",
+    "Approach ::: Morphologically Motivated Segmentation",
+    "Approach ::: Morphologically Motivated Segmentation ::: BPE on Stem with Combined Suffix",
+    "Approach ::: Morphologically Motivated Segmentation ::: BPE on Stem with Singular Suffix",
+    "Experiments ::: Experimental Setup ::: Turkish-English Data :",
+    "Experiments ::: Experimental Setup ::: Uyghur-Chinese Data :",
+    "Experiments ::: Experimental Setup ::: Data Preprocessing :",
+    "Experiments ::: Experimental Setup ::: Number of Merge Operations :",
+    "Experiments ::: NMT Configuration",
+    "Results",
+    "Discussion",
+    "Related Work",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Neural machine translation (NMT) has achieved impressive performance on machine translation task in recent years for many language pairs BIBREF0, BIBREF1, BIBREF2. However, in consideration of time cost and space capacity, the NMT model generally employs a limited-size vocabulary that only contains the top-N highest frequency words (commonly in the range of 30K to 80K) BIBREF3, which leads to the Out-of-Vocabulary (OOV) problem following with inaccurate and terrible translation results. Research indicated that sentences with too many unknown words tend to be translated much more poorly than sentences with mainly frequent words. For the low-resource and source-side morphologically-rich machine translation tasks, such as Turkish-English and Uyghur-Chinese, all the above issues are more serious due to the fact that the NMT model cannot effectively identify the complex morpheme structure or capture the linguistic and semantic information with too many rare and unknown words in the training corpus.",
+      "Both the Turkish and Uyghur are agglutinative and highly-inflected languages in which the word is formed by suffixes attaching to a stem BIBREF4. The word consists of smaller morpheme units without any splitter between them and its structure can be denoted as \u201cstem + suffix1 + suffix2 + ... + suffixN\u201d. A stem is attached in the rear by zero to many suffixes that have many inflected and morphological variants depending on case, number, gender, and so on. The complex morpheme structure and relatively free constituent order can produce very large vocabulary because of the derivational morphology, so when translating from the agglutinative languages, many words are unseen at training time. Moreover, due to the semantic context, the same word generally has different segmentation forms in the training corpus.",
+      "For the purpose of incorporating morphology knowledge of agglutinative languages into word segmentation for NMT, we propose a morphological word segmentation method on the source-side of Turkish-English and Uyghur-Chinese machine translation tasks, which segments the complex words into simple and effective morpheme units while reducing the vocabulary size for model training. In this paper, we investigate and compare the following segmentation strategies:",
+      "Stem with combined suffix",
+      "Stem with singular suffix",
+      "Byte Pair Encoding (BPE)",
+      "BPE on stem with combined suffix",
+      "BPE on stem with singular suffix",
+      "The latter two segmentation strategies are our newly proposed methods. Experimental results show that our morphologically motivated word segmentation method can achieve significant improvement of up to 1.2 and 2.5 BLEU points on Turkish-English and Uyghur-Chinese machine translation tasks over the strong baseline of pure BPE method respectively, indicating that it can provide better translation performance for the NMT model."
+    ],
+    [
+      "We will elaborate two popular word segmentation methods and our newly proposed segmentation strategies in this section. The two popular segmentation methods are morpheme segmentation BIBREF4 and Byte Pair Encoding (BPE) BIBREF5. After word segmentation, we additionally add an specific symbol behind each separated subword unit, which aims to assist the NMT model to identify the morpheme boundaries and capture the semantic information effectively. The sentence examples with different segmentation strategies for Turkish-English machine translation task are shown in Table 1."
+    ],
+    [
+      "The words of Turkish and Uyghur are formed by a stem followed with unlimited number of suffixes. Both of the stem and suffix are called morphemes, and they are the smallest functional unit in agglutinative languages. Study indicated that modeling language based on the morpheme units can provide better performance BIBREF6. Morpheme segmentation can segment the complex word into morpheme units of stem and suffix. This representation maintains a full description of the morphological properties of subwords while minimizing the data sparseness caused by inflection and allomorphy phenomenon in highly-inflected languages."
+    ],
+    [
+      "In this segmentation strategy, each word is segmented into a stem unit and a combined suffix unit. We add \u201c##\u201d behind the stem unit and add \u201c$$\u201d behind the combined suffix unit. We denote this method as SCS. The segmented word can be denoted as two parts of \u201cstem##\u201d and \u201csuffix1suffix2...suffixN$$\u201d. If the original word has no suffix unit, the word is treated as its stem unit. All the following segmentation strategies will follow this rule."
+    ],
+    [
+      "In this segmentation strategy, each word is segmented into a stem unit and a sequence of suffix units. We add \u201c##\u201d behind the stem unit and add \u201c$$\u201d behind each singular suffix unit. We denote this method as SSS. The segmented word can be denoted as a sequence of \u201cstem##\u201d, \u201csuffix1$$\u201d, \u201csuffix2$$\u201d until \u201csuffixN$$\u201d."
+    ],
+    [
+      "BPE BIBREF7 is originally a data compression technique and it is adapted by BIBREF5 for word segmentation and vocabulary reduction by encoding the rare and unknown words as a sequence of subword units, in which the most frequent character sequences are merged iteratively. Frequent character n-grams are eventually merged into a single symbol. This is based on the intuition that various word classes are translatable via smaller units than words. This method making the NMT model capable of open-vocabulary translation, which can generalize to translate and produce new words on the basis of these subword units. The BPE algorithm can be run on the dictionary extracted from a training text, with each word being weighted by its frequency. In this segmentation strategy, we add \u201c@@\u201d behind each no-final subword unit of the segmented word."
+    ],
+    [
+      "The problem with morpheme segmentation is that the vocabulary of stem units is still very large, which leads to many rare and unknown words at the training time. The problem with BPE is that it do not consider the morpheme boundaries inside words, which might cause a loss of morphological properties and semantic information. Hence, on the analyses of the above popular word segmentation methods, we propose the morphologically motivated segmentation strategy that combines the morpheme segmentation and BPE for further improving the translation performance of NMT.",
+      "Compared with the sentence of word surface forms, the corresponding sentence of stem units only contains the structure information without considering morphological information, which can make better generalization over inflectional variants of the same word and reduce data sparseness BIBREF8. Therefore, we learn a BPE model on the stem units in the training corpus rather than the words, and then apply it on the stem unit of each word after morpheme segmentation."
+    ],
+    [
+      "In this segmentation strategy, firstly we segment each word into a stem unit and a combined suffix unit as SCS. Secondly, we apply BPE on the stem unit. Thirdly, we add \u201c$$\u201d behind the combined suffix unit. If the stem unit is not segmented, we add \u201c##\u201d behind itself. Otherwise, we add \u201c@@\u201d behind each no-final subword of the segmented stem unit. We denote this method as BPE-SCS."
+    ],
+    [
+      "In this segmentation strategy, firstly we segment each word into a stem unit and a sequence of suffix units as SSS. Secondly, we apply BPE on the stem unit. Thirdly, we add \u201c$$\u201d behind each singular suffix unit. If the stem unit is not segmented, we add \u201c##\u201d behind itself. Otherwise, we add \u201c@@\u201d behind each no-final subword of the segmented stem unit. We denote this method as BPE-SSS."
+    ],
+    [
+      "Following BIBREF9, we use the WIT corpus BIBREF10 and SETimes corpus BIBREF11 for model training, and use the newsdev2016 from Workshop on Machine Translation in 2016 (WMT2016) for validation. The test data are newstest2016 and newstest2017."
+    ],
+    [
+      "We use the news data from China Workshop on Machine Translation in 2017 (CWMT2017) for model training, validation and test."
+    ],
+    [
+      "We utilize the Zemberek with a morphological disambiguation tool to segment the Turkish words into morpheme units, and utilize the morphology analysis tool BIBREF12 to segment the Uyghur words into morpheme units. We employ the python toolkits of jieba for Chinese word segmentation. We apply BPE on the target-side words and we set the number of merge operations to 35K for Chinese and 30K for English and we set the maximum sentence length to 150 tokens. The training corpus statistics of Turkish-English and Uyghur-Chinese machine translation tasks are shown in Table 2 and Table 3 respectively."
+    ],
+    [
+      "We set the number of merge operations on the stem units in the consideration of keeping the vocabulary size of BPE, BPE-SCS and BPE-SSS segmentation strategies on the same scale. We will elaborate the number settings for our proposed word segmentation strategies in this section.",
+      "In the Turkish-English machine translation task, for the pure BPE strategy, we set the number of merge operations on the words to 35K, set the number of merge operations on the stem units for BPE-SCS strategy to 15K, and set the number of merge operations on the stem units for BPE-SSS strategy to 25K. In the Uyghur-Chinese machine translation task, for the pure BPE strategy, we set the number of merge operations on the words to 38K, set the number of merge operations on the stem units for BPE-SCS strategy to 10K, and set the number of merge operations on the stem units for BPE-SSS strategy to 35K. The detailed training corpus statistics with different segmentation strategies of Turkish and Uyghur are shown in Table 4 and Table 5 respectively.",
+      "According to Table 4 and Table 5, we can find that both the Turkish and Uyghur have a very large vocabulary even in the low-resource training corpus. So we propose the morphological word segmentation strategies of BPE-SCS and BPE-SSS that additionally applying BPE on the stem units after morpheme segmentation, which not only consider the morphological properties but also eliminate the rare and unknown words."
+    ],
+    [
+      "We employ the Transformer model BIBREF13 with self-attention mechanism architecture implemented in Sockeye toolkit BIBREF14. Both the encoder and decoder have 6 layers. We set the number of hidden units to 512, the number of heads for self-attention to 8, the source and target word embedding size to 512, and the number of hidden units in feed-forward layers to 2048. We train the NMT model by using the Adam optimizer BIBREF15 with a batch size of 128 sentences, and we shuffle all the training data at each epoch. The label smoothing is set to 0.1. We report the result of averaging the parameters of the 4 best checkpoints on the validation perplexity. Decoding is performed by beam search with beam size of 5. To effectively evaluate the machine translation quality, we report case-sensitive BLEU score with standard tokenization and character n-gram ChrF3 score ."
+    ],
+    [
+      "In this paper, we investigate and compare morpheme segmentation, BPE and our proposed morphological segmentation strategies on the low resource and morphologically-rich agglutinative languages. Experimental results of Turkish-English and Uyghur-Chinese machine translation tasks are shown in Table 6 and Table 7 respectively."
+    ],
+    [
+      "According to Table 6 and Table 7, we can find that both the BPE-SCS and BPE-SSS strategies outperform morpheme segmentation and the strong baseline of pure BPE method. Especially, the BPE-SSS strategy is better and it achieves significant improvement of up to 1.2 BLEU points on Turkish-English machine translation task and 2.5 BLEU points on Uyghur-Chinese machine translation task. Furthermore, we also find that the translation performance of our proposed segmentation strategy on Turkish-English machine translation task is not obvious than Uyghur-Chinese machine translation task, the probable reasons are: the training corpus of Turkish-English consists of talk and news data while most of the talk data are short informal sentences compared with the news data, which cannot provide more language information for the NMT model. Moreover, the test corpus consists of news data, so due to the data domain is different, the improvement of machine translation quality is limited.",
+      "In addition, we estimate how the number of merge operations on the stem units for BPE-SSS strategy effects the machine translation quality. Experimental results are shown in Table 8 and Table 9. We find that the number of 25K for Turkish, 30K and 35K for Uyghur maximizes the translation performance. The probable reason is that these numbers of merge operations are able to generate a more appropriate vocabulary that containing effective morpheme units and moderate subword units, which makes better generalization over the morphologically-rich words."
+    ],
+    [
+      "The NMT system is typically trained with a limited vocabulary, which creates bottleneck on translation accuracy and generalization capability. Many word segmentation methods have been proposed to cope with the above problems, which consider the morphological properties of different languages.",
+      "Bradbury and Socher BIBREF16 employed the modified Morfessor to provide morphology knowledge into word segmentation, but they neglected the morphological varieties between subword units, which might result in ambiguous translation results. Sanchez-Cartagena and Toral BIBREF17 proposed a rule-based morphological word segmentation for Finnish, which applies BPE on all the morpheme units uniformly without distinguishing their inner morphological roles. Huck BIBREF18 explored target-side segmentation method for German, which shows that the cascading of suffix splitting and compound splitting with BPE can achieve better translation results. Ataman et al. BIBREF19 presented a linguistically motivated vocabulary reduction approach for Turkish, which optimizes the segmentation complexity with constraint on the vocabulary based on a category-based hidden markov model (HMM). Our work is closely related to their idea while ours are more simple and realizable. Tawfik et al. BIBREF20 confirmed that there is some advantage from using a high accuracy dialectal segmenter jointly with a language independent word segmentation method like BPE. The main difference is that their approach needs sufficient monolingual data additionally to train a segmentation model while ours do not need any external resources, which is very convenient for word segmentation on the low-resource and morphologically-rich agglutinative languages."
+    ],
+    [
+      "In this paper, we investigate morphological segmentation strategies on the low-resource and morphologically-rich languages of Turkish and Uyghur. Experimental results show that our proposed morphologically motivated word segmentation method is better suitable for NMT. And the BPE-SSS strategy achieves the best machine translation performance, as it can better preserve the syntactic and semantic information of the words with complex morphology as well as reduce the vocabulary size for model training. Moreover, we also estimate how the number of merge operations on the stem units for BPE-SSS strategy effects the translation quality, and we find that an appropriate vocabulary size is more useful for the NMT model.",
+      "In future work, we are planning to incorporate more linguistic and morphology knowledge into the training process of NMT to enhance its capacity of capturing syntactic structure and semantic information on the low-resource and morphologically-rich languages."
+    ],
+    [
+      "This work is supported by the National Natural Science Foundation of China, the Open Project of Key Laboratory of Xinjiang Uygur Autonomous Region, the Youth Innovation Promotion Association of the Chinese Academy of Sciences, and the High-level Talents Introduction Project of Xinjiang Uyghur Autonomous Region."
+    ]
+  ]
+}
+```

qasper-0542/instruction.md

@@ -0,0 +1,41 @@
+Name of Paper: How Does Language Influence Documentation Workflow? Unsupervised Word Discovery Using Translations in Multiple Languages
+
+Question: Does the paper report the accuracy of the model?
+
+## 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-0545/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: How Does Language Influence Documentation Workflow? Unsupervised Word Discovery Using Translations in Multiple Languages
+
+Question: How does the well-resourced language impact the quality of the output?

qasper-0589/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis
+
+Question: Is the model evaluated against other Aspect-Based models?

qasper-0656/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: A framework for anomaly detection using language modeling, and its applications to finance
+
+Question: What is the performance of the models discussed in the paper?

qasper-0669/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Automatic Reminiscence Therapy for Dementia.
+
+Question: Is machine learning system underneath similar to image caption ML systems?

qasper-0713/instruction.md

@@ -0,0 +1,116 @@
+Name of Paper: MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning
+
+Question: What are three main machine translation tasks?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "MUSE: Parallel Multi-Scale Attention",
+    "MUSE: Parallel Multi-Scale Attention ::: Attention Mechanism for Global Context Representation",
+    "MUSE: Parallel Multi-Scale Attention ::: Convolution for Local Context Modeling",
+    "MUSE: Parallel Multi-Scale Attention ::: Point-wise Feed-forward Network for Capturing Token Representations",
+    "Experiment",
+    "Experiment ::: Datasets",
+    "Experiment ::: Experimental Settings ::: Model",
+    "Experiment ::: Experimental Settings ::: Training",
+    "Experiment ::: Experimental Settings ::: Evaluation",
+    "Experiment ::: Results",
+    "Experiment ::: How do we propose effective parallel multi-scale attention?",
+    "Experiment ::: Further Analysis ::: Parallel multi-scale attention brings time efficiency on GPUs",
+    "Related Work",
+    "Conclusion and Future work",
+    "Conclusion and Future work ::: Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "In recent years, Transformer has been remarkably adept at sequence learning tasks like machine translation BIBREF0, BIBREF1, text classification BIBREF2, BIBREF3, language modeling BIBREF4, BIBREF5, etc. It is solely based on an attention mechanism that captures global dependencies between input tokens, dispensing with recurrence and convolutions entirely. The key idea of the self-attention mechanism is updating token representations based on a weighted sum of all input representations.",
+      "However, recent research BIBREF6 has shown that the Transformer has surprising shortcomings in long sequence learning, exactly because of its use of self-attention. As shown in Figure 1 (a), in the task of machine translation, the performance of Transformer drops with the increase of the source sentence length, especially for long sequences. The reason is that the attention can be over-concentrated and disperse, as shown in Figure 1 (b), and only a small number of tokens are represented by attention. It may work fine for shorter sequences, but for longer sequences, it causes insufficient representation of information and brings difficulty for the model to comprehend the source information intactly. In recent work, local attention that constrains the attention to focus on only part of the sequences BIBREF7, BIBREF8 is used to address this problem. However, it costs self-attention the ability to capture long-range dependencies and also does not demonstrate effectiveness in sequence to sequence learning tasks.",
+      "To build a module with both inductive bias of local and global context modelling in sequence to sequence learning, we hybrid self-attention with convolution and present Parallel multi-scale attention called MUSE. It encodes inputs into hidden representations and then applies self-attention and depth-separable convolution transformations in parallel. The convolution compensates for the insufficient use of local information while the self-attention focuses on capturing the dependencies. Moreover, this parallel structure is highly extensible, and new transformations can be easily introduced as new parallel branches, and is also favourable to parallel computation.",
+      "The main contributions are summarized as follows:",
+      "We find that the attention mechanism alone suffers from dispersed weights and is not suitable for long sequence representation learning. The proposed method tries to address this problem and achieves much better performance on generating long sequence.",
+      "We propose a parallel multi-scale attention and explore a simple but efficient method to successfully combine convolution with self-attention all in one module.",
+      "MUSE outperforms all previous models with same training data and the comparable model size, with state-of-the-art BLEU scores on three main machine translation tasks.",
+      "MUSE-simple introduce parallel representation learning and brings expansibility and parallelism. Experiments show that the inference speed can be increased by 31% on GPUs."
+    ],
+    [
+      "Like other sequence-to-sequence models, MUSE also adopts an encoder-decoder framework. The encoder takes a sequence of word embeddings $(x_1, \\cdots , x_n)$ as input where $n$ is the length of input. It transfers word embeddings to a sequence of hidden representation ${z} = (z_1, \\cdots , z_n)$. Given ${z}$, the decoder is responsible for generating a sequence of text $(y_1, \\cdots , y_m)$ token by token.",
+      "The encoder is a stack of $N$ MUSE modules. Residual mechanism and layer normalization are used to connect two adjacent layers. The decoder is similar to encoder, except that each MUSE module in the decoder not only captures features from the generated text representations but also performs attention over the output of the encoder stack through additional context attention. Residual mechanism and layer normalization are also used to connect two modules and two adjacent layers.",
+      "The key part in the proposed model is the MUSE module, which contains three main parts: self-attention for capturing global features, depth-wise separable convolution for capturing local features, and a position-wise feed-forward network for capturing token features. The module takes the output of $(i-1)$ layer as input and generates the output representation in a fusion way:",
+      "where \u201cAttention\u201d refers to self-attention, \u201cConv\u201d refers to dynamic convolution, \u201cPointwise\u201d refers to a position-wise feed-forward network. The followings list the details of each part. We also propose MUSE-simple, a simple version of MUSE, which generates the output representation similar to the MUSE model except for that it dose not the include convolution operation:"
+    ],
+    [
+      "Self-attention is responsible for learning representations of global context. For a given input sequence $X$, it first projects $X$ into three representations, key $K$, query $Q$, and value $V$. Then, it uses a self-attention mechanism to get the output representation:",
+      "Where $W^O$, $W^Q$, $W^K$, and $W^V$ are projection parameters. The self-attention operation $\\sigma $ is the dot-production between key, query, and value pairs:",
+      "Note that we conduct a projecting operation over the value in our self-attention mechanism $V_1=VW^V$ here."
+    ],
+    [
+      "We introduce convolution operations into MUSE to capture local context. To learn contextual sequence representations in the same hidden space, we choose depth-wise convolution BIBREF9 (we denote it as DepthConv in the experiments) as the convolution operation because it includes two separate transformations, namely, point-wise projecting transformation and contextual transformation. It is because that original convolution operator is not separable, but DepthConv can share the same point-wise projecting transformation with self-attention mechanism. We choose dynamic convolution BIBREF10, the best variant of DepthConv, as our implementation.",
+      "Each convolution sub-module contains multiple cells with different kernel sizes. They are used for capturing different-range features. The output of the convolution cell with kernel size $k$ is:",
+      "where $W^{V}$ and $W^{out}$ are parameters, $W^{V}$ is a point-wise projecting transformation matrix. The $Depth\\_conv$ refers to depth convolution in the work of BIBREF10. For an input sequence $X$, the output $O$ is computed as:",
+      "where $d$ is the hidden size. Note that we conduct the same projecting operation over the input in our convolution mechanism $V_2=XW^V$ here with that in self-attention mechanism.",
+      "Shared projection To learn contextual sequence representations in the same hidden space, the projection in the self-attention mechanism $V_1=VW_V$ and that in the convolution mechanism $V_2=XW^V$ is shared. Because the shared projection can project the input feature into the same hidden space. If we conduct two independent projection here: $V_1=VW_1^V$ and $V_2=XW^V_2$, where $W_1^V$ and $W_2^V$ are two parameter matrices, we call it as separate projection. We will analyze the necessity of applying shared projection here instead of separate projection.",
+      "Dynamically Selected Convolution Kernels We introduce a gating mechanism to automatically select the weight of different convolution cells."
+    ],
+    [
+      "To learn token level representations, MUSE concatenates an self-attention network with a position-wise feed-forward network at each layer. Since the linear transformations are the same across different positions, the position-wise feed-forward network can be seen as a token feature extractor.",
+      "where $W_1$, $b_1$, $W_2$, and $b_2$ are projection parameters."
+    ],
+    [
+      "We evaluate MUSE on four machine translation tasks. This section describes the datasets, experimental settings, detailed results, and analysis."
+    ],
+    [
+      "WMT14 En-Fr and En-De datasets The WMT 2014 English-French translation dataset, consisting of $36M$ sentence pairs, is adopted as a big dataset to test our model. We use the standard split of development set and test set. We use newstest2014 as the test set and use newstest2012 +newstest2013 as the development set. Following BIBREF11, we also adopt a joint source and target BPE factorization with the vocabulary size of $40K$. For medium dataset, we borrow the setup of BIBREF0 and adopt the WMT 2014 English-German translation dataset which consists of $4.5M$ sentence pairs, the BPE vocabulary size is set to $32K$. The test and validation datasets we used are the same as BIBREF0.",
+      "IWSLT De-En and En-Vi datasets Besides, we perform experiments on two small IWSLT datasets to test the small version of MUSE with other comparable models. The IWSLT 2014 German-English translation dataset consists of $160k$ sentence pairs. We also adopt a joint source and target BPE factorization with the vocabulary size of $32K$. The IWSLT 2015 English-Vietnamese translation dataset consists of $133K$ training sentence pairs. For the En-Vi task, we build a dictionary including all source and target tokens. The vocabulary size for English is $17.2K$, and the vocabulary size for the Vietnamese is $6.8K$."
+    ],
+    [
+      "For fair comparisons, we only compare models reported with the comparable model size and the same training data. We do not compare BIBREF12 because it is an ensemble method. We build MUSE-base and MUSE-large with the parameter size comparable to Transformer-base and Transformer-large. We adopt multi-head attention BIBREF0 as implementation of self-attention in MUSE module. The number of attention head is set to 4 for MUSE-base and 16 for MUSE-large. We also add the network architecture built by MUSE-simple in the similar way into the comparison.",
+      "MUSE consists of 12 residual blocks for encoder and 12 residual blocks for decoder, the dimension is set to 384 for MUSE-base and 768 for MUSE-large. The hidden dimension of non linear transformation is set to 768 for MUSE-base and 3072 for MUSE-large.",
+      "The MUSE-large is trained on 4 Titan RTX GPUs while the MUSE-base is trained on a single NVIDIA RTX 2080Ti GPU. The batch size is calculated at the token level, which is called dynamic batching BIBREF0. We adopt dynamic convolution as the variant of depth-wise separable convolution. We tune the kernel size on the validation set. For convolution with a single kernel, we use the kernel size of 7 for all layers. In case of dynamic selected kernels, the kernel size is 3 for small kernels and 15 for large kernels for all layers."
+    ],
+    [
+      "The training hyper-parameters are tuned on the validation set.",
+      "MUSE-large For training MUSE-large, following BIBREF13, parameters are updated every 32 steps. We train the model for $80K$ updates with a batch size of 5120 for En-Fr, and train the model for ${30K}$ updates with a batch size of 3584 for En-De. The dropout rate is set to $0.1$ for En-Fr and ${0.3}$ for En-De. We borrow the setup of optimizer from BIBREF10 and use the cosine learning rate schedule with 10000 warmup steps. The max learning rate is set to $0.001$ on En-De translation and ${0.0007}$ on En-Fr translation. For checkpoint averaging, following BIBREF10, we tune the average checkpoints for En-De translation tasks. For En-Fr translation, we do not average checkpoint but use the final single checkpoint.",
+      "MUSE-base We train and test MUSE-base on two small datasets, IWSLT 2014 De-En translation and IWSLT2015 En-Vi translation. Following BIBREF0, we use Adam optimizer with a learning rate of $0.001$. We use the warmup mechanism and invert the learning rate decay with warmup updates of $4K$. For the De-En dataset, we train the model for $20K$ steps with a batch size of $4K$. The parameters are updated every 4 steps. The dropout rate is set to $0.4$. For the En-Vi dataset, we train the model for $10K$ steps with a batch size of $4K$. The parameters are also updated every 4 steps. The dropout rate is set to $0.3$. We save checkpoints every epoch and average the last 10 checkpoints for inference."
+    ],
+    [
+      "During inference, we adopt beam search with a beam size of 5 for De-En, En-Fr and En-Vi translation tasks. The length penalty is set to 0.8 for En-Fr according to the validation results, 1 for the two small datasets following the default setting of BIBREF14. We do not tune beam width and length penalty but use the setting reported in BIBREF0. The BLEU metric is adopted to evaluate the model performance during evaluation."
+    ],
+    [
+      "As shown in Table TABREF24, MUSE outperforms all previously models on En-De and En-Fr translation, including both state-of-the-art models of stand alone self-attention BIBREF0, BIBREF13, and convolutional models BIBREF11, BIBREF15, BIBREF10. This result shows that either self-attention or convolution alone is not enough for sequence to sequence learning. The proposed parallel multi-scale attention improves over them both on En-De and En-Fr.",
+      "Compared to Evolved Transformer BIBREF19 which is constructed by NAS and also mixes convolutions of different kernel size, MUSE achieves 2.2 BLEU gains in En-Fr translation.",
+      "Relative position or local attention constraints bring improvements over origin self-attention model, but parallel multi-scale outperforms them.",
+      "MUSE can also scale to small model and small datasets, as depicted in Table TABREF25, MUSE-base pushes the state-of-the-art from 35.7 to 36.3 on IWSLT De-En translation dataset.",
+      "It is shown in Table TABREF24 and Table TABREF25 that MUSE-simple which contains the basic idea of parallel multi-scale attention achieves state-of-the-art performance on three major machine translation datasets."
+    ],
+    [
+      "In this subsection we compare MUSE and its variants on IWSLT 2015 De-En translation to answer the question.",
+      "Does concatenating self-attention with convolution certainly improve the model? To bridge the gap between point-wise transformation which learns token level representations and self-attention which learns representations of global context, we introduce convolution to enhance our multi-scale attention. As we can see from the first experiment group of Table TABREF27, convolution is important in the parallel multi-scale attention. However, it is not easy to combine convolution and self-attention in one module to build better representations on sequence to sequence tasks. As shown in the first line of both second and third group of Table TABREF27, simply learning local representations by using convolution or depth-wise separable convolution in parallel with self-attention harms the performance. Furthermore, combining depth-wise separable convolution (in this work we choose its best variant dynamic convolution as implementation) is even worse than combining convolution.",
+      "Why do we choose DepthConv and what is the importance of sharing Projection of DepthConv and self-attention? We conjecture that convolution and self-attention both learn contextual sequence representations and they should share the point transformation and perform the contextual transformation in the same hidden space. We first project the input to a hidden representation and perform a variant of depth-wise convolution and self-attention transformations in parallel. The fist two experiments in third group of Table TABREF27 show that validating the utility of sharing Projection in parallel multi-scale attention, shared projection gain 1.4 BLEU scores over separate projection, and bring improvement of 0.5 BLEU scores over MUSE-simple (without DepthConv).",
+      "How much is the kernel size? Comparative experiments show that the too large kernel harms performance both for DepthConv and convolution. Since there exists self-attention and point-wise transformations, simply applying the growing kernel size schedule proposed in SliceNet BIBREF15 doesn't work. Thus, we propose to use dynamically selected kernel size to let the learned network decide the kernel size for each layer."
+    ],
+    [
+      "The underlying parallel structure (compared to the sequential structure in each block of Transformer) allows MUSE to be efficiently computed on GPUs. For example, we can combine small matrices into large matrices, and while it does not reduce the number of actual operations, it can be better paralleled by GPUs to speed up computation. Concretely, for each MUSE module, we first concentrate $W^Q,W^K,W^V$ of self-attention and $W_1$ of point feed-forward transformation into a single encoder matrix $W^{Enc}$, and then perform transformation such as self-attention, depth-separable convolution, and nonlinear transformation, in parallel, to learn multi-scale representations in the hidden layer. $W^O,W_2,W^{out}$ can also be combined a single decoder matrix $W^{Dec}$. The decoder of sequence to sequence architecture can be implemented similarly.",
+      "In Table TABREF31, we conduct comparisons to show the speed gains with the aforementioned implementation, and the batch size is set to one sample per batch to simulate online inference environment. Under the settings, where the numbers of parameters are similar for MUSE and Transformer, about 31% increase in inference speed can be obtained. The experiments use MUSE with 6 MUSE-simple modules and Transformer with 6 base blocks. The hidden size is set to 512.",
+      "Parallel multi-scale attention generates much better long sequence As demonstrated in Figure FIGREF32, MUSE generates better sequences of various length than self-attention, but it is remarkably adept at generate long sequence, e.g. for sequence longer than 100, MUSE is two times better.",
+      "Lower layers prefer local context and higher layers prefer more contextual representations MUSE contains multiple dynamic convolution cells, whose streams are fused by a gated mechanism. The weight for each dynamic cell is a scalar. Here we analyze the weight of different dynamic convolution cells in different layers. Figure FIGREF32 shows that as the layer depth increases, the weight of dynamic convolution cells with small kernel sizes gradually decreases. It demonstrates that lower layers prefer local features while higher layers prefer global features. It is corresponding to the finding in BIBREF26.",
+      "MUSE not only gains BLEU scores, but also generates more reasonable sentences and increases the translation quality. We conduct the case study on the De-En dataset and the cases are shown in Table TABREF34 in Appendix. In case 1, although the baseline transformer translates many correct words according to the source sentence, the translated sentence is not fluent at all. It indicates that Transformer does not capture the relationship between some words and their neighbors, such as \u201cright\u201d and \u201cclap\u201d. By contrast, MUSE captures them well by combining local convolution with global self-attention. In case 2, the cause adverbial clause is correctly translated by MUSE while transformer misses the word \u201cwhy\u201d and fails to translate it."
+    ],
+    [
+      "Sequence to sequence learning is an important task in machine learning. It evolves understanding and generating sequence. Machine translation is the touchstone of sequence to sequence learning. Traditional approaches usually adopt long-short term memory networks BIBREF27, BIBREF28 to learn the representation of sequences. However, these models either are built upon auto-regressive structures requiring longer encoding time or perform worse on real-world natural language processing tasks. Recent studies explore convolutional neural networks (CNN) BIBREF11 or self-attention BIBREF0 to support high-parallel sequence modeling and does not require auto-regressive structure during encoding, thus bringing large efficiency improvements. They are strong at capturing local or global dependencies.",
+      "There are several studies on combining self-attention and convolution. However, they do not surpass both convectional and self-attention mechanisms. BIBREF4 propose to augment convolution with self attention by directly concentrating them in computer vision tasks. However, as demonstrated in Table TABREF27 there method does not work for sequence to sequence learning task. Since state-of-the-art models on question answering tasks still consist on self-attention and do no adopt ideas in QAnet BIBREF29. Both self-attention BIBREF13 and convolution BIBREF10 outperforms Evolved transformer by near 2 BLEU scores on En-Fr translation. It seems that learning global and local context through stacking self-attention and convolution layers does not beat either self-attention or convolution models. In contrast, the proposed parallel multi-scale attention outperforms previous convolution or self-attention based models on main translation tasks, showing its effectiveness for sequence to sequence learning."
+    ],
+    [
+      "Although the self-attention mechanism has been prevalent in sequence modeling, we find that attention suffers from dispersed weights especially for long sequences, resulting from the insufficient local information.",
+      "To address this problem, we present Parallel Multi-scale Attention (MUSE) and MUSE-simple. MUSE-simple introduces the idea of parallel multi-scale attention into sequence to sequence learning. And MUSE fuses self-attention, convolution, and point-wise transformation together to explicitly learn global, local and token level sequence representations. Especially, we find from empirical results that the shared projection plays important part in its success, and is essential for our multi-scale learning.",
+      "Beyond the inspiring new state-of-the-art results on three major machine translation datasets, detailed analysis and model variants also verify the effectiveness of MUSE.",
+      "For future work, the parallel structure is highly extensible and provide many opportunities to improve these models. In addition, given the success of shared projection, we would like to explore its detailed effects on contextual representation learning. Finally, we are exited about future of parallel multi-scale attention and plan to apply this simple but effective idea to other tasks including image and speech."
+    ],
+    [
+      "This work was supported in part by National Natural Science Foundation of China (No. 61673028)."
+    ]
+  ]
+}
+```

qasper-0724/instruction.md

@@ -0,0 +1,97 @@
+Name of Paper: Using word embeddings to improve the discriminability of co-occurrence text networks
+
+Question: Do the use word embeddings alone or they replace some previous features of the model with word embeddings?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related works",
+    "Material and Methods",
+    "Results and Discussion",
+    "Results and Discussion ::: Performance analysis",
+    "Results and Discussion ::: Effects of considering stopwords and local thresholding",
+    "Conclusion",
+    "Acknowledgments",
+    "Supplementary Information ::: Stopwords",
+    "Supplementary Information ::: List of books",
+    "Supplementary Information ::: Additional results"
+  ],
+  "paragraphs": [
+    [
+      "The ability to construct complex and diverse linguistic structures is one of the main features that set us apart from all other species. Despite its ubiquity, some language aspects remain unknown. Topics such as language origin and evolution have been studied by researchers from diverse disciplines, including Linguistic, Computer Science, Physics and Mathematics BIBREF0, BIBREF1, BIBREF2. In order to better understand the underlying language mechanisms and universal linguistic properties, several models have been developed BIBREF3, BIBREF4. A particular language representation regards texts as complex systems BIBREF5. Written texts can be considered as complex networks (or graphs), where nodes could represent syllables, words, sentences, paragraphs or even larger chunks BIBREF5. In such models, network edges represent the proximity between nodes, e.g. the frequency of the co-occurrence of words. Several interesting results have been obtained from networked models, such as the explanation of Zipf's Law as a consequence of the least effort principle and theories on the nature of syntactical relationships BIBREF6, BIBREF7.",
+      "In a more practical scenario, text networks have been used in text classification tasks BIBREF8, BIBREF9, BIBREF10. The main advantage of the model is that it does not rely on deep semantical information to obtain competitive results. Another advantage of graph-based approaches is that, when combined with other approaches, it yields competitive results BIBREF11. A simple, yet recurrent text model is the well-known word co-occurrence network. After optional textual pre-processing steps, in a co-occurrence network each different word becomes a node and edges are established via co-occurrence in a desired window. A common strategy connects only adjacent words in the so called word adjacency networks.",
+      "While the co-occurrence representation yields good results in classification scenarios, some important features are not considered in the model. For example, long-range syntactical links, though less frequent than adjacent syntactical relationships, might be disregarded from a simple word adjacency approach BIBREF12. In addition, semantically similar words not sharing the same lemma are mapped into distinct nodes. In order to address these issues, here we introduce a modification of the traditional network representation by establishing additional edges, referred to as \u201cvirtual\u201d edges. In the proposed model, in addition to the co-occurrence edges, we link two nodes (words) if the corresponding word embedding representation is similar. While this approach still does not merge similar nodes into the same concept, similar nodes are explicitly linked via virtual edges.",
+      "Our main objective here is to evaluate whether such an approach is able to improve the discriminability of word co-occurrence networks in a typical text network classification task. We evaluate the methodology for different embedding techniques, including GloVe, Word2Vec and FastText. We also investigated different thresholding strategies to establish virtual links. Our results revealed, as a proof of principle, that the proposed approach is able to improve the discriminability of the classification when compared to the traditional co-occurrence network. While the gain in performance depended upon the text length being considered, we found relevant gains for intermediary text lengths. Additional results also revealed that a simple thresholding strategy combined with the use of stopwords tends to yield the best results.",
+      "We believe that the proposed representation could be applied in other text classification tasks, which could lead to potential gains in performance. Because the inclusion of virtual edges is a simple technique to make the network denser, such an approach can benefit networked representations with a limited number of nodes and edges. This representation could also shed light into language mechanisms in theoretical studies relying on the representation of text as complex networks. Potential novel research lines leveraging the adopted approach to improve the characterization of texts in other applications are presented in the conclusion."
+    ],
+    [
+      "Complex networks have been used in a wide range of fields, including in Social Sciences BIBREF13, Neuroscience BIBREF14, Biology BIBREF15, Scientometry BIBREF16 and Pattern Recognition BIBREF17, BIBREF18, BIBREF19, BIBREF20. In text analysis, networks are used to uncover language patterns, including the origins of the ever present Zipf's Law BIBREF21 and the analysis of linguistic properties of natural and unknown texts BIBREF22, BIBREF23. Applications of network science in text mining and text classification encompasses applications in semantic analysis BIBREF24, BIBREF25, BIBREF26, BIBREF27, authorship attribution BIBREF28, BIBREF29 and stylometry BIBREF28, BIBREF30, BIBREF31. Here we focus in the stylometric analysis of texts using complex networks.",
+      "In BIBREF28, the authors used a co-occurrence network to study a corpus of English and Polish books. They considered a dataset of 48 novels, which were written by 8 different authors. Differently from traditional co-occurrence networks, some punctuation marks were considered as words when mapping texts as networks. The authors also decided to create a methodology to normalize the obtained network metrics, since they considered documents with variations in length. A similar approach was adopted in a similar study BIBREF32, with a focus on comparing novel measurements and measuring the effect of considering stopwords in the network structure.",
+      "A different approach to analyze co-occurrence networks was devised in BIBREF33. Whilst most approaches only considered traditional network measurements or devised novel topological and dynamical measurements, the authors combined networked and semantic information to improve the performance of network-based classification. Interesting, the combined use of network motifs and node labels (representing the corresponding words) allowed an improvement in performance in the considered task. A similar combination of techniques using a hybrid approach was proposed in BIBREF8. Networked-based approaches has also been applied to the authorship recognition tasks in other languages, including Persian texts BIBREF9.",
+      "Co-occurrence networks have been used in other contexts other than stylometric analysis. The main advantage of this approach is illustrated in the task aimed at diagnosing diseases via text analysis BIBREF11. Because the topological analysis of co-occurrence language networks do not require deep semantic analysis, this model is able to model text created by patients suffering from cognitive impairment BIBREF11. Recently, it has been shown that the combination of network and traditional features could be used to improve the diagnosis of patients with cognitive impairment BIBREF11. Interestingly, this was one of the first approaches suggesting the use of embeddings to address the particular problem of lack of statistics to create a co-occurrence network in short documents BIBREF34.",
+      "While many of the works dealing with word co-occurrence networks have been proposed in the last few years, no systematic study of the effects of including information from word embeddings in such networks has been analyzed. This work studies how links created via embeddings information modify the underlying structure of networks and, most importantly, how it can improve the model to provide improved classification performance in the stylometry task."
+    ],
+    [
+      "To represent texts as networks, we used the so-called word adjacency network representation BIBREF35, BIBREF28, BIBREF32. Typically, before creating the networks, the text is pre-processed. An optional pre-processing step is the removal of stopwords. This step is optional because such words include mostly article and prepositions, which may be artlessly represented by network edges. However, in some applications \u2013 including the authorship attribution task \u2013 stopwords (or function words) play an important role in the stylistic characterization of texts BIBREF32. A list of stopwords considered in this study is available in the Supplementary Information.",
+      "The pre-processing step may also include a lemmatization procedure. This step aims at mapping words conveying the same meaning into the same node. In the lemmatization process, nouns and verbs are mapped into their singular and infinite forms. Note that, while this step is useful to merge words sharing a lemma into the same node, more complex semantical relationships are overlooked. For example, if \u201ccar\u201d and \u201cvehicle\u201d co-occur in the same text, they are considered as distinct nodes, which may result in an inaccurate representation of the text.",
+      "Such a drawback is addressed by including \u201cvirtual\u201d edges connecting nodes. In other words, even if two words are not adjacent in the text, we include \u201cvirtual\u201d edges to indicate that two distant words are semantically related. The inclusion of such virtual edges is illustrated in Figure FIGREF1. In order to measure the semantical similarity between two concepts, we use the concept of word embeddings BIBREF36, BIBREF37. Thus, each word is represented using a vector representation encoding the semantical and contextual characteristics of the word. Several interesting properties have been obtained from distributed representation of words. One particular property encoded in the embeddings representation is the fact the semantical similarity between concepts is proportional to the similarity of vectors representing the words. Similarly to several other works, here we measure the similarity of the vectors via cosine similarity BIBREF38.",
+      "The following strategies to create word embedding were considered in this paper:",
+      "GloVe: the Global Vectors (GloVe) algorithm is an extension of the Word2vec model BIBREF39 for efficient word vector learning BIBREF40. This approach combines global statistics from matrix factorization techniques (such as latent semantic analysis) with context-based and predictive methods like Word2Vec. This method is called as Global Vector method because the global corpus statistics are captured by GloVe. Instead of using a window to define the local context, GloVe constructs an explicit word-context matrix (or co-occurrence matrix) using statistics across the entire corpus. The final result is a learning model that oftentimes yields better word vector representations BIBREF40.",
+      "Word2Vec: this is a predictive model that finds dense vector representations of words using a three-layer neural network with a single hidden layer BIBREF39. It can be defined in a two-fold way: continuous bag-of-words and skip-gram model. In the latter, the model analyzes the words of a set of sentences (or corpus) and attempts to predict the neighbors of such words. For example, taking as reference the word \u201cRobin\u201d, the model decides that \u201cHood\u201d is more likely to follow the reference word than any other word. The vectors are obtained as follows: given the vocabulary (generated from all corpus words), the model trains a neural network with the sentences of the corpus. Then, for a given word, the probabilities that each word follows the reference word are obtained. Once the neural network is trained, the weights of the hidden layer are used as vectors of each corpus word.",
+      "FastText: this method is another extension of the Word2Vec model BIBREF41. Unlike Word2Vec, FastText represents each word as a bag of character n-grams. Therefore, the neural network not only trains individual words, but also several n-grams of such words. The vector for a word is the sum of vectors obtained for the character n-grams composing the word. For example, the embedding obtained for the word \u201ccomputer\u201d with $n\\le 3$ is the sum of the embeddings obtained for \u201cco\u201d, \u201ccom\u201d, \u201comp\u201d, \u201cmpu\u201d, \u201cput\u201d, \u201cute\u201d, \u201cter\u201d and \u201cer\u201d. In this way, this method obtains improved representations for rare words, since n-grams composing rare words might be present in other words. The FastText representation also allows the model to understand suffixes and prefixes. Another advantage of FastText is its efficiency to be trained in very large corpora.",
+      "Concerning the thresholding process, we considered two main strategies. First, we used a global strategy: in addition to the co-occurrence links (continuous lines in Figure FIGREF1), only \u201cvirtual\u201d edges stronger than a given threshold are left in the network. Thus only the most similar concepts are connected via virtual links. This strategy is hereafter referred to as global strategy. Unfortunately, this method may introduce an undesired bias towards hubs BIBREF42.",
+      "To overcome the potential disadvantages of the global thresholding method, we also considered a more refined thresholding approach that takes into account the local structure to decide whether a weighted link is statistically significant BIBREF42. This method relies on the idea that the importance of an edge should be considered in the the context in which it appears. In other words, the relevance of an edge should be evaluated by analyzing the nodes connected to its ending points. Using the concept of disparity filter, the method devised in BIBREF42 defines a null model that quantifies the probability of a node to be connected to an edge with a given weight, based on its other connections. This probability is used to define the significance of the edge. The parameter that is used to measure the significance of an edge $e_{ij}$ is $\\alpha _{ij}$, defined as:",
+      "where $w_{ij}$ is the weight of the edge $e_{ij}$ and $k_i$ is the degree of the $i$-th node. The obtained network corresponds to the set of nodes and edges obtained by removing all edges with $\\alpha $ higher than the considered threshold. Note that while the similarity between co-occurrence links might be considered to compute $\\alpha _{ij}$, only \u201cvirtual\u201d edges (i.e. the dashed lines in Figure FIGREF1) are eligible to be removed from the network in the filtering step. This strategy is hereafter referred to as local strategy.",
+      "After co-occurrence networks are created and virtual edges are included, in the next step we used a characterization based on topological analysis. Because a global topological analysis is prone to variations in network size, we focused our analysis in the local characterization of complex networks. In a local topological analysis, we use as features the value of topological/dynamical measurements obtained for a set of words. In this case, we selected as feature the words occurring in all books of the dataset. For each word, we considered the following network measurements: degree, betweenness, clustering coefficient, average shortest path length, PageRank, concentric symmetry (at the second and third hierarchical level) BIBREF32 and accessibility BIBREF43, BIBREF44 (at the second and third hierarchical level). We chose these measurements because all of them capture some particular linguistic feature of texts BIBREF45, BIBREF46, BIBREF47, BIBREF48. After network measurements are extracted, they are used in machine learning algorithms. In our experiments, we considered Decision Trees (DT), nearest neighbors (kNN), Naive Bayes (NB) and Support Vector Machines (SVM). We used some heuristics to optimize classifier parameters. Such techniques are described in the literature BIBREF49. The accuracy of the pattern recognition methods were evaluated using cross-validation BIBREF50.",
+      "In summary, the methodology used in this paper encompasses the following steps:",
+      "Network construction: here texts are mapped into a co-occurrence networks. Some variations exists in the literature, however here we focused in the most usual variation, i.e. the possibility of considering or disregarding stopwords. A network with co-occurrence links is obtained after this step.",
+      "Network enrichment: in this step, the network is enriched with virtual edges established via similarity of word embeddings. After this step, we are given a complete network with weighted links. Virtually, any embedding technique could be used to gauge the similarity between nodes.",
+      "Network filtering: in order to eliminate spurious links included in the last step, the weakest edges are filtered. Two approaches were considered: a simple approach based on a global threshold and a local thresholding strategy that preserves network community structure. The outcome of this network filtering step is a network with two types of links: co-occurrence and virtual links (as shown in Figure FIGREF1).",
+      "Feature extraction: In this step, topological and dynamical network features are extracted. Here, we do not discriminate co-occurrence from virtual edges to compute the network metrics.",
+      "Pattern classification: once features are extracted from complex networks, they are used in pattern classification methods. This might include supervised, unsupervised and semi-supervised classification. This framework is exemplified in the supervised scenario.",
+      "The above framework is exemplified with the most common technique(s). It should be noted that the methods used, however, can be replaced by similar techniques. For example, the network construction could consider stopwords or even punctuation marks BIBREF51. Another possibility is the use of different strategies of thresholding. While a systematic analysis of techniques and parameters is still required to reveal other potential advantages of the framework based on the addition of virtual edges, in this paper we provide a first analysis showing that virtual edges could be useful to improve the discriminability of texts modeled as complex networks.",
+      "Here we used a dataset compatible with datasets used recently in the literature (see e.g. BIBREF28, BIBREF10, BIBREF52). The objective of the studied stylometric task is to identify the authorship of an unknown document BIBREF53. All data and some statistics of each book are shown in the Supplementary Information."
+    ],
+    [
+      "In Section SECREF13, we probe whether the inclusion of virtual edges is able to improve the performance of the traditional co-occurrence network-based classification in a usual stylometry task. While the focus of this paper is not to perform a systematic analysis of different methods comprising the adopted network, we consider two variations in the adopted methodology. In Section SECREF19, we consider the use of stopwords and the adoption of a local thresholding process to establish different criteria to create new virtual edges."
+    ],
+    [
+      "In Figure FIGREF14, we show some of the improvements in performance obtained when including a fixed amount of virtual edges using GloVe as embedding method. In each subpanel, we show the relative improvement in performance obtained as a function of the fraction of additional edges. In this section, we considered the traditional co-occurrence as starting point. In other words, the network construction disregarded stopwords. The list of stopwords considered in this paper is available in the Supplementary Information. We also considered the global approach to filter edges.",
+      "The relative improvement in performance is given by $\\Gamma _+{(p)}/\\Gamma _0$, where $\\Gamma _+{(p)}$ is the accuracy rate obtained when $p\\%$ additional edges are included and $\\Gamma _0 = \\Gamma _+{(p=0)}$, i.e. $\\Gamma _0$ is the accuracy rate measured from the traditional co-occurrence model. We only show the highest relative improvements in performance for each classifier. In our analysis, we considered also samples of text with distinct length, since the performance of network-based methods is sensitive to text length BIBREF34. In this figure, we considered samples comprising $w=\\lbrace 1.0, 2.5, 5.0, 10.0\\rbrace $ thousand words.",
+      "The results obtained for GloVe show that the highest relative improvements in performance occur for decision trees. This is apparent specially for the shortest samples. For $w=1,000$ words, the decision tree accuracy is enhanced by a factor of almost 50% when $p=20\\%$. An excellent gain in performance is also observed for both Naive Bayes and SVM classifiers, when $p=18\\%$ and $p=12\\%$, respectively. When $w=2,500$ words, the highest improvements was observed for the decision tree algorithm. A minor improvement was observed for the kNN method. A similar behavior occurred for $w=5,000$ words. Interestingly, SVM seems to benefit from the use of additional edges when larger documents are considered. When only 5% virtual edges are included, the relative gain in performance is about 45%.",
+      "The relative gain in performance obtained for Word2vec is shown in Figure FIGREF15. Overall, once again decision trees obtained the highest gain in performance when short texts are considered. Similar to the analysis based on the GloVe method, the gain for kNN is low when compared to the benefit received by other methods. Here, a considerable gain for SVM in only clear for $w=2,500$ and $p=10\\%$. When large texts are considered, Naive Bayes obtained the largest gain in performance.",
+      "Finally, the relative gain in performance obtained for FastText is shown in Figure FIGREF16. The prominent role of virtual edges in decision tree algorithm in the classification of short texts once again is evident. Conversely, the classification of large documents using virtual edges mostly benefit the classification based on the Naive Bayes classifier. Similarly to the results observed for Glove and Word2vec, the gain in performance obtained for kNN is low compared when compared to other methods.",
+      "While Figures FIGREF14 \u2013 FIGREF16 show the relative behavior in the accuracy, it still interesting to observe the absolute accuracy rate obtained with the classifiers. In Table TABREF17, we show the best accuracy rate (i.e. $\\max \\Gamma _+ = \\max _p \\Gamma _+(p)$) for GloVe. We also show the average difference in performance ($\\langle \\Gamma _+ - \\Gamma _0 \\rangle $) and the total number of cases in which an improvement in performance was observed ($N_+$). $N_+$ ranges in the interval $0 \\le N_+ \\le 20$. Table TABREF17 summarizes the results obtained for $w = \\lbrace 1.0, 5.0, 10.0\\rbrace $ thousand words. Additional results for other text length are available in Tables TABREF28\u2013TABREF30 of the Supplementary Information.",
+      "In very short texts, despite the low accuracy rates, an improvement can be observed in all classifiers. The best results was obtained with SVM when virtual edges were included. For $w=5,000$ words, the inclusion of new edges has no positive effect on both kNN and Naive Bayes algorithms. On the other hand, once again SVM could be improved, yielding an optimized performance. For $w=10,000$ words, SVM could not be improved. However, even without improvement it yielded the maximum accuracy rate. The Naive Bayes algorithm, in average, could be improved by a margin of about 10%.",
+      "The results obtained for Word2vec are summarized in Table TABREF29 of the Supplementary Information. Considering short documents ($w=1,000$ words), here the best results occurs only with the decision tree method combined with enriched networks. Differently from the GloVe approach, SVM does not yield the best results. Nonetheless, the highest accuracy across all classifiers and values of $p$ is the same. For larger documents ($w=5,000$ and $w=10,000$ words), no significant difference in performance between Word2vec and GloVe is apparent.",
+      "The results obtained for FastText are shown in Table TABREF18. In short texts, only kNN and Naive Bayes have their performance improved with virtual edges. However, none of the optimized results for these classifiers outperformed SVM applied to the traditional co-occurrence model. Conversely, when $w=5,000$ words, the optimized results are obtained with virtual edges in the SVM classifier. Apart from kNN, the enriched networks improved the traditional approach in all classifiers. For large chunks of texts ($w=10,000$), once again the approach based on SVM and virtual edges yielded optimized results. All classifiers benefited from the inclusion of additional edges. Remarkably, Naive Bayes improved by a margin of about $13\\%$."
+    ],
+    [
+      "While in the previous section we focused our analysis in the traditional word co-occurrence model, here we probe if the idea of considering virtual edges can also yield optimized results in particular modifications of the framework described in the methodology. The first modification in the co-occurrence model is the use of stopwords. While in semantical application of network language modeling stopwords are disregarded, in other application it can unravel interesting linguistic patterns BIBREF10. Here we analyzed the effect of using stopwords in enriched networks. We summarize the obtained results in Table TABREF20. We only show the results obtained with SVM, as it yielded the best results in comparison to other classifiers. The accuracy rate for other classifiers is shown in the Supplementary Information.",
+      "The results in Table TABREF20 reveals that even when stopwords are considered in the original model, an improvement can be observed with the addition of virtual edges. However, the results show that the degree of improvement depends upon the text length. In very short texts ($w=1,000$), none of the embeddings strategy was able to improve the performance of the classification. For $w=1,500$, a minor improvement was observed with FastText: the accuracy increased from $\\Gamma _0 = 37.18\\%$ to $38.46\\%$. A larger improvement could be observed for $w=2,000$. Both Word2vec and FastText approaches allowed an increase of more than 5% in performance. A gain higher than 10% was observed for $w=2,500$ with Word2vec. For larger pieces of texts, the gain is less expressive or absent. All in all, the results show that the use of virtual edges can also benefit the network approach based on stopwords. However, no significant improvement could be observed with very short and very large documents. The comparison of all three embedding methods showed that no method performed better than the others in all cases.",
+      "We also investigated if more informed thresholding strategies could provide better results. While the simple global thresholding approach might not be able to represent more complex structures, we also tested a more robust approach based on the local approach proposed by Serrano et al. BIBREF42. In Table TABREF21, we summarize the results obtained with this thresholding strategies. The table shows $\\max \\Gamma _+^{(L)} / \\max \\Gamma _+^{(G)}$, where $\\Gamma _+^{(L)}$ and $\\Gamma _+^{(G)}$ are the accuracy obtained with the local and global thresholding strategy, respectively. The results were obtained with the SVM classifier, as it turned to be the most efficient classification method. We found that there is no gain in performance when the local strategy is used. In particular cases, the global strategy is considerably more efficient. This is the case e.g. when GloVe is employed in texts with $w=1,500$ words. The performance of the global strategy is $12.2\\%$ higher than the one obtained with the global method. A minor difference in performance was found in texts comprising $w=1,000$ words, yet the global strategy is still more efficient than the global one.",
+      "To summarize all results obtained in this study we show in Table TABREF22 the best results obtained for each text length. We also show the relative gain in performance with the proposed approach and the embedding technique yielding the best result. All optimized results were obtained with the use of stopwords, global thresholding strategy and SVM as classification algorithm. A significant gain is more evident for intermediary text lengths."
+    ],
+    [
+      "Textual classification remains one of the most important facets of the Natural Language Processing area. Here we studied a family of classification methods, the word co-occurrence networks. Despite this apparent simplicity, this model has been useful in several practical and theoretical scenarios. We proposed a modification of the traditional model by establishing virtual edges to connect nodes that are semantically similar via word embeddings. The reasoning behind this strategy is the fact the similar words are not properly linked in the traditional model and, thus, important links might be overlooked if only adjacent words are linked.",
+      "Taking as reference task a stylometric problem, we showed \u2013 as a proof of principle \u2013 that the use of virtual edges might improve the discriminability of networks. When analyzing the best results for each text length, apart from very short and long texts, the proposed strategy yielded optimized results in all cases. The best classification performance was always obtained with the SVM classifier. In addition, we found an improved performance when stopwords are used in the construction of the enriched co-occurrence networks. Finally, a simple global thresholding strategy was found to be more efficient than a local approach that preserves the community structure of the networks. Because complex networks are usually combined with other strategies BIBREF8, BIBREF11, we believe that the proposed could be used in combination with other methods to improve the classification performance of other text classification tasks.",
+      "Our findings paves the way for research in several new directions. While we probed the effectiveness of virtual edges in a specific text classification task, we could extend this approach for general classification tasks. A systematic comparison of embeddings techniques could also be performed to include other recent techniques BIBREF54, BIBREF55. We could also identify other relevant techniques to create virtual edges, allowing thus the use of the methodology in other networked systems other than texts. For example, a network could be enriched with embeddings obtained from graph embeddings techniques. A simpler approach could also consider link prediction BIBREF56 to create virtual edges. Finally, other interesting family of studies concerns the discrimination between co-occurrence and virtual edges, possibly by creating novel network measurements considering heterogeneous links."
+    ],
+    [
+      "The authors acknowledge financial support from FAPESP (Grant no. 16/19069-9), CNPq-Brazil (Grant no. 304026/2018-2). This study was financed in part by the Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior - Brasil (CAPES) - Finance Code 001."
+    ],
+    [
+      "The following words were considered as stopwords in our analysis: all, just, don't, being, over, both, through, yourselves, its, before, o, don, hadn, herself, ll, had, should, to, only, won, under, ours,has, should've, haven't, do, them, his, very, you've, they, not, during, now, him, nor, wasn't, d, did, didn, this, she, each, further, won't, where, mustn't, isn't, few, because, you'd, doing, some, hasn, hasn't, are, our, ourselves, out, what, for, needn't, below, re, does, shouldn't, above, between, mustn, t, be, we, who, mightn't, doesn't, were, here, shouldn, hers, aren't, by, on, about, couldn, of, wouldn't, against, s, isn, or, own, into, yourself, down, hadn't, mightn, couldn't, wasn, your, you're, from, her, their, aren, it's, there, been, whom, too, wouldn, themselves, weren, was, until, more, himself, that, didn't, but, that'll, with, than, those, he, me, myself, ma, weren't, these, up, will, while, ain, can, theirs, my, and, ve, then, is, am, it, doesn, an, as, itself, at, have, in, any, if, again, no, when, same, how, other, which, you, shan't, shan, needn, haven, after, most, such, why, a, off i, m, yours, you'll, so, y, she's, the, having, once."
+    ],
+    [
+      "The list of books is shown in Tables TABREF25 and TABREF26. For each book we show the respective authors (Aut.) and the following quantities: total number of words ($N_W$), total number of sentences ($N_S$), total number of paragraphs ($N_P$) and the average sentence length ($\\langle S_L \\rangle $), measured in number of words. The following authors were considered: Hector Hugh (HH), Thomas Hardy (TH), Daniel Defoe (DD), Allan Poe (AP), Bram Stoker (BS), Mark Twain (MT), Charles Dickens (CD), Pelham Grenville (PG), Charles Darwin (CD), Arthur Doyle (AD), George Eliot (GE), Jane Austen (JA), and Joseph Conrad (JC)."
+    ],
+    [
+      "In this section we show additional results obtained for different text length. More specifically, we show the results obtained for GloVe, Word2vec and FastText when stopwords are either considered in the text or disregarded from the analysis."
+    ]
+  ]
+}
+```

qasper-0748/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Uncover Sexual Harassment Patterns from Personal Stories by Joint Key Element Extraction and Categorization
+
+Question: What is the size of the dataset?

qasper-0770/instruction.md

@@ -0,0 +1,65 @@
+Name of Paper: The First Evaluation of Chinese Human-Computer Dialogue Technology
+
+Question: How is the data annotated?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "The First Evaluation of Chinese Human-Computer Dialogue Technology",
+    "Task 1: User Intent Classification",
+    "Task 2: Online Testing of Task-oriented Dialogue",
+    "Evaluation Data",
+    "Evaluation Results",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "Recently, human-computer dialogue has been emerged as a hot topic, which has attracted the attention of both academia and industry. In research, the natural language understanding (NLU), dialogue management (DM) and natural language generation (NLG) have been promoted by the technologies of big data and deep learning BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 . Following the development of machine reading comprehension BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , the NLU technology has made great progress. The development of DM technology is from rule-based approach and supervised learning based approach to reinforcement learning based approach BIBREF15 . The NLG technology is through pattern-based approach, sentence planning approach and end-to-end deep learning approach BIBREF16 , BIBREF17 , BIBREF18 . In application, there are massive products that are based on the technology of human-computer dialogue, such as Apple Siri, Amazon Echo, Microsoft Cortana, Facebook Messenger and Google Allo etc.",
+      "Although the blooming of human-computer dialogue technology in both academia and industry, how to evaluate a dialogue system, especially an open domain chit-chat system, is still an open question. Figure FIGREF6 presents a brief comparison of the open domain chit-chat system and the task-oriented dialogue system.",
+      "From Figure FIGREF6 , we can see that it is quite different between the open domain chit-chat system and the task-oriented dialogue system. For the open domain chit-chat system, as it has no exact goal in a conversation, given an input message, the responses can be various. For example, for the input message \u201cHow is it going today?\u201d, the responses can be \u201cI'm fine!\u201d, \u201cNot bad.\u201d, \u201cI feel so depressed!\u201d, \u201cWhat a bad day!\u201d, etc. There may be infinite number of responses for an open domain messages. Hence, it is difficult to construct a gold standard (usually a reference set) to evaluate a response which is generated by an open domain chit-chat system. For the task-oriented system, although there are some objective evaluation metrics, such as the number of turns in a dialogue, the ratio of task completion, etc., there is no gold standard for automatically evaluating two (or more) dialogue systems when considering the satisfaction of the human and the fluency of the generated dialogue.",
+      "To promote the development of the evaluation technology for dialogue systems, especially considering the language characteristics of Chinese, we organize the first evaluation of Chinese human-computer dialogue technology. In this paper, we will present the evaluation scheme and the released corpus in detail.",
+      "The rest of this paper is as follows. In Section 2, we will briefly introduce the first evaluation of Chinese human-computer dialogue technology, which includes the descriptions and the evaluation metrics of the two tasks. We then present the evaluation data and final results in Section 3 and 4 respectively, following the conclusion and acknowledgements in the last two sections."
+    ],
+    [
+      "The First Evaluation of Chinese Human-Computer Dialogue Technology includes two tasks, namely user intent classification and online testing of task-oriented dialogue."
+    ],
+    [
+      "In using of human-computer dialogue based applications, human may have various intent, for example, chit-chatting, asking questions, booking air tickets, inquiring weather, etc. Therefore, after receiving an input message (text or ASR result) from a user, the first step is to classify the user intent into a specific domain for further processing. Table TABREF7 shows an example of user intent with category information.",
+      "In task 1, there are two top categories, namely, chit-chat and task-oriented dialogue. The task-oriented dialogue also includes 30 sub categories. In this evaluation, we only consider to classify the user intent in single utterance.",
+      "It is worth noting that besides the released data for training and developing, we also allow to collect external data for training and developing. To considering that, the task 1 is indeed includes two sub tasks. One is a closed evaluation, in which only the released data can be used for training and developing. The other is an open evaluation that allow to collect external data for training and developing. For task 1, we use F1-score as evaluation metric."
+    ],
+    [
+      "For the task-oriented dialogue systems, the best way for evaluation is to use the online human-computer dialogue. After finishing an online human-computer dialogue with a dialogue system, the human then manually evaluate the system by using the metrics of user satisfaction degree, dialogue fluency, etc. Therefore, in the task 2, we use an online testing of task-oriented dialogue for dialogue systems. For a human tester, we will give a complete intent with an initial sentence, which is used to start the online human-computer dialogue. Table TABREF12 shows an example of the task-oriented human-computer dialogue. Here \u201cU\u201d and \u201cR\u201d denote user and robot respectively. The complete intent is as following:",
+      "\u201c\u00e6\u009f\u00a5\u00e8\u00af\u00a2\u00e6\u0098\u008e\u00e5\u00a4\u00a9\u00e4\u00bb\u008e\u00e5\u0093\u0088\u00e5\u00b0\u0094\u00e6\u00bb\u00a8\u00e5\u0088\u00b0\u00e5\u008c\u0097\u00e4\u00ba\u00ac\u00e7\u009a\u0084\u00e6\u0099\u009a\u00e9\u0097\u00b4\u00e8\u00bd\u00af\u00e5\u008d\u00a7\u00e7\u0081\u00ab\u00e8\u00bd\u00a6\u00e7\u00a5\u00a8\u00ef\u00bc\u008c\u00e4\u00b8\u008a\u00e4\u00b8\u008b\u00e9\u0093\u00ba\u00e5\u009d\u0087\u00e5\u008f\u00af\u00e3\u0080\u0082",
+      "Inquire the soft berth ticket at tomorrow evening, from Harbin to Beijing, either upper or lower berth is okay.\u201d",
+      "In task 2, there are three categories. They are \u201cair tickets\u201d, \u201ctrain tickets\u201d and \u201chotel\u201d. Correspondingly, there are three type of tasks. All the tasks are in the scope of the three categories. However, a complete user intent may include more than one task. For example, a user may first inquiring the air tickets. However, due to the high price, the user decide to buy a train tickets. Furthermore, the user may also need to book a hotel room at the destination.",
+      "We use manual evaluation for task 2. For each system and each complete user intent, the initial sentence, which is used to start the dialogue, is the same. The tester then begin to converse to each system. A dialogue is finished if the system successfully returns the information which the user inquires or the number of dialogue turns is larger than 30 for a single task. For building the dialogue systems of participants, we release an example set of complete user intent and three data files of flight, train and hotel in JSON format. There are five evaluation metrics for task 2 as following.",
+      "Task completion ratio: The number of completed tasks divided by the number of total tasks.",
+      "User satisfaction degree: There are five scores -2, -1, 0, 1, 2, which denote very dissatisfied, dissatisfied, neutral, satisfied and very satisfied, respectively.",
+      "Response fluency: There are three scores -1, 0, 1, which indicate nonfluency, neutral, fluency.",
+      "Number of dialogue turns: The number of utterances in a task-completed dialogue.",
+      "Guidance ability for out of scope input: There are two scores 0, 1, which represent able to guide or unable to guide.",
+      "For the number of dialogue turns, we have a penalty rule that for a dialogue task, if the system cannot return the result (or accomplish the task) in 30 turns, the dialogue task is end by force. Meanwhile, if a system cannot accomplish a task in less than 30 dialogue turns, the number of dialogue turns is set to 30."
+    ],
+    [
+      "In the evaluation, all the data for training, developing and test is provided by the iFLYTEK Corporation.",
+      "For task 1, as the descriptions in Section SECREF10 , the two top categories are chit-chat (chat in Table TABREF13 ) and task-oriented dialogue. Meanwhile, the task-oriented dialogue also includes 30 sub categories. Actually, the task 1 is a 31 categories classification task. In task 1, besides the data we released for training and developing, we also allow the participants to extend the training and developing corpus. Hence, there are two sub tasks for the task 1. One is closed test, which means the participants can only use the released data for training and developing. The other is open test, which allows the participants to explore external corpus for training and developing. Note that there is a same test set for both the closed test and the open test.",
+      "For task 2, we release 11 examples of the complete user intent and 3 data file, which includes about one month of flight, hotel and train information, for participants to build their dialogue systems. The current date for online test is set to April 18, 2017. If the tester says \u201ctoday\u201d, the systems developed by the participants should understand that he/she indicates the date of April 18, 2017."
+    ],
+    [
+      "There are 74 participants who are signing up the evaluation. The final number of participants is 28 and the number of submitted systems is 43. Table TABREF14 and TABREF15 show the evaluation results of the closed test and open test of the task 1 respectively. Due to the space limitation, we only present the top 5 results of task 1. We will add the complete lists of the evaluation results in the version of full paper.",
+      "Note that for task 2, there are 7 submitted systems. However, only 4 systems can provide correct results or be connected in a right way at the test phase. Therefore, Table TABREF16 shows the complete results of the task 2."
+    ],
+    [
+      "In this paper, we introduce the first evaluation of Chinese human-computer dialogue technology. In detail, we first present the two tasks of the evaluation as well as the evaluation metrics. We then describe the released data for evaluation. Finally, we also show the evaluation results of the two tasks. As the evaluation data is provided by the iFLYTEK Corporation from their real online applications, we believe that the released data will further promote the research of human-computer dialogue and fill the blank of the data on the two tasks."
+    ],
+    [
+      "We would like to thank the Social Media Processing (SMP) committee of Chinese Information Processing Society of China. We thank all the participants of the first evaluation of Chinese human-computer dialogue technology. We also thank the testers from the voice resource department of the iFLYTEK Corporation for their effort to the online real-time human-computer dialogue test and offline dialogue evaluation. We thank Lingzhi Li, Yangzi Zhang, Jiaqi Zhu and Xiaoming Shi from the research center for social computing and information retrieval for their support on the data annotation, establishing the system testing environment and the communication to the participants and help connect their systems to the testing environment."
+    ]
+  ]
+}
+```

qasper-0777/instruction.md

@@ -0,0 +1,127 @@
+Name of Paper: Multi-style Generative Reading Comprehension
+
+Question: What do they mean by answer styles?
+
+## 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-0783/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: A Cascade Sequence-to-Sequence Model for Chinese Mandarin Lip Reading
+
+Question: What syntactic structure is used to model tones?

qasper-0784/instruction.md

@@ -0,0 +1,94 @@
+Name of Paper: A Cascade Sequence-to-Sequence Model for Chinese Mandarin Lip Reading
+
+Question: What visual information characterizes tones?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "The Proposed Method",
+    "Pinyin Prediction Sub-network",
+    "Tone Prediction Sub-network",
+    "Character Prediction Sub-network",
+    "CSSMCM Architecture",
+    "Training Strategy",
+    "Dataset",
+    "Implementation Details",
+    "Compared Methods and Evaluation Protocol",
+    "Results",
+    "Attention Visualisation",
+    "Summary and Extension"
+  ],
+  "paragraphs": [
+    [
+      "Lip reading, also known as visual speech recognition, aims to predict the sentence being spoken, given a silent video of a talking face. In noisy environments, where speech recognition is difficult, visual speech recognition offers an alternative way to understand speech. Besides, lip reading has practical potential in improved hearing aids, security, and silent dictation in public spaces. Lip reading is essentially a difficult problem, as most lip reading actuations, besides the lips and sometimes tongue and teeth, are latent and ambiguous. Several seemingly identical lip movements can produce different words.",
+      "Thanks to the recent development of deep learning, English-based lip reading methods have made great progress, at both word-level BIBREF0 , BIBREF1 and sentence-level BIBREF2 , BIBREF3 . However, as the language of the most number of speakers, there is only a little work for Chinese Mandarin lip reading in the multimedia community. Yang et al. BIBREF4 present a naturally-distributed large-scale benchmark for Chinese Mandarin lip-reading in the wild, named LRW-1000, which contains 1,000 classes with 718,018 samples from more than 2,000 individual speakers. Each class corresponds to the syllables of a Mandarin word composed of one or several Chinese characters. However, they perform only word classification for Chinese Mandarin lip reading but not at the complete sentence level. LipCH-Net BIBREF5 is the first paper aiming for sentence-level Chinese Mandarin lip reading. LipCH-Net is a two-step end-to-end architecture, in which two deep neural network models are employed to perform the recognition of Picture-to-Pinyin (mouth motion pictures to pronunciations) and the recognition of Pinyin-to-Hanzi (pronunciations to texts) respectively. Then a joint optimization is performed to improve the overall performance.",
+      "Belong to two different language families, English and Chinese Mandarin have many differences. The most significant one might be that: Chinese Mandarin is a tone language, while English is not. The tone is the use of pitch in language to distinguish lexical or grammatical meaning - that is, to distinguish or to inflect words . Even two words look the same on the face when pronounced, they can have different tones, thus have different meanings. For example, even though \"UTF8gbsn\u7ec3\u4e60\" (which means practice) and \"UTF8gbsn\u8054\u7cfb\" (which means contact) have different meanings, but they have the same mouth movement. This increases ambiguity when lip reading. So the tone is an important factor for Chinese Mandarin lip reading.",
+      "Based on the above considerations, in this paper, we present CSSMCM, a sentence-level Chinese Mandarin lip reading network, which contains three sub-networks. Same as BIBREF5 , in the first sub-network, pinyin sequence is predicted from the video. Different from BIBREF5 , which predicts pinyin characters from video, pinyin is taken as a whole in CSSMCM, also known as syllables. As we know, Mandarin Chinese is a syllable-based language and syllables are their logical unit of pronunciation. Compared with pinyin characters, syllables are a longer linguistic unit, and can reduce the difficulty of syllable choices in the decoder by sequence-to-sequence attention-based models BIBREF6 . Chen et al. BIBREF7 find that there might be a relationship between the production of lexical tones and the visible movements of the neck, head, and mouth. Motivated by this observation, in the second sub-network, both video and pinyin sequence is used as input to predict tone. Then in the third sub-network, video, pinyin, and tone sequence work together to predict the Chinese character sequence. At last, three sub-networks are jointly finetuned to improve overall performance.",
+      "As there is no public sentence-level Chinese Mandarin lip reading dataset, we collect a new Chinese Mandarin Lip Reading dataset called CMLR based on China Network Television broadcasts containing talking faces together with subtitles of what is said.",
+      "In summary, our major contributions are as follows."
+    ],
+    [
+      "In this section, we present CSSMCM, a lip reading model for Chinese Mandarin. As mention in Section SECREF1 , pinyin and tone are both important for Chinese Mandarin lip reading. Pinyin represents how to pronounce a Chinese character and is related to mouth movement. Tone can alleviate the ambiguity of visemes (several speech sounds that look the same) to some extent and can be inferred from visible movements. Based on this, the lip reading task is defined as follow: DISPLAYFORM0 ",
+      "The meaning of these symbols is given in Table TABREF5 .",
+      "As shown in Equation ( EQREF6 ), the whole problem is divided into three parts, which corresponds to pinyin prediction, tone prediction, and character prediction separately. Each part will be described in detail below."
+    ],
+    [
+      "The pinyin prediction sub-network transforms video sequence into pinyin sequence, which corresponds to INLINEFORM0 in Equation ( EQREF6 ). This sub-network is based on the sequence-to-sequence architecture with attention mechanism BIBREF8 . We name the encoder and decoder the video encoder and pinyin decoder, for the encoder process video sequence, and the decoder predicts pinyin sequence. The input video sequence is first fed into the VGG model BIBREF9 to extract visual feature. The output of conv5 of VGG is appended with global average pooling BIBREF10 to get the 512-dim feature vector. Then the 512-dim feature vector is fed into video encoder. The video encoder can be denoted as: DISPLAYFORM0 ",
+      "When predicting pinyin sequence, at each timestep INLINEFORM0 , video encoder outputs are attended to calculate a context vector INLINEFORM1 : DISPLAYFORM0 DISPLAYFORM1 "
+    ],
+    [
+      "As shown in Equation ( EQREF6 ), tone prediction sub-network ( INLINEFORM0 ) takes video and pinyin sequence as inputs and predict corresponding tone sequence. This problem is modeled as a sequence-to-sequence learning problem too. The corresponding model architecture is shown in Figure FIGREF8 .",
+      "In order to take both video and pinyin information into consideration when producing tone, a dual attention mechanism BIBREF3 is employed. Two independent attention mechanisms are used for video and pinyin sequence. Video context vectors INLINEFORM0 and pinyin context vectors INLINEFORM1 are fused when predicting a tone character at each decoder step.",
+      "The video encoder is the same as in Section SECREF7 and the pinyin encoder is: DISPLAYFORM0 ",
+      "The tone decoder takes both video encoder outputs and pinyin encoder outputs to calculate context vector, and then predicts tones: DISPLAYFORM0 DISPLAYFORM1 "
+    ],
+    [
+      "The character prediction sub-network corresponds to INLINEFORM0 in Equation ( EQREF6 ). It considers all the pinyin sequence, tone sequence and video sequence when predicting Chinese character. Similarly, we also use attention based sequence-to-sequence architecture to model this equation. Here the attention mechanism is modified into triplet attention mechanism: DISPLAYFORM0 DISPLAYFORM1 ",
+      "For the following needs, the formula of tone encoder is also listed as follows: DISPLAYFORM0 "
+    ],
+    [
+      "The architecture of the proposed approach is demonstrated in Figure FIGREF32 . For better display, the three attention mechanisms are not shown in the figure. During the training of CSSMCM, the outputs of pinyin decoder are fed into pinyin encoder, the outputs of tone decoder into tone encoder: DISPLAYFORM0 DISPLAYFORM1 ",
+      "We replace Equation ( EQREF14 ) with Equation ( EQREF28 ), Equation ( EQREF26 ) with Equation ( EQREF29 ). Then, the three sub-networks are jointly trained and the overall loss function is defined as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 stand for loss of pinyin prediction sub-network, tone prediction sub-network and character prediction sub-network respectively, as defined below. DISPLAYFORM0 "
+    ],
+    [
+      "To accelerate training and reduce overfitting, curriculum learning BIBREF3 is employed. The sentences are grouped into subsets according to the length of less than 11, 12-17, 18-23, more than 24 Chinese characters. Scheduled sampling proposed by BIBREF11 is used to eliminate the discrepancy between training and inference. At the training stage, the sampling rate from the previous output is selected from 0.7 to 1. Greedy decoder is used for fast decoding."
+    ],
+    [
+      "In this section, a three-stage pipeline for generating the Chinese Mandarin Lip Reading (CMLR) dataset is described, which includes video pre-processing, text acquisition, and data generation. This three-stage pipeline is similar to the method mentioned in BIBREF3 , but considering the characteristics of our Chinese Mandarin dataset, we have optimized some steps and parts to generate a better quality lip reading dataset. The three-stage pipeline is detailed below.",
+      "Video Pre-processing. First, national news program \"News Broadcast\" recorded between June 2009 and June 2018 is obtained from China Network Television website. Then, the HOG-based face detection method is performed BIBREF12 , followed by an open source platform for face recognition and alignment. The video clip set of eleven different hosts who broadcast the news is captured. During the face detection step, using frame skipping can improve efficiency while ensuring the program quality.",
+      "Text Acquisition. Since there is no subtitle or text annotation in the original \"News Broadcast\" program, FFmpeg tools are used to extract the corresponding audio track from the video clip set. Then through the iFLYTEK ASR, the corresponding text annotation of the video clip set is obtained. However, there is some noise in these text annotation. English letters, Arabic numerals, and rare punctuation are deleted to get a more pure Chinese Mandarin lip reading dataset.",
+      "Data Generation. The text annotation acquired in the previous step also contains timestamp information. Therefore, video clip set is intercepted according to these timestamp information, and then the corresponding word, phrase, or sentence video segment of the text annotation are obtained. Since the text timestamp information may have a few uncertain errors, some adjustments are made to the start frame and the end frame when intercepting the video segment. It is worth noting that through experiments, we found that using OpenCV can capture clearer video segment than the FFmpeg tools.",
+      "Through the three-stage pipeline mentioned above, we can obtain the Chinese Mandarin Lip Reading (CMLR) dataset containing more than 100,000 sentences, 25,000 phrases, 3,500 characters. The dataset is randomly divided into training set, validation set, and test set in a ratio of 7:1:2. Details are listed in Table TABREF37 ."
+    ],
+    [
+      "The input images are 64 INLINEFORM0 128 in dimension. Lip frames are transformed into gray-scale, and the VGG network takes every 5 lip frames as an input, moving 2 frames at each timestep. For all sub-networks, a two-layer bi-direction GRU BIBREF13 with a cell size of 256 is used for the encoder and a two-layer uni-direction GRU with a cell size of 512 for the decoder. For character and pinyin vocabulary, we keep characters and pinyin that appear more than 20 times. [sos], [eos] and [pad] are also included in these three vocabularies. The final vocabulary size is 371 for pinyin prediction sub-network, 8 for tone prediction sub-network (four tones plus a neutral tone), and 1,779 for character prediction sub-network.",
+      "The initial learning rate was 0.0001 and decreased by 50% every time the training error did not improve for 4 epochs. CSSMCM is implemented using pytorch library and trained on a Quadro 64C P5000 with 16GB memory. The total end-to-end model was trained for around 12 days."
+    ],
+    [
+      "WAS: The architecture used in BIBREF3 without the audio input. The decoder output Chinese character at each timestep. Others keep unchanged to the original implementation.",
+      "LipCH-Net-seq: For a fair comparison, we use sequence-to-sequence with attention framework to replace the Connectionist temporal classification (CTC) loss BIBREF14 used in LipCH-Net BIBREF5 when converting picture to pinyin.",
+      "CSSMCM-w/o video: To evaluate the necessity of video information when predicting tone, the video stream is removed when predicting tone and Chinese characters. In other word, video is only used when predicting the pinyin sequence. The tone is predicted from the pinyin sequence. Tone information and pinyin information work together to predict Chinese character.",
+      "We tried to implement the Lipnet architecture BIBREF2 to predict Chinese character at each timestep. However, the model did not converge. The possible reasons are due to the way CTC loss works and the difference between English and Chinese Mandarin. Compared to English, which only contains 26 characters, Chinese Mandarin contains thousands of Chinese characters. When CTC calculates loss, it first adds blank between every character in a sentence, that causes the number of the blank label is far more than any other Chinese character. Thus, when Lipnet starts training, it predicts only the blank label. After a certain epoch, \"UTF8gbsn\u7684\" character will occasionally appear until the learning rate decays to close to zero.",
+      "For all experiments, Character Error Rate (CER) and Pinyin Error Rate (PER) are used as evaluation metrics. CER is defined as INLINEFORM0 , where INLINEFORM1 is the number of substitutions, INLINEFORM2 is the number of deletions, INLINEFORM3 is the number of insertions to get from the reference to the hypothesis and INLINEFORM4 is the number of words in the reference. PER is calculated in the same way as CER. Tone Error Rate (TER) is also included when analyzing CSSMCM, which is calculated in the same way as above."
+    ],
+    [
+      "Table TABREF40 shows a detailed comparison between various sub-network of different methods. Comparing P2T and VP2T, VP2T considers video information when predicting the pinyin sequence and achieves a lower error rate. This verifies the conjecture of BIBREF7 that the generation of tones is related to the motion of the head. In terms of overall performance, CSSMCM exceeds all the other architecture on the CMLR dataset and achieves 32.48% character error rate. It is worth noting that CSSMCM-w/o video achieves the worst result (42.23% CER) even though its sub-networks perform well when trained separately. This may be due to the lack of visual information to support, and the accumulation of errors. CSSMCM using tone information performs better compared to LipCH-Net-seq, which does not use tone information. The comparison results show that tone is important when lip reading, and when predicting tone, visual information should be considered.",
+      "Table TABREF41 shows some generated sentences from different methods. CSSMCM-w/o video architecture is not included due to its relatively lower performance. These are sentences other methods fail to predict but CSSMCM succeeds. The phrase \"UTF8gbsn\u5b9e\u60e0\" (which means affordable) in the first example sentence, has a tone of 2, 4 and its corresponding pinyin are shi, hui. WAS predicts it as \"UTF8gbsn\u4e8b\u4f1a\" (which means opportunity). Although the pinyin prediction is correct, the tone is wrong. LipCH-Net-seq predicts \"UTF8gbsn\u5b9e\u60e0\" as \"UTF8gbsn\u5403\u8d35\" (not a word), which have the same finals \"ui\" and the corresponding mouth shapes are the same. It's the same in the second example. \"UTF8gbsn\u524d, \u5929, \u5e74\" have the same finals and mouth shapes, but the tone is different.",
+      "These show that when predicting characters with the same lip shape but different tones, other methods are often unable to predict correctly. However, CSSMCM can leverage the tone information to predict successfully.",
+      "Apart from the above results, Table TABREF42 also lists some failure cases of CSSMCM. The characters that CSSMCM predicts wrong are usually homophones or characters with the same final as the ground truth. In the first example, \"UTF8gbsn\u4ef7\" and \"UTF8gbsn\u4e0b\" have the same final, ia, while \"UTF8gbsn\u4e00\" and \"UTF8gbsn\u533b\" are homophones in the second example. Unlike English, if one character in an English word is predicted wrong, the understanding of the transcriptions has little effect. However, if there is a character predicted wrong in Chinese words, it will greatly affect the understandability of transcriptions. In the second example, CSSMCM mispredicts \"UTF8gbsn\u533b\u5b66\" ( which means medical) to \"UTF8gbsn\u4e00\u6c34\" (which means all). Although their first characters are pronounced the same, the meaning of the sentence changed from Now with the progress of medical science and technology in our country to It is now with the footsteps of China's Yishui Technology."
+    ],
+    [
+      "Figure FIGREF44 (a) and Figure FIGREF44 (b) visualise the alignment of video frames and Chinese characters predicted by CSSMCM and WAS respectively. The ground truth sequence is \"UTF8gbsn\u540c\u65f6\u4ed6\u8fd8\u5411\u5a92\u4f53\u8868\u793a\". Comparing Figure FIGREF44 (a) with Figure FIGREF44 (b), the diagonal trend of the video attention map got by CSSMCM is more obvious. The video attention is more focused where WAS predicts wrong, i.e. the area corresponding to \"UTF8gbsn\u8fd8\u5411\". Although WAS mistakenly predicts the \"UTF8gbsn\u5a92\u4f53\" as \"UTF8gbsn\u4e48\u4f53\", the \"UTF8gbsn\u5a92\u4f53\" and the \"UTF8gbsn\u4e48\u4f53\" have the same mouth shape, so the attention concentrates on the correct frame.",
+      "It's interesting to mention that in Figure FIGREF47 , when predicting the INLINEFORM0 -th character, attention is concentrated on the INLINEFORM1 -th tone. This may be because attention is applied to the outputs of the encoder, which actually includes all the information from the previous INLINEFORM2 timesteps. The attention to the tone of INLINEFORM3 -th timestep serves as the language model, which reduces the options for generating the character at INLINEFORM4 -th timestep, making prediction more accurate."
+    ],
+    [
+      "In this paper, we propose the CSSMCM, a Cascade Sequence-to-Sequence Model for Chinese Mandarin lip reading. CSSMCM is designed to predicting pinyin sequence, tone sequence, and Chinese character sequence one by one. When predicting tone sequence, a dual attention mechanism is used to consider video sequence and pinyin sequence at the same time. When predicting the Chinese character sequence, a triplet attention mechanism is proposed to take all the video sequence, pinyin sequence, and tone sequence information into consideration. CSSMCM consistently outperforms other lip reading architectures on the proposed CMLR dataset.",
+      "Lip reading and speech recognition are very similar. In Chinese Mandarin speech recognition, there have been kinds of different acoustic representations like syllable initial/final approach, syllable initial/final with tone approach, syllable approach, syllable with tone approach, preme/toneme approach BIBREF15 and Chinese Character approach BIBREF16 . In this paper, the Chinese character is chosen as the output unit. However, we find that the wrongly predicted characters severely affect the understandability of transcriptions. Using larger output units, like Chinese words, maybe can alleviate this problem."
+    ]
+  ]
+}
+```

qasper-0803/instruction.md

@@ -0,0 +1,114 @@
+Name of Paper: Style Transfer for Texts: to Err is Human, but Error Margins Matter
+
+Question: By how much do proposed architectures autperform state-of-the-art?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Style transfer",
+    "Experiments",
+    "Experiments ::: Error margins matter",
+    "Experiments ::: Delete, duplicate and conquer",
+    "Conclusion",
+    "Supplemental Material"
+  ],
+  "paragraphs": [
+    [
+      "Deep generative models attract a lot of attention in recent years BIBREF0. Such methods as variational autoencoders BIBREF1 or generative adversarial networks BIBREF2 are successfully applied to a variety of machine vision problems including image generation BIBREF3, learning interpretable image representations BIBREF4 and style transfer for images BIBREF5. However, natural language generation is more challenging due to many reasons, such as the discrete nature of textual information BIBREF6, the absence of local information continuity and non-smooth disentangled representations BIBREF7. Due to these difficulties, text generation is mostly limited to specific narrow applications and is usually working in supervised settings.",
+      "Content and style are deeply fused in natural language, but style transfer for texts is often addressed in the context of disentangled latent representations BIBREF6, BIBREF8, BIBREF9, BIBREF10, BIBREF11, BIBREF12. Intuitive understanding of this problem is apparent: if an input text has some attribute $A$, a system generates new text similar to the input on a given set of attributes with only one attribute $A$ changed to the target attribute $\\tilde{A}$. In the majority of previous works, style transfer is obtained through an encoder-decoder architecture with one or multiple style discriminators to learn disentangled representations. The encoder takes a sentence as an input and generates a style-independent content representation. The decoder then takes the content representation and the target style representation to generate the transformed sentence. In BIBREF13 authors question the quality and usability of the disentangled representations for texts and suggest an end-to-end approach to style transfer similar to an end-to-end machine translation.",
+      "Contribution of this paper is three-fold: 1) we show that different style transfer architectures have varying results on test and that reporting error margins for various training re-runs of the same model is especially important for adequate assessment of the models accuracy, see Figure FIGREF1; 2) we show that BLEU BIBREF14 between input and output and accuracy of style transfer measured in terms of the accuracy of a pre-trained external style classifier can be manipulated and naturally diverge from the intuitive goal of the style transfer task starting from a certain threshold; 3) new architectures that perform style transfer using improved latent representations are shown to outperform state of the art in terms of BLEU between output and human-written reformulations."
+    ],
+    [
+      "Style of a text is a very general notion that is hard to define in rigorous terms BIBREF15. However, the style of a text can be characterized quantitatively BIBREF16; stylized texts could be generated if a system is trained on a dataset of stylistically similar texts BIBREF17; and author-style could be learned end-to-end BIBREF18, BIBREF19, BIBREF20. A majority of recent works on style transfer focus on the sentiment of text and use it as a target attribute. For example, in BIBREF21, BIBREF22, BIBREF23 estimate the quality of the style transfer with binary sentiment classifier trained on the corpora further used for the training of the style-transfer system. BIBREF24 and especially BIBREF9 generalize this ad-hoc approach defining a style as a set of arbitrary quantitively measurable categorial or continuous parameters. Such parameters could include the 'style of the time' BIBREF16, author-specific attributes (see BIBREF25 or BIBREF26 on 'shakespearization'), politeness BIBREF27, formality of speech BIBREF28, and gender or even political slant BIBREF29.",
+      "A significant challenge associated with narrowly defined style-transfer problems is that finding a good solution for one aspect of a style does not guarantee that you can use the same solution for a different aspect of it. For example, BIBREF30 build a generative model for sentiment transfer with a retrieve-edit approach. In BIBREF21 a delete-retrieve model shows good results for sentiment transfer. However, it is hard to imagine that these retrieval approaches could be used, say, for the style of the time or formality, since in these cases the system is often expected to paraphrase a given sentence to achieve the target style.",
+      "In BIBREF6 the authors propose a more general approach to the controlled text generation combining variational autoencoder (VAE) with an extended wake-sleep mechanism in which the sleep procedure updates both the generator and external classifier that assesses generated samples and feedbacks learning signals to the generator. Authors had concatenated labels for style with the text representation of the encoder and used this vector with \"hard-coded\" information about the sentiment of the output as the input of the decoder. This approach seems promising, and some other papers either extend it or use similar ideas. BIBREF8 applied a GAN to align the hidden representations of sentences from two corpora using an adversarial loss to decompose information about the form. In BIBREF31 model learns a smooth code space and can be used as a discrete GAN with the ability to generate coherent discrete outputs from continuous samples. Authors use two different generators for two different styles. In BIBREF9 an adversarial network is used to make sure that the output of the encoder does not have style representation. BIBREF6 also uses an adversarial component that ensures there is no stylistic information within the representation. BIBREF9 do not use a dedicated component that controls the semantic component of the latent representation. Such a component is proposed by BIBREF10 who demonstrate that decomposition of style and content could be improved with an auxiliary multi-task for label prediction and adversarial objective for bag-of-words prediction. BIBREF11 also introduces a dedicated component to control semantic aspects of latent representations and an adversarial-motivational training that includes a special motivational loss to encourage a better decomposition. Speaking about preservation of semantics one also has to mention works on paraphrase systems, see, for example BIBREF32, BIBREF33, BIBREF34. The methodology described in this paper could be extended to paraphrasing systems in terms of semantic preservation measurement, however, this is the matter of future work.",
+      "BIBREF13 state that learning a latent representation, which is independent of the attributes specifying its style, is rarely attainable. There are other works on style transfer that are based on the ideas of neural machine translation with BIBREF35 and without parallel corpora BIBREF36 in line with BIBREF37 and BIBREF38.",
+      "It is important to underline here that majority of the papers dedicated to style transfer for texts treat sentiment of a sentence as a stylistic rather than semantic attribute despite particular concerns BIBREF39. It is also crucial to mention that in line with BIBREF9 majority of the state of the art methods for style transfer use an external pre-trained classifier to measure the accuracy of the style transfer. BLEU computes the harmonic mean of precision of exact matching n-grams between a reference and a target sentence across the corpus. It is not sensitive to minute changes, but BLEU between input and output is often used as the coarse measure of the semantics preservation. For the corpora that have human written reformulations, BLEU between the output of the model and human text is used. These metrics are used alongside with a handful of others such as PINC (Paraphrase In N-gram Changes) score BIBREF35, POS distance BIBREF12, language fluency BIBREF10, etc. Figure FIGREF2 shows self-reported results of different models in terms of two most frequently measured performance metrics, namely, BLEU and Accuracy of the style transfer.",
+      "This paper focuses on Yelp! reviews dataset that was lately enhanced with human written reformulations by BIBREF21. These are Yelp! reviews, where each short English review of a place is labeled as a negative or as a positive once. This paper studies three metrics that are most common in the field at the moment and questions to which extent can they be used for the performance assessment. These metrics are the accuracy of an external style classifier that is trained to measure the accuracy of the style transfer, BLEU between input and output of a system, and BLEU between output and human-written texts."
+    ],
+    [
+      "In this work we experiment with extensions of a model, described in BIBREF6, using Texar BIBREF40 framework. To generate plausible sentences with specific semantic and stylistic features every sentence is conditioned on a representation vector $z$ which is concatenated with a particular code $c$ that specifies desired attribute, see Figure FIGREF8. Under notation introduced in BIBREF6 the base autoencoder (AE) includes a conditional probabilistic encoder $E$ defined with parameters $\\theta _E$ to infer the latent representation $z$ given input $x$",
+      "Generator $G$ defined with parameters $\\theta _G$ is a GRU-RNN for generating and output $\\hat{x}$ defined as a sequence of tokens $\\hat{x} = {\\hat{x}_1, ..., \\hat{x}_T}$ conditioned on the latent representation $z$ and a stylistic component $c$ that are concatenated and give rise to a generative distribution",
+      "These encoder and generator form an AE with the following loss",
+      "This standard reconstruction loss that drives the generator to produce realistic sentences is combined with two additional losses. The first discriminator provides extra learning signals which enforce the generator to produce coherent attributes that match the structured code in $c$. Since it is impossible to propagate gradients from the discriminator through the discrete sample $\\hat{x}$, we use a deterministic continuous approximation a \"soft\" generated sentence, denoted as $\\tilde{G} = \\tilde{G}_\\tau (z, c)$ with \"temperature\" $\\tau $ set to $\\tau \\rightarrow 0$ as training proceeds. The resulting \u201csoft\u201d generated sentence is fed into the discriminator to measure the fitness to the target attribute, leading to the following loss",
+      "Finally, under the assumption that each structured attribute of generated sentences is controlled through the corresponding code in $c$ and is independent from $z$ one would like to control that other not explicitly modelled attributes do not entangle with $c$. This is addressed by the dedicated loss",
+      "The training objective for the baseline, shown in Figure FIGREF8, is therefore a sum of the losses from Equations (DISPLAY_FORM4) \u2013 (DISPLAY_FORM6) defined as",
+      "where $\\lambda _c$ and $\\lambda _z$ are balancing parameters.",
+      "Let us propose two further extensions of this baseline architecture. To improve reproducibility of the research the code of the studied models is open. Both extensions aim to improve the quality of information decomposition within the latent representation. In the first one, shown in Figure FIGREF12, a special dedicated discriminator is added to the model to control that the latent representation does not contain stylistic information. The loss of this discriminator is defined as",
+      "Here a discriminator denoted as $D_z$ is trying to predict code $c$ using representation $z$. Combining the loss defined by Equation (DISPLAY_FORM7) with the adversarial component defined in Equation (DISPLAY_FORM10) the following learning objective is formed",
+      "where $\\mathcal {L}_{baseline}$ is a sum defined in Equation (DISPLAY_FORM7), $\\lambda _{D_z}$ is a balancing parameter.",
+      "The second extension of the baseline architecture does not use an adversarial component $D_z$ that is trying to eradicate information on $c$ from component $z$. Instead, the system, shown in Figure FIGREF16 feeds the \"soft\" generated sentence $\\tilde{G}$ into encoder $E$ and checks how close is the representation $E(\\tilde{G} )$ to the original representation $z = E(x)$ in terms of the cosine distance. We further refer to it as shifted autoencoder or SAE. Ideally, both $E(\\tilde{G} (E(x), c))$ and $E(\\tilde{G} (E(x), \\bar{c}))$, where $\\bar{c}$ denotes an inverse style code, should be both equal to $E(x)$. The loss of the shifted autoencoder is",
+      "where $\\lambda _{cos}$ and $\\lambda _{cos^{-}}$ are two balancing parameters, with two additional terms in the loss, namely, cosine distances between the softened output processed by the encoder and the encoded original input, defined as",
+      "We also study a combination of both approaches described above, shown on Figure FIGREF17.",
+      "In Section SECREF4 we describe a series of experiments that we have carried out for these architectures using Yelp! reviews dataset."
+    ],
+    [
+      "We have found that the baseline, as well as the proposed extensions, have noisy outcomes, when retrained from scratch, see Figure FIGREF1. Most of the papers mentioned in Section SECREF2 measure the performance of the methods proposed for the sentiment transfer with two metrics: accuracy of the external sentiment classifier measured on test data, and BLEU between the input and output that is regarded as a coarse metric for semantic similarity.",
+      "In the first part of this section, we demonstrate that reporting error margins is essential for the performance assessment in terms that are prevalent in the field at the moment, i.e., BLEU between input and output and accuracy of the external sentiment classifier. In the second part, we also show that both of these two metrics after a certain threshold start to diverge from an intuitive goal of the style transfer and could be manipulated."
+    ],
+    [
+      "On Figure FIGREF1 one can see that the outcomes for every single rerun differ significantly. Namely, accuracy can change up to 5 percentage points, whereas BLEU can vary up to 8 points. This variance can be partially explained with the stochasticity incurred due to sampling from the latent variables. However, we show that results for state of the art models sometimes end up within error margins from one another, so one has to report the margins to compare the results rigorously. More importantly, one can see that there is an inherent trade-off between these two performance metrics. This trade-off is not only visible across models but is also present for the same retrained architecture. Therefore, improving one of the two metrics is not enough to confidently state that one system solves the style-transfer problem better than the other. One has to report error margins after several consecutive retrains and instead of comparing one of the two metrics has to talk about Pareto-like optimization that would show confident improvement of both.",
+      "To put obtained results into perspective, we have retrained every model from scratch five times in a row. We have also retrained the models of BIBREF12 five times since their code is published online. Figure FIGREF19 shows the results of all models with error margins. It is also enhanced with other self-reported results on the same Yelp! review dataset for which no code was published.",
+      "One can see that error margins of the models, for which several reruns could be performed, overlap significantly. In the next subsection, we carefully study BLEU and accuracy of the external classifier and discuss their aptness to measure style transfer performance."
+    ],
+    [
+      "One can argue that as there is an inevitable entanglement between semantics and stylistics in natural language, there is also an apparent entanglement between BLEU of input and output and accuracy estimation of the style. Indeed, the output that copies input gives maximal BLEU yet clearly fails in terms of the style transfer. On the other hand, a wholly rephrased sentence could provide a low BLEU between input and output but high accuracy. These two issues are not problematic when both BLEU between input and output and accuracy of the transfer are relatively low. However, since style transfer methods have significantly evolved in recent years, some state of the art methods are now sensitive to these issues. The trade-off between these two metrics can be seen in Figure FIGREF1 as well as in Figure FIGREF19.",
+      "As we have mentioned above, the accuracy of an external classifier and BLEU between output and input are the most widely used methods to assess the performance of style transfer at this moment. However, both of these metrics can be manipulated in a relatively simple manner. One can extend the generative architecture with internal pre-trained classifier of style and then perform the following heuristic procedure:",
+      "measure the style accuracy on the output for a given batch;",
+      "choose the sentences that style classifier labels as incorrect;",
+      "replace them with duplicates of sentences from the given batch that have correct style according to the internal classifier and show the highest BLEU with given inputs.",
+      "This way One can replace all sentences that push measured accuracy down and boost reported accuracy to 100%. To see the effect that this manipulation has on the key performance metric we split all sentences with wrong style in 10 groups of equal size and replaces them with the best possible duplicates of the stylistically correct sentences group after group. The results of this process are shown in Figure FIGREF24.",
+      "This result is disconcerting. Simply replacing part of the output with duplicates of the sentences that happen to have relatively high BLEU with given inputs allows to \"boost\" accuracy to 100% and \"improve\" BLEU. The change of BLEU during such manipulation stays within error margins of the architecture, but accuracy is significantly manipulated. What is even more disturbing is that BLEU between such manipulated output of the batch and human-written reformulations provided in BIBREF12 also grows. Figure FIGREF24 shows that for SAE but all four architectures described in Section SECREF3 demonstrate similar behavior.",
+      "Our experiments show that though we can manipulate BLEU between output and human-written text, it tends to change monotonically. That might be because of the fact that this metric incorporates information on stylistics and semantics of the text at the same time, preserving inevitable entanglement that we have mentioned earlier. Despite being costly, human-written reformulations are needed for future experiments with style transfer. It seems that modern architectures have reached a certain level of complexity for which naive proxy metrics such as accuracy of an external classifier or BLEU between output and input are already not enough for performance estimation and should be combined with BLEU between output and human-written texts. As the quality of style transfer grows further one has to improve the human-written data sets: for example, one would like to have data sets similar to the ones used for machine translation with several reformulations of the same sentence.",
+      "On Figure FIGREF25 one can see how new proposed architectures compare with another state of the art approaches in terms of BLEU between output and human-written reformulations."
+    ],
+    [
+      "Style transfer is not a rigorously defined NLP problem. Starting from definitions of style and semantics and finishing with metrics that could be used to evaluate the performance of a proposed system. There is a surge of recent contributions that work on this problem. This paper highlights several issues connected with this lack of rigor. First, it shows that the state of the art algorithms are inherently noisy on the two most widely accepted metrics, namely, BLEU between input and output and accuracy of the external style classifier. This noise can be partially attributed to the adversarial components that are often used in the state of the art architectures and partly due to certain methodological inconsistencies in the assessment of the performance. Second, it shows that reporting error margins of several consecutive retrains for the same model is crucial for the comparison of different architectures, since error margins for some of the models overlap significantly. Finally, it demonstrates that even BLEU on human-written reformulations can be manipulated in a relatively simple way."
+    ],
+    [
+      "Here are some examples characteristic for different systems. An output of a system follows the input. Here are some successful examples produced by the system with additional discriminator:",
+      "it's not much like an actual irish pub, which is depressing. $\\rightarrow $ it's definitely much like an actual irish pub, which is grateful.",
+      "i got a bagel breakfast sandwich and it was delicious! $\\rightarrow $ i got a bagel breakfast sandwich and it was disgusting!",
+      "i love their flavored coffee. $\\rightarrow $ i dumb their flavored coffee.",
+      "i got a bagel breakfast sandwich and it was delicious! $\\rightarrow $ i got a bagel breakfast sandwich and it was disgusting!",
+      "i love their flavored coffee. $\\rightarrow $ i dumb their flavored coffee.",
+      "nice selection of games to play. $\\rightarrow $ typical selection of games to play.",
+      "i'm not a fan of huge chain restaurants. $\\rightarrow $ i'm definitely a fan of huge chain restaurants.",
+      "Here are some examples of typical faulty reformulations:",
+      "only now i'm really hungry, and really pissed off. $\\rightarrow $ kids now i'm really hungry, and really extraordinary off.",
+      "what a waste of my time and theirs. $\\rightarrow $ what a wow. of my time and theirs.",
+      "cooked to perfection and very flavorful. $\\rightarrow $ cooked to pain and very outdated.",
+      "the beer was nice and cold! $\\rightarrow $ the beer was nice and consistant!",
+      "corn bread was also good! $\\rightarrow $ corn bread was also unethical bagged",
+      "Here are some successful examples produced by the SAE:",
+      "our waitress was the best, very accommodating. $\\rightarrow $ our waitress was the worst, very accommodating.",
+      "great food and awesome service! $\\rightarrow $ horrible food and nasty service!",
+      "their sandwiches were really tasty. $\\rightarrow $ their sandwiches were really bland.",
+      "i highly recommend the ahi tuna. $\\rightarrow $ i highly hated the ahi tuna.",
+      "other than that, it's great! $\\rightarrow $ other than that, it's horrible!",
+      "Here are some examples of typical faulty reformulations by SAE:",
+      "good drinks, and good company. $\\rightarrow $ 9:30 drinks, and 9:30 company.",
+      "like it's been in a fridge for a week. $\\rightarrow $ like it's been in a fridge for a true.",
+      "save your money & your patience. $\\rightarrow $ save your smile & your patience.",
+      "no call, no nothing. $\\rightarrow $ deliciously call, deliciously community.",
+      "sounds good doesn't it? $\\rightarrow $ sounds good does keeps it talented",
+      "Here are some successful examples produced by the SAE with additional discriminator:",
+      "best green corn tamales around. $\\rightarrow $ worst green corn tamales around.",
+      "she did the most amazing job. $\\rightarrow $ she did the most desperate job.",
+      "very friendly staff and manager. $\\rightarrow $ very inconsistent staff and manager.",
+      "even the water tasted horrible. $\\rightarrow $ even the water tasted great.",
+      "go here, you will love it. $\\rightarrow $ go here, you will avoid it.",
+      "Here are some examples of typical faulty reformulations by the SAE with additional discriminator:",
+      "_num_ - _num_ % capacity at most , i was the only one in the pool. $\\rightarrow $ sweetness - stylish % fountains at most, i was the new one in the",
+      "this is pretty darn good pizza! $\\rightarrow $ this is pretty darn unsafe pizza misleading",
+      "enjoyed the dolly a lot. $\\rightarrow $ remove the shortage a lot.",
+      "so, it went in the trash. $\\rightarrow $ so, it improved in the hooked.",
+      "they are so fresh and yummy. $\\rightarrow $ they are so bland and yummy."
+    ]
+  ]
+}
+```

qasper-0804/instruction.md

@@ -0,0 +1,114 @@
+Name of Paper: Style Transfer for Texts: to Err is Human, but Error Margins Matter
+
+Question: What are three new proposed architectures?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Style transfer",
+    "Experiments",
+    "Experiments ::: Error margins matter",
+    "Experiments ::: Delete, duplicate and conquer",
+    "Conclusion",
+    "Supplemental Material"
+  ],
+  "paragraphs": [
+    [
+      "Deep generative models attract a lot of attention in recent years BIBREF0. Such methods as variational autoencoders BIBREF1 or generative adversarial networks BIBREF2 are successfully applied to a variety of machine vision problems including image generation BIBREF3, learning interpretable image representations BIBREF4 and style transfer for images BIBREF5. However, natural language generation is more challenging due to many reasons, such as the discrete nature of textual information BIBREF6, the absence of local information continuity and non-smooth disentangled representations BIBREF7. Due to these difficulties, text generation is mostly limited to specific narrow applications and is usually working in supervised settings.",
+      "Content and style are deeply fused in natural language, but style transfer for texts is often addressed in the context of disentangled latent representations BIBREF6, BIBREF8, BIBREF9, BIBREF10, BIBREF11, BIBREF12. Intuitive understanding of this problem is apparent: if an input text has some attribute $A$, a system generates new text similar to the input on a given set of attributes with only one attribute $A$ changed to the target attribute $\\tilde{A}$. In the majority of previous works, style transfer is obtained through an encoder-decoder architecture with one or multiple style discriminators to learn disentangled representations. The encoder takes a sentence as an input and generates a style-independent content representation. The decoder then takes the content representation and the target style representation to generate the transformed sentence. In BIBREF13 authors question the quality and usability of the disentangled representations for texts and suggest an end-to-end approach to style transfer similar to an end-to-end machine translation.",
+      "Contribution of this paper is three-fold: 1) we show that different style transfer architectures have varying results on test and that reporting error margins for various training re-runs of the same model is especially important for adequate assessment of the models accuracy, see Figure FIGREF1; 2) we show that BLEU BIBREF14 between input and output and accuracy of style transfer measured in terms of the accuracy of a pre-trained external style classifier can be manipulated and naturally diverge from the intuitive goal of the style transfer task starting from a certain threshold; 3) new architectures that perform style transfer using improved latent representations are shown to outperform state of the art in terms of BLEU between output and human-written reformulations."
+    ],
+    [
+      "Style of a text is a very general notion that is hard to define in rigorous terms BIBREF15. However, the style of a text can be characterized quantitatively BIBREF16; stylized texts could be generated if a system is trained on a dataset of stylistically similar texts BIBREF17; and author-style could be learned end-to-end BIBREF18, BIBREF19, BIBREF20. A majority of recent works on style transfer focus on the sentiment of text and use it as a target attribute. For example, in BIBREF21, BIBREF22, BIBREF23 estimate the quality of the style transfer with binary sentiment classifier trained on the corpora further used for the training of the style-transfer system. BIBREF24 and especially BIBREF9 generalize this ad-hoc approach defining a style as a set of arbitrary quantitively measurable categorial or continuous parameters. Such parameters could include the 'style of the time' BIBREF16, author-specific attributes (see BIBREF25 or BIBREF26 on 'shakespearization'), politeness BIBREF27, formality of speech BIBREF28, and gender or even political slant BIBREF29.",
+      "A significant challenge associated with narrowly defined style-transfer problems is that finding a good solution for one aspect of a style does not guarantee that you can use the same solution for a different aspect of it. For example, BIBREF30 build a generative model for sentiment transfer with a retrieve-edit approach. In BIBREF21 a delete-retrieve model shows good results for sentiment transfer. However, it is hard to imagine that these retrieval approaches could be used, say, for the style of the time or formality, since in these cases the system is often expected to paraphrase a given sentence to achieve the target style.",
+      "In BIBREF6 the authors propose a more general approach to the controlled text generation combining variational autoencoder (VAE) with an extended wake-sleep mechanism in which the sleep procedure updates both the generator and external classifier that assesses generated samples and feedbacks learning signals to the generator. Authors had concatenated labels for style with the text representation of the encoder and used this vector with \"hard-coded\" information about the sentiment of the output as the input of the decoder. This approach seems promising, and some other papers either extend it or use similar ideas. BIBREF8 applied a GAN to align the hidden representations of sentences from two corpora using an adversarial loss to decompose information about the form. In BIBREF31 model learns a smooth code space and can be used as a discrete GAN with the ability to generate coherent discrete outputs from continuous samples. Authors use two different generators for two different styles. In BIBREF9 an adversarial network is used to make sure that the output of the encoder does not have style representation. BIBREF6 also uses an adversarial component that ensures there is no stylistic information within the representation. BIBREF9 do not use a dedicated component that controls the semantic component of the latent representation. Such a component is proposed by BIBREF10 who demonstrate that decomposition of style and content could be improved with an auxiliary multi-task for label prediction and adversarial objective for bag-of-words prediction. BIBREF11 also introduces a dedicated component to control semantic aspects of latent representations and an adversarial-motivational training that includes a special motivational loss to encourage a better decomposition. Speaking about preservation of semantics one also has to mention works on paraphrase systems, see, for example BIBREF32, BIBREF33, BIBREF34. The methodology described in this paper could be extended to paraphrasing systems in terms of semantic preservation measurement, however, this is the matter of future work.",
+      "BIBREF13 state that learning a latent representation, which is independent of the attributes specifying its style, is rarely attainable. There are other works on style transfer that are based on the ideas of neural machine translation with BIBREF35 and without parallel corpora BIBREF36 in line with BIBREF37 and BIBREF38.",
+      "It is important to underline here that majority of the papers dedicated to style transfer for texts treat sentiment of a sentence as a stylistic rather than semantic attribute despite particular concerns BIBREF39. It is also crucial to mention that in line with BIBREF9 majority of the state of the art methods for style transfer use an external pre-trained classifier to measure the accuracy of the style transfer. BLEU computes the harmonic mean of precision of exact matching n-grams between a reference and a target sentence across the corpus. It is not sensitive to minute changes, but BLEU between input and output is often used as the coarse measure of the semantics preservation. For the corpora that have human written reformulations, BLEU between the output of the model and human text is used. These metrics are used alongside with a handful of others such as PINC (Paraphrase In N-gram Changes) score BIBREF35, POS distance BIBREF12, language fluency BIBREF10, etc. Figure FIGREF2 shows self-reported results of different models in terms of two most frequently measured performance metrics, namely, BLEU and Accuracy of the style transfer.",
+      "This paper focuses on Yelp! reviews dataset that was lately enhanced with human written reformulations by BIBREF21. These are Yelp! reviews, where each short English review of a place is labeled as a negative or as a positive once. This paper studies three metrics that are most common in the field at the moment and questions to which extent can they be used for the performance assessment. These metrics are the accuracy of an external style classifier that is trained to measure the accuracy of the style transfer, BLEU between input and output of a system, and BLEU between output and human-written texts."
+    ],
+    [
+      "In this work we experiment with extensions of a model, described in BIBREF6, using Texar BIBREF40 framework. To generate plausible sentences with specific semantic and stylistic features every sentence is conditioned on a representation vector $z$ which is concatenated with a particular code $c$ that specifies desired attribute, see Figure FIGREF8. Under notation introduced in BIBREF6 the base autoencoder (AE) includes a conditional probabilistic encoder $E$ defined with parameters $\\theta _E$ to infer the latent representation $z$ given input $x$",
+      "Generator $G$ defined with parameters $\\theta _G$ is a GRU-RNN for generating and output $\\hat{x}$ defined as a sequence of tokens $\\hat{x} = {\\hat{x}_1, ..., \\hat{x}_T}$ conditioned on the latent representation $z$ and a stylistic component $c$ that are concatenated and give rise to a generative distribution",
+      "These encoder and generator form an AE with the following loss",
+      "This standard reconstruction loss that drives the generator to produce realistic sentences is combined with two additional losses. The first discriminator provides extra learning signals which enforce the generator to produce coherent attributes that match the structured code in $c$. Since it is impossible to propagate gradients from the discriminator through the discrete sample $\\hat{x}$, we use a deterministic continuous approximation a \"soft\" generated sentence, denoted as $\\tilde{G} = \\tilde{G}_\\tau (z, c)$ with \"temperature\" $\\tau $ set to $\\tau \\rightarrow 0$ as training proceeds. The resulting \u201csoft\u201d generated sentence is fed into the discriminator to measure the fitness to the target attribute, leading to the following loss",
+      "Finally, under the assumption that each structured attribute of generated sentences is controlled through the corresponding code in $c$ and is independent from $z$ one would like to control that other not explicitly modelled attributes do not entangle with $c$. This is addressed by the dedicated loss",
+      "The training objective for the baseline, shown in Figure FIGREF8, is therefore a sum of the losses from Equations (DISPLAY_FORM4) \u2013 (DISPLAY_FORM6) defined as",
+      "where $\\lambda _c$ and $\\lambda _z$ are balancing parameters.",
+      "Let us propose two further extensions of this baseline architecture. To improve reproducibility of the research the code of the studied models is open. Both extensions aim to improve the quality of information decomposition within the latent representation. In the first one, shown in Figure FIGREF12, a special dedicated discriminator is added to the model to control that the latent representation does not contain stylistic information. The loss of this discriminator is defined as",
+      "Here a discriminator denoted as $D_z$ is trying to predict code $c$ using representation $z$. Combining the loss defined by Equation (DISPLAY_FORM7) with the adversarial component defined in Equation (DISPLAY_FORM10) the following learning objective is formed",
+      "where $\\mathcal {L}_{baseline}$ is a sum defined in Equation (DISPLAY_FORM7), $\\lambda _{D_z}$ is a balancing parameter.",
+      "The second extension of the baseline architecture does not use an adversarial component $D_z$ that is trying to eradicate information on $c$ from component $z$. Instead, the system, shown in Figure FIGREF16 feeds the \"soft\" generated sentence $\\tilde{G}$ into encoder $E$ and checks how close is the representation $E(\\tilde{G} )$ to the original representation $z = E(x)$ in terms of the cosine distance. We further refer to it as shifted autoencoder or SAE. Ideally, both $E(\\tilde{G} (E(x), c))$ and $E(\\tilde{G} (E(x), \\bar{c}))$, where $\\bar{c}$ denotes an inverse style code, should be both equal to $E(x)$. The loss of the shifted autoencoder is",
+      "where $\\lambda _{cos}$ and $\\lambda _{cos^{-}}$ are two balancing parameters, with two additional terms in the loss, namely, cosine distances between the softened output processed by the encoder and the encoded original input, defined as",
+      "We also study a combination of both approaches described above, shown on Figure FIGREF17.",
+      "In Section SECREF4 we describe a series of experiments that we have carried out for these architectures using Yelp! reviews dataset."
+    ],
+    [
+      "We have found that the baseline, as well as the proposed extensions, have noisy outcomes, when retrained from scratch, see Figure FIGREF1. Most of the papers mentioned in Section SECREF2 measure the performance of the methods proposed for the sentiment transfer with two metrics: accuracy of the external sentiment classifier measured on test data, and BLEU between the input and output that is regarded as a coarse metric for semantic similarity.",
+      "In the first part of this section, we demonstrate that reporting error margins is essential for the performance assessment in terms that are prevalent in the field at the moment, i.e., BLEU between input and output and accuracy of the external sentiment classifier. In the second part, we also show that both of these two metrics after a certain threshold start to diverge from an intuitive goal of the style transfer and could be manipulated."
+    ],
+    [
+      "On Figure FIGREF1 one can see that the outcomes for every single rerun differ significantly. Namely, accuracy can change up to 5 percentage points, whereas BLEU can vary up to 8 points. This variance can be partially explained with the stochasticity incurred due to sampling from the latent variables. However, we show that results for state of the art models sometimes end up within error margins from one another, so one has to report the margins to compare the results rigorously. More importantly, one can see that there is an inherent trade-off between these two performance metrics. This trade-off is not only visible across models but is also present for the same retrained architecture. Therefore, improving one of the two metrics is not enough to confidently state that one system solves the style-transfer problem better than the other. One has to report error margins after several consecutive retrains and instead of comparing one of the two metrics has to talk about Pareto-like optimization that would show confident improvement of both.",
+      "To put obtained results into perspective, we have retrained every model from scratch five times in a row. We have also retrained the models of BIBREF12 five times since their code is published online. Figure FIGREF19 shows the results of all models with error margins. It is also enhanced with other self-reported results on the same Yelp! review dataset for which no code was published.",
+      "One can see that error margins of the models, for which several reruns could be performed, overlap significantly. In the next subsection, we carefully study BLEU and accuracy of the external classifier and discuss their aptness to measure style transfer performance."
+    ],
+    [
+      "One can argue that as there is an inevitable entanglement between semantics and stylistics in natural language, there is also an apparent entanglement between BLEU of input and output and accuracy estimation of the style. Indeed, the output that copies input gives maximal BLEU yet clearly fails in terms of the style transfer. On the other hand, a wholly rephrased sentence could provide a low BLEU between input and output but high accuracy. These two issues are not problematic when both BLEU between input and output and accuracy of the transfer are relatively low. However, since style transfer methods have significantly evolved in recent years, some state of the art methods are now sensitive to these issues. The trade-off between these two metrics can be seen in Figure FIGREF1 as well as in Figure FIGREF19.",
+      "As we have mentioned above, the accuracy of an external classifier and BLEU between output and input are the most widely used methods to assess the performance of style transfer at this moment. However, both of these metrics can be manipulated in a relatively simple manner. One can extend the generative architecture with internal pre-trained classifier of style and then perform the following heuristic procedure:",
+      "measure the style accuracy on the output for a given batch;",
+      "choose the sentences that style classifier labels as incorrect;",
+      "replace them with duplicates of sentences from the given batch that have correct style according to the internal classifier and show the highest BLEU with given inputs.",
+      "This way One can replace all sentences that push measured accuracy down and boost reported accuracy to 100%. To see the effect that this manipulation has on the key performance metric we split all sentences with wrong style in 10 groups of equal size and replaces them with the best possible duplicates of the stylistically correct sentences group after group. The results of this process are shown in Figure FIGREF24.",
+      "This result is disconcerting. Simply replacing part of the output with duplicates of the sentences that happen to have relatively high BLEU with given inputs allows to \"boost\" accuracy to 100% and \"improve\" BLEU. The change of BLEU during such manipulation stays within error margins of the architecture, but accuracy is significantly manipulated. What is even more disturbing is that BLEU between such manipulated output of the batch and human-written reformulations provided in BIBREF12 also grows. Figure FIGREF24 shows that for SAE but all four architectures described in Section SECREF3 demonstrate similar behavior.",
+      "Our experiments show that though we can manipulate BLEU between output and human-written text, it tends to change monotonically. That might be because of the fact that this metric incorporates information on stylistics and semantics of the text at the same time, preserving inevitable entanglement that we have mentioned earlier. Despite being costly, human-written reformulations are needed for future experiments with style transfer. It seems that modern architectures have reached a certain level of complexity for which naive proxy metrics such as accuracy of an external classifier or BLEU between output and input are already not enough for performance estimation and should be combined with BLEU between output and human-written texts. As the quality of style transfer grows further one has to improve the human-written data sets: for example, one would like to have data sets similar to the ones used for machine translation with several reformulations of the same sentence.",
+      "On Figure FIGREF25 one can see how new proposed architectures compare with another state of the art approaches in terms of BLEU between output and human-written reformulations."
+    ],
+    [
+      "Style transfer is not a rigorously defined NLP problem. Starting from definitions of style and semantics and finishing with metrics that could be used to evaluate the performance of a proposed system. There is a surge of recent contributions that work on this problem. This paper highlights several issues connected with this lack of rigor. First, it shows that the state of the art algorithms are inherently noisy on the two most widely accepted metrics, namely, BLEU between input and output and accuracy of the external style classifier. This noise can be partially attributed to the adversarial components that are often used in the state of the art architectures and partly due to certain methodological inconsistencies in the assessment of the performance. Second, it shows that reporting error margins of several consecutive retrains for the same model is crucial for the comparison of different architectures, since error margins for some of the models overlap significantly. Finally, it demonstrates that even BLEU on human-written reformulations can be manipulated in a relatively simple way."
+    ],
+    [
+      "Here are some examples characteristic for different systems. An output of a system follows the input. Here are some successful examples produced by the system with additional discriminator:",
+      "it's not much like an actual irish pub, which is depressing. $\\rightarrow $ it's definitely much like an actual irish pub, which is grateful.",
+      "i got a bagel breakfast sandwich and it was delicious! $\\rightarrow $ i got a bagel breakfast sandwich and it was disgusting!",
+      "i love their flavored coffee. $\\rightarrow $ i dumb their flavored coffee.",
+      "i got a bagel breakfast sandwich and it was delicious! $\\rightarrow $ i got a bagel breakfast sandwich and it was disgusting!",
+      "i love their flavored coffee. $\\rightarrow $ i dumb their flavored coffee.",
+      "nice selection of games to play. $\\rightarrow $ typical selection of games to play.",
+      "i'm not a fan of huge chain restaurants. $\\rightarrow $ i'm definitely a fan of huge chain restaurants.",
+      "Here are some examples of typical faulty reformulations:",
+      "only now i'm really hungry, and really pissed off. $\\rightarrow $ kids now i'm really hungry, and really extraordinary off.",
+      "what a waste of my time and theirs. $\\rightarrow $ what a wow. of my time and theirs.",
+      "cooked to perfection and very flavorful. $\\rightarrow $ cooked to pain and very outdated.",
+      "the beer was nice and cold! $\\rightarrow $ the beer was nice and consistant!",
+      "corn bread was also good! $\\rightarrow $ corn bread was also unethical bagged",
+      "Here are some successful examples produced by the SAE:",
+      "our waitress was the best, very accommodating. $\\rightarrow $ our waitress was the worst, very accommodating.",
+      "great food and awesome service! $\\rightarrow $ horrible food and nasty service!",
+      "their sandwiches were really tasty. $\\rightarrow $ their sandwiches were really bland.",
+      "i highly recommend the ahi tuna. $\\rightarrow $ i highly hated the ahi tuna.",
+      "other than that, it's great! $\\rightarrow $ other than that, it's horrible!",
+      "Here are some examples of typical faulty reformulations by SAE:",
+      "good drinks, and good company. $\\rightarrow $ 9:30 drinks, and 9:30 company.",
+      "like it's been in a fridge for a week. $\\rightarrow $ like it's been in a fridge for a true.",
+      "save your money & your patience. $\\rightarrow $ save your smile & your patience.",
+      "no call, no nothing. $\\rightarrow $ deliciously call, deliciously community.",
+      "sounds good doesn't it? $\\rightarrow $ sounds good does keeps it talented",
+      "Here are some successful examples produced by the SAE with additional discriminator:",
+      "best green corn tamales around. $\\rightarrow $ worst green corn tamales around.",
+      "she did the most amazing job. $\\rightarrow $ she did the most desperate job.",
+      "very friendly staff and manager. $\\rightarrow $ very inconsistent staff and manager.",
+      "even the water tasted horrible. $\\rightarrow $ even the water tasted great.",
+      "go here, you will love it. $\\rightarrow $ go here, you will avoid it.",
+      "Here are some examples of typical faulty reformulations by the SAE with additional discriminator:",
+      "_num_ - _num_ % capacity at most , i was the only one in the pool. $\\rightarrow $ sweetness - stylish % fountains at most, i was the new one in the",
+      "this is pretty darn good pizza! $\\rightarrow $ this is pretty darn unsafe pizza misleading",
+      "enjoyed the dolly a lot. $\\rightarrow $ remove the shortage a lot.",
+      "so, it went in the trash. $\\rightarrow $ so, it improved in the hooked.",
+      "they are so fresh and yummy. $\\rightarrow $ they are so bland and yummy."
+    ]
+  ]
+}
+```

qasper-0922/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing
+
+Question: What latent variables are modeled in the PCFG?

qasper-0925/instruction.md

@@ -0,0 +1,75 @@
+Name of Paper: Characterizing Diabetes, Diet, Exercise, and Obesity Comments on Twitter
+
+Question: How strong was the correlation between exercise and diabetes?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Methods",
+    "Data Collection",
+    "Topic Discovery",
+    "Topic Content Analysis",
+    "Results",
+    "Discussion",
+    "Conclusion",
+    "Conflict of interest",
+    "Acknowledgement"
+  ],
+  "paragraphs": [
+    [
+      "The global prevalence of obesity has doubled between 1980 and 2014, with more than 1.9 billion adults considered as overweight and over 600 million adults considered as obese in 2014 BIBREF0 . Since the 1970s, obesity has risen 37 percent affecting 25 percent of the U.S. adults BIBREF1 . Similar upward trends of obesity have been found in youth populations, with a 60% increase in preschool aged children between 1990 and 2010 BIBREF2 . Overweight and obesity are the fifth leading risk for global deaths according to the European Association for the Study of Obesity BIBREF0 . Excess energy intake and inadequate energy expenditure both contribute to weight gain and diabetes BIBREF3 , BIBREF4 .",
+      "Obesity can be reduced through modifiable lifestyle behaviors such as diet and exercise BIBREF4 . There are several comorbidities associated with being overweight or obese, such as diabetes BIBREF5 . The prevalence of diabetes in adults has risen globally from 4.7% in 1980 to 8.5% in 2014. Current projections estimate that by 2050, 29 million Americans will be diagnosed with type 2 diabetes, which is a 165% increase from the 11 million diagnosed in 2002 BIBREF6 . Studies show that there are strong relations among diabetes, diet, exercise, and obesity (DDEO) BIBREF7 , BIBREF4 , BIBREF8 , BIBREF9 ; however, the general public's perception of DDEO remains limited to survey-based studies BIBREF10 .",
+      "The growth of social media has provided a research opportunity to track public behaviors, information, and opinions about common health issues. It is estimated that the number of social media users will increase from 2.34 billion in 2016 to 2.95 billion in 2020 BIBREF11 . Twitter has 316 million users worldwide BIBREF12 providing a unique opportunity to understand users' opinions with respect to the most common health issues BIBREF13 . Publicly available Twitter posts have facilitated data collection and leveraged the research at the intersection of public health and data science; thus, informing the research community of major opinions and topics of interest among the general population BIBREF14 , BIBREF15 , BIBREF16 that cannot otherwise be collected through traditional means of research (e.g., surveys, interviews, focus groups) BIBREF17 , BIBREF18 . Furthermore, analyzing Twitter data can help health organizations such as state health departments and large healthcare systems to provide health advice and track health opinions of their populations and provide effective health advice when needed BIBREF13 .",
+      "Among computational methods to analyze tweets, computational linguistics is a well-known developed approach to gain insight into a population, track health issues, and discover new knowledge BIBREF19 , BIBREF20 , BIBREF21 , BIBREF22 . Twitter data has been used for a wide range of health and non-health related applications, such as stock market BIBREF23 and election analysis BIBREF24 . Some examples of Twitter data analysis for health-related topics include: flu BIBREF25 , BIBREF26 , BIBREF27 , BIBREF28 , BIBREF29 , BIBREF30 , mental health BIBREF31 , Ebola BIBREF32 , BIBREF33 , Zika BIBREF34 , medication use BIBREF35 , BIBREF36 , BIBREF37 , diabetes BIBREF38 , and weight loss and obesity BIBREF39 , BIBREF40 , BIBREF41 , BIBREF42 , BIBREF21 .",
+      "The previous Twitter studies have dealt with extracting common topics of one health issue discussed by the users to better understand common themes; however, this study utilizes an innovative approach to computationally analyze unstructured health related text data exchanged via Twitter to characterize health opinions regarding four common health issues, including diabetes, diet, exercise and obesity (DDEO) on a population level. This study identifies the characteristics of the most common health opinions with respect to DDEO and discloses public perception of the relationship among diabetes, diet, exercise and obesity. These common public opinions/topics and perceptions can be used by providers and public health agencies to better understand the common opinions of their population denominators in regard to DDEO, and reflect upon those opinions accordingly."
+    ],
+    [
+      "Our approach uses semantic and linguistics analyses for disclosing health characteristics of opinions in tweets containing DDEO words. The present study included three phases: data collection, topic discovery, and topic-content analysis."
+    ],
+    [
+      "This phase collected tweets using Twitter's Application Programming Interfaces (API) BIBREF43 . Within the Twitter API, diabetes, diet, exercise, and obesity were selected as the related words BIBREF4 and the related health areas BIBREF19 . Twitter's APIs provides both historic and real-time data collections. The latter method randomly collects 1% of publicly available tweets. This paper used the real-time method to randomly collect 10% of publicly available English tweets using several pre-defined DDEO-related queries (Table TABREF6 ) within a specific time frame. We used the queries to collect approximately 4.5 million related tweets between 06/01/2016 and 06/30/2016. The data will be available in the first author's website. Figure FIGREF3 shows a sample of collected tweets in this research."
+    ],
+    [
+      "To discover topics from the collected tweets, we used a topic modeling approach that fuzzy clusters the semantically related words such as assigning \u201cdiabetes\", \u201ccancer\", and \u201cinfluenza\" into a topic that has an overall \u201cdisease\" theme BIBREF44 , BIBREF45 . Topic modeling has a wide range of applications in health and medical domains such as predicting protein-protein relationships based on the literature knowledge BIBREF46 , discovering relevant clinical concepts and structures in patients' health records BIBREF47 , and identifying patterns of clinical events in a cohort of brain cancer patients BIBREF48 .",
+      "Among topic models, Latent Dirichlet Allocation (LDA) BIBREF49 is the most popular effective model BIBREF50 , BIBREF19 as studies have shown that LDA is an effective computational linguistics model for discovering topics in a corpus BIBREF51 , BIBREF52 . LDA assumes that a corpus contains topics such that each word in each document can be assigned to the topics with different degrees of membership BIBREF53 , BIBREF54 , BIBREF55 .",
+      "Twitter users can post their opinions or share information about a subject to the public. Identifying the main topics of users' tweets provides an interesting point of reference, but conceptualizing larger subtopics of millions of tweets can reveal valuable insight to users' opinions. The topic discovery component of the study approach uses LDA to find main topics, themes, and opinions in the collected tweets.",
+      "We used the Mallet implementation of LDA BIBREF49 , BIBREF56 with its default settings to explore opinions in the tweets. Before identifying the opinions, two pre-processing steps were implemented: (1) using a standard list for removing stop words, that do not have semantic value for analysis (such as \u201cthe\"); and, (2) finding the optimum number of topics. To determine a proper number of topics, log-likelihood estimation with 80% of tweets for training and 20% of tweets for testing was used to find the highest log-likelihood, as it is the optimum number of topics BIBREF57 . The highest log-likelihood was determined 425 topics."
+    ],
+    [
+      "The topic content analysis component used an objective interpretation approach with a lexicon-based approach to analyze the content of topics. The lexicon-based approach uses dictionaries to disclose the semantic orientation of words in a topic. Linguistic Inquiry and Word Count (LIWC) is a linguistics analysis tool that reveals thoughts, feelings, personality, and motivations in a corpus BIBREF58 , BIBREF59 , BIBREF60 . LIWC has accepted rate of sensitivity, specificity, and English proficiency measures BIBREF61 . LIWC has a health related dictionary that can help to find whether a topic contains words associated with health. In this analysis, we used LIWC to find health related topics."
+    ],
+    [
+      "Obesity and Diabetes showed the highest and the lowest number of tweets (51.7% and 8.0%). Diet and Exercise formed 23.7% and 16.6% of the tweets (Table TABREF6 ).",
+      "Out of all 4.5 million DDEO-related tweets returned by Tweeter's API, the LDA found 425 topics. We used LIWC to filter the detected 425 topics and found 222 health-related topics. Additionally, we labeled topics based on the availability of DDEO words. For example, if a topic had \u201cdiet\", we labeled it as a diet-related topic. As expected and driven by the initial Tweeter API's query, common topics were Diabetes, Diet, Exercise, and Obesity (DDEO). (Table TABREF7 ) shows that the highest and the lowest number of topics were related to exercise and diabetes (80 and 21 out of 222). Diet and Obesity had almost similar rates (58 and 63 out of 222).",
+      "Each of the DDEO topics included several common subtopics including both DDEO and non-DDEO terms discovered by the LDA algorithm (Table TABREF7 ). Common subtopics for \u201cDiabetes\", in order of frequency, included type 2 diabetes, obesity, diet, exercise, blood pressure, heart attack, yoga, and Alzheimer. Common subtopics for \u201cDiet\" included obesity, exercise, weight loss [medicine], celebrities, vegetarian, diabetes, religious diet, pregnancy, and mental health. Frequent subtopics for \u201cExercise\" included fitness, obesity, daily plan, diet, brain, diabetes, and computer games. And finally, the most common subtopics for \u201cObesity\" included diet, exercise, children, diabetes, Alzheimer, and cancer (Table TABREF7 ). Table TABREF8 provides illustrative examples for each of the topics and subtopics.",
+      "Further exploration of the subtopics revealed additional patterns of interest (Tables TABREF7 and TABREF8 ). We found 21 diabetes-related topics with 8 subtopics. While type 2 diabetes was the most frequent of the sub-topics, heart attack, Yoga, and Alzheimer are the least frequent subtopics for diabetes. Diet had a wide variety of emerging themes ranging from celebrity diet (e.g., Beyonce) to religious diet (e.g., Ramadan). Diet was detected in 63 topics with 10 subtopics; obesity, and pregnancy and mental health were the most and the least discussed obesity-related topics, respectively. Exploring the themes for Exercise subtopics revealed subjects such as computer games (e.g., Pokemon-Go) and brain exercises (e.g., memory improvement). Exercise had 7 subtopics with fitness as the most discussed subtopic and computer games as the least discussed subtopic. Finally, Obesity themes showed topics such as Alzheimer (e.g., research studies) and cancer (e.g., breast cancer). Obesity had the lowest diversity of subtopics: six with diet as the most discussed subtopic, and Alzheimer and cancer as the least discussed subtopics.",
+      "Diabetes subtopics show the relation between diabetes and exercise, diet, and obesity. Subtopics of diabetes revealed that users post about the relationship between diabetes and other diseases such as heart attack (Tables TABREF7 and TABREF8 ). The subtopic Alzheimer is also shown in the obesity subtopics. This overlap between categories prompts the discussion of research and linkages among obesity, diabetes, and Alzheimer's disease. Type 2 diabetes was another subtopic expressed by users and scientifically documented in the literature.",
+      "The main DDEO topics showed some level of interrelationship by appearing as subtopics of other DDEO topics. The words with italic and underline styles in Table 2 demonstrate the relation among the four DDEO areas. Our results show users' interest about posting their opinions, sharing information, and conversing about exercise & diabetes, exercise & diet, diabetes & diet, diabetes & obesity, and diet & obesity (Figure FIGREF9 ). The strongest correlation among the topics was determined to be between exercise and obesity ( INLINEFORM0 ). Other notable correlations were: diabetes and obesity ( INLINEFORM1 ), and diet and obesity ( INLINEFORM2 )."
+    ],
+    [
+      "Diabetes, diet, exercise, and obesity are common public health related opinions. Analyzing individual- level opinions by automated algorithmic techniques can be a useful approach to better characterize health opinions of a population. Traditional public health polls and surveys are limited by a small sample size; however, Twitter provides a platform to capture an array of opinions and shared information a expressed in the words of the tweeter. Studies show that Twitter data can be used to discover trending topics, and that there is a strong correlation between Twitter health conversations and Centers for Disease Control and Prevention (CDC) statistics BIBREF62 .",
+      "This research provides a computational content analysis approach to conduct a deep analysis using a large data set of tweets. Our framework decodes public health opinions in DDEO related tweets, which can be applied to other public health issues. Among health-related subtopics, there are a wide range of topics from diseases to personal experiences such as participating in religious activities or vegetarian diets.",
+      "Diabetes subtopics showed the relationship between diabetes and exercise, diet, and obesity (Tables TABREF7 and TABREF8 ). Subtopics of diabetes revealed that users posted about the relation between diabetes and other diseases such as heart attack. The subtopic Alzheimer is also shown in the obesity subtopics. This overlap between categories prompts the discussion of research and linkages among obesity, diabetes, and Alzheimer's disease. Type 2 diabetes was another subtopic that was also expressed by users and scientifically documented in the literature. The inclusion of Yoga in posts about diabetes is interesting. While yoga would certainly be labeled as a form of fitness, when considering the post, it was insightful to see discussion on the mental health benefits that yoga offers to those living with diabetes BIBREF63 .",
+      "Diet had the highest number of subtopics. For example, religious diet activities such as fasting during the month of Ramadan for Muslims incorporated two subtopics categorized under the diet topic (Tables TABREF7 and TABREF8 ). This information has implications for the type of diets that are being practiced in the religious community, but may help inform religious scholars who focus on health and psychological conditions during fasting. Other religions such as Judaism, Christianity, and Taoism have periods of fasting that were not captured in our data collection, which may have been due to lack of posts or the timeframe in which we collected data. The diet plans of celebrities were also considered influential to explaining and informing diet opinions of Twitter users BIBREF64 .",
+      "Exercise themes show the Twitter users' association of exercise with \u201cbrain\" benefits such as increased memory and cognitive performance (Tables TABREF7 and TABREF8 ) BIBREF65 . The topics also confirm that exercising is associated with controlling diabetes and assisting with meal planning BIBREF66 , BIBREF9 , and obesity BIBREF67 . Additionally, we found the Twitter users mentioned exercise topics about the use of computer games that assist with exercising. The recent mobile gaming phenomenon Pokeman-Go game BIBREF68 was highly associated with the exercise topic. Pokemon-Go allows users to operate in a virtual environment while simultaneously functioning in the real word. Capturing Pokemons, battling characters, and finding physical locations for meeting other users required physically activity to reach predefined locations. These themes reflect on the potential of augmented reality in increasing patients' physical activity levels BIBREF69 .",
+      "Obesity had the lowest number of subtopics in our study. Three of the subtopics were related to other diseases such as diabetes (Tables TABREF7 and TABREF8 ). The scholarly literature has well documented the possible linkages between obesity and chronic diseases such as diabetes BIBREF1 as supported by the study results. The topic of children is another prominent subtopic associated with obesity. There has been an increasing number of opinions in regard to child obesity and national health campaigns that have been developed to encourage physical activity among children BIBREF70 . Alzheimer was also identified as a topic under obesity. Although considered a perplexing finding, recent studies have been conducted to identify possible correlation between obesity and Alzheimer's disease BIBREF71 , BIBREF72 , BIBREF73 . Indeed, Twitter users have expressed opinions about the study of Alzheimer's disease and the linkage between these two topics.",
+      "This paper addresses a need for clinical providers, public health experts, and social scientists to utilize a large conversational dataset to collect and utilize population level opinions and information needs. Although our framework is applied to Twitter, the applications from this study can be used in patient communication devices monitored by physicians or weight management interventions with social media accounts, and support large scale population-wide initiatives to promote healthy behaviors and preventative measures for diabetes, diet, exercise, and obesity.",
+      "This research has some limitations. First, our DDEO analysis does not take geographical location of the Twitter users into consideration and thus does not reveal if certain geographical differences exists. Second, we used a limited number of queries to select the initial pool of tweets, thus perhaps missing tweets that may have been relevant to DDEO but have used unusual terms referenced. Third, our analysis only included tweets generated in one month; however, as our previous work has demonstrated BIBREF42 , public opinion can change during a year. Additionally, we did not track individuals across time to detect changes in common themes discussed. Our future research plans includes introducing a dynamic framework to collect and analyze DDEO related tweets during extended time periods (multiple months) and incorporating spatial analysis of DDEO-related tweets."
+    ],
+    [
+      "This study represents the first step in developing routine processes to collect, analyze, and interpret DDEO-related posts to social media around health-related topics and presents a transdisciplinary approach to analyzing public discussions around health topics. With 2.34 billion social media users in 2016, the ability to collect and synthesize social media data will continue to grow. Developing methods to make this process more streamlined and robust will allow for more rapid identification of public health trends in real time.",
+      "Note: Amir Karami will handle correspondence at all stages of refereeing and publication."
+    ],
+    [
+      "The authors state that they have no conflict of interest."
+    ],
+    [
+      "This research was partially supported by the first author's startup research funding provided by the University of South Carolina, School of Library and Information Science. We thank Jill Chappell-Fail and Jeff Salter at the University of South Carolina College of Information and Communications for assistance with technical support.",
+      "References"
+    ]
+  ]
+}
+```

qasper-0941/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Measuring Compositional Generalization: A Comprehensive Method on Realistic Data
+
+Question: What are results of comparison between novel method to other approaches for creating compositional generalization benchmarks?

qasper-0946/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Prototypical Metric Transfer Learning for Continuous Speech Keyword Spotting With Limited Training Data
+
+Question: What problem do they apply transfer learning to?

qasper-0971/instruction.md

@@ -0,0 +1,75 @@
+Name of Paper: Recognizing Musical Entities in User-generated Content
+
+Question: What task-specific features are used?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Methodology",
+    "Dataset",
+    "NER system",
+    "Schedule matching",
+    "Results",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "The increasing use of social media and microblogging services has broken new ground in the field of Information Extraction (IE) from user-generated content (UGC). Understanding the information contained in users' content has become one of the main goal for many applications, due to the uniqueness and the variety of this data BIBREF0 . However, the highly informal and noisy status of these sources makes it difficult to apply techniques proposed by the NLP community for dealing with formal and structured content BIBREF1 .",
+      "In this work, we analyze a set of tweets related to a specific classical music radio channel, BBC Radio 3, interested in detecting two types of musical named entities, Contributor and Musical Work.",
+      "The method proposed makes use of the information extracted from the radio schedule for creating links between users' tweets and tracks broadcasted. Thanks to this linking, we aim to detect when users refer to entities included into the schedule. Apart from that, we consider a series of linguistic features, partly taken from the NLP literature and partly specifically designed for this task, for building statistical models able to recognize the musical entities. To that aim, we perform several experiments with a supervised learning model, Support Vector Machine (SVM), and a recurrent neural network architecture, a bidirectional LSTM with a CRF layer (biLSTM-CRF).",
+      "The contributions in this work are summarized as follows:",
+      "The paper is structured as follows. In Section 2, we present a review of the previous works related to Named Entity Recognition, focusing on its application on UGC and MIR. Afterwards, in Section 3 it is presented the methodology of this work, describing the dataset and the method proposed. In Section 4, the results obtained are shown. Finally, in Section 5 conclusions are discussed."
+    ],
+    [
+      "Named Entity Recognition (NER), or alternatively Named Entity Recognition and Classification (NERC), is the task of detecting entities in an input text and to assign them to a specific class. It starts to be defined in the early '80, and over the years several approaches have been proposed BIBREF2 . Early systems were based on handcrafted rule-based algorithms, while recently several contributions by Machine Learning scientists have helped in integrating probabilistic models into NER systems.",
+      "In particular, new developments in neural architectures have become an important resource for this task. Their main advantages are that they do not need language-specific knowledge resources BIBREF3 , and they are robust to the noisy and short nature of social media messages BIBREF4 . Indeed, according to a performance analysis of several Named Entity Recognition and Linking systems presented in BIBREF5 , it has been found that poor capitalization is one of the main issues when dealing with microblog content. Apart from that, typographic errors and the ubiquitous occurrence of out-of-vocabulary (OOV) words also cause drops in NER recall and precision, together with shortenings and slang, particularly pronounced in tweets.",
+      "Music Information Retrieval (MIR) is an interdisciplinary field which borrows tools of several disciplines, such as signal processing, musicology, machine learning, psychology and many others, for extracting knowledge from musical objects (be them audio, texts, etc.) BIBREF6 . In the last decade, several MIR tasks have benefited from NLP, such as sound and music recommendation BIBREF7 , automatic summary of song review BIBREF8 , artist similarity BIBREF9 and genre classification BIBREF10 .",
+      "In the field of IE, a first approach for detecting musical named entities from raw text, based on Hidden Markov Models, has been proposed in BIBREF11 . In BIBREF12 , the authors combine state-of-the-art Entity Linking (EL) systems to tackle the problem of detecting musical entities from raw texts. The method proposed relies on the argumentum ad populum intuition, so if two or more different EL systems perform the same prediction in linking a named entity mention, the more likely this prediction is to be correct. In detail, the off-the-shelf systems used are: DBpedia Spotlight BIBREF13 , TagMe BIBREF14 , Babelfy BIBREF15 . Moreover, a first Musical Entity Linking, MEL has been presented in BIBREF16 which combines different state-of-the-art NLP libraries and SimpleBrainz, an RDF knowledge base created from MusicBrainz after a simplification process.",
+      "Furthermore, Twitter has also been at the center of many studies done by the MIR community. As example, for building a music recommender system BIBREF17 analyzes tweets containing keywords like nowplaying or listeningto. In BIBREF9 , a similar dataset it is used for discovering cultural listening patterns. Publicly available Twitter corpora built for MIR investigations have been created, among others the Million Musical Tweets dataset BIBREF18 and the #nowplaying dataset BIBREF19 ."
+    ],
+    [
+      "We propose a hybrid method which recognizes musical entities in UGC using both contextual and linguistic information. We focus on detecting two types of entities: Contributor: person who is related to a musical work (composer, performer, conductor, etc). Musical Work: musical composition or recording (symphony, concerto, overture, etc).",
+      "As case study, we have chosen to analyze tweets extracted from the channel of a classical music radio, BBC Radio 3. The choice to focus on classical music has been mostly motivated by the particular discrepancy between the informal language used in the social platform and the formal nomenclature of contributors and musical works. Indeed, users when referring to a musician or to a classical piece in a tweet, rarely use the full name of the person or of the work, as shown in Table 2.",
+      "We extract information from the radio schedule for recreating the musical context to analyze user-generated tweets, detecting when they are referring to a specific work or contributor recently played. We manage to associate to every track broadcasted a list of entities, thanks to the tweets automatically posted by the BBC Radio3 Music Bot, where it is described the track actually on air in the radio. In Table 3, examples of bot-generated tweets are shown.",
+      "Afterwards, we detect the entities on the user-generated content by means of two methods: on one side, we use the entities extracted from the radio schedule for generating candidates entities in the user-generated tweets, thanks to a matching algorithm based on time proximity and string similarity. On the other side, we create a statistical model capable of detecting entities directly from the UGC, aimed to model the informal language of the raw texts. In Figure 1, an overview of the system proposed is presented."
+    ],
+    [
+      "In May 2018, we crawled Twitter using the Python library Tweepy, creating two datasets on which Contributor and Musical Work entities have been manually annotated, using IOB tags.",
+      "The first set contains user-generated tweets related to the BBC Radio 3 channel. It represents the source of user-generated content on which we aim to predict the named entities. We create it filtering the messages containing hashtags related to BBC Radio 3, such as #BBCRadio3 or #BBCR3. We obtain a set of 2,225 unique user-generated tweets. The second set consists of the messages automatically generated by the BBC Radio 3 Music Bot. This set contains 5,093 automatically generated tweets, thanks to which we have recreated the schedule.",
+      "In Table 4, the amount of tokens and relative entities annotated are reported for the two datasets. For evaluation purposes, both sets are split in a training part (80%) and two test sets (10% each one) randomly chosen. Within the user-generated corpora, entities annotated are only about 5% of the whole amount of tokens. In the case of the automatically generated tweets, the percentage is significantly greater and entities represent about the 50%."
+    ],
+    [
+      "According to the literature reviewed, state-of-the-art NER systems proposed by the NLP community are not tailored to detect musical entities in user-generated content. Consequently, our first objective has been to understand how to adapt existing systems for achieving significant results in this task.",
+      "In the following sections, we describe separately the features, the word embeddings and the models considered. All the resources used are publicy available.",
+      "We define a set of features for characterizing the text at the token level. We mix standard linguistic features, such as Part-Of-Speech (POS) and chunk tag, together with several gazetteers specifically built for classical music, and a series of features representing tokens' left and right context. For extracting the POS and the chunk tag we use the Python library twitter_nlp, presented in BIBREF1 .",
+      "In total, we define 26 features for describing each token: 1)POS tag; 2)Chunk tag; 3)Position of the token within the text, normalized between 0 and 1; 4)If the token starts with a capital letter; 5)If the token is a digit. Gazetteers: 6)Contributor first names; 7)Contributor last names; 8)Contributor types (\"soprano\", \"violinist\", etc.); 9)Classical work types (\"symphony\", \"overture\", etc.); 10)Musical instruments; 11)Opus forms (\"op\", \"opus\"); 12)Work number forms (\"no\", \"number\"); 13)Work keys (\"C\", \"D\", \"E\", \"F\" , \"G\" , \"A\", \"B\", \"flat\", \"sharp\"); 14)Work Modes (\"major\", \"minor\", \"m\"). Finally, we complete the tokens' description including as token's features the surface form, the POS and the chunk tag of the previous and the following two tokens (12 features).",
+      "We consider two sets of GloVe word embeddings BIBREF20 for training the neural architecture, one pre-trained with 2B of tweets, publicy downloadable, one trained with a corpora of 300K tweets collected during the 2014-2017 BBC Proms Festivals and disjoint from the data used in our experiments.",
+      "The first model considered for this task has been the John Platt's sequential minimal optimization algorithm for training a support vector classifier BIBREF21 , implemented in WEKA BIBREF22 . Indeed, in BIBREF23 results shown that SVM outperforms other machine learning models, such as Decision Trees and Naive Bayes, obtaining the best accuracy when detecting named entities from the user-generated tweets.",
+      "However, recent advances in Deep Learning techniques have shown that the NER task can benefit from the use of neural architectures, such as biLSTM-networks BIBREF3 , BIBREF4 . We use the implementation proposed in BIBREF24 for conducting three different experiments. In the first, we train the model using only the word embeddings as feature. In the second, together with the word embeddings we use the POS and chunk tag. In the third, all the features previously defined are included, in addition to the word embeddings. For every experiment, we use both the pre-trained embeddings and the ones that we created with our Twitter corpora. In section 4, results obtained from the several experiments are reported."
+    ],
+    [
+      "The bot-generated tweets present a predefined structure and a formal language, which facilitates the entities detection. In this dataset, our goal is to assign to each track played on the radio, represented by a tweet, a list of entities extracted from the tweet raw text. For achieving that, we experiment with the algorithms and features presented previously, obtaining an high level of accuracy, as presented in section 4. The hypothesis considered is that when a radio listener posts a tweet, it is possible that she is referring to a track which has been played a relatively short time before. In this cases, we want to show that knowing the radio schedule can help improving the results when detecting entities.",
+      "Once assigned a list of entities to each track, we perform two types of matching. Firstly, within the tracks we identify the ones which have been played in a fixed range of time (t) before and after the generation of the user's tweet. Using the resulting tracks, we create a list of candidates entities on which performing string similarity. The score of the matching based on string similarity is computed as the ratio of the number of tokens in common between an entity and the input tweet, and the total number of token of the entity: DISPLAYFORM0 ",
+      "In order to exclude trivial matches, tokens within a list of stop words are not considered while performing string matching. The final score is a weighted combination of the string matching score and the time proximity of the track, aimed to enhance matches from tracks played closer to the time when the user is posting the tweet.",
+      "The performance of the algorithm depends, apart from the time proximity threshold t, also on other two thresholds related to the string matching, one for the Musical Work (w) and one for the Contributor (c) entities. It has been necessary for avoiding to include candidate entities matched against the schedule with a low score, often source of false positives or negatives. Consequently, as last step Contributor and Musical Work candidates entities with respectively a string matching score lower than c and w, are filtered out. In Figure 2, an example of Musical Work entity recognized in an user-generated tweet using the schedule information is presented.",
+      "The entities recognized from the schedule matching are joined with the ones obtained directly from the statistical models. In the joined results, the criteria is to give priority to the entities recognized from the machine learning techniques. If they do not return any entities, the entities predicted by the schedule matching are considered. Our strategy is justified by the poorer results obtained by the NER based only on the schedule matching, compared to the other models used in the experiments, to be presented in the next section."
+    ],
+    [
+      "The performances of the NER experiments are reported separately for three different parts of the system proposed.",
+      "Table 6 presents the comparison of the various methods while performing NER on the bot-generated corpora and the user-generated corpora. Results shown that, in the first case, in the training set the F1 score is always greater than 97%, with a maximum of 99.65%. With both test sets performances decrease, varying between 94-97%. In the case of UGC, comparing the F1 score we can observe how performances significantly decrease. It can be considered a natural consequence of the complex nature of the users' informal language in comparison to the structured message created by the bot.",
+      "In Table 7, results of the schedule matching are reported. We can observe how the quality of the linking performed by the algorithm is correlated to the choice of the three thresholds. Indeed, the Precision score increase when the time threshold decrease, admitting less candidates as entities during the matching, and when the string similarity thresholds increase, accepting only candidates with an higher degree of similarity. The behaviour of the Recall score is inverted.",
+      "Finally, we test the impact of using the schedule matching together with a biLSTM-CRF network. In this experiment, we consider the network trained using all the features proposed, and the embeddings not pre-trained. Table 8 reports the results obtained. We can observe how generally the system benefits from the use of the schedule information. Especially in the testing part, where the neural network recognizes with less accuracy, the explicit information contained in the schedule can be exploited for identifying the entities at which users are referring while listening to the radio and posting the tweets."
+    ],
+    [
+      "We have presented in this work a novel method for detecting musical entities from user-generated content, modelling linguistic features with statistical models and extracting contextual information from a radio schedule. We analyzed tweets related to a classical music radio station, integrating its schedule to connect users' messages to tracks broadcasted. We focus on the recognition of two kinds of entities related to the music field, Contributor and Musical Work.",
+      "According to the results obtained, we have seen a pronounced difference between the system performances when dealing with the Contributor instead of the Musical Work entities. Indeed, the former type of entity has been shown to be more easily detected in comparison to the latter, and we identify several reasons behind this fact. Firstly, Contributor entities are less prone to be shorten or modified, while due to their longness, Musical Work entities often represent only a part of the complete title of a musical piece. Furthermore, Musical Work titles are typically composed by more tokens, including common words which can be easily misclassified. The low performances obtained in the case of Musical Work entities can be a consequences of these observations. On the other hand, when referring to a Contributor users often use only the surname, but in most of the cases it is enough for the system to recognizing the entities.",
+      "From the experiments we have seen that generally the biLSTM-CRF architecture outperforms the SVM model. The benefit of using the whole set of features is evident in the training part, but while testing the inclusion of the features not always leads to better results. In addition, some of the features designed in our experiments are tailored to the case of classical music, hence they might not be representative if applied to other fields. We do not exclude that our method can be adapted for detecting other kinds of entity, but it might be needed to redefine the features according to the case considered. Similarly, it has not been found a particular advantage of using the pre-trained embeddings instead of the one trained with our corpora. Furthermore, we verified the statistical significance of our experiment by using Wilcoxon Rank-Sum Test, obtaining that there have been not significant difference between the various model considered while testing.",
+      "The information extracted from the schedule also present several limitations. In fact, the hypothesis that a tweet is referring to a track broadcasted is not always verified. Even if it is common that radios listeners do comments about tracks played, or give suggestion to the radio host about what they would like to listen, it is also true that they might refer to a Contributor or Musical Work unrelated to the radio schedule."
+    ]
+  ]
+}
+```

qasper-0979/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Bias in Semantic and Discourse Interpretation
+
+Question: What factors contribute to interpretive biases according to this research?

qasper-0985/instruction.md

@@ -0,0 +1,120 @@
+Name of Paper: QuaRel: A Dataset and Models for Answering Questions about Qualitative Relationships
+
+Question: How do they obtain the logical forms of their questions in their dataset?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Knowledge Representation ",
+    "Qualitative Background Knowledge",
+    "Representing Questions",
+    "Logical Forms for Questions",
+    "Inference",
+    "The QuaRel Dataset ",
+    "Baseline Systems ",
+    "Baseline Experiments",
+    "New Models ",
+    "QuaSP+: A Model Incorporating World Tracking",
+    "QuaSP+Zero: A Model for the Zero-Shot Task",
+    "Summary and Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Many natural language tasks require recognizing and reasoning with qualitative relationships. For example, we may read about temperatures rising (climate science), a drug dose being increased (medicine), or the supply of goods being reduced (economics), and want to reason about the effects. Qualitative story problems, of the kind found in elementary exams (e.g., Figure FIGREF1 ), form a natural example of many of these linguistic and reasoning challenges, and is the target of this work.",
+      "Understanding and answering such questions is particularly challenging. Corpus-based methods perform poorly in this setting, as the questions ask about novel scenarios rather than facts that can be looked up. Similarly, word association methods struggle, as a single word change (e.g., \u201cmore\u201d to \u201cless\u201d) can flip the answer. Rather, the task appears to require knowledge of the underlying qualitative relations (e.g., \u201cmore friction implies less speed\u201d).",
+      "Qualitative modeling BIBREF0 , BIBREF1 , BIBREF2 provides a means for encoding and reasoning about such relationships. Relationships are expressed in a natural, qualitative way (e.g., if X increases, then so will Y), rather than requiring numeric equations, and inference allows complex questions to be answered. However, the semantic parsing task of mapping real world questions into these models is formidable and presents unique challenges. These challenges must be solved if natural questions involving qualitative relationships are to be reliably answered.",
+      "We make three contributions: (1) a simple and flexible conceptual framework for formally representing these kinds of questions, in particular ones that express qualitative comparisons between two scenarios; (2) a challenging new dataset (QuaRel), including logical forms, exemplifying the parsing challenges; and (3) two novel models that extend type-constrained semantic parsing to address these challenges.",
+      "Our first model, QuaSP+, addresses the problem of tracking different \u201cworlds\u201d in questions, resulting in significantly higher scores than with off-the-shelf tools (Section SECREF36 ). The second model, QuaSP+Zero, demonstrates zero-shot capability, i.e., the ability to handle new qualitative relationships on unseen properties, without requiring additional training data, something not possible with previous models (Section SECREF44 ). Together these contributions make inroads into answering complex, qualitative questions by linking language and reasoning, and offer a new dataset and models to spur further progress by the community."
+    ],
+    [
+      "There has been rapid progress in question-answering (QA), spanning a wide variety of tasks and phenomena, including factoid QA BIBREF3 , entailment BIBREF4 , sentiment BIBREF5 , and ellipsis and coreference BIBREF6 . Our contribution here is the first dataset specifically targeted at qualitative relationships, an important category of language that has been less explored. While questions requiring reasoning about qualitative relations sometimes appear in other datasets, e.g., BIBREF7 , our dataset specifically focuses on them so their challenges can be studied.",
+      "For answering such questions, we treat the problem as mapping language to a structured formalism (semantic parsing) where simple qualitative reasoning can occur. Semantic parsing has a long history BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , using datasets about geography BIBREF8 , travel booking BIBREF12 , factoid QA over knowledge bases BIBREF10 , Wikipedia tables BIBREF13 , and many more. Our contributions to this line of research are: a dataset that features phenomena under-represented in prior datasets, namely (1) highly diverse language describing open-domain qualitative problems, and (2) the need to reason over entities that have no explicit formal representation; and methods for adapting existing semantic parsers to address these phenomena.",
+      "For the target formalism itself, we draw on the extensive body of work on qualitative reasoning BIBREF0 , BIBREF1 , BIBREF2 to create a logical form language that can express the required qualitative knowledge, yet is sufficiently constrained that parsing into it is feasible, described in more detail in Section SECREF3 .",
+      "There has been some work connecting language with qualitative reasoning, although mainly focused on extracting qualitative models themselves from text rather than question interpretation, e.g., BIBREF14 , BIBREF15 . Recent work by BIBREF16 crouse2018learning also includes interpreting questions that require identifying qualitative processes in text, in constrast to our setting of interpreting NL story questions that involve qualitative comparisons.",
+      "Answering story problems has received attention in the domain of arithmetic, where simple algebra story questions (e.g., \u201cSue had 5 cookies, then gave 2 to Joe...\u201d) are mapped to a system of equations, e.g., BIBREF17 , BIBREF18 , BIBREF19 , BIBREF20 . This task is loosely analogous to ours (we instead map to qualitative relations) except that in arithmetic the entities to relate are often identifiable (namely, the numbers). Our qualitative story questions lack this structure, adding an extra challenge.",
+      "The QuaRel dataset shares some structure with the Winograd Schema Challenge BIBREF21 , being 2-way multiple choice questions invoking both commonsense and coreference. However, they test different aspects of commonsense: Winograd uses coreference resolution to test commonsense understanding of scenarios, while QuaRel tests reasoning about qualitative relationships requiring tracking of coreferent \u201cworlds.\u201d",
+      "Finally, crowdsourcing datasets has become a driving force in AI, producing significant progress, e.g., BIBREF3 , BIBREF22 , BIBREF23 . However, for semantic parsing tasks, one obstacle has been the difficulty in crowdsourcing target logical forms for questions. Here, we show how those logical forms can be obtained indirectly from workers without training the workers in the formalism, loosely similar to BIBREF24 ."
+    ],
+    [
+      "We first describe our framework for representing questions and the knowledge to answer them. Our dataset, described later, includes logical forms expressed in this language."
+    ],
+    [
+      "We use a simple representation of qualitative relationships, leveraging prior work in qualitative reasoning BIBREF0 . Let INLINEFORM0 be the set of properties relevant to the question set's domain (e.g., smoothness, friction, speed). Let INLINEFORM1 be a set of qualitative values for property INLINEFORM2 (e.g., fast, slow). For the background knowledge about the domain itself (a qualitative model), following BIBREF0 Forbus1984QualitativePT, we use the following predicates: [vskip=1mm,leftmargin=5mm] q+(property1, property2)",
+      "q-(property1, property2) q+ denotes that property1 and property2 are qualitatively proportional, e.g., if property1 goes up, property2 will too, while q- denotes inverse proportionality, e.g., [vskip=1mm,leftmargin=5mm] # If friction goes up, speed goes down.",
+      "q-(friction, speed). We also introduce the predicate: [vskip=1mm,leftmargin=5mm] higher-than( INLINEFORM0 , INLINEFORM1 , property INLINEFORM2 ) where INLINEFORM3 , allowing an ordering of property values to be specified, e.g., higher-than(fast, slow, speed). For our purposes here, we simplify to use just two property values, low and high, for all properties. (The parser learns mappings from words to these values, described later).",
+      "Given these primitives, compact theories can be authored for a particular domain by choosing relevant properties INLINEFORM0 , and specifying qualitative relationships (q+,q-) and ordinal values (higher-than) for them. For example, a simple theory about friction is sketched graphically in Figure FIGREF3 . Our observation is that these theories are relatively small, simple, and easy to author. Rather, the primary challenge is in mapping the complex and varied language of questions into a form that interfaces with this representation.",
+      "This language can be extended to include additional primitives from qualitative modeling, e.g., i+(x,y) (\u201cthe rate of change of x is qualitatively proportional to y\u201d). That is, the techniques we present are not specific to our particular qualitative modeling subset. The only requirement is that, given a set of absolute values or qualitative relationships from a question, the theory can compute an answer."
+    ],
+    [
+      "A key feature of our representation is the conceptualization of questions as describing events happening in two worlds, world1 and world2, that are being compared. That comparison may be between two different entities, or the same entity at different time points. E.g., in Figure FIGREF1 the two worlds being compared are the car on wood, and the car on carpet. The tags world1 and world2 denote these different situations, and semantic parsing (Section SECREF5 ) requires learning to correctly associate these tags with parts of the question describing those situations. This abstracts away irrelevant details of the worlds, while still keeping track of which world is which.",
+      "We define the following two predicates to express qualitative information in questions: [vskip=1mm,leftmargin=5mm] qrel(property, direction, world)",
+      "qval(property, value, world) where property ( INLINEFORM0 ) INLINEFORM1 P, value INLINEFORM2 INLINEFORM3 , direction INLINEFORM4 {higher, lower}, and world INLINEFORM5 {world1, world2}. qrel() denotes the relative assertion that property is higher/lower in world compared with the other world, which is left implicit, e.g., from Figure FIGREF1 : [vskip=1mm,leftmargin=5mm] # The car rolls further on wood.",
+      "qrel(distance, higher, world1) where world1 is a tag for the \u201ccar on wood\u201d situation (hence world2 becomes a tag for the opposite \u201ccar on carpet\u201d situation). qval() denotes that property has an absolute value in world, e.g., [vskip=1mm,leftmargin=5mm] # The car's speed is slow on carpet.",
+      "qval(speed, low, world2)"
+    ],
+    [
+      "Despite the wide variation in language, the space of logical forms (LFs) for the questions that we consider is relatively compact. In each question, the question body establishes a scenario and each answer option then probes an implication. We thus express a question's LF as a tuple: [vskip=1mm,leftmargin=5mm] (setup, answer-A, answer-B) where setup is the predicate(s) describing the scenario, and answer-* are the predicate(s) being queried for. If answer-A follows from setup, as inferred by the reasoner, then the answer is (A); similarly for (B). For readability we will write this as [vskip=1mm,leftmargin=5mm] setup INLINEFORM0 answer-A ; answer-B We consider two styles of LF, covering a large range of questions. The first is: [vskip=1mm,leftmargin=5mm] (1) qrel( INLINEFORM1 ) INLINEFORM2 ",
+      " qrel( INLINEFORM0 ) ; qrel( INLINEFORM1 ) which deals with relative values of properties between worlds, and applies when the question setup includes a comparative. An example of this is in Figure FIGREF1 . The second is: [vskip=1mm,leftmargin=5mm] (2) qval( INLINEFORM2 ), qval( INLINEFORM3 ) INLINEFORM4 ",
+      " qrel( INLINEFORM0 ) ; qrel( INLINEFORM1 ) which deals with absolute values of properties, and applies when the setup uses absolute terms instead of comparatives. An example is the first question in Figure FIGREF4 , shown simplified below, whose LF looks as follows (colors showing approximate correspondences): [vskip=1mm,leftmargin=5mm] # Does a bar stool orangeslide faster along the redbar surface with tealdecorative raised bumps or the magentasmooth wooden bluefloor? (A) redbar (B) bluefloor",
+      "",
+      "qval(tealsmoothness, low, redworld1),",
+      "qval(magentasmoothness, high, blueworld2) INLINEFORM0 ",
+      " qrel(orangespeed, higher, redworld1) ;",
+      " qrel(orangespeed, higher, blueworld2)"
+    ],
+    [
+      "A small set of rules for qualitative reasoning connects these predicates together. For example, (in logic) if the value of P is higher in world1 than the value of P in world2 and q+(P,Q) then the value of Q will be higher in world1 than the value of Q in world2. Given a question's logical form, a qualitative model, and these rules, a Prolog-style inference engine determines which answer option follows from the premise."
+    ],
+    [
+      "QuaRel is a crowdsourced dataset of 2771 multiple-choice story questions, including their logical forms. The size of the dataset is similar to several other datasets with annotated logical forms used for semantic parsing BIBREF8 , BIBREF25 , BIBREF24 . As the space of LFs is constrained, the dataset is sufficient for a rich exploration of this space.",
+      "We crowdsourced multiple-choice questions in two parts, encouraging workers to be imaginative and varied in their use of language. First, workers were given a seed qualitative relation q+/-( INLINEFORM0 ) in the domain, expressed in English (e.g., \u201cIf a surface has more friction, then an object will travel slower\u201d), and asked to enter two objects, people, or situations to compare. They then created a question, guided by a large number of examples, and were encouraged to be imaginative and use their own words. The results are a remarkable variety of situations and phrasings (Figure FIGREF4 ).",
+      "Second, the LFs were elicited using a novel technique of reverse-engineering them from a set of follow-up questions, without exposing workers to the underlying formalism. This is possible because of the constrained space of LFs. Referring to LF templates (1) and (2) earlier (Section SECREF13 ), these questions are as follows:",
+      "From this information, we can deduce the target LF ( INLINEFORM0 is the complement of INLINEFORM1 , INLINEFORM2 , we arbitrarily set INLINEFORM3 =world1, hence all other variables can be inferred). Three independent workers answer these follow-up questions to ensure reliable results.",
+      "We also had a human answer the questions in the dev partition (in principle, they should all be answerable). The human scored 96.4%, the few failures caused by occasional annotation errors or ambiguities in the question set itself, suggesting high fidelity of the content.",
+      "About half of the dataset are questions about friction, relating five different properties (friction, heat, distance, speed, smoothness). These questions form a meaningful, connected subset of the dataset which we denote QuaRel INLINEFORM0 . The remaining questions involve a wide variety of 14 additional properties and their relations, such as \u201cexercise intensity vs. sweat\u201d or \u201cdistance vs. brightness\u201d.",
+      "Figure FIGREF4 shows typical examples of questions in QuaRel, and Table TABREF26 provides summary statistics. In particular, the vocabulary is highly varied (5226 unique words), given the dataset size. Figure FIGREF27 shows some examples of the varied phrases used to describe smoothness."
+    ],
+    [
+      "We use four systems to evaluate the difficulty of this dataset. (We subsequently present two new models, extending the baseline neural semantic parser, in Sections SECREF36 and SECREF44 ). The first two are an information retrieval system and a word-association method, following the designs of BIBREF26 Clark2016CombiningRS. These are naive baselines that do not parse the question, but nevertheless may find some signal in a large corpus of text that helps guess the correct answer. The third is a CCG-style rule-based semantic parser written specifically for friction questions (the QuaRel INLINEFORM0 subset), but prior to data being collected. The last is a state-of-the-art neural semantic parser. We briefly describe each in turn."
+    ],
+    [
+      "We ran the above systems on the QuaRel dataset. QuaSP was trained on the training set, using the model with highest parse accuracy on the dev set (similarly BiLSTM used highest answer accuracy on the dev set) . The results are shown in Table TABREF34 . The 95% confidence interval is +/- 4% on the full test set. The human score is the sanity check on the dev set (Section SECREF4 ).",
+      "As Table TABREF34 shows, the QuaSP model performs better than other baseline approaches which are only slightly above random. QuaSP scores 56.1% (61.7% on the friction subset), indicating the challenges of this dataset.",
+      "For the rule-based system, we observe that it is unable to parse the majority (66%) of questions (hence scoring 0.5 for those questions, reflecting a random guess), due to the varied and unexpected vocabulary present in the dataset. For example, Figure FIGREF27 shows some of the ways that the notion of \u201csmoother/rougher\u201d is expressed in questions, many of which are not covered by the hand-written CCG grammar. This reflects the typical brittleness of hand-built systems.",
+      "For QuaSP, we also analyzed the parse accuracies, shown in Table TABREF35 , the score reflecting the percentage of times it produced exactly the right logical form. The random baseline for parse accuracy is near zero given the large space of logical forms, while the model parse accuracies are relatively high, much better than a random baseline.",
+      "Further analysis of the predicted LFs indicates that the neural model does well at predicting the properties ( INLINEFORM0 25% of errors on dev set), but struggles to predict the worlds in the LFs reliably ( INLINEFORM1 70% of errors on dev set). This helps explain why non-trivial parse accuracy does not necessarily translate into correspondingly higher answer accuracy: If only the world assignment is wrong, the answer will flip and give a score of zero, rather than the average 0.5."
+    ],
+    [
+      "We now present two new models, both extensions of the neural baseline QuaSP. The first, QuaSP+, addresses the leading cause of failure just described, namely the problem of identifying the two worlds being compared, and significantly outperforms all the baseline systems. The second, QuaSP+Zero, addresses the scaling problem, namely the costly requirement of needing many training examples each time a new qualitative property is introduced. It does this by instead using only a small amount of lexical information about the new property, thus achieving \u201czero shot\u201d performance, i.e., handling properties unseen in the training examples BIBREF34 , a capability not present in the baseline systems. We present the models and results for each."
+    ],
+    [
+      "We define the world tracking problem as identifying and tracking references to different \u201cworlds\u201d being compared in text, i.e., correctly mapping phrases to world identifiers, a critical aspect of the semantic parsing task. There are three reasons why this is challenging. First, unlike properties, the worlds being compared in questions are distinct in almost every question, and thus there is no obvious, learnable mapping from phrases to worlds. For example, while a property (like speed) has learnable ways to refer to it (\u201cfaster\u201d, \u201cmoves rapidly\u201d, \u201cspeeds\u201d, \u201cbarely moves\u201d), worlds are different in each question (e.g., \u201con a road\u201d, \u201ccountertop\u201d, \u201cwhile cutting grass\u201d) and thus learning to identify them is hard. Second, different phrases may be used to refer to the same world in the same question (see Figure FIGREF43 ), further complicating the task. Finally, even if the model could learn to identify worlds in other ways, e.g., by syntactic position in the question, there is the problem of selecting world1 or world2 consistently throughout the parse, so that the equivalent phrasings are assigned the same world.",
+      "This problem of mapping phrases to world identifiers is similar to the task of entity linking BIBREF35 . In prior semantic parsing work, entity linking is relatively straightforward: simple string-matching heuristics are often sufficient BIBREF36 , BIBREF37 , or an external entity linking system can be used BIBREF38 , BIBREF39 . In QuaRel, however, because the phrases denoting world1 and world2 are different in almost every question, and the word \u201cworld\u201d is never used, such methods cannot be applied.",
+      "To address this, we have developed QuaSP+, a new model that extends QuaSP by adding an extra initial step to identify and delexicalize world references in the question. In this delexicalization process, potentially new linguistic descriptions of worlds are replaced by canonical tokens, creating the opportunity for the model to generalize across questions. For example, the world mentions in the question: [vskip=1mm,leftmargin=5mm] \u201cA ball rolls further on wood than carpet because the (A) carpet is smoother (B) wood is smoother\u201d are delexicalized to: [vskip=1mm,leftmargin=5mm] \u201cA ball rolls further on World1 than World2 because the (A) World2 is smoother (B) World1 is smoother\u201d This approach is analogous to BIBREF40 Herzig2018DecouplingSA, who delexicalized words to POS tags to avoid memorization. Similar delexicalized features have also been employed in Open Information Extraction BIBREF41 , so the Open IE system could learn a general model of how relations are expressed. In our case, however, delexicalizing to World1 and World2 is itself a significant challenge, because identifying phrases referring to worlds is substantially more complex than (say) identifying parts of speech.",
+      "To perform this delexicalization step, we use the world annotations included as part of the training dataset (Section SECREF4 ) to train a separate tagger to identify \u201cworld mentions\u201d (text spans) in the question using BIO tags (BiLSTM encoder followed by a CRF). The spans are then sorted into World1 and World2 using the following algorithm:",
+      "If one span is a substring of another, they are are grouped together. Remaining spans are singleton groups.",
+      "The two groups containing the longest spans are labeled as the two worlds being compared.",
+      "Any additional spans are assigned to one of these two groups based on closest edit distance (or ignored if zero overlap).",
+      "The group appearing first in the question is labeled World1, the other World2.",
+      "The result is a question in which world mentions are canonicalized. The semantic parser QuaSP is then trained using these questions. We call the combined system (delexicalization plus semantic parser) QuaSP+.",
+      "The results for QuaSP+ are included in Table TABREF34 . Most importantly, QuaSP+ significantly outperforms the baselines by over 12% absolute. Similarly, the parse accuracies are significantly improved from 32.2% to 43.8% (Table TABREF35 ). This suggests that this delexicalization technique is an effective way of making progress on this dataset, and more generally on problems where multiple situations are being compared, a common characteristic of qualitative problems."
+    ],
+    [
+      "While our delexicalization procedure demonstrates a way of addressing the world tracking problem, the approach still relies on annotated data; if we were to add new qualitative relations, new training data would be needed, which is a significant scalability obstacle. To address this, we define the zero-shot problem as being able to answer questions involving a new predicate p given training data only about other predicates P different from p. For example, if we add a new property (e.g., heat) to the qualitative model (e.g., adding q+(friction, heat); \u201cmore friction implies more heat\u201d), we want to answer questions involving heat without creating new annotated training questions, and instead only use minimal extra information about the new property. A parser that achieved good zero-shot performance, i.e., worked well for new properties unseen at training time, would be a substantial advance, allowing a new qualitative model to link to questions with minimal effort.",
+      "QuaRel provides an environment in which methods for this zero-shot theory extension can be devised and evaluated. To do this, we consider the following experimental setting: All questions mentioning a particular property are removed, the parser is trained on the remainder, and then tested on those withheld questions, i.e., questions mentioning a property unseen in the training data.",
+      "We present and evaluate a model that we have developed for this, called QuaSP+Zero, that modifies the QuaSP+ parser as follows: During decoding, at points where the parser is selecting which property to include in the LF (e.g., Figure FIGREF31 ), it does not just consider the question tokens, but also the relationship between those tokens and the properties INLINEFORM0 used in the qualitative model. For example, a question token such as \u201clonger\u201d can act as a cue for (the property) length, even if unseen in the training data, because \u201clonger\u201d and a lexical form of length (e.g.,\u201clength\u201d) are similar. This approach follows the entity-linking approach used by BIBREF11 Krishnamurthy2017NeuralSP, where the similarity between question tokens and (words associated with) entities - called the entity linking score - help decide which entities to include in the LF during parsing. Here, we modify their entity linking score INLINEFORM1 , linking question tokens INLINEFORM2 and property \u201centities\u201d INLINEFORM3 , to be: INLINEFORM4 ",
+      "where INLINEFORM0 is a diagonal matrix connecting the embedding of the question token INLINEFORM1 and words INLINEFORM2 associated with the property INLINEFORM3 . For INLINEFORM4 , we provide a small list of words for each property (such as \u201cspeed\u201d, \u201cvelocity\u201d, and \u201cfast\u201d for the speed property), a small-cost requirement.",
+      "The results with QuaSP+Zero are in Table TABREF45 , shown in detail on the QuaRel INLINEFORM0 subset and (due to space constraints) summarized for the full QuaRel. We can measure overall performance of QuaSP+Zero by averaging each of the zero-shot test sets (weighted by the number of questions in each set), resulting in an overall parse accuracy of 38.9% and answer accuracy 61.0% on QuaRel INLINEFORM1 , and 25.7% (parse) and 59.5% (answer) on QuaRel, both significantly better than random. These initial results are encouraging, suggesting that it may be possible to parse into modified qualitative models that include new relations, with minimal annotation effort, significantly opening up qualitative reasoning methods for QA."
+    ],
+    [
+      "Our goal is to answer questions that involve qualitative relationships, an important genre of task that involves both language and knowledge, but also one that presents significant challenges for semantic parsing. To this end we have developed a simple and flexible formalism for representing these questions; constructed QuaRel, the first dataset of qualitative story questions that exemplifies these challenges; and presented two new models that adapt existing parsing techniques to this task. The first model, QuaSP+, illustrates how delexicalization can help with world tracking (identifying different \u201cworlds\u201d in questions), resulting in state-of-the-art performance on QuaRel. The second model, QuaSP+Zero, illustrates how zero-shot learning can be achieved (i.e., adding new qualitative relationships without requiring new training examples) by using an entity-linking approach applied to properties - a capability not present in previous models.",
+      "There are several directions in which this work can be expanded. First, quantitative property values (e.g., \u201c10 mph\u201d) are currently not handled well, as their mapping to \u201clow\u201d or \u201chigh\u201d is context-dependent. Second, some questions do not fit our two question templates (Section SECREF13 ), e.g., where two property values are a single answer option (e.g., \u201c....(A) one floor is smooth and the other floor is rough\u201d). Finally, some questions include an additional level of indirection, requiring an inference step to map to qualitative relations. For example, \u201cWhich surface would be best for a race? (A) gravel (B) blacktop\u201d requires the additional commonsense inference that \u201cbest for a race\u201d implies \u201chigher speed\u201d.",
+      "Given the ubiquity of qualitative comparisons in natural text, recognizing and reasoning with qualitative relationships is likely to remain an important task for AI. This work makes inroads into this task, and contributes a dataset and models to encourage progress by others. The dataset and models are publicly available at http://data.allenai.org/quarel."
+    ]
+  ]
+}
+```

qasper-1000/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Semantic Document Distance Measures and Unsupervised Document Revision Detection
+
+Question: Which are the state-of-the-art models?

qasper-1007/instruction.md

@@ -0,0 +1,86 @@
+Name of Paper: Gated Convolutional Neural Networks for Domain Adaptation
+
+Question: For the purposes of this paper, how is something determined to be domain specific knowledge?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Gated Convolutional Neural Networks",
+    "Problem Definition",
+    "Model Architecture",
+    "Gating mechanisms",
+    "Datasets",
+    "Baselines",
+    "Implementation details",
+    "Results",
+    "Discussion",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "With the advancement in technology and invention of modern web applications like Facebook and Twitter, users started expressing their opinions and ideologies at a scale unseen before. The growth of e-commerce companies like Amazon, Walmart have created a revolutionary impact in the field of consumer business. People buy products online through these companies and write reviews for their products. These consumer reviews act as a bridge between consumers and companies. Through these reviews, companies polish the quality of their services. Sentiment Classification (SC) is one of the major applications of Natural Language Processing (NLP) which aims to find the polarity of text. In the early stages BIBREF0 of text classification, sentiment classification was performed using traditional feature selection techniques like Bag-of-Words (BoW) BIBREF1 or TF-IDF. These features were further used to train machine learning classifiers like Naive Bayes (NB) BIBREF2 and Support Vector Machines (SVM) BIBREF3 . They are shown to act as strong baselines for text classification BIBREF4 . However, these models ignore word level semantic knowledge and sequential nature of text. Neural networks were proposed to learn distributed representations of words BIBREF5 . Skip-gram and CBOW architectures BIBREF6 were introduced to learn high quality word representations which constituted a major breakthrough in NLP. Several neural network architectures like recursive neural networks BIBREF7 and convolutional neural networks BIBREF8 achieved excellent results in text classification. Recurrent neural networks which were proposed for dealing sequential inputs suffer from vanishing BIBREF9 and exploding gradient problems BIBREF10 . To overcome this problem, Long Short Term Memory (LSTM) was introduced BIBREF11 .",
+      "All these architectures have been successful in performing sentiment classification for a specific domain utilizing large amounts of labelled data. However, there exists insufficient labelled data for a target domain of interest. Therefore, Domain Adaptation (DA) exploits knowledge from a relevant domain with abundant labeled data to perform sentiment classification on an unseen target domain. However, expressions of sentiment vary in each domain. For example, in $\\textit {Books}$ domain, words $\\textit {thoughtful}$ and $\\textit {comprehensive}$ are used to express sentiment whereas $\\textit {cheap}$ and $\\textit {costly}$ are used in $\\textit {Electronics}$ domain. Hence, models should generalize well for all domains. Several methods have been introduced for performing Domain Adaptation. Blitzer BIBREF12 proposed Structural Correspondence Learning (SCL) which relies on pivot features between source and target domains. Pan BIBREF13 performed Domain Adaptation using Spectral Feature Alignment (SFA) that aligns features across different domains. Glorot BIBREF14 proposed Stacked Denoising Autoencoder (SDA) that learns generalized feature representations across domains. Zheng BIBREF15 proposed end-to-end adversarial network for Domain Adaptation. Qi BIBREF16 proposed a memory network for Domain Adaptation. Zheng BIBREF17 proposed a Hierarchical transfer network relying on attention for Domain Adaptation.",
+      "However, all the above architectures use a different sub-network altogether to incorporate domain agnostic knowledge and is combined with main network in the final layers. This makes these architectures computationally intensive. To address this issue, we propose a Gated Convolutional Neural Network (GCN) model that learns domain agnostic knowledge using gated mechanism BIBREF18 . Convolution layers learns the higher level representations for source domain and gated layer selects domain agnostic representations. Unlike other models, GCN doesn't rely on a special sub-network for learning domain agnostic representations. As, gated mechanism is applied on Convolution layers, GCN is computationally efficient."
+    ],
+    [
+      "Traditionally methods for tackling Domain Adaptation are lexicon based. Blitzer BIBREF19 used a pivot method to select features that occur frequently in both domains. It assumes that the selected pivot features can reliably represent the source domain. The pivots are selected using mutual information between selected features and the source domain labels. SFA BIBREF13 method argues that pivot features selected from source domain cannot attest a representation of target domain. Hence, SFA tries to exploit the relationship between domain-specific and domain independent words via simultaneously co-clustering them in a common latent space. SDA BIBREF14 performs Domain Adaptation by learning intermediate representations through auto-encoders. Yu BIBREF20 used two auxiliary tasks to help induce sentence embeddings that work well across different domains. These embeddings are trained using Convolutional Neural Networks (CNN).",
+      "Gated convolutional neural networks have achieved state-of-art results in language modelling BIBREF18 . Since then, they have been used in different areas of natural language processing (NLP) like sentence similarity BIBREF21 and aspect based sentiment analysis BIBREF22 ."
+    ],
+    [
+      "In this section, we introduce a model based on Gated Convolutional Neural Networks for Domain Adaptation. We present the problem definition of Domain Adaptation, followed by the architecture of the proposed model."
+    ],
+    [
+      "Given a source domain $D_{S}$ represented as $D_{S}$ = { $(x_{s_{1}},y_{s_{1}})$ , $(x_{s_{2}},y_{s_{2}})$ .... $(x_{s_{n}},y_{s_{n}})$ } where $x_{s_{i}} \\in \\mathbb {R}$ represents the vector of $i^{th}$ source text and $y_{s_{i}}$ represents the corresponding source domain label. Let $T_{S}$ represent the task in source domain. Given a target domain $D_{T}$ represented as $D_{S}$0 = { $D_{S}$1 , $D_{S}$2 .... $D_{S}$3 }, where $D_{S}$4 represents the vector of $D_{S}$5 target text and $D_{S}$6 represents corresponding target domain label. Let $D_{S}$7 represent the task in target domain. Domain Adaptation (DA) is defined by the target predictive function $D_{S}$8 calculated using the knowledge of $D_{S}$9 and $(x_{s_{1}},y_{s_{1}})$0 where $(x_{s_{1}},y_{s_{1}})$1 but $(x_{s_{1}},y_{s_{1}})$2 . It is imperative to note that the domains are different but only a single task. In this paper, the task is sentiment classification."
+    ],
+    [
+      "The proposed model architecture is shown in the Figure 1 . Recurrent Neural Networks like LSTM, GRU update their weights at every timestep sequentially and hence lack parallelization over inputs in training. In case of attention based models, the attention layer has to wait for outputs from all timesteps. Hence, these models fail to take the advantage of parallelism either. Since, proposed model is based on convolution layers and gated mechanism, it can be parallelized efficiently. The convolution layers learn higher level representations for the source domain. The gated mechanism learn the domain agnostic representations. They together control the information that has to flow through further fully connected output layer after max pooling.",
+      "Let $I$ denote the input sentence represented as $I$ = { $w_{1}$ $w_{2}$ $w_{3}$ ... $w_{N}$ } where $w_{i}$ represents the $i_{th}$ word in $I$ and $N$ is the maximum sentence length considered. Let $I$0 be the vocabulary size for each dataset and $I$1 denote the word embedding matrix where each $I$2 is a $I$3 dimensional vector. Input sentences whose length is less than $I$4 are padded with 0s to reach maximum sentence length. Words absent in the pretrained word embeddings are initialized to 0s. Therefore each input sentence $I$5 is converted to $I$6 dimensional vector. Convolution operation is applied on $I$7 with kernel $I$8 . The convolution operation is one-dimensional, applied with a fixed window size across words. We consider kernel size of 3,4 and 5. The weight initialization of these kernels is done using glorot uniform BIBREF23 . Each kernel is a feature detector which extracts patterns from n-grams. After convolution we obtain a new feature map $I$9 = [ $w_{1}$0 ] for each kernel $w_{1}$1 . ",
+      "$$C_{i} = f(P_{i:i+h} \\ast W_{a} + b_{a})$$   (Eq. 5) ",
+      "where $f$ represents the activation function in convolution layer. The gated mechanism is applied on each convolution layer. Each gated layer learns to filter domain agnostic representations for every time step $i$ . ",
+      "$$S_{i} = g(P_{i:i+h} \\ast W_{s} + b_{s})$$   (Eq. 6) ",
+      "where $g$ is the activation function used in gated convolution layer. The outputs from convolution layer and gated convolution layer are element wise multiplied to compute a new feature representation $G_{i}$ ",
+      "$$G_{i} = C_{i} \\times S_{i}$$   (Eq. 7) ",
+      "Maxpooling operation is applied across each filter in this new feature representation to get the most important features BIBREF8 . As shown in Figure 1 the outputs from maxpooling layer across all filters are concatenated. The concatenated layer is fully connected to output layer. Sigmoid is used as the activation function in the output layer."
+    ],
+    [
+      "Gating mechanisms have been effective in Recurrent Neural Networks like GRU and LSTM. They control the information flow through their recurrent cells. In case of GCN, these gated units control the domain information that flows to pooling layers. The model must be robust to change in domain knowledge and should be able to generalize well across different domains. We use the gated mechanisms Gated Tanh Unit (GTU) and Gated Linear Unit (GLU) and Gated Tanh ReLU Unit (GTRU) BIBREF22 in proposed model. The gated architectures are shown in figure 2 . The outputs from Gated Tanh Unit is calculated as $tanh(P \\ast W + c) \\times \\sigma (P \\ast V + c)$ . In case of Gated Linear Unit, it is calculated as $(P \\ast W + c) \\times \\sigma (P \\ast V + c)$ where $tanh$ and $\\sigma $ denotes Tanh and Sigmoid activation functions respectively. In case of Gated Tanh ReLU Unit, output is calculated as $tanh(P \\ast W + c) \\times relu(P \\ast V + c)$ "
+    ],
+    [
+      "Multi Domain Dataset BIBREF19 is a short dataset with reviews from distinct domains namely Books(B), DVD(D), Electronics(E) and Kitchen(K). Each domain consists of 2000 reviews equally divided among positive and negative sentiment. We consider 1280 reviews for training, 320 reviews for validation and 400 reviews for testing from each domain.",
+      "Amazon Reviews Dataset BIBREF24 is a large dataset with millions of reviews from different product categories. For our experiments, we consider a subset of 20000 reviews from the domains Cell Phones and Accessories(C), Clothing and Shoes(S), Home and Kitchen(H) and Tools and Home Improvement(T). Out of 20000 reviews, 10000 are positive and 10000 are negative. We use 12800 reviews for training, 3200 reviews for validation and 4000 reviews for testing from each domain."
+    ],
+    [
+      "To evaluate the performance of proposed model, we consider various baselines like traditional lexicon approaches, CNN models without gating mechanisms and LSTM models.",
+      "Bag-of-words (BoW) is one of the strongest baselines in text classification BIBREF4 . We consider all the words as features with a minimum frequency of 5. These features are trained using Logistic Regression (LR).",
+      "TF-IDF is a feature selection technique built upon Bag-of-Words. We consider all the words with a minimum frequency of 5. The features selected are trained using Logistic Regression (LR).",
+      "Paragraph2vec or doc2vec BIBREF25 is a strong and popularly used baseline for text classification. Paragraph2Vec represents each sentence or paragraph in the form of a distributed representation. We trained our own doc2vec model using DBOW model. The paragraph vectors obtained are trained using Feed Forward Neural Network (FNN).",
+      "To show the effectiveness of gated layer, we consider a CNN model which does not contain gated layers. Hence, we consider Static CNN model, a popular CNN architecture proposed in Kim BIBREF8 as a baseline.",
+      "Wang BIBREF26 proposed a combination of Convolutional and Recurrent Neural Network for sentiment Analysis of short texts. This model takes the advantages of features learned by CNN and long-distance dependencies learned by RNN. It achieved remarkable results on benchmark datasets. We report the results using code published by the authors.",
+      "We offer a comparison with LSTM model with a single hidden layer. This model is trained with equivalent experimental settings as proposed model.",
+      "In this baseline, attention mechanism BIBREF27 is applied on the top of LSTM outputs across different timesteps."
+    ],
+    [
+      "All the models are experimented with approximately matching number of parameters for a solid comparison using a Tesla K80 GPU.",
+      "Input Each word in the input sentence is converted to a 300 dimensional vector using GloVe pretrained vectors BIBREF28 . A maximum sentence length 100 is considered for all the datasets. Sentences with length less than 100 are padded with 0s.",
+      "Architecture details: The model is implemented using keras. We considered 100 convolution filters for each of the kernels of sizes 3,4 and 5. To get the same sentence length after convolution operation zero padding is done on the input.",
+      "Training Each sentence or paragraph is converted to lower case. Stopword removal is not done. A vocabulary size of 20000 is considered for all the datasets. We apply a dropout layer BIBREF29 with a probability of 0.5, on the embedding layer and probability 0.2, on the dense layer that connects the output layer. Adadelta BIBREF30 is used as the optimizer for training with gradient descent updates. Batch-size of 16 is taken for MDD and 50 for ARD. The model is trained for 50 epochs. We employ an early stopping mechanism based on validation loss for a patience of 10 epochs. The models are trained on source domain and tested on unseen target domain in all experiments."
+    ],
+    [
+      "The performance of all models on MDD is shown in Tables 2 and 3 while for ARD, in Tables 4 and 5 . All values are shown in accuracy percentage. Furthermore time complexity of each model is presented in Table 1 ."
+    ],
+    [
+      "We find that gated architectures vastly outperform non gated CNN model. The effectiveness of gated architectures rely on the idea of training a gate with sole purpose of identifying a weightage. In the task of sentiment analysis this weightage corresponds to what weights will lead to a decrement in final loss or in other words, most accurate prediction of sentiment. In doing so, the gate architecture learns which words or n-grams contribute to the sentiment the most, these words or n-grams often co-relate with domain independent words. On the other hand the gate gives less weightage to n-grams which are largely either specific to domain or function word chunks which contribute negligible to the overall sentiment. This is what makes gated architectures effective at Domain Adaptation.",
+      "In Figure 3 , we have illustrated the visualization of convolution outputs(kernel size = 3) from the sigmoid gate in GLU across domains. As the kernel size is 3, each row in the output corresponds to a trigram from input sentence. This heat map visualizes values of all 100 filters and their average for every input trigram. These examples demonstrate what the convolution gate learns. Trigrams with domain independent but heavy polarity like \u201c_ _ good\u201d and \u201c_ costly would\u201d have higher weightage. Meanwhile, Trigrams with domain specific terms like \u201cquality functional case\u201d and \u201csell entire kitchen\u201d get some of the least weights. In Figure 3 (b) example, the trigram \u201cwould have to\u201d just consists of function words, hence gets the least weight. While \u201csell entire kitchen\u201d gets more weight comparatively. This might be because while function words are merely grammatical units which contribute minimal to overall sentiment, domain specific terms like \u201csell\u201d may contain sentiment level knowledge only relevant within the domain. In such a case it is possible that the filters effectively propagate sentiment level knowledge from domain specific terms as well.",
+      "We see that gated architectures almost always outperform recurrent, attention and linear models BoW, TFIDF, PV. This is largely because while training and testing on same domains, these models especially recurrent and attention based may perform better. However, for Domain Adaptation, as they lack gated structure which is trained in parallel to learn importance, their performance on target domain is poor as compared to gated architectures. As gated architectures are based on convolutions, they exploit parallelization to give significant boost in time complexity as compared to other models. This is depicted in Table 1 .",
+      "While the gated architectures outperform other baselines, within them as well we make observations. Gated Linear Unit (GLU) performs the best often over other gated architectures. In case of GTU, outputs from Sigmoid and Tanh are multiplied together, this may result in small gradients, and hence resulting in the vanishing gradient problem. However, this will not be the in the case of GLU, as the activation is linear. In case of GTRU, outputs from Tanh and ReLU are multiplied. In ReLU, because of absence of negative activations, corresponding Tanh outputs will be completely ignored, resulting in loss of some domain independent knowledge."
+    ],
+    [
+      "In this paper, we proposed Gated Convolutional Neural Network(GCN) model for Domain Adaptation in Sentiment Analysis. We show that gates in GCN, filter out domain dependant knowledge, hence performing better at an unseen target domain. Our experiments reveal that gated architectures outperform other popular recurrent and non-gated architectures. Furthermore, because these architectures rely on convolutions, they take advantage of parellalization, vastly reducing time complexity."
+    ]
+  ]
+}
+```

qasper-1038/instruction.md

@@ -0,0 +1,69 @@
+Name of Paper: Contextualized Word Embeddings Enhanced Event Temporal Relation Extraction for Story Understanding
+
+Question: What conclusions do the authors draw from their detailed analyses?
+
+## 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-1054/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Kurdish (Sorani) Speech to Text: Presenting an Experimental Dataset
+
+Question: How long is the dataset?