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qasper-0002/instruction.md

@@ -0,0 +1,149 @@
+Name of Paper: Minimally Supervised Learning of Affective Events Using Discourse Relations
+
+Question: How are relations used to propagate polarity?
+
+## 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-0003/instruction.md

@@ -0,0 +1,149 @@
+Name of Paper: Minimally Supervised Learning of Affective Events Using Discourse Relations
+
+Question: How big is the Japanese data?
+
+## 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-0004/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Minimally Supervised Learning of Affective Events Using Discourse Relations
+
+Question: What are labels available in dataset for supervision?

qasper-0005/instruction.md

@@ -0,0 +1,149 @@
+Name of Paper: Minimally Supervised Learning of Affective Events Using Discourse Relations
+
+Question: How big are improvements of supervszed learning results trained on smalled labeled data enhanced with proposed approach copared to basic approach?
+
+## 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-0010/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry
+
+Question: How is the annotation experiment evaluated?

qasper-0011/instruction.md

@@ -0,0 +1,169 @@
+Name of Paper: PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry
+
+Question: What are the aesthetic emotions formalized?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "",
+    " ::: ",
+    " :::  ::: ",
+    "Introduction",
+    "Related Work ::: Poetry in Natural Language Processing",
+    "Related Work ::: Emotion Annotation",
+    "Related Work ::: Emotion Classification",
+    "Data Collection",
+    "Data Collection ::: German",
+    "Data Collection ::: English",
+    "Expert Annotation",
+    "Expert Annotation ::: Workflow",
+    "Expert Annotation ::: Emotion Labels",
+    "Expert Annotation ::: Agreement",
+    "Crowdsourcing Annotation",
+    "Crowdsourcing Annotation ::: Data and Setup",
+    "Crowdsourcing Annotation ::: Results",
+    "Crowdsourcing Annotation ::: Comparing Experts with Crowds",
+    "Modeling",
+    "Concluding Remarks",
+    "Acknowledgements",
+    "Appendix",
+    "Appendix ::: Friedrich H\u00f6lderlin: H\u00e4lfte des Lebens (1804)",
+    "Appendix ::: Georg Trakl: In den Nachmittag gefl\u00fcstert (1912)",
+    "Appendix ::: Walt Whitman: O Captain! My Captain! (1865)"
+  ],
+  "paragraphs": [
+    [
+      "1.1em"
+    ],
+    [
+      "1.1.1em"
+    ],
+    [
+      "1.1.1.1em",
+      "Thomas Haider$^{1,3}$, Steffen Eger$^2$, Evgeny Kim$^3$, Roman Klinger$^3$, Winfried Menninghaus$^1$",
+      "$^{1}$Department of Language and Literature, Max Planck Institute for Empirical Aesthetics",
+      "$^{2}$NLLG, Department of Computer Science, Technische Universitat Darmstadt",
+      "$^{3}$Institut f\u00fcr Maschinelle Sprachverarbeitung, University of Stuttgart",
+      "{thomas.haider, w.m}@ae.mpg.de, eger@aiphes.tu-darmstadt.de",
+      "{roman.klinger, evgeny.kim}@ims.uni-stuttgart.de",
+      "Most approaches to emotion analysis regarding social media, literature, news, and other domains focus exclusively on basic emotion categories as defined by Ekman or Plutchik. However, art (such as literature) enables engagement in a broader range of more complex and subtle emotions that have been shown to also include mixed emotional responses. We consider emotions as they are elicited in the reader, rather than what is expressed in the text or intended by the author. Thus, we conceptualize a set of aesthetic emotions that are predictive of aesthetic appreciation in the reader, and allow the annotation of multiple labels per line to capture mixed emotions within context. We evaluate this novel setting in an annotation experiment both with carefully trained experts and via crowdsourcing. Our annotation with experts leads to an acceptable agreement of $\\kappa =.70$, resulting in a consistent dataset for future large scale analysis. Finally, we conduct first emotion classification experiments based on BERT, showing that identifying aesthetic emotions is challenging in our data, with up to .52 F1-micro on the German subset. Data and resources are available at https://github.com/tnhaider/poetry-emotion.",
+      "Emotion, Aesthetic Emotions, Literature, Poetry, Annotation, Corpora, Emotion Recognition, Multi-Label"
+    ],
+    [
+      "Emotions are central to human experience, creativity and behavior. Models of affect and emotion, both in psychology and natural language processing, commonly operate on predefined categories, designated either by continuous scales of, e.g., Valence, Arousal and Dominance BIBREF0 or discrete emotion labels (which can also vary in intensity). Discrete sets of emotions often have been motivated by theories of basic emotions, as proposed by Ekman1992\u2014Anger, Fear, Joy, Disgust, Surprise, Sadness\u2014and Plutchik1991, who added Trust and Anticipation. These categories are likely to have evolved as they motivate behavior that is directly relevant for survival. However, art reception typically presupposes a situation of safety and therefore offers special opportunities to engage in a broader range of more complex and subtle emotions. These differences between real-life and art contexts have not been considered in natural language processing work so far.",
+      "To emotionally move readers is considered a prime goal of literature since Latin antiquity BIBREF1, BIBREF2, BIBREF3. Deeply moved readers shed tears or get chills and goosebumps even in lab settings BIBREF4. In cases like these, the emotional response actually implies an aesthetic evaluation: narratives that have the capacity to move readers are evaluated as good and powerful texts for this very reason. Similarly, feelings of suspense experienced in narratives not only respond to the trajectory of the plot's content, but are also directly predictive of aesthetic liking (or disliking). Emotions that exhibit this dual capacity have been defined as \u201caesthetic emotions\u201d BIBREF2. Contrary to the negativity bias of classical emotion catalogues, emotion terms used for aesthetic evaluation purposes include far more positive than negative emotions. At the same time, many overall positive aesthetic emotions encompass negative or mixed emotional ingredients BIBREF2, e.g., feelings of suspense include both hopeful and fearful anticipations.",
+      "For these reasons, we argue that the analysis of literature (with a focus on poetry) should rely on specifically selected emotion items rather than on the narrow range of basic emotions only. Our selection is based on previous research on this issue in psychological studies on art reception and, specifically, on poetry. For instance, knoop2016mapping found that Beauty is a major factor in poetry reception.",
+      "We primarily adopt and adapt emotion terms that schindler2017measuring have identified as aesthetic emotions in their study on how to measure and categorize such particular affective states. Further, we consider the aspect that, when selecting specific emotion labels, the perspective of annotators plays a major role. Whether emotions are elicited in the reader, expressed in the text, or intended by the author largely changes the permissible labels. For example, feelings of Disgust or Love might be intended or expressed in the text, but the text might still fail to elicit corresponding feelings as these concepts presume a strong reaction in the reader. Our focus here was on the actual emotional experience of the readers rather than on hypothetical intentions of authors. We opted for this reader perspective based on previous research in NLP BIBREF5, BIBREF6 and work in empirical aesthetics BIBREF7, that specifically measured the reception of poetry. Our final set of emotion labels consists of Beauty/Joy, Sadness, Uneasiness, Vitality, Suspense, Awe/Sublime, Humor, Annoyance, and Nostalgia.",
+      "In addition to selecting an adapted set of emotions, the annotation of poetry brings further challenges, one of which is the choice of the appropriate unit of annotation. Previous work considers words BIBREF8, BIBREF9, sentences BIBREF10, BIBREF11, utterances BIBREF12, sentence triples BIBREF13, or paragraphs BIBREF14 as the units of annotation. For poetry, reasonable units follow the logical document structure of poems, i.e., verse (line), stanza, and, owing to its relative shortness, the complete text. The more coarse-grained the unit, the more difficult the annotation is likely to be, but the more it may also enable the annotation of emotions in context. We find that annotating fine-grained units (lines) that are hierarchically ordered within a larger context (stanza, poem) caters to the specific structure of poems, where emotions are regularly mixed and are more interpretable within the whole poem. Consequently, we allow the mixing of emotions already at line level through multi-label annotation.",
+      "The remainder of this paper includes (1) a report of the annotation process that takes these challenges into consideration, (2) a description of our annotated corpora, and (3) an implementation of baseline models for the novel task of aesthetic emotion annotation in poetry. In a first study, the annotators work on the annotations in a closely supervised fashion, carefully reading each verse, stanza, and poem. In a second study, the annotations are performed via crowdsourcing within relatively short time periods with annotators not seeing the entire poem while reading the stanza. Using these two settings, we aim at obtaining a better understanding of the advantages and disadvantages of an expert vs. crowdsourcing setting in this novel annotation task. Particularly, we are interested in estimating the potential of a crowdsourcing environment for the task of self-perceived emotion annotation in poetry, given time and cost overhead associated with in-house annotation process (that usually involve training and close supervision of the annotators).",
+      "We provide the final datasets of German and English language poems annotated with reader emotions on verse level at https://github.com/tnhaider/poetry-emotion."
+    ],
+    [
+      "Natural language understanding research on poetry has investigated stylistic variation BIBREF15, BIBREF16, BIBREF17, with a focus on broadly accepted formal features such as meter BIBREF18, BIBREF19, BIBREF20 and rhyme BIBREF21, BIBREF22, as well as enjambement BIBREF23, BIBREF24 and metaphor BIBREF25, BIBREF26. Recent work has also explored the relationship of poetry and prose, mainly on a syntactic level BIBREF27, BIBREF28. Furthermore, poetry also lends itself well to semantic (change) analysis BIBREF29, BIBREF30, as linguistic invention BIBREF31, BIBREF32 and succinctness BIBREF33 are at the core of poetic production.",
+      "Corpus-based analysis of emotions in poetry has been considered, but there is no work on German, and little on English. kao2015computational analyze English poems with word associations from the Harvard Inquirer and LIWC, within the categories positive/negative outlook, positive/negative emotion and phys./psych. well-being. hou-frank-2015-analyzing examine the binary sentiment polarity of Chinese poems with a weighted personalized PageRank algorithm. barros2013automatic followed a tagging approach with a thesaurus to annotate words that are similar to the words `Joy', `Anger', `Fear' and `Sadness' (moreover translating these from English to Spanish). With these word lists, they distinguish the categories `Love', `Songs to Lisi', `Satire' and `Philosophical-Moral-Religious' in Quevedo's poetry. Similarly, alsharif2013emotion classify unique Arabic `emotional text forms' based on word unigrams.",
+      "Mohanty2018 create a corpus of 788 poems in the Indian Odia language, annotate it on text (poem) level with binary negative and positive sentiment, and are able to distinguish these with moderate success. Sreeja2019 construct a corpus of 736 Indian language poems and annotate the texts on Ekman's six categories + Love + Courage. They achieve a Fleiss Kappa of .48.",
+      "In contrast to our work, these studies focus on basic emotions and binary sentiment polarity only, rather than addressing aesthetic emotions. Moreover, they annotate on the level of complete poems (instead of fine-grained verse and stanza-level)."
+    ],
+    [
+      "Emotion corpora have been created for different tasks and with different annotation strategies, with different units of analysis and different foci of emotion perspective (reader, writer, text). Examples include the ISEAR dataset BIBREF34 (document-level); emotion annotation in children stories BIBREF10 and news headlines BIBREF35 (sentence-level); and fine-grained emotion annotation in literature by Kim2018 (phrase- and word-level). We refer the interested reader to an overview paper on existing corpora BIBREF36.",
+      "We are only aware of a limited number of publications which look in more depth into the emotion perspective. buechel-hahn-2017-emobank report on an annotation study that focuses both on writer's and reader's emotions associated with English sentences. The results show that the reader perspective yields better inter-annotator agreement. Yang2009 also study the difference between writer and reader emotions, but not with a modeling perspective. The authors find that positive reader emotions tend to be linked to positive writer emotions in online blogs."
+    ],
+    [
+      "The task of emotion classification has been tackled before using rule-based and machine learning approaches. Rule-based emotion classification typically relies on lexical resources of emotionally charged words BIBREF9, BIBREF37, BIBREF8 and offers a straightforward and transparent way to detect emotions in text.",
+      "In contrast to rule-based approaches, current models for emotion classification are often based on neural networks and commonly use word embeddings as features. Schuff2017 applied models from the classes of CNN, BiLSTM, and LSTM and compare them to linear classifiers (SVM and MaxEnt), where the BiLSTM shows best results with the most balanced precision and recall. AbdulMageed2017 claim the highest F$_1$ with gated recurrent unit networks BIBREF38 for Plutchik's emotion model. More recently, shared tasks on emotion analysis BIBREF39, BIBREF40 triggered a set of more advanced deep learning approaches, including BERT BIBREF41 and other transfer learning methods BIBREF42."
+    ],
+    [
+      "For our annotation and modeling studies, we build on top of two poetry corpora (in English and German), which we refer to as PO-EMO. This collection represents important contributions to the literary canon over the last 400 years. We make this resource available in TEI P5 XML and an easy-to-use tab separated format. Table TABREF9 shows a size overview of these data sets. Figure FIGREF8 shows the distribution of our data over time via density plots. Note that both corpora show a relative underrepresentation before the onset of the romantic period (around 1750)."
+    ],
+    [
+      "The German corpus contains poems available from the website lyrik.antikoerperchen.de (ANTI-K), which provides a platform for students to upload essays about poems. The data is available in the Hypertext Markup Language, with clean line and stanza segmentation. ANTI-K also has extensive metadata, including author names, years of publication, numbers of sentences, poetic genres, and literary periods, that enable us to gauge the distribution of poems according to periods. The 158 poems we consider (731 stanzas) are dispersed over 51 authors and the New High German timeline (1575\u20131936 A.D.). This data has been annotated, besides emotions, for meter, rhythm, and rhyme in other studies BIBREF22, BIBREF43."
+    ],
+    [
+      "The English corpus contains 64 poems of popular English writers. It was partly collected from Project Gutenberg with the GutenTag tool, and, in addition, includes a number of hand selected poems from the modern period and represents a cross section of popular English poets. We took care to include a number of female authors, who would have been underrepresented in a uniform sample. Time stamps in the corpus are organized by the birth year of the author, as assigned in Project Gutenberg."
+    ],
+    [
+      "In the following, we will explain how we compiled and annotated three data subsets, namely, (1) 48 German poems with gold annotation. These were originally annotated by three annotators. The labels were then aggregated with majority voting and based on discussions among the annotators. Finally, they were curated to only include one gold annotation. (2) The remaining 110 German poems that are used to compute the agreement in table TABREF20 and (3) 64 English poems contain the raw annotation from two annotators.",
+      "We report the genesis of our annotation guidelines including the emotion classes. With the intention to provide a language resource for the computational analysis of emotion in poetry, we aimed at maximizing the consistency of our annotation, while doing justice to the diversity of poetry. We iteratively improved the guidelines and the annotation workflow by annotating in batches, cleaning the class set, and the compilation of a gold standard. The final overall cost of producing this expert annotated dataset amounts to approximately 3,500."
+    ],
+    [
+      "The annotation process was initially conducted by three female university students majoring in linguistics and/or literary studies, which we refer to as our \u201cexpert annotators\u201d. We used the INCePTION platform for annotation BIBREF44. Starting with the German poems, we annotated in batches of about 16 (and later in some cases 32) poems. After each batch, we computed agreement statistics including heatmaps, and provided this feedback to the annotators. For the first three batches, the three annotators produced a gold standard using a majority vote for each line. Where this was inconclusive, they developed an adjudicated annotation based on discussion. Where necessary, we encouraged the annotators to aim for more consistency, as most of the frequent switching of emotions within a stanza could not be reconstructed or justified.",
+      "In poems, emotions are regularly mixed (already on line level) and are more interpretable within the whole poem. We therefore annotate lines hierarchically within the larger context of stanzas and the whole poem. Hence, we instruct the annotators to read a complete stanza or full poem, and then annotate each line in the context of its stanza. To reflect on the emotional complexity of poetry, we allow a maximum of two labels per line while avoiding heavy label fluctuations by encouraging annotators to reflect on their feelings to avoid `empty' annotations. Rather, they were advised to use fewer labels and more consistent annotation. This additional constraint is necessary to avoid \u201cwild\u201d, non-reconstructable or non-justified annotations.",
+      "All subsequent batches (all except the first three) were only annotated by two out of the three initial annotators, coincidentally those two who had the lowest initial agreement with each other. We asked these two experts to use the generated gold standard (48 poems; majority votes of 3 annotators plus manual curation) as a reference (\u201cif in doubt, annotate according to the gold standard\u201d). This eliminated some systematic differences between them and markedly improved the agreement levels, roughly from 0.3\u20130.5 Cohen's $\\kappa $ in the first three batches to around 0.6\u20130.8 $\\kappa $ for all subsequent batches. This annotation procedure relaxes the reader perspective, as we encourage annotators (if in doubt) to annotate how they think the other annotators would annotate. However, we found that this formulation improves the usability of the data and leads to a more consistent annotation."
+    ],
+    [
+      "We opt for measuring the reader perspective rather than the text surface or author's intent. To closer define and support conceptualizing our labels, we use particular `items', as they are used in psychological self-evaluations. These items consist of adjectives, verbs or short phrases. We build on top of schindler2017measuring who proposed 43 items that were then grouped by a factor analysis based on self-evaluations of participants. The resulting factors are shown in Table TABREF17. We attempt to cover all identified factors and supplement with basic emotions BIBREF46, BIBREF47, where possible.",
+      "We started with a larger set of labels to then delete and substitute (tone down) labels during the initial annotation process to avoid infrequent classes and inconsistencies. Further, we conflate labels if they show considerable confusion with each other. These iterative improvements particularly affected Confusion, Boredom and Other that were very infrequently annotated and had little agreement among annotators ($\\kappa <.2$). For German, we also removed Nostalgia ($\\kappa =.218$) after gold standard creation, but after consideration, added it back for English, then achieving agreement. Nostalgia is still available in the gold standard (then with a second label Beauty/Joy or Sadness to keep consistency). However, Confusion, Boredom and Other are not available in any sub-corpus.",
+      "Our final set consists of nine classes, i.e., (in order of frequency) Beauty/Joy, Sadness, Uneasiness, Vitality, Suspense, Awe/Sublime, Humor, Annoyance, and Nostalgia. In the following, we describe the labels and give further details on the aggregation process.",
+      "Annoyance (annoys me/angers me/felt frustrated): Annoyance implies feeling annoyed, frustrated or even angry while reading the line/stanza. We include the class Anger here, as this was found to be too strong in intensity.",
+      "Awe/Sublime (found it overwhelming/sense of greatness): Awe/Sublime implies being overwhelmed by the line/stanza, i.e., if one gets the impression of facing something sublime or if the line/stanza inspires one with awe (or that the expression itself is sublime). Such emotions are often associated with subjects like god, death, life, truth, etc. The term Sublime originated with kant2000critique as one of the first aesthetic emotion terms. Awe is a more common English term.",
+      "Beauty/Joy (found it beautiful/pleasing/makes me happy/joyful): kant2000critique already spoke of a \u201cfeeling of beauty\u201d, and it should be noted that it is not a `merely pleasing emotion'. Therefore, in our pilot annotations, Beauty and Joy were separate labels. However, schindler2017measuring found that items for Beauty and Joy load into the same factors. Furthermore, our pilot annotations revealed, while Beauty is the more dominant and frequent feeling, both labels regularly accompany each other, and they often get confused across annotators. Therefore, we add Joy to form an inclusive label Beauty/Joy that increases annotation consistency.",
+      "Humor (found it funny/amusing): Implies feeling amused by the line/stanza or if it makes one laugh.",
+      "Nostalgia (makes me nostalgic): Nostalgia is defined as a sentimental longing for things, persons or situations in the past. It often carries both positive and negative feelings. However, since this label is quite infrequent, and not available in all subsets of the data, we annotated it with an additional Beauty/Joy or Sadness label to ensure annotation consistency.",
+      "Sadness (makes me sad/touches me): If the line/stanza makes one feel sad. It also includes a more general `being touched / moved'.",
+      "Suspense (found it gripping/sparked my interest): Choose Suspense if the line/stanza keeps one in suspense (if the line/stanza excites one or triggers one's curiosity). We further removed Anticipation from Suspense/Anticipation, as Anticipation appeared to us as being a more cognitive prediction whereas Suspense is a far more straightforward emotion item.",
+      "Uneasiness (found it ugly/unsettling/disturbing / frightening/distasteful): This label covers situations when one feels discomfort about the line/stanza (if the line/stanza feels distasteful/ugly, unsettling/disturbing or frightens one). The labels Ugliness and Disgust were conflated into Uneasiness, as both are seldom felt in poetry (being inadequate/too strong/high in arousal), and typically lead to Uneasiness.",
+      "Vitality (found it invigorating/spurs me on/inspires me): This label is meant for a line/stanza that has an inciting, encouraging effect (if the line/stanza conveys a feeling of movement, energy and vitality which animates to action). Similar terms are Activation and Stimulation."
+    ],
+    [
+      "Table TABREF20 shows the Cohen's $\\kappa $ agreement scores among our two expert annotators for each emotion category $e$ as follows. We assign each instance (a line in a poem) a binary label indicating whether or not the annotator has annotated the emotion category $e$ in question. From this, we obtain vectors $v_i^e$, for annotators $i=0,1$, where each entry of $v_i^e$ holds the binary value for the corresponding line. We then apply the $\\kappa $ statistics to the two binary vectors $v_i^e$. Additionally to averaged $\\kappa $, we report micro-F1 values in Table TABREF21 between the multi-label annotations of both expert annotators as well as the micro-F1 score of a random baseline as well as of the majority emotion baseline (which labels each line as Beauty/Joy).",
+      "We find that Cohen $\\kappa $ agreement ranges from .84 for Uneasiness in the English data, .81 for Humor and Nostalgia, down to German Suspense (.65), Awe/Sublime (.61) and Vitality for both languages (.50 English, .63 German). Both annotators have a similar emotion frequency profile, where the ranking is almost identical, especially for German. However, for English, Annotator 2 annotates more Vitality than Uneasiness. Figure FIGREF18 shows the confusion matrices of labels between annotators as heatmaps. Notably, Beauty/Joy and Sadness are confused across annotators more often than other labels. This is topical for poetry, and therefore not surprising: One might argue that the beauty of beings and situations is only beautiful because it is not enduring and therefore not to divorce from the sadness of the vanishing of beauty BIBREF48. We also find considerable confusion of Sadness with Awe/Sublime and Vitality, while the latter is also regularly confused with Beauty/Joy.",
+      "Furthermore, as shown in Figure FIGREF23, we find that no single poem aggregates to more than six emotion labels, while no stanza aggregates to more than four emotion labels. However, most lines and stanzas prefer one or two labels. German poems seem more emotionally diverse where more poems have three labels than two labels, while the majority of English poems have only two labels. This is however attributable to the generally shorter English texts."
+    ],
+    [
+      "After concluding the expert annotation, we performed a focused crowdsourcing experiment, based on the final label set and items as they are listed in Table TABREF27 and Section SECREF19. With this experiment, we aim to understand whether it is possible to collect reliable judgements for aesthetic perception of poetry from a crowdsourcing platform. A second goal is to see whether we can replicate the expensive expert annotations with less costly crowd annotations.",
+      "We opted for a maximally simple annotation environment, where we asked participants to annotate English 4-line stanzas with self-perceived reader emotions. We choose English due to the higher availability of English language annotators on crowdsourcing platforms. Each annotator rates each stanza independently of surrounding context."
+    ],
+    [
+      "For consistency and to simplify the task for the annotators, we opt for a trade-off between completeness and granularity of the annotation. Specifically, we subselect stanzas composed of four verses from the corpus of 64 hand selected English poems. The resulting selection of 59 stanzas is uploaded to Figure Eight for annotation.",
+      "The annotators are asked to answer the following questions for each instance.",
+      "Question 1 (single-choice): Read the following stanza and decide for yourself which emotions it evokes.",
+      "Question 2 (multiple-choice): Which additional emotions does the stanza evoke?",
+      "The answers to both questions correspond to the emotion labels we defined to use in our annotation, as described in Section SECREF19. We add an additional answer choice \u201cNone\u201d to Question 2 to allow annotators to say that a stanza does not evoke any additional emotions.",
+      "Each instance is annotated by ten people. We restrict the task geographically to the United Kingdom and Ireland and set the internal parameters on Figure Eight to only include the highest quality annotators to join the task. We pay 0.09 per instance. The final cost of the crowdsourcing experiment is 74."
+    ],
+    [
+      "In the following, we determine the best aggregation strategy regarding the 10 annotators with bootstrap resampling. For instance, one could assign the label of a specific emotion to an instance if just one annotators picks it, or one could assign the label only if all annotators agree on this emotion. To evaluate this, we repeatedly pick two sets of 5 annotators each out of the 10 annotators for each of the 59 stanzas, 1000 times overall (i.e., 1000$\\times $59 times, bootstrap resampling). For each of these repetitions, we compare the agreement of these two groups of 5 annotators. Each group gets assigned with an adjudicated emotion which is accepted if at least one annotator picks it, at least two annotators pick it, etc. up to all five pick it.",
+      "We show the results in Table TABREF27. The $\\kappa $ scores show the average agreement between the two groups of five annotators, when the adjudicated class is picked based on the particular threshold of annotators with the same label choice. We see that some emotions tend to have higher agreement scores than others, namely Annoyance (.66), Sadness (up to .52), and Awe/Sublime, Beauty/Joy, Humor (all .46). The maximum agreement is reached mostly with a threshold of 2 (4 times) or 3 (3 times).",
+      "We further show in the same table the average numbers of labels from each strategy. Obviously, a lower threshold leads to higher numbers (corresponding to a disjunction of annotations for each emotion). The drop in label counts is comparably drastic, with on average 18 labels per class. Overall, the best average $\\kappa $ agreement (.32) is less than half of what we saw for the expert annotators (roughly .70). Crowds especially disagree on many more intricate emotion labels (Uneasiness, Vitality, Nostalgia, Suspense).",
+      "We visualize how often two emotions are used to label an instance in a confusion table in Figure FIGREF18. Sadness is used most often to annotate a stanza, and it is often confused with Suspense, Uneasiness, and Nostalgia. Further, Beauty/Joy partially overlaps with Awe/Sublime, Nostalgia, and Sadness.",
+      "On average, each crowd annotator uses two emotion labels per stanza (56% of cases); only in 36% of the cases the annotators use one label, and in 6% and 1% of the cases three and four labels, respectively. This contrasts with the expert annotators, who use one label in about 70% of the cases and two labels in 30% of the cases for the same 59 four-liners. Concerning frequency distribution for emotion labels, both experts and crowds name Sadness and Beauty/Joy as the most frequent emotions (for the `best' threshold of 3) and Nostalgia as one of the least frequent emotions. The Spearman rank correlation between experts and crowds is about 0.55 with respect to the label frequency distribution, indicating that crowds could replace experts to a moderate degree when it comes to extracting, e.g., emotion distributions for an author or time period. Now, we further compare crowds and experts in terms of whether crowds could replicate expert annotations also on a finer stanza level (rather than only on a distributional level)."
+    ],
+    [
+      "To gauge the quality of the crowd annotations in comparison with our experts, we calculate agreement on the emotions between experts and an increasing group size from the crowd. For each stanza instance $s$, we pick $N$ crowd workers, where $N\\in \\lbrace 4,6,8,10\\rbrace $, then pick their majority emotion for $s$, and additionally pick their second ranked majority emotion if at least $\\frac{N}{2}-1$ workers have chosen it. For the experts, we aggregate their emotion labels on stanza level, then perform the same strategy for selection of emotion labels. Thus, for $s$, both crowds and experts have 1 or 2 emotions. For each emotion, we then compute Cohen's $\\kappa $ as before. Note that, compared to our previous experiments in Section SECREF26 with a threshold, each stanza now receives an emotion annotation (exactly one or two emotion labels), both by the experts and the crowd-workers.",
+      "In Figure FIGREF30, we plot agreement between experts and crowds on stanza level as we vary the number $N$ of crowd workers involved. On average, there is roughly a steady linear increase in agreement as $N$ grows, which may indicate that $N=20$ or $N=30$ would still lead to better agreement. Concerning individual emotions, Nostalgia is the emotion with the least agreement, as opposed to Sadness (in our sample of 59 four-liners): the agreement for this emotion grows from $.47$ $\\kappa $ with $N=4$ to $.65$ $\\kappa $ with $N=10$. Sadness is also the most frequent emotion, both according to experts and crowds. Other emotions for which a reasonable agreement is achieved are Annoyance, Awe/Sublime, Beauty/Joy, Humor ($\\kappa $ > 0.2). Emotions with little agreement are Vitality, Uneasiness, Suspense, Nostalgia ($\\kappa $ < 0.2).",
+      "By and large, we note from Figure FIGREF18 that expert annotation is more restrictive, with experts agreeing more often on particular emotion labels (seen in the darker diagonal). The results of the crowdsourcing experiment, on the other hand, are a mixed bag as evidenced by a much sparser distribution of emotion labels. However, we note that these differences can be caused by 1) the disparate training procedure for the experts and crowds, and 2) the lack of opportunities for close supervision and on-going training of the crowds, as opposed to the in-house expert annotators.",
+      "In general, however, we find that substituting experts with crowds is possible to a certain degree. Even though the crowds' labels look inconsistent at first view, there appears to be a good signal in their aggregated annotations, helping to approximate expert annotations to a certain degree. The average $\\kappa $ agreement (with the experts) we get from $N=10$ crowd workers (0.24) is still considerably below the agreement among the experts (0.70)."
+    ],
+    [
+      "To estimate the difficulty of automatic classification of our data set, we perform multi-label document classification (of stanzas) with BERT BIBREF41. For this experiment we aggregate all labels for a stanza and sort them by frequency, both for the gold standard and the raw expert annotations. As can be seen in Figure FIGREF23, a stanza bears a minimum of one and a maximum of four emotions. Unfortunately, the label Nostalgia is only available 16 times in the German data (the gold standard) as a second label (as discussed in Section SECREF19). None of our models was able to learn this label for German. Therefore we omit it, leaving us with eight proper labels.",
+      "We use the code and the pre-trained BERT models of Farm, provided by deepset.ai. We test the multilingual-uncased model (Multiling), the german-base-cased model (Base), the german-dbmdz-uncased model (Dbmdz), and we tune the Base model on 80k stanzas of the German Poetry Corpus DLK BIBREF30 for 2 epochs, both on token (masked words) and sequence (next line) prediction (Base$_{\\textsc {Tuned}}$).",
+      "We split the randomized German dataset so that each label is at least 10 times in the validation set (63 instances, 113 labels), and at least 10 times in the test set (56 instances, 108 labels) and leave the rest for training (617 instances, 946 labels). We train BERT for 10 epochs (with a batch size of 8), optimize with entropy loss, and report F1-micro on the test set. See Table TABREF36 for the results.",
+      "We find that the multilingual model cannot handle infrequent categories, i.e., Awe/Sublime, Suspense and Humor. However, increasing the dataset with English data improves the results, suggesting that the classification would largely benefit from more annotated data. The best model overall is DBMDZ (.520), showing a balanced response on both validation and test set. See Table TABREF37 for a breakdown of all emotions as predicted by the this model. Precision is mostly higher than recall. The labels Awe/Sublime, Suspense and Humor are harder to predict than the other labels.",
+      "The BASE and BASE$_{\\textsc {TUNED}}$ models perform slightly worse than DBMDZ. The effect of tuning of the BASE model is questionable, probably because of the restricted vocabulary (30k). We found that tuning on poetry does not show obvious improvements. Lastly, we find that models that were trained on lines (instead of stanzas) do not achieve the same F1 (~.42 for the German models)."
+    ],
+    [
+      "In this paper, we presented a dataset of German and English poetry annotated with reader response to reading poetry. We argued that basic emotions as proposed by psychologists (such as Ekman and Plutchik) that are often used in emotion analysis from text are of little use for the annotation of poetry reception. We instead conceptualized aesthetic emotion labels and showed that a closely supervised annotation task results in substantial agreement\u2014in terms of $\\kappa $ score\u2014on the final dataset.",
+      "The task of collecting reader-perceived emotion response to poetry in a crowdsourcing setting is not straightforward. In contrast to expert annotators, who were closely supervised and reflected upon the task, the annotators on crowdsourcing platforms are difficult to control and may lack necessary background knowledge to perform the task at hand. However, using a larger number of crowd annotators may lead to finding an aggregation strategy with a better trade-off between quality and quantity of adjudicated labels. For future work, we thus propose to repeat the experiment with larger number of crowdworkers, and develop an improved training strategy that would suit the crowdsourcing environment.",
+      "The dataset presented in this paper can be of use for different application scenarios, including multi-label emotion classification, style-conditioned poetry generation, investigating the influence of rhythm/prosodic features on emotion, or analysis of authors, genres and diachronic variation (e.g., how emotions are represented differently in certain periods).",
+      "Further, though our modeling experiments are still rudimentary, we propose that this data set can be used to investigate the intra-poem relations either through multi-task learning BIBREF49 and/or with the help of hierarchical sequence classification approaches."
+    ],
+    [
+      "A special thanks goes to Gesine Fuhrmann, who created the guidelines and tirelessly documented the annotation progress. Also thanks to Annika Palm and Debby Trzeciak who annotated and gave lively feedback. For help with the conceptualization of labels we thank Ines Schindler. This research has been partially conducted within the CRETA center (http://www.creta.uni-stuttgart.de/) which is funded by the German Ministry for Education and Research (BMBF) and partially funded by the German Research Council (DFG), projects SEAT (Structured Multi-Domain Emotion Analysis from Text, KL 2869/1-1). This work has also been supported by the German Research Foundation as part of the Research Training Group Adaptive Preparation of Information from Heterogeneous Sources (AIPHES) at the Technische Universit\u00e4t Darmstadt under grant No. GRK 1994/1."
+    ],
+    [
+      "We illustrate two examples of our German gold standard annotation, a poem each by Friedrich H\u00f6lderlin and Georg Trakl, and an English poem by Walt Whitman. H\u00f6lderlin's text stands out, because the mood changes starkly from the first stanza to the second, from Beauty/Joy to Sadness. Trakl's text is a bit more complex with bits of Nostalgia and, most importantly, a mixture of Uneasiness with Awe/Sublime. Whitman's poem is an example of Vitality and its mixing with Sadness. The English annotation was unified by us for space constraints. For the full annotation please see https://github.com/tnhaider/poetry-emotion/"
+    ],
+    [
+      ""
+    ],
+    [
+      ""
+    ],
+    [
+      ""
+    ]
+  ]
+}
+```

qasper-0016/instruction.md

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+Name of Paper: Community Identity and User Engagement in a Multi-Community Landscape
+
+Question: How do the authors measure how temporally dynamic a community is?

qasper-0017/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Community Identity and User Engagement in a Multi-Community Landscape
+
+Question: How do the authors measure how distinctive a community is?

qasper-0018/instruction.md

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+Name of Paper: Question Answering based Clinical Text Structuring Using Pre-trained Language Model
+
+Question: What data is the language model pretrained on?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work ::: Clinical Text Structuring",
+    "Related Work ::: Pre-trained Language Model",
+    "Question Answering based Clinical Text Structuring",
+    "The Proposed Model for QA-CTS Task",
+    "The Proposed Model for QA-CTS Task ::: Contextualized Representation of Sentence Text and Query Text",
+    "The Proposed Model for QA-CTS Task ::: Clinical Named Entity Information",
+    "The Proposed Model for QA-CTS Task ::: Integration Method",
+    "The Proposed Model for QA-CTS Task ::: Final Prediction",
+    "The Proposed Model for QA-CTS Task ::: Two-Stage Training Mechanism",
+    "Experimental Studies",
+    "Experimental Studies ::: Dataset and Evaluation Metrics",
+    "Experimental Studies ::: Experimental Settings",
+    "Experimental Studies ::: Comparison with State-of-the-art Methods",
+    "Experimental Studies ::: Ablation Analysis",
+    "Experimental Studies ::: Comparisons Between Two Integration Methods",
+    "Experimental Studies ::: Data Integration Analysis",
+    "Conclusion",
+    "Acknowledgment"
+  ],
+  "paragraphs": [
+    [
+      "Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surgery, or what the specific laboratory test result is, are obtained. It is important to extract structured data from clinical text because bio-medical systems or bio-medical researches greatly rely on structured data but they cannot obtain them directly. In addition, clinical text often contains abundant healthcare information. CTS is able to provide large-scale extracted structured data for enormous down-stream clinical researches.",
+      "However, end-to-end CTS is a very challenging task. Different CTS tasks often have non-uniform output formats, such as specific-class classifications (e.g. tumor stage), strings in the original text (e.g. result for a laboratory test) and inferred values from part of the original text (e.g. calculated tumor size). Researchers have to construct different models for it, which is already costly, and hence it calls for a lot of labeled data for each model. Moreover, labeling necessary amount of data for training neural network requires expensive labor cost. To handle it, researchers turn to some rule-based structuring methods which often have lower labor cost.",
+      "Traditionally, CTS tasks can be addressed by rule and dictionary based methods BIBREF0, BIBREF1, BIBREF2, task-specific end-to-end methods BIBREF3, BIBREF4, BIBREF5, BIBREF6 and pipeline methods BIBREF7, BIBREF8, BIBREF9. Rule and dictionary based methods suffer from costly human-designed extraction rules, while task-specific end-to-end methods have non-uniform output formats and require task-specific training dataset. Pipeline methods break down the entire process into several pieces which improves the performance and generality. However, when the pipeline depth grows, error propagation will have a greater impact on the performance.",
+      "To reduce the pipeline depth and break the barrier of non-uniform output formats, we present a question answering based clinical text structuring (QA-CTS) task (see Fig. FIGREF1). Unlike the traditional CTS task, our QA-CTS task aims to discover the most related text from original paragraph text. For some cases, it is already the final answer in deed (e.g., extracting sub-string). While for other cases, it needs several steps to obtain the final answer, such as entity names conversion and negative words recognition. Our presented QA-CTS task unifies the output format of the traditional CTS task and make the training data shareable, thus enriching the training data. The main contributions of this work can be summarized as follows.",
+      "We first present a question answering based clinical text structuring (QA-CTS) task, which unifies different specific tasks and make dataset shareable. We also propose an effective model to integrate clinical named entity information into pre-trained language model.",
+      "Experimental results show that QA-CTS task leads to significant improvement due to shared dataset. Our proposed model also achieves significantly better performance than the strong baseline methods. In addition, we also show that two-stage training mechanism has a great improvement on QA-CTS task.",
+      "The rest of the paper is organized as follows. We briefly review the related work on clinical text structuring in Section SECREF2. Then, we present question answer based clinical text structuring task in Section SECREF3. In Section SECREF4, we present an effective model for this task. Section SECREF5 is devoted to computational studies and several investigations on the key issues of our proposed model. Finally, conclusions are given in Section SECREF6."
+    ],
+    [
+      "Clinical text structuring is a final problem which is highly related to practical applications. Most of existing studies are case-by-case. Few of them are developed for the general purpose structuring task. These studies can be roughly divided into three categories: rule and dictionary based methods, task-specific end-to-end methods and pipeline methods.",
+      "Rule and dictionary based methods BIBREF0, BIBREF1, BIBREF2 rely extremely on heuristics and handcrafted extraction rules which is more of an art than a science and incurring extensive trial-and-error experiments. Fukuda et al. BIBREF0 identified protein names from biological papers by dictionaries and several features of protein names. Wang et al. BIBREF1 developed some linguistic rules (i.e. normalised/expanded term matching and substring term matching) to map specific terminology to SNOMED CT. Song et al. BIBREF2 proposed a hybrid dictionary-based bio-entity extraction technique and expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. This kind of approach features its interpretability and easy modifiability. However, with the increase of the rule amount, supplementing new rules to existing system will turn to be a rule disaster.",
+      "Task-specific end-to-end methods BIBREF3, BIBREF4 use large amount of data to automatically model the specific task. Topaz et al. BIBREF3 constructed an automated wound information identification model with five output. Tan et al. BIBREF4 identified patients undergoing radical cystectomy for bladder cancer. Although they achieved good performance, none of their models could be used to another task due to output format difference. This makes building a new model for a new task a costly job.",
+      "Pipeline methods BIBREF7, BIBREF8, BIBREF9 break down the entire task into several basic natural language processing tasks. Bill et al. BIBREF7 focused on attributes extraction which mainly relied on dependency parsing and named entity recognition BIBREF10, BIBREF11, BIBREF12. Meanwhile, Fonferko et al. BIBREF9 used more components like noun phrase chunking BIBREF13, BIBREF14, BIBREF15, part-of-speech tagging BIBREF16, BIBREF17, BIBREF18, sentence splitter, named entity linking BIBREF19, BIBREF20, BIBREF21, relation extraction BIBREF22, BIBREF23. This kind of method focus on language itself, so it can handle tasks more general. However, as the depth of pipeline grows, it is obvious that error propagation will be more and more serious. In contrary, using less components to decrease the pipeline depth will lead to a poor performance. So the upper limit of this method depends mainly on the worst component."
+    ],
+    [
+      "Recently, some works focused on pre-trained language representation models to capture language information from text and then utilizing the information to improve the performance of specific natural language processing tasks BIBREF24, BIBREF25, BIBREF26, BIBREF27 which makes language model a shared model to all natural language processing tasks. Radford et al. BIBREF24 proposed a framework for fine-tuning pre-trained language model. Peters et al. BIBREF25 proposed ELMo which concatenates forward and backward language models in a shallow manner. Devlin et al. BIBREF26 used bidirectional Transformers to model deep interactions between the two directions. Yang et al. BIBREF27 replaced the fixed forward or backward factorization order with all possible permutations of the factorization order and avoided using the [MASK] tag which causes pretrain-finetune discrepancy that BERT is subject to.",
+      "The main motivation of introducing pre-trained language model is to solve the shortage of labeled data and polysemy problem. Although polysemy problem is not a common phenomenon in biomedical domain, shortage of labeled data is always a non-trivial problem. Lee et al. BIBREF28 applied BERT on large-scale biomedical unannotated data and achieved improvement on biomedical named entity recognition, relation extraction and question answering. Kim et al. BIBREF29 adapted BioBERT into multi-type named entity recognition and discovered new entities. Both of them demonstrates the usefulness of introducing pre-trained language model into biomedical domain."
+    ],
+    [
+      "Given a sequence of paragraph text $X=<x_1, x_2, ..., x_n>$, clinical text structuring (CTS) can be regarded to extract or generate a key-value pair where key $Q$ is typically a query term such as proximal resection margin and value $V$ is a result of query term $Q$ according to the paragraph text $X$.",
+      "Generally, researchers solve CTS problem in two steps. Firstly, the answer-related text is pick out. And then several steps such as entity names conversion and negative words recognition are deployed to generate the final answer. While final answer varies from task to task, which truly causes non-uniform output formats, finding the answer-related text is a common action among all tasks. Traditional methods regard both the steps as a whole. In this paper, we focus on finding the answer-related substring $Xs = <X_i, X_i+1, X_i+2, ... X_j> (1 <= i < j <= n)$ from paragraph text $X$. For example, given sentence UTF8gkai\u201c\u8fdc\u7aef\u80c3\u5207\u9664\u6807\u672c\uff1a\u5c0f\u5f2f\u957f11.5cm\uff0c\u5927\u5f2f\u957f17.0cm\u3002\u8ddd\u4e0a\u5207\u7aef6.0cm\u3001\u4e0b\u5207\u7aef8.0cm\" (Distal gastrectomy specimen: measuring 11.5cm in length along the lesser curvature, 17.0cm in length along the greater curvature; 6.0cm from the proximal resection margin, and 8.0cm from the distal resection margin) and query UTF8gkai\u201c\u4e0a\u5207\u7f18\u8ddd\u79bb\"(proximal resection margin), the answer should be 6.0cm which is located in original text from index 32 to 37. With such definition, it unifies the output format of CTS tasks and therefore make the training data shareable, in order to reduce the training data quantity requirement.",
+      "Since BERT BIBREF26 has already demonstrated the usefulness of shared model, we suppose extracting commonality of this problem and unifying the output format will make the model more powerful than dedicated model and meanwhile, for a specific clinical task, use the data for other tasks to supplement the training data."
+    ],
+    [
+      "In this section, we present an effective model for the question answering based clinical text structuring (QA-CTS). As shown in Fig. FIGREF8, paragraph text $X$ is first passed to a clinical named entity recognition (CNER) model BIBREF12 to capture named entity information and obtain one-hot CNER output tagging sequence for query text $I_{nq}$ and paragraph text $I_{nt}$ with BIEOS (Begin, Inside, End, Outside, Single) tag scheme. $I_{nq}$ and $I_{nt}$ are then integrated together into $I_n$. Meanwhile, the paragraph text $X$ and query text $Q$ are organized and passed to contextualized representation model which is pre-trained language model BERT BIBREF26 here to obtain the contextualized representation vector $V_s$ of both text and query. Afterwards, $V_s$ and $I_n$ are integrated together and fed into a feed forward network to calculate the start and end index of answer-related text. Here we define this calculation problem as a classification for each word to be the start or end word."
+    ],
+    [
+      "For any clinical free-text paragraph $X$ and query $Q$, contextualized representation is to generate the encoded vector of both of them. Here we use pre-trained language model BERT-base BIBREF26 model to capture contextual information.",
+      "The text input is constructed as `[CLS] $Q$ [SEP] $X$ [SEP]'. For Chinese sentence, each word in this input will be mapped to a pre-trained embedding $e_i$. To tell the model $Q$ and $X$ is two different sentence, a sentence type input is generated which is a binary label sequence to denote what sentence each character in the input belongs to. Positional encoding and mask matrix is also constructed automatically to bring in absolute position information and eliminate the impact of zero padding respectively. Then a hidden vector $V_s$ which contains both query and text information is generated through BERT-base model."
+    ],
+    [
+      "Since BERT is trained on general corpus, its performance on biomedical domain can be improved by introducing biomedical domain-specific features. In this paper, we introduce clinical named entity information into the model.",
+      "The CNER task aims to identify and classify important clinical terms such as diseases, symptoms, treatments, exams, and body parts from Chinese EHRs. It can be regarded as a sequence labeling task. A CNER model typically outputs a sequence of tags. Each character of the original sentence will be tagged a label following a tag scheme. In this paper we recognize the entities by the model of our previous work BIBREF12 but trained on another corpus which has 44 entity types including operations, numbers, unit words, examinations, symptoms, negative words, etc. An illustrative example of named entity information sequence is demonstrated in Table TABREF2. In Table TABREF2, UTF8gkai\u201c\u8fdc\u7aef\u80c3\u5207\u9664\" is tagged as an operation, `11.5' is a number word and `cm' is an unit word. The named entity tag sequence is organized in one-hot type. We denote the sequence for clinical sentence and query term as $I_{nt}$ and $I_{nq}$, respectively."
+    ],
+    [
+      "There are two ways to integrate two named entity information vectors $I_{nt}$ and $I_{nq}$ or hidden contextualized representation $V_s$ and named entity information $I_n$, where $I_n = [I_{nt}; I_{nq}]$. The first one is to concatenate them together because they have sequence output with a common dimension. The second one is to transform them into a new hidden representation. For the concatenation method, the integrated representation is described as follows.",
+      "While for the transformation method, we use multi-head attention BIBREF30 to encode the two vectors. It can be defined as follows where $h$ is the number of heads and $W_o$ is used to projects back the dimension of concatenated matrix.",
+      "$Attention$ denotes the traditional attention and it can be defined as follows.",
+      "where $d_k$ is the length of hidden vector."
+    ],
+    [
+      "The final step is to use integrated representation $H_i$ to predict the start and end index of answer-related text. Here we define this calculation problem as a classification for each word to be the start or end word. We use a feed forward network (FFN) to compress and calculate the score of each word $H_f$ which makes the dimension to $\\left\\langle l_s, 2\\right\\rangle $ where $l_s$ denotes the length of sequence.",
+      "Then we permute the two dimensions for softmax calculation. The calculation process of loss function can be defined as followed.",
+      "where $O_s = softmax(permute(H_f)_0)$ denotes the probability score of each word to be the start word and similarly $O_e = softmax(permute(H_f)_1)$ denotes the end. $y_s$ and $y_e$ denotes the true answer of the output for start word and end word respectively."
+    ],
+    [
+      "Two-stage training mechanism is previously applied on bilinear model in fine-grained visual recognition BIBREF31, BIBREF32, BIBREF33. Two CNNs are deployed in the model. One is trained at first for coarse-graind features while freezing the parameter of the other. Then unfreeze the other one and train the entire model in a low learning rate for fetching fine-grained features.",
+      "Inspired by this and due to the large amount of parameters in BERT model, to speed up the training process, we fine tune the BERT model with new prediction layer first to achieve a better contextualized representation performance. Then we deploy the proposed model and load the fine tuned BERT weights, attach named entity information layers and retrain the model."
+    ],
+    [
+      "In this section, we devote to experimentally evaluating our proposed task and approach. The best results in tables are in bold."
+    ],
+    [
+      "Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. These annotated instances have been partitioned into 1,899 training instances (12,412 sentences) and 815 test instances (5,421 sentences). Each instance has one or several sentences. Detailed statistics of different types of entities are listed in Table TABREF20.",
+      "In the following experiments, two widely-used performance measures (i.e., EM-score BIBREF34 and (macro-averaged) F$_1$-score BIBREF35) are used to evaluate the methods. The Exact Match (EM-score) metric measures the percentage of predictions that match any one of the ground truth answers exactly. The F$_1$-score metric is a looser metric measures the average overlap between the prediction and ground truth answer."
+    ],
+    [
+      "To implement deep neural network models, we utilize the Keras library BIBREF36 with TensorFlow BIBREF37 backend. Each model is run on a single NVIDIA GeForce GTX 1080 Ti GPU. The models are trained by Adam optimization algorithm BIBREF38 whose parameters are the same as the default settings except for learning rate set to $5\\times 10^{-5}$. Batch size is set to 3 or 4 due to the lack of graphical memory. We select BERT-base as the pre-trained language model in this paper. Due to the high cost of pre-training BERT language model, we directly adopt parameters pre-trained by Google in Chinese general corpus. The named entity recognition is applied on both pathology report texts and query texts."
+    ],
+    [
+      "Since BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26. As BERT has two versions: BERT-Base and BERT-Large, due to the lack of computational resource, we can only compare with BERT-Base model instead of BERT-Large. Prediction layer is attached at the end of the original BERT-Base model and we fine tune it on our dataset. In this section, the named entity integration method is chosen to pure concatenation (Concatenate the named entity information on pathology report text and query text first and then concatenate contextualized representation and concatenated named entity information). Comparative results are summarized in Table TABREF23.",
+      "Table TABREF23 indicates that our proposed model achieved the best performance both in EM-score and F$_1$-score with EM-score of 91.84% and F$_1$-score of 93.75%. QANet outperformed BERT-Base with 3.56% score in F$_1$-score but underperformed it with 0.75% score in EM-score. Compared with BERT-Base, our model led to a 5.64% performance improvement in EM-score and 3.69% in F$_1$-score. Although our model didn't outperform much with QANet in F$_1$-score (only 0.13%), our model significantly outperformed it with 6.39% score in EM-score."
+    ],
+    [
+      "To further investigate the effects of named entity information and two-stage training mechanism for our model, we apply ablation analysis to see the improvement brought by each of them, where $\\times $ refers to removing that part from our model.",
+      "As demonstrated in Table TABREF25, with named entity information enabled, two-stage training mechanism improved the result by 4.36% in EM-score and 3.8% in F$_1$-score. Without two-stage training mechanism, named entity information led to an improvement by 1.28% in EM-score but it also led to a weak deterioration by 0.12% in F$_1$-score. With both of them enabled, our proposed model achieved a 5.64% score improvement in EM-score and a 3.69% score improvement in F$_1$-score. The experimental results show that both named entity information and two-stage training mechanism are helpful to our model."
+    ],
+    [
+      "There are two methods to integrate named entity information into existing model, we experimentally compare these two integration methods. As named entity recognition has been applied on both pathology report text and query text, there will be two integration here. One is for two named entity information and the other is for contextualized representation and integrated named entity information. For multi-head attention BIBREF30, we set heads number $h = 16$ with 256-dimension hidden vector size for each head.",
+      "From Table TABREF27, we can observe that applying concatenation on both periods achieved the best performance on both EM-score and F$_1$-score. Unfortunately, applying multi-head attention on both period one and period two can not reach convergence in our experiments. This probably because it makes the model too complex to train. The difference on other two methods are the order of concatenation and multi-head attention. Applying multi-head attention on two named entity information $I_{nt}$ and $I_{nq}$ first achieved a better performance with 89.87% in EM-score and 92.88% in F$_1$-score. Applying Concatenation first can only achieve 80.74% in EM-score and 84.42% in F$_1$-score. This is probably due to the processing depth of hidden vectors and dataset size. BERT's output has been modified after many layers but named entity information representation is very close to input. With big amount of parameters in multi-head attention, it requires massive training to find out the optimal parameters. However, our dataset is significantly smaller than what pre-trained BERT uses. This probably can also explain why applying multi-head attention method on both periods can not converge.",
+      "Although Table TABREF27 shows the best integration method is concatenation, multi-head attention still has great potential. Due to the lack of computational resources, our experiment fixed the head number and hidden vector size. However, tuning these hyper parameters may have impact on the result. Tuning integration method and try to utilize larger datasets may give help to improving the performance."
+    ],
+    [
+      "To investigate how shared task and shared model can benefit, we split our dataset by query types, train our proposed model with different datasets and demonstrate their performance on different datasets. Firstly, we investigate the performance on model without two-stage training and named entity information.",
+      "As indicated in Table TABREF30, The model trained by mixed data outperforms 2 of the 3 original tasks in EM-score with 81.55% for proximal resection margin and 86.85% for distal resection margin. The performance on tumor size declined by 1.57% score in EM-score and 3.14% score in F$_1$-score but they were still above 90%. 0.69% and 0.37% score improvement in EM-score was brought by shared model for proximal and distal resection margin prediction. Meanwhile F$_1$-score for those two tasks declined 3.11% and 0.77% score.",
+      "Then we investigate the performance on model with two-stage training and named entity information. In this experiment, pre-training process only use the specific dataset not the mixed data. From Table TABREF31 we can observe that the performance on proximal and distal resection margin achieved the best performance on both EM-score and F$_1$-score. Compared with Table TABREF30, the best performance on proximal resection margin improved by 6.9% in EM-score and 7.94% in F$_1$-score. Meanwhile, the best performance on distal resection margin improved by 5.56% in EM-score and 6.32% in F$_1$-score. Other performances also usually improved a lot. This proves the usefulness of two-stage training and named entity information as well.",
+      "Lastly, we fine tune the model for each task with a pre-trained parameter. Table TABREF32 summarizes the result. (Add some explanations for the Table TABREF32). Comparing Table TABREF32 with Table TABREF31, using mixed-data pre-trained parameters can significantly improve the model performance than task-specific data trained model. Except tumor size, the result was improved by 0.52% score in EM-score, 1.39% score in F$_1$-score for proximal resection margin and 2.6% score in EM-score, 2.96% score in F$_1$-score for distal resection margin. This proves mixed-data pre-trained parameters can lead to a great benefit for specific task. Meanwhile, the model performance on other tasks which are not trained in the final stage was also improved from around 0 to 60 or 70 percent. This proves that there is commonality between different tasks and our proposed QA-CTS task make this learnable. In conclusion, to achieve the best performance for a specific dataset, pre-training the model in multiple datasets and then fine tuning the model on the specific dataset is the best way."
+    ],
+    [
+      "In this paper, we present a question answering based clinical text structuring (QA-CTS) task, which unifies different clinical text structuring tasks and utilize different datasets. A novel model is also proposed to integrate named entity information into a pre-trained language model and adapt it to QA-CTS task. Initially, sequential results of named entity recognition on both paragraph and query texts are integrated together. Contextualized representation on both paragraph and query texts are transformed by a pre-trained language model. Then, the integrated named entity information and contextualized representation are integrated together and fed into a feed forward network for final prediction. Experimental results on real-world dataset demonstrate that our proposed model competes favorably with strong baseline models in all three specific tasks. The shared task and shared model introduced by QA-CTS task has also been proved to be useful for improving the performance on most of the task-specific datasets. In conclusion, the best way to achieve the best performance for a specific dataset is to pre-train the model in multiple datasets and then fine tune it on the specific dataset."
+    ],
+    [
+      "We would like to thank Ting Li and Xizhou Hong (Ruijin Hospital) who have helped us very much in data fetching and data cleansing. This work is supported by the National Key R&D Program of China for \u201cPrecision Medical Research\" (No. 2018YFC0910500)."
+    ]
+  ]
+}
+```

qasper-0019/instruction.md

@@ -0,0 +1,118 @@
+Name of Paper: Question Answering based Clinical Text Structuring Using Pre-trained Language Model
+
+Question: What baselines is the proposed model compared against?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work ::: Clinical Text Structuring",
+    "Related Work ::: Pre-trained Language Model",
+    "Question Answering based Clinical Text Structuring",
+    "The Proposed Model for QA-CTS Task",
+    "The Proposed Model for QA-CTS Task ::: Contextualized Representation of Sentence Text and Query Text",
+    "The Proposed Model for QA-CTS Task ::: Clinical Named Entity Information",
+    "The Proposed Model for QA-CTS Task ::: Integration Method",
+    "The Proposed Model for QA-CTS Task ::: Final Prediction",
+    "The Proposed Model for QA-CTS Task ::: Two-Stage Training Mechanism",
+    "Experimental Studies",
+    "Experimental Studies ::: Dataset and Evaluation Metrics",
+    "Experimental Studies ::: Experimental Settings",
+    "Experimental Studies ::: Comparison with State-of-the-art Methods",
+    "Experimental Studies ::: Ablation Analysis",
+    "Experimental Studies ::: Comparisons Between Two Integration Methods",
+    "Experimental Studies ::: Data Integration Analysis",
+    "Conclusion",
+    "Acknowledgment"
+  ],
+  "paragraphs": [
+    [
+      "Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surgery, or what the specific laboratory test result is, are obtained. It is important to extract structured data from clinical text because bio-medical systems or bio-medical researches greatly rely on structured data but they cannot obtain them directly. In addition, clinical text often contains abundant healthcare information. CTS is able to provide large-scale extracted structured data for enormous down-stream clinical researches.",
+      "However, end-to-end CTS is a very challenging task. Different CTS tasks often have non-uniform output formats, such as specific-class classifications (e.g. tumor stage), strings in the original text (e.g. result for a laboratory test) and inferred values from part of the original text (e.g. calculated tumor size). Researchers have to construct different models for it, which is already costly, and hence it calls for a lot of labeled data for each model. Moreover, labeling necessary amount of data for training neural network requires expensive labor cost. To handle it, researchers turn to some rule-based structuring methods which often have lower labor cost.",
+      "Traditionally, CTS tasks can be addressed by rule and dictionary based methods BIBREF0, BIBREF1, BIBREF2, task-specific end-to-end methods BIBREF3, BIBREF4, BIBREF5, BIBREF6 and pipeline methods BIBREF7, BIBREF8, BIBREF9. Rule and dictionary based methods suffer from costly human-designed extraction rules, while task-specific end-to-end methods have non-uniform output formats and require task-specific training dataset. Pipeline methods break down the entire process into several pieces which improves the performance and generality. However, when the pipeline depth grows, error propagation will have a greater impact on the performance.",
+      "To reduce the pipeline depth and break the barrier of non-uniform output formats, we present a question answering based clinical text structuring (QA-CTS) task (see Fig. FIGREF1). Unlike the traditional CTS task, our QA-CTS task aims to discover the most related text from original paragraph text. For some cases, it is already the final answer in deed (e.g., extracting sub-string). While for other cases, it needs several steps to obtain the final answer, such as entity names conversion and negative words recognition. Our presented QA-CTS task unifies the output format of the traditional CTS task and make the training data shareable, thus enriching the training data. The main contributions of this work can be summarized as follows.",
+      "We first present a question answering based clinical text structuring (QA-CTS) task, which unifies different specific tasks and make dataset shareable. We also propose an effective model to integrate clinical named entity information into pre-trained language model.",
+      "Experimental results show that QA-CTS task leads to significant improvement due to shared dataset. Our proposed model also achieves significantly better performance than the strong baseline methods. In addition, we also show that two-stage training mechanism has a great improvement on QA-CTS task.",
+      "The rest of the paper is organized as follows. We briefly review the related work on clinical text structuring in Section SECREF2. Then, we present question answer based clinical text structuring task in Section SECREF3. In Section SECREF4, we present an effective model for this task. Section SECREF5 is devoted to computational studies and several investigations on the key issues of our proposed model. Finally, conclusions are given in Section SECREF6."
+    ],
+    [
+      "Clinical text structuring is a final problem which is highly related to practical applications. Most of existing studies are case-by-case. Few of them are developed for the general purpose structuring task. These studies can be roughly divided into three categories: rule and dictionary based methods, task-specific end-to-end methods and pipeline methods.",
+      "Rule and dictionary based methods BIBREF0, BIBREF1, BIBREF2 rely extremely on heuristics and handcrafted extraction rules which is more of an art than a science and incurring extensive trial-and-error experiments. Fukuda et al. BIBREF0 identified protein names from biological papers by dictionaries and several features of protein names. Wang et al. BIBREF1 developed some linguistic rules (i.e. normalised/expanded term matching and substring term matching) to map specific terminology to SNOMED CT. Song et al. BIBREF2 proposed a hybrid dictionary-based bio-entity extraction technique and expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. This kind of approach features its interpretability and easy modifiability. However, with the increase of the rule amount, supplementing new rules to existing system will turn to be a rule disaster.",
+      "Task-specific end-to-end methods BIBREF3, BIBREF4 use large amount of data to automatically model the specific task. Topaz et al. BIBREF3 constructed an automated wound information identification model with five output. Tan et al. BIBREF4 identified patients undergoing radical cystectomy for bladder cancer. Although they achieved good performance, none of their models could be used to another task due to output format difference. This makes building a new model for a new task a costly job.",
+      "Pipeline methods BIBREF7, BIBREF8, BIBREF9 break down the entire task into several basic natural language processing tasks. Bill et al. BIBREF7 focused on attributes extraction which mainly relied on dependency parsing and named entity recognition BIBREF10, BIBREF11, BIBREF12. Meanwhile, Fonferko et al. BIBREF9 used more components like noun phrase chunking BIBREF13, BIBREF14, BIBREF15, part-of-speech tagging BIBREF16, BIBREF17, BIBREF18, sentence splitter, named entity linking BIBREF19, BIBREF20, BIBREF21, relation extraction BIBREF22, BIBREF23. This kind of method focus on language itself, so it can handle tasks more general. However, as the depth of pipeline grows, it is obvious that error propagation will be more and more serious. In contrary, using less components to decrease the pipeline depth will lead to a poor performance. So the upper limit of this method depends mainly on the worst component."
+    ],
+    [
+      "Recently, some works focused on pre-trained language representation models to capture language information from text and then utilizing the information to improve the performance of specific natural language processing tasks BIBREF24, BIBREF25, BIBREF26, BIBREF27 which makes language model a shared model to all natural language processing tasks. Radford et al. BIBREF24 proposed a framework for fine-tuning pre-trained language model. Peters et al. BIBREF25 proposed ELMo which concatenates forward and backward language models in a shallow manner. Devlin et al. BIBREF26 used bidirectional Transformers to model deep interactions between the two directions. Yang et al. BIBREF27 replaced the fixed forward or backward factorization order with all possible permutations of the factorization order and avoided using the [MASK] tag which causes pretrain-finetune discrepancy that BERT is subject to.",
+      "The main motivation of introducing pre-trained language model is to solve the shortage of labeled data and polysemy problem. Although polysemy problem is not a common phenomenon in biomedical domain, shortage of labeled data is always a non-trivial problem. Lee et al. BIBREF28 applied BERT on large-scale biomedical unannotated data and achieved improvement on biomedical named entity recognition, relation extraction and question answering. Kim et al. BIBREF29 adapted BioBERT into multi-type named entity recognition and discovered new entities. Both of them demonstrates the usefulness of introducing pre-trained language model into biomedical domain."
+    ],
+    [
+      "Given a sequence of paragraph text $X=<x_1, x_2, ..., x_n>$, clinical text structuring (CTS) can be regarded to extract or generate a key-value pair where key $Q$ is typically a query term such as proximal resection margin and value $V$ is a result of query term $Q$ according to the paragraph text $X$.",
+      "Generally, researchers solve CTS problem in two steps. Firstly, the answer-related text is pick out. And then several steps such as entity names conversion and negative words recognition are deployed to generate the final answer. While final answer varies from task to task, which truly causes non-uniform output formats, finding the answer-related text is a common action among all tasks. Traditional methods regard both the steps as a whole. In this paper, we focus on finding the answer-related substring $Xs = <X_i, X_i+1, X_i+2, ... X_j> (1 <= i < j <= n)$ from paragraph text $X$. For example, given sentence UTF8gkai\u201c\u8fdc\u7aef\u80c3\u5207\u9664\u6807\u672c\uff1a\u5c0f\u5f2f\u957f11.5cm\uff0c\u5927\u5f2f\u957f17.0cm\u3002\u8ddd\u4e0a\u5207\u7aef6.0cm\u3001\u4e0b\u5207\u7aef8.0cm\" (Distal gastrectomy specimen: measuring 11.5cm in length along the lesser curvature, 17.0cm in length along the greater curvature; 6.0cm from the proximal resection margin, and 8.0cm from the distal resection margin) and query UTF8gkai\u201c\u4e0a\u5207\u7f18\u8ddd\u79bb\"(proximal resection margin), the answer should be 6.0cm which is located in original text from index 32 to 37. With such definition, it unifies the output format of CTS tasks and therefore make the training data shareable, in order to reduce the training data quantity requirement.",
+      "Since BERT BIBREF26 has already demonstrated the usefulness of shared model, we suppose extracting commonality of this problem and unifying the output format will make the model more powerful than dedicated model and meanwhile, for a specific clinical task, use the data for other tasks to supplement the training data."
+    ],
+    [
+      "In this section, we present an effective model for the question answering based clinical text structuring (QA-CTS). As shown in Fig. FIGREF8, paragraph text $X$ is first passed to a clinical named entity recognition (CNER) model BIBREF12 to capture named entity information and obtain one-hot CNER output tagging sequence for query text $I_{nq}$ and paragraph text $I_{nt}$ with BIEOS (Begin, Inside, End, Outside, Single) tag scheme. $I_{nq}$ and $I_{nt}$ are then integrated together into $I_n$. Meanwhile, the paragraph text $X$ and query text $Q$ are organized and passed to contextualized representation model which is pre-trained language model BERT BIBREF26 here to obtain the contextualized representation vector $V_s$ of both text and query. Afterwards, $V_s$ and $I_n$ are integrated together and fed into a feed forward network to calculate the start and end index of answer-related text. Here we define this calculation problem as a classification for each word to be the start or end word."
+    ],
+    [
+      "For any clinical free-text paragraph $X$ and query $Q$, contextualized representation is to generate the encoded vector of both of them. Here we use pre-trained language model BERT-base BIBREF26 model to capture contextual information.",
+      "The text input is constructed as `[CLS] $Q$ [SEP] $X$ [SEP]'. For Chinese sentence, each word in this input will be mapped to a pre-trained embedding $e_i$. To tell the model $Q$ and $X$ is two different sentence, a sentence type input is generated which is a binary label sequence to denote what sentence each character in the input belongs to. Positional encoding and mask matrix is also constructed automatically to bring in absolute position information and eliminate the impact of zero padding respectively. Then a hidden vector $V_s$ which contains both query and text information is generated through BERT-base model."
+    ],
+    [
+      "Since BERT is trained on general corpus, its performance on biomedical domain can be improved by introducing biomedical domain-specific features. In this paper, we introduce clinical named entity information into the model.",
+      "The CNER task aims to identify and classify important clinical terms such as diseases, symptoms, treatments, exams, and body parts from Chinese EHRs. It can be regarded as a sequence labeling task. A CNER model typically outputs a sequence of tags. Each character of the original sentence will be tagged a label following a tag scheme. In this paper we recognize the entities by the model of our previous work BIBREF12 but trained on another corpus which has 44 entity types including operations, numbers, unit words, examinations, symptoms, negative words, etc. An illustrative example of named entity information sequence is demonstrated in Table TABREF2. In Table TABREF2, UTF8gkai\u201c\u8fdc\u7aef\u80c3\u5207\u9664\" is tagged as an operation, `11.5' is a number word and `cm' is an unit word. The named entity tag sequence is organized in one-hot type. We denote the sequence for clinical sentence and query term as $I_{nt}$ and $I_{nq}$, respectively."
+    ],
+    [
+      "There are two ways to integrate two named entity information vectors $I_{nt}$ and $I_{nq}$ or hidden contextualized representation $V_s$ and named entity information $I_n$, where $I_n = [I_{nt}; I_{nq}]$. The first one is to concatenate them together because they have sequence output with a common dimension. The second one is to transform them into a new hidden representation. For the concatenation method, the integrated representation is described as follows.",
+      "While for the transformation method, we use multi-head attention BIBREF30 to encode the two vectors. It can be defined as follows where $h$ is the number of heads and $W_o$ is used to projects back the dimension of concatenated matrix.",
+      "$Attention$ denotes the traditional attention and it can be defined as follows.",
+      "where $d_k$ is the length of hidden vector."
+    ],
+    [
+      "The final step is to use integrated representation $H_i$ to predict the start and end index of answer-related text. Here we define this calculation problem as a classification for each word to be the start or end word. We use a feed forward network (FFN) to compress and calculate the score of each word $H_f$ which makes the dimension to $\\left\\langle l_s, 2\\right\\rangle $ where $l_s$ denotes the length of sequence.",
+      "Then we permute the two dimensions for softmax calculation. The calculation process of loss function can be defined as followed.",
+      "where $O_s = softmax(permute(H_f)_0)$ denotes the probability score of each word to be the start word and similarly $O_e = softmax(permute(H_f)_1)$ denotes the end. $y_s$ and $y_e$ denotes the true answer of the output for start word and end word respectively."
+    ],
+    [
+      "Two-stage training mechanism is previously applied on bilinear model in fine-grained visual recognition BIBREF31, BIBREF32, BIBREF33. Two CNNs are deployed in the model. One is trained at first for coarse-graind features while freezing the parameter of the other. Then unfreeze the other one and train the entire model in a low learning rate for fetching fine-grained features.",
+      "Inspired by this and due to the large amount of parameters in BERT model, to speed up the training process, we fine tune the BERT model with new prediction layer first to achieve a better contextualized representation performance. Then we deploy the proposed model and load the fine tuned BERT weights, attach named entity information layers and retrain the model."
+    ],
+    [
+      "In this section, we devote to experimentally evaluating our proposed task and approach. The best results in tables are in bold."
+    ],
+    [
+      "Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. These annotated instances have been partitioned into 1,899 training instances (12,412 sentences) and 815 test instances (5,421 sentences). Each instance has one or several sentences. Detailed statistics of different types of entities are listed in Table TABREF20.",
+      "In the following experiments, two widely-used performance measures (i.e., EM-score BIBREF34 and (macro-averaged) F$_1$-score BIBREF35) are used to evaluate the methods. The Exact Match (EM-score) metric measures the percentage of predictions that match any one of the ground truth answers exactly. The F$_1$-score metric is a looser metric measures the average overlap between the prediction and ground truth answer."
+    ],
+    [
+      "To implement deep neural network models, we utilize the Keras library BIBREF36 with TensorFlow BIBREF37 backend. Each model is run on a single NVIDIA GeForce GTX 1080 Ti GPU. The models are trained by Adam optimization algorithm BIBREF38 whose parameters are the same as the default settings except for learning rate set to $5\\times 10^{-5}$. Batch size is set to 3 or 4 due to the lack of graphical memory. We select BERT-base as the pre-trained language model in this paper. Due to the high cost of pre-training BERT language model, we directly adopt parameters pre-trained by Google in Chinese general corpus. The named entity recognition is applied on both pathology report texts and query texts."
+    ],
+    [
+      "Since BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26. As BERT has two versions: BERT-Base and BERT-Large, due to the lack of computational resource, we can only compare with BERT-Base model instead of BERT-Large. Prediction layer is attached at the end of the original BERT-Base model and we fine tune it on our dataset. In this section, the named entity integration method is chosen to pure concatenation (Concatenate the named entity information on pathology report text and query text first and then concatenate contextualized representation and concatenated named entity information). Comparative results are summarized in Table TABREF23.",
+      "Table TABREF23 indicates that our proposed model achieved the best performance both in EM-score and F$_1$-score with EM-score of 91.84% and F$_1$-score of 93.75%. QANet outperformed BERT-Base with 3.56% score in F$_1$-score but underperformed it with 0.75% score in EM-score. Compared with BERT-Base, our model led to a 5.64% performance improvement in EM-score and 3.69% in F$_1$-score. Although our model didn't outperform much with QANet in F$_1$-score (only 0.13%), our model significantly outperformed it with 6.39% score in EM-score."
+    ],
+    [
+      "To further investigate the effects of named entity information and two-stage training mechanism for our model, we apply ablation analysis to see the improvement brought by each of them, where $\\times $ refers to removing that part from our model.",
+      "As demonstrated in Table TABREF25, with named entity information enabled, two-stage training mechanism improved the result by 4.36% in EM-score and 3.8% in F$_1$-score. Without two-stage training mechanism, named entity information led to an improvement by 1.28% in EM-score but it also led to a weak deterioration by 0.12% in F$_1$-score. With both of them enabled, our proposed model achieved a 5.64% score improvement in EM-score and a 3.69% score improvement in F$_1$-score. The experimental results show that both named entity information and two-stage training mechanism are helpful to our model."
+    ],
+    [
+      "There are two methods to integrate named entity information into existing model, we experimentally compare these two integration methods. As named entity recognition has been applied on both pathology report text and query text, there will be two integration here. One is for two named entity information and the other is for contextualized representation and integrated named entity information. For multi-head attention BIBREF30, we set heads number $h = 16$ with 256-dimension hidden vector size for each head.",
+      "From Table TABREF27, we can observe that applying concatenation on both periods achieved the best performance on both EM-score and F$_1$-score. Unfortunately, applying multi-head attention on both period one and period two can not reach convergence in our experiments. This probably because it makes the model too complex to train. The difference on other two methods are the order of concatenation and multi-head attention. Applying multi-head attention on two named entity information $I_{nt}$ and $I_{nq}$ first achieved a better performance with 89.87% in EM-score and 92.88% in F$_1$-score. Applying Concatenation first can only achieve 80.74% in EM-score and 84.42% in F$_1$-score. This is probably due to the processing depth of hidden vectors and dataset size. BERT's output has been modified after many layers but named entity information representation is very close to input. With big amount of parameters in multi-head attention, it requires massive training to find out the optimal parameters. However, our dataset is significantly smaller than what pre-trained BERT uses. This probably can also explain why applying multi-head attention method on both periods can not converge.",
+      "Although Table TABREF27 shows the best integration method is concatenation, multi-head attention still has great potential. Due to the lack of computational resources, our experiment fixed the head number and hidden vector size. However, tuning these hyper parameters may have impact on the result. Tuning integration method and try to utilize larger datasets may give help to improving the performance."
+    ],
+    [
+      "To investigate how shared task and shared model can benefit, we split our dataset by query types, train our proposed model with different datasets and demonstrate their performance on different datasets. Firstly, we investigate the performance on model without two-stage training and named entity information.",
+      "As indicated in Table TABREF30, The model trained by mixed data outperforms 2 of the 3 original tasks in EM-score with 81.55% for proximal resection margin and 86.85% for distal resection margin. The performance on tumor size declined by 1.57% score in EM-score and 3.14% score in F$_1$-score but they were still above 90%. 0.69% and 0.37% score improvement in EM-score was brought by shared model for proximal and distal resection margin prediction. Meanwhile F$_1$-score for those two tasks declined 3.11% and 0.77% score.",
+      "Then we investigate the performance on model with two-stage training and named entity information. In this experiment, pre-training process only use the specific dataset not the mixed data. From Table TABREF31 we can observe that the performance on proximal and distal resection margin achieved the best performance on both EM-score and F$_1$-score. Compared with Table TABREF30, the best performance on proximal resection margin improved by 6.9% in EM-score and 7.94% in F$_1$-score. Meanwhile, the best performance on distal resection margin improved by 5.56% in EM-score and 6.32% in F$_1$-score. Other performances also usually improved a lot. This proves the usefulness of two-stage training and named entity information as well.",
+      "Lastly, we fine tune the model for each task with a pre-trained parameter. Table TABREF32 summarizes the result. (Add some explanations for the Table TABREF32). Comparing Table TABREF32 with Table TABREF31, using mixed-data pre-trained parameters can significantly improve the model performance than task-specific data trained model. Except tumor size, the result was improved by 0.52% score in EM-score, 1.39% score in F$_1$-score for proximal resection margin and 2.6% score in EM-score, 2.96% score in F$_1$-score for distal resection margin. This proves mixed-data pre-trained parameters can lead to a great benefit for specific task. Meanwhile, the model performance on other tasks which are not trained in the final stage was also improved from around 0 to 60 or 70 percent. This proves that there is commonality between different tasks and our proposed QA-CTS task make this learnable. In conclusion, to achieve the best performance for a specific dataset, pre-training the model in multiple datasets and then fine tuning the model on the specific dataset is the best way."
+    ],
+    [
+      "In this paper, we present a question answering based clinical text structuring (QA-CTS) task, which unifies different clinical text structuring tasks and utilize different datasets. A novel model is also proposed to integrate named entity information into a pre-trained language model and adapt it to QA-CTS task. Initially, sequential results of named entity recognition on both paragraph and query texts are integrated together. Contextualized representation on both paragraph and query texts are transformed by a pre-trained language model. Then, the integrated named entity information and contextualized representation are integrated together and fed into a feed forward network for final prediction. Experimental results on real-world dataset demonstrate that our proposed model competes favorably with strong baseline models in all three specific tasks. The shared task and shared model introduced by QA-CTS task has also been proved to be useful for improving the performance on most of the task-specific datasets. In conclusion, the best way to achieve the best performance for a specific dataset is to pre-train the model in multiple datasets and then fine tune it on the specific dataset."
+    ],
+    [
+      "We would like to thank Ting Li and Xizhou Hong (Ruijin Hospital) who have helped us very much in data fetching and data cleansing. This work is supported by the National Key R&D Program of China for \u201cPrecision Medical Research\" (No. 2018YFC0910500)."
+    ]
+  ]
+}
+```

qasper-0020/instruction.md

@@ -0,0 +1,118 @@
+Name of Paper: Question Answering based Clinical Text Structuring Using Pre-trained Language Model
+
+Question: How is the clinical text structuring task defined?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work ::: Clinical Text Structuring",
+    "Related Work ::: Pre-trained Language Model",
+    "Question Answering based Clinical Text Structuring",
+    "The Proposed Model for QA-CTS Task",
+    "The Proposed Model for QA-CTS Task ::: Contextualized Representation of Sentence Text and Query Text",
+    "The Proposed Model for QA-CTS Task ::: Clinical Named Entity Information",
+    "The Proposed Model for QA-CTS Task ::: Integration Method",
+    "The Proposed Model for QA-CTS Task ::: Final Prediction",
+    "The Proposed Model for QA-CTS Task ::: Two-Stage Training Mechanism",
+    "Experimental Studies",
+    "Experimental Studies ::: Dataset and Evaluation Metrics",
+    "Experimental Studies ::: Experimental Settings",
+    "Experimental Studies ::: Comparison with State-of-the-art Methods",
+    "Experimental Studies ::: Ablation Analysis",
+    "Experimental Studies ::: Comparisons Between Two Integration Methods",
+    "Experimental Studies ::: Data Integration Analysis",
+    "Conclusion",
+    "Acknowledgment"
+  ],
+  "paragraphs": [
+    [
+      "Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surgery, or what the specific laboratory test result is, are obtained. It is important to extract structured data from clinical text because bio-medical systems or bio-medical researches greatly rely on structured data but they cannot obtain them directly. In addition, clinical text often contains abundant healthcare information. CTS is able to provide large-scale extracted structured data for enormous down-stream clinical researches.",
+      "However, end-to-end CTS is a very challenging task. Different CTS tasks often have non-uniform output formats, such as specific-class classifications (e.g. tumor stage), strings in the original text (e.g. result for a laboratory test) and inferred values from part of the original text (e.g. calculated tumor size). Researchers have to construct different models for it, which is already costly, and hence it calls for a lot of labeled data for each model. Moreover, labeling necessary amount of data for training neural network requires expensive labor cost. To handle it, researchers turn to some rule-based structuring methods which often have lower labor cost.",
+      "Traditionally, CTS tasks can be addressed by rule and dictionary based methods BIBREF0, BIBREF1, BIBREF2, task-specific end-to-end methods BIBREF3, BIBREF4, BIBREF5, BIBREF6 and pipeline methods BIBREF7, BIBREF8, BIBREF9. Rule and dictionary based methods suffer from costly human-designed extraction rules, while task-specific end-to-end methods have non-uniform output formats and require task-specific training dataset. Pipeline methods break down the entire process into several pieces which improves the performance and generality. However, when the pipeline depth grows, error propagation will have a greater impact on the performance.",
+      "To reduce the pipeline depth and break the barrier of non-uniform output formats, we present a question answering based clinical text structuring (QA-CTS) task (see Fig. FIGREF1). Unlike the traditional CTS task, our QA-CTS task aims to discover the most related text from original paragraph text. For some cases, it is already the final answer in deed (e.g., extracting sub-string). While for other cases, it needs several steps to obtain the final answer, such as entity names conversion and negative words recognition. Our presented QA-CTS task unifies the output format of the traditional CTS task and make the training data shareable, thus enriching the training data. The main contributions of this work can be summarized as follows.",
+      "We first present a question answering based clinical text structuring (QA-CTS) task, which unifies different specific tasks and make dataset shareable. We also propose an effective model to integrate clinical named entity information into pre-trained language model.",
+      "Experimental results show that QA-CTS task leads to significant improvement due to shared dataset. Our proposed model also achieves significantly better performance than the strong baseline methods. In addition, we also show that two-stage training mechanism has a great improvement on QA-CTS task.",
+      "The rest of the paper is organized as follows. We briefly review the related work on clinical text structuring in Section SECREF2. Then, we present question answer based clinical text structuring task in Section SECREF3. In Section SECREF4, we present an effective model for this task. Section SECREF5 is devoted to computational studies and several investigations on the key issues of our proposed model. Finally, conclusions are given in Section SECREF6."
+    ],
+    [
+      "Clinical text structuring is a final problem which is highly related to practical applications. Most of existing studies are case-by-case. Few of them are developed for the general purpose structuring task. These studies can be roughly divided into three categories: rule and dictionary based methods, task-specific end-to-end methods and pipeline methods.",
+      "Rule and dictionary based methods BIBREF0, BIBREF1, BIBREF2 rely extremely on heuristics and handcrafted extraction rules which is more of an art than a science and incurring extensive trial-and-error experiments. Fukuda et al. BIBREF0 identified protein names from biological papers by dictionaries and several features of protein names. Wang et al. BIBREF1 developed some linguistic rules (i.e. normalised/expanded term matching and substring term matching) to map specific terminology to SNOMED CT. Song et al. BIBREF2 proposed a hybrid dictionary-based bio-entity extraction technique and expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. This kind of approach features its interpretability and easy modifiability. However, with the increase of the rule amount, supplementing new rules to existing system will turn to be a rule disaster.",
+      "Task-specific end-to-end methods BIBREF3, BIBREF4 use large amount of data to automatically model the specific task. Topaz et al. BIBREF3 constructed an automated wound information identification model with five output. Tan et al. BIBREF4 identified patients undergoing radical cystectomy for bladder cancer. Although they achieved good performance, none of their models could be used to another task due to output format difference. This makes building a new model for a new task a costly job.",
+      "Pipeline methods BIBREF7, BIBREF8, BIBREF9 break down the entire task into several basic natural language processing tasks. Bill et al. BIBREF7 focused on attributes extraction which mainly relied on dependency parsing and named entity recognition BIBREF10, BIBREF11, BIBREF12. Meanwhile, Fonferko et al. BIBREF9 used more components like noun phrase chunking BIBREF13, BIBREF14, BIBREF15, part-of-speech tagging BIBREF16, BIBREF17, BIBREF18, sentence splitter, named entity linking BIBREF19, BIBREF20, BIBREF21, relation extraction BIBREF22, BIBREF23. This kind of method focus on language itself, so it can handle tasks more general. However, as the depth of pipeline grows, it is obvious that error propagation will be more and more serious. In contrary, using less components to decrease the pipeline depth will lead to a poor performance. So the upper limit of this method depends mainly on the worst component."
+    ],
+    [
+      "Recently, some works focused on pre-trained language representation models to capture language information from text and then utilizing the information to improve the performance of specific natural language processing tasks BIBREF24, BIBREF25, BIBREF26, BIBREF27 which makes language model a shared model to all natural language processing tasks. Radford et al. BIBREF24 proposed a framework for fine-tuning pre-trained language model. Peters et al. BIBREF25 proposed ELMo which concatenates forward and backward language models in a shallow manner. Devlin et al. BIBREF26 used bidirectional Transformers to model deep interactions between the two directions. Yang et al. BIBREF27 replaced the fixed forward or backward factorization order with all possible permutations of the factorization order and avoided using the [MASK] tag which causes pretrain-finetune discrepancy that BERT is subject to.",
+      "The main motivation of introducing pre-trained language model is to solve the shortage of labeled data and polysemy problem. Although polysemy problem is not a common phenomenon in biomedical domain, shortage of labeled data is always a non-trivial problem. Lee et al. BIBREF28 applied BERT on large-scale biomedical unannotated data and achieved improvement on biomedical named entity recognition, relation extraction and question answering. Kim et al. BIBREF29 adapted BioBERT into multi-type named entity recognition and discovered new entities. Both of them demonstrates the usefulness of introducing pre-trained language model into biomedical domain."
+    ],
+    [
+      "Given a sequence of paragraph text $X=<x_1, x_2, ..., x_n>$, clinical text structuring (CTS) can be regarded to extract or generate a key-value pair where key $Q$ is typically a query term such as proximal resection margin and value $V$ is a result of query term $Q$ according to the paragraph text $X$.",
+      "Generally, researchers solve CTS problem in two steps. Firstly, the answer-related text is pick out. And then several steps such as entity names conversion and negative words recognition are deployed to generate the final answer. While final answer varies from task to task, which truly causes non-uniform output formats, finding the answer-related text is a common action among all tasks. Traditional methods regard both the steps as a whole. In this paper, we focus on finding the answer-related substring $Xs = <X_i, X_i+1, X_i+2, ... X_j> (1 <= i < j <= n)$ from paragraph text $X$. For example, given sentence UTF8gkai\u201c\u8fdc\u7aef\u80c3\u5207\u9664\u6807\u672c\uff1a\u5c0f\u5f2f\u957f11.5cm\uff0c\u5927\u5f2f\u957f17.0cm\u3002\u8ddd\u4e0a\u5207\u7aef6.0cm\u3001\u4e0b\u5207\u7aef8.0cm\" (Distal gastrectomy specimen: measuring 11.5cm in length along the lesser curvature, 17.0cm in length along the greater curvature; 6.0cm from the proximal resection margin, and 8.0cm from the distal resection margin) and query UTF8gkai\u201c\u4e0a\u5207\u7f18\u8ddd\u79bb\"(proximal resection margin), the answer should be 6.0cm which is located in original text from index 32 to 37. With such definition, it unifies the output format of CTS tasks and therefore make the training data shareable, in order to reduce the training data quantity requirement.",
+      "Since BERT BIBREF26 has already demonstrated the usefulness of shared model, we suppose extracting commonality of this problem and unifying the output format will make the model more powerful than dedicated model and meanwhile, for a specific clinical task, use the data for other tasks to supplement the training data."
+    ],
+    [
+      "In this section, we present an effective model for the question answering based clinical text structuring (QA-CTS). As shown in Fig. FIGREF8, paragraph text $X$ is first passed to a clinical named entity recognition (CNER) model BIBREF12 to capture named entity information and obtain one-hot CNER output tagging sequence for query text $I_{nq}$ and paragraph text $I_{nt}$ with BIEOS (Begin, Inside, End, Outside, Single) tag scheme. $I_{nq}$ and $I_{nt}$ are then integrated together into $I_n$. Meanwhile, the paragraph text $X$ and query text $Q$ are organized and passed to contextualized representation model which is pre-trained language model BERT BIBREF26 here to obtain the contextualized representation vector $V_s$ of both text and query. Afterwards, $V_s$ and $I_n$ are integrated together and fed into a feed forward network to calculate the start and end index of answer-related text. Here we define this calculation problem as a classification for each word to be the start or end word."
+    ],
+    [
+      "For any clinical free-text paragraph $X$ and query $Q$, contextualized representation is to generate the encoded vector of both of them. Here we use pre-trained language model BERT-base BIBREF26 model to capture contextual information.",
+      "The text input is constructed as `[CLS] $Q$ [SEP] $X$ [SEP]'. For Chinese sentence, each word in this input will be mapped to a pre-trained embedding $e_i$. To tell the model $Q$ and $X$ is two different sentence, a sentence type input is generated which is a binary label sequence to denote what sentence each character in the input belongs to. Positional encoding and mask matrix is also constructed automatically to bring in absolute position information and eliminate the impact of zero padding respectively. Then a hidden vector $V_s$ which contains both query and text information is generated through BERT-base model."
+    ],
+    [
+      "Since BERT is trained on general corpus, its performance on biomedical domain can be improved by introducing biomedical domain-specific features. In this paper, we introduce clinical named entity information into the model.",
+      "The CNER task aims to identify and classify important clinical terms such as diseases, symptoms, treatments, exams, and body parts from Chinese EHRs. It can be regarded as a sequence labeling task. A CNER model typically outputs a sequence of tags. Each character of the original sentence will be tagged a label following a tag scheme. In this paper we recognize the entities by the model of our previous work BIBREF12 but trained on another corpus which has 44 entity types including operations, numbers, unit words, examinations, symptoms, negative words, etc. An illustrative example of named entity information sequence is demonstrated in Table TABREF2. In Table TABREF2, UTF8gkai\u201c\u8fdc\u7aef\u80c3\u5207\u9664\" is tagged as an operation, `11.5' is a number word and `cm' is an unit word. The named entity tag sequence is organized in one-hot type. We denote the sequence for clinical sentence and query term as $I_{nt}$ and $I_{nq}$, respectively."
+    ],
+    [
+      "There are two ways to integrate two named entity information vectors $I_{nt}$ and $I_{nq}$ or hidden contextualized representation $V_s$ and named entity information $I_n$, where $I_n = [I_{nt}; I_{nq}]$. The first one is to concatenate them together because they have sequence output with a common dimension. The second one is to transform them into a new hidden representation. For the concatenation method, the integrated representation is described as follows.",
+      "While for the transformation method, we use multi-head attention BIBREF30 to encode the two vectors. It can be defined as follows where $h$ is the number of heads and $W_o$ is used to projects back the dimension of concatenated matrix.",
+      "$Attention$ denotes the traditional attention and it can be defined as follows.",
+      "where $d_k$ is the length of hidden vector."
+    ],
+    [
+      "The final step is to use integrated representation $H_i$ to predict the start and end index of answer-related text. Here we define this calculation problem as a classification for each word to be the start or end word. We use a feed forward network (FFN) to compress and calculate the score of each word $H_f$ which makes the dimension to $\\left\\langle l_s, 2\\right\\rangle $ where $l_s$ denotes the length of sequence.",
+      "Then we permute the two dimensions for softmax calculation. The calculation process of loss function can be defined as followed.",
+      "where $O_s = softmax(permute(H_f)_0)$ denotes the probability score of each word to be the start word and similarly $O_e = softmax(permute(H_f)_1)$ denotes the end. $y_s$ and $y_e$ denotes the true answer of the output for start word and end word respectively."
+    ],
+    [
+      "Two-stage training mechanism is previously applied on bilinear model in fine-grained visual recognition BIBREF31, BIBREF32, BIBREF33. Two CNNs are deployed in the model. One is trained at first for coarse-graind features while freezing the parameter of the other. Then unfreeze the other one and train the entire model in a low learning rate for fetching fine-grained features.",
+      "Inspired by this and due to the large amount of parameters in BERT model, to speed up the training process, we fine tune the BERT model with new prediction layer first to achieve a better contextualized representation performance. Then we deploy the proposed model and load the fine tuned BERT weights, attach named entity information layers and retrain the model."
+    ],
+    [
+      "In this section, we devote to experimentally evaluating our proposed task and approach. The best results in tables are in bold."
+    ],
+    [
+      "Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. These annotated instances have been partitioned into 1,899 training instances (12,412 sentences) and 815 test instances (5,421 sentences). Each instance has one or several sentences. Detailed statistics of different types of entities are listed in Table TABREF20.",
+      "In the following experiments, two widely-used performance measures (i.e., EM-score BIBREF34 and (macro-averaged) F$_1$-score BIBREF35) are used to evaluate the methods. The Exact Match (EM-score) metric measures the percentage of predictions that match any one of the ground truth answers exactly. The F$_1$-score metric is a looser metric measures the average overlap between the prediction and ground truth answer."
+    ],
+    [
+      "To implement deep neural network models, we utilize the Keras library BIBREF36 with TensorFlow BIBREF37 backend. Each model is run on a single NVIDIA GeForce GTX 1080 Ti GPU. The models are trained by Adam optimization algorithm BIBREF38 whose parameters are the same as the default settings except for learning rate set to $5\\times 10^{-5}$. Batch size is set to 3 or 4 due to the lack of graphical memory. We select BERT-base as the pre-trained language model in this paper. Due to the high cost of pre-training BERT language model, we directly adopt parameters pre-trained by Google in Chinese general corpus. The named entity recognition is applied on both pathology report texts and query texts."
+    ],
+    [
+      "Since BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26. As BERT has two versions: BERT-Base and BERT-Large, due to the lack of computational resource, we can only compare with BERT-Base model instead of BERT-Large. Prediction layer is attached at the end of the original BERT-Base model and we fine tune it on our dataset. In this section, the named entity integration method is chosen to pure concatenation (Concatenate the named entity information on pathology report text and query text first and then concatenate contextualized representation and concatenated named entity information). Comparative results are summarized in Table TABREF23.",
+      "Table TABREF23 indicates that our proposed model achieved the best performance both in EM-score and F$_1$-score with EM-score of 91.84% and F$_1$-score of 93.75%. QANet outperformed BERT-Base with 3.56% score in F$_1$-score but underperformed it with 0.75% score in EM-score. Compared with BERT-Base, our model led to a 5.64% performance improvement in EM-score and 3.69% in F$_1$-score. Although our model didn't outperform much with QANet in F$_1$-score (only 0.13%), our model significantly outperformed it with 6.39% score in EM-score."
+    ],
+    [
+      "To further investigate the effects of named entity information and two-stage training mechanism for our model, we apply ablation analysis to see the improvement brought by each of them, where $\\times $ refers to removing that part from our model.",
+      "As demonstrated in Table TABREF25, with named entity information enabled, two-stage training mechanism improved the result by 4.36% in EM-score and 3.8% in F$_1$-score. Without two-stage training mechanism, named entity information led to an improvement by 1.28% in EM-score but it also led to a weak deterioration by 0.12% in F$_1$-score. With both of them enabled, our proposed model achieved a 5.64% score improvement in EM-score and a 3.69% score improvement in F$_1$-score. The experimental results show that both named entity information and two-stage training mechanism are helpful to our model."
+    ],
+    [
+      "There are two methods to integrate named entity information into existing model, we experimentally compare these two integration methods. As named entity recognition has been applied on both pathology report text and query text, there will be two integration here. One is for two named entity information and the other is for contextualized representation and integrated named entity information. For multi-head attention BIBREF30, we set heads number $h = 16$ with 256-dimension hidden vector size for each head.",
+      "From Table TABREF27, we can observe that applying concatenation on both periods achieved the best performance on both EM-score and F$_1$-score. Unfortunately, applying multi-head attention on both period one and period two can not reach convergence in our experiments. This probably because it makes the model too complex to train. The difference on other two methods are the order of concatenation and multi-head attention. Applying multi-head attention on two named entity information $I_{nt}$ and $I_{nq}$ first achieved a better performance with 89.87% in EM-score and 92.88% in F$_1$-score. Applying Concatenation first can only achieve 80.74% in EM-score and 84.42% in F$_1$-score. This is probably due to the processing depth of hidden vectors and dataset size. BERT's output has been modified after many layers but named entity information representation is very close to input. With big amount of parameters in multi-head attention, it requires massive training to find out the optimal parameters. However, our dataset is significantly smaller than what pre-trained BERT uses. This probably can also explain why applying multi-head attention method on both periods can not converge.",
+      "Although Table TABREF27 shows the best integration method is concatenation, multi-head attention still has great potential. Due to the lack of computational resources, our experiment fixed the head number and hidden vector size. However, tuning these hyper parameters may have impact on the result. Tuning integration method and try to utilize larger datasets may give help to improving the performance."
+    ],
+    [
+      "To investigate how shared task and shared model can benefit, we split our dataset by query types, train our proposed model with different datasets and demonstrate their performance on different datasets. Firstly, we investigate the performance on model without two-stage training and named entity information.",
+      "As indicated in Table TABREF30, The model trained by mixed data outperforms 2 of the 3 original tasks in EM-score with 81.55% for proximal resection margin and 86.85% for distal resection margin. The performance on tumor size declined by 1.57% score in EM-score and 3.14% score in F$_1$-score but they were still above 90%. 0.69% and 0.37% score improvement in EM-score was brought by shared model for proximal and distal resection margin prediction. Meanwhile F$_1$-score for those two tasks declined 3.11% and 0.77% score.",
+      "Then we investigate the performance on model with two-stage training and named entity information. In this experiment, pre-training process only use the specific dataset not the mixed data. From Table TABREF31 we can observe that the performance on proximal and distal resection margin achieved the best performance on both EM-score and F$_1$-score. Compared with Table TABREF30, the best performance on proximal resection margin improved by 6.9% in EM-score and 7.94% in F$_1$-score. Meanwhile, the best performance on distal resection margin improved by 5.56% in EM-score and 6.32% in F$_1$-score. Other performances also usually improved a lot. This proves the usefulness of two-stage training and named entity information as well.",
+      "Lastly, we fine tune the model for each task with a pre-trained parameter. Table TABREF32 summarizes the result. (Add some explanations for the Table TABREF32). Comparing Table TABREF32 with Table TABREF31, using mixed-data pre-trained parameters can significantly improve the model performance than task-specific data trained model. Except tumor size, the result was improved by 0.52% score in EM-score, 1.39% score in F$_1$-score for proximal resection margin and 2.6% score in EM-score, 2.96% score in F$_1$-score for distal resection margin. This proves mixed-data pre-trained parameters can lead to a great benefit for specific task. Meanwhile, the model performance on other tasks which are not trained in the final stage was also improved from around 0 to 60 or 70 percent. This proves that there is commonality between different tasks and our proposed QA-CTS task make this learnable. In conclusion, to achieve the best performance for a specific dataset, pre-training the model in multiple datasets and then fine tuning the model on the specific dataset is the best way."
+    ],
+    [
+      "In this paper, we present a question answering based clinical text structuring (QA-CTS) task, which unifies different clinical text structuring tasks and utilize different datasets. A novel model is also proposed to integrate named entity information into a pre-trained language model and adapt it to QA-CTS task. Initially, sequential results of named entity recognition on both paragraph and query texts are integrated together. Contextualized representation on both paragraph and query texts are transformed by a pre-trained language model. Then, the integrated named entity information and contextualized representation are integrated together and fed into a feed forward network for final prediction. Experimental results on real-world dataset demonstrate that our proposed model competes favorably with strong baseline models in all three specific tasks. The shared task and shared model introduced by QA-CTS task has also been proved to be useful for improving the performance on most of the task-specific datasets. In conclusion, the best way to achieve the best performance for a specific dataset is to pre-train the model in multiple datasets and then fine tune it on the specific dataset."
+    ],
+    [
+      "We would like to thank Ting Li and Xizhou Hong (Ruijin Hospital) who have helped us very much in data fetching and data cleansing. This work is supported by the National Key R&D Program of China for \u201cPrecision Medical Research\" (No. 2018YFC0910500)."
+    ]
+  ]
+}
+```

qasper-0021/instruction.md

@@ -0,0 +1,118 @@
+Name of Paper: Question Answering based Clinical Text Structuring Using Pre-trained Language Model
+
+Question: What are the specific tasks being unified?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work ::: Clinical Text Structuring",
+    "Related Work ::: Pre-trained Language Model",
+    "Question Answering based Clinical Text Structuring",
+    "The Proposed Model for QA-CTS Task",
+    "The Proposed Model for QA-CTS Task ::: Contextualized Representation of Sentence Text and Query Text",
+    "The Proposed Model for QA-CTS Task ::: Clinical Named Entity Information",
+    "The Proposed Model for QA-CTS Task ::: Integration Method",
+    "The Proposed Model for QA-CTS Task ::: Final Prediction",
+    "The Proposed Model for QA-CTS Task ::: Two-Stage Training Mechanism",
+    "Experimental Studies",
+    "Experimental Studies ::: Dataset and Evaluation Metrics",
+    "Experimental Studies ::: Experimental Settings",
+    "Experimental Studies ::: Comparison with State-of-the-art Methods",
+    "Experimental Studies ::: Ablation Analysis",
+    "Experimental Studies ::: Comparisons Between Two Integration Methods",
+    "Experimental Studies ::: Data Integration Analysis",
+    "Conclusion",
+    "Acknowledgment"
+  ],
+  "paragraphs": [
+    [
+      "Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surgery, or what the specific laboratory test result is, are obtained. It is important to extract structured data from clinical text because bio-medical systems or bio-medical researches greatly rely on structured data but they cannot obtain them directly. In addition, clinical text often contains abundant healthcare information. CTS is able to provide large-scale extracted structured data for enormous down-stream clinical researches.",
+      "However, end-to-end CTS is a very challenging task. Different CTS tasks often have non-uniform output formats, such as specific-class classifications (e.g. tumor stage), strings in the original text (e.g. result for a laboratory test) and inferred values from part of the original text (e.g. calculated tumor size). Researchers have to construct different models for it, which is already costly, and hence it calls for a lot of labeled data for each model. Moreover, labeling necessary amount of data for training neural network requires expensive labor cost. To handle it, researchers turn to some rule-based structuring methods which often have lower labor cost.",
+      "Traditionally, CTS tasks can be addressed by rule and dictionary based methods BIBREF0, BIBREF1, BIBREF2, task-specific end-to-end methods BIBREF3, BIBREF4, BIBREF5, BIBREF6 and pipeline methods BIBREF7, BIBREF8, BIBREF9. Rule and dictionary based methods suffer from costly human-designed extraction rules, while task-specific end-to-end methods have non-uniform output formats and require task-specific training dataset. Pipeline methods break down the entire process into several pieces which improves the performance and generality. However, when the pipeline depth grows, error propagation will have a greater impact on the performance.",
+      "To reduce the pipeline depth and break the barrier of non-uniform output formats, we present a question answering based clinical text structuring (QA-CTS) task (see Fig. FIGREF1). Unlike the traditional CTS task, our QA-CTS task aims to discover the most related text from original paragraph text. For some cases, it is already the final answer in deed (e.g., extracting sub-string). While for other cases, it needs several steps to obtain the final answer, such as entity names conversion and negative words recognition. Our presented QA-CTS task unifies the output format of the traditional CTS task and make the training data shareable, thus enriching the training data. The main contributions of this work can be summarized as follows.",
+      "We first present a question answering based clinical text structuring (QA-CTS) task, which unifies different specific tasks and make dataset shareable. We also propose an effective model to integrate clinical named entity information into pre-trained language model.",
+      "Experimental results show that QA-CTS task leads to significant improvement due to shared dataset. Our proposed model also achieves significantly better performance than the strong baseline methods. In addition, we also show that two-stage training mechanism has a great improvement on QA-CTS task.",
+      "The rest of the paper is organized as follows. We briefly review the related work on clinical text structuring in Section SECREF2. Then, we present question answer based clinical text structuring task in Section SECREF3. In Section SECREF4, we present an effective model for this task. Section SECREF5 is devoted to computational studies and several investigations on the key issues of our proposed model. Finally, conclusions are given in Section SECREF6."
+    ],
+    [
+      "Clinical text structuring is a final problem which is highly related to practical applications. Most of existing studies are case-by-case. Few of them are developed for the general purpose structuring task. These studies can be roughly divided into three categories: rule and dictionary based methods, task-specific end-to-end methods and pipeline methods.",
+      "Rule and dictionary based methods BIBREF0, BIBREF1, BIBREF2 rely extremely on heuristics and handcrafted extraction rules which is more of an art than a science and incurring extensive trial-and-error experiments. Fukuda et al. BIBREF0 identified protein names from biological papers by dictionaries and several features of protein names. Wang et al. BIBREF1 developed some linguistic rules (i.e. normalised/expanded term matching and substring term matching) to map specific terminology to SNOMED CT. Song et al. BIBREF2 proposed a hybrid dictionary-based bio-entity extraction technique and expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. This kind of approach features its interpretability and easy modifiability. However, with the increase of the rule amount, supplementing new rules to existing system will turn to be a rule disaster.",
+      "Task-specific end-to-end methods BIBREF3, BIBREF4 use large amount of data to automatically model the specific task. Topaz et al. BIBREF3 constructed an automated wound information identification model with five output. Tan et al. BIBREF4 identified patients undergoing radical cystectomy for bladder cancer. Although they achieved good performance, none of their models could be used to another task due to output format difference. This makes building a new model for a new task a costly job.",
+      "Pipeline methods BIBREF7, BIBREF8, BIBREF9 break down the entire task into several basic natural language processing tasks. Bill et al. BIBREF7 focused on attributes extraction which mainly relied on dependency parsing and named entity recognition BIBREF10, BIBREF11, BIBREF12. Meanwhile, Fonferko et al. BIBREF9 used more components like noun phrase chunking BIBREF13, BIBREF14, BIBREF15, part-of-speech tagging BIBREF16, BIBREF17, BIBREF18, sentence splitter, named entity linking BIBREF19, BIBREF20, BIBREF21, relation extraction BIBREF22, BIBREF23. This kind of method focus on language itself, so it can handle tasks more general. However, as the depth of pipeline grows, it is obvious that error propagation will be more and more serious. In contrary, using less components to decrease the pipeline depth will lead to a poor performance. So the upper limit of this method depends mainly on the worst component."
+    ],
+    [
+      "Recently, some works focused on pre-trained language representation models to capture language information from text and then utilizing the information to improve the performance of specific natural language processing tasks BIBREF24, BIBREF25, BIBREF26, BIBREF27 which makes language model a shared model to all natural language processing tasks. Radford et al. BIBREF24 proposed a framework for fine-tuning pre-trained language model. Peters et al. BIBREF25 proposed ELMo which concatenates forward and backward language models in a shallow manner. Devlin et al. BIBREF26 used bidirectional Transformers to model deep interactions between the two directions. Yang et al. BIBREF27 replaced the fixed forward or backward factorization order with all possible permutations of the factorization order and avoided using the [MASK] tag which causes pretrain-finetune discrepancy that BERT is subject to.",
+      "The main motivation of introducing pre-trained language model is to solve the shortage of labeled data and polysemy problem. Although polysemy problem is not a common phenomenon in biomedical domain, shortage of labeled data is always a non-trivial problem. Lee et al. BIBREF28 applied BERT on large-scale biomedical unannotated data and achieved improvement on biomedical named entity recognition, relation extraction and question answering. Kim et al. BIBREF29 adapted BioBERT into multi-type named entity recognition and discovered new entities. Both of them demonstrates the usefulness of introducing pre-trained language model into biomedical domain."
+    ],
+    [
+      "Given a sequence of paragraph text $X=<x_1, x_2, ..., x_n>$, clinical text structuring (CTS) can be regarded to extract or generate a key-value pair where key $Q$ is typically a query term such as proximal resection margin and value $V$ is a result of query term $Q$ according to the paragraph text $X$.",
+      "Generally, researchers solve CTS problem in two steps. Firstly, the answer-related text is pick out. And then several steps such as entity names conversion and negative words recognition are deployed to generate the final answer. While final answer varies from task to task, which truly causes non-uniform output formats, finding the answer-related text is a common action among all tasks. Traditional methods regard both the steps as a whole. In this paper, we focus on finding the answer-related substring $Xs = <X_i, X_i+1, X_i+2, ... X_j> (1 <= i < j <= n)$ from paragraph text $X$. For example, given sentence UTF8gkai\u201c\u8fdc\u7aef\u80c3\u5207\u9664\u6807\u672c\uff1a\u5c0f\u5f2f\u957f11.5cm\uff0c\u5927\u5f2f\u957f17.0cm\u3002\u8ddd\u4e0a\u5207\u7aef6.0cm\u3001\u4e0b\u5207\u7aef8.0cm\" (Distal gastrectomy specimen: measuring 11.5cm in length along the lesser curvature, 17.0cm in length along the greater curvature; 6.0cm from the proximal resection margin, and 8.0cm from the distal resection margin) and query UTF8gkai\u201c\u4e0a\u5207\u7f18\u8ddd\u79bb\"(proximal resection margin), the answer should be 6.0cm which is located in original text from index 32 to 37. With such definition, it unifies the output format of CTS tasks and therefore make the training data shareable, in order to reduce the training data quantity requirement.",
+      "Since BERT BIBREF26 has already demonstrated the usefulness of shared model, we suppose extracting commonality of this problem and unifying the output format will make the model more powerful than dedicated model and meanwhile, for a specific clinical task, use the data for other tasks to supplement the training data."
+    ],
+    [
+      "In this section, we present an effective model for the question answering based clinical text structuring (QA-CTS). As shown in Fig. FIGREF8, paragraph text $X$ is first passed to a clinical named entity recognition (CNER) model BIBREF12 to capture named entity information and obtain one-hot CNER output tagging sequence for query text $I_{nq}$ and paragraph text $I_{nt}$ with BIEOS (Begin, Inside, End, Outside, Single) tag scheme. $I_{nq}$ and $I_{nt}$ are then integrated together into $I_n$. Meanwhile, the paragraph text $X$ and query text $Q$ are organized and passed to contextualized representation model which is pre-trained language model BERT BIBREF26 here to obtain the contextualized representation vector $V_s$ of both text and query. Afterwards, $V_s$ and $I_n$ are integrated together and fed into a feed forward network to calculate the start and end index of answer-related text. Here we define this calculation problem as a classification for each word to be the start or end word."
+    ],
+    [
+      "For any clinical free-text paragraph $X$ and query $Q$, contextualized representation is to generate the encoded vector of both of them. Here we use pre-trained language model BERT-base BIBREF26 model to capture contextual information.",
+      "The text input is constructed as `[CLS] $Q$ [SEP] $X$ [SEP]'. For Chinese sentence, each word in this input will be mapped to a pre-trained embedding $e_i$. To tell the model $Q$ and $X$ is two different sentence, a sentence type input is generated which is a binary label sequence to denote what sentence each character in the input belongs to. Positional encoding and mask matrix is also constructed automatically to bring in absolute position information and eliminate the impact of zero padding respectively. Then a hidden vector $V_s$ which contains both query and text information is generated through BERT-base model."
+    ],
+    [
+      "Since BERT is trained on general corpus, its performance on biomedical domain can be improved by introducing biomedical domain-specific features. In this paper, we introduce clinical named entity information into the model.",
+      "The CNER task aims to identify and classify important clinical terms such as diseases, symptoms, treatments, exams, and body parts from Chinese EHRs. It can be regarded as a sequence labeling task. A CNER model typically outputs a sequence of tags. Each character of the original sentence will be tagged a label following a tag scheme. In this paper we recognize the entities by the model of our previous work BIBREF12 but trained on another corpus which has 44 entity types including operations, numbers, unit words, examinations, symptoms, negative words, etc. An illustrative example of named entity information sequence is demonstrated in Table TABREF2. In Table TABREF2, UTF8gkai\u201c\u8fdc\u7aef\u80c3\u5207\u9664\" is tagged as an operation, `11.5' is a number word and `cm' is an unit word. The named entity tag sequence is organized in one-hot type. We denote the sequence for clinical sentence and query term as $I_{nt}$ and $I_{nq}$, respectively."
+    ],
+    [
+      "There are two ways to integrate two named entity information vectors $I_{nt}$ and $I_{nq}$ or hidden contextualized representation $V_s$ and named entity information $I_n$, where $I_n = [I_{nt}; I_{nq}]$. The first one is to concatenate them together because they have sequence output with a common dimension. The second one is to transform them into a new hidden representation. For the concatenation method, the integrated representation is described as follows.",
+      "While for the transformation method, we use multi-head attention BIBREF30 to encode the two vectors. It can be defined as follows where $h$ is the number of heads and $W_o$ is used to projects back the dimension of concatenated matrix.",
+      "$Attention$ denotes the traditional attention and it can be defined as follows.",
+      "where $d_k$ is the length of hidden vector."
+    ],
+    [
+      "The final step is to use integrated representation $H_i$ to predict the start and end index of answer-related text. Here we define this calculation problem as a classification for each word to be the start or end word. We use a feed forward network (FFN) to compress and calculate the score of each word $H_f$ which makes the dimension to $\\left\\langle l_s, 2\\right\\rangle $ where $l_s$ denotes the length of sequence.",
+      "Then we permute the two dimensions for softmax calculation. The calculation process of loss function can be defined as followed.",
+      "where $O_s = softmax(permute(H_f)_0)$ denotes the probability score of each word to be the start word and similarly $O_e = softmax(permute(H_f)_1)$ denotes the end. $y_s$ and $y_e$ denotes the true answer of the output for start word and end word respectively."
+    ],
+    [
+      "Two-stage training mechanism is previously applied on bilinear model in fine-grained visual recognition BIBREF31, BIBREF32, BIBREF33. Two CNNs are deployed in the model. One is trained at first for coarse-graind features while freezing the parameter of the other. Then unfreeze the other one and train the entire model in a low learning rate for fetching fine-grained features.",
+      "Inspired by this and due to the large amount of parameters in BERT model, to speed up the training process, we fine tune the BERT model with new prediction layer first to achieve a better contextualized representation performance. Then we deploy the proposed model and load the fine tuned BERT weights, attach named entity information layers and retrain the model."
+    ],
+    [
+      "In this section, we devote to experimentally evaluating our proposed task and approach. The best results in tables are in bold."
+    ],
+    [
+      "Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. These annotated instances have been partitioned into 1,899 training instances (12,412 sentences) and 815 test instances (5,421 sentences). Each instance has one or several sentences. Detailed statistics of different types of entities are listed in Table TABREF20.",
+      "In the following experiments, two widely-used performance measures (i.e., EM-score BIBREF34 and (macro-averaged) F$_1$-score BIBREF35) are used to evaluate the methods. The Exact Match (EM-score) metric measures the percentage of predictions that match any one of the ground truth answers exactly. The F$_1$-score metric is a looser metric measures the average overlap between the prediction and ground truth answer."
+    ],
+    [
+      "To implement deep neural network models, we utilize the Keras library BIBREF36 with TensorFlow BIBREF37 backend. Each model is run on a single NVIDIA GeForce GTX 1080 Ti GPU. The models are trained by Adam optimization algorithm BIBREF38 whose parameters are the same as the default settings except for learning rate set to $5\\times 10^{-5}$. Batch size is set to 3 or 4 due to the lack of graphical memory. We select BERT-base as the pre-trained language model in this paper. Due to the high cost of pre-training BERT language model, we directly adopt parameters pre-trained by Google in Chinese general corpus. The named entity recognition is applied on both pathology report texts and query texts."
+    ],
+    [
+      "Since BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26. As BERT has two versions: BERT-Base and BERT-Large, due to the lack of computational resource, we can only compare with BERT-Base model instead of BERT-Large. Prediction layer is attached at the end of the original BERT-Base model and we fine tune it on our dataset. In this section, the named entity integration method is chosen to pure concatenation (Concatenate the named entity information on pathology report text and query text first and then concatenate contextualized representation and concatenated named entity information). Comparative results are summarized in Table TABREF23.",
+      "Table TABREF23 indicates that our proposed model achieved the best performance both in EM-score and F$_1$-score with EM-score of 91.84% and F$_1$-score of 93.75%. QANet outperformed BERT-Base with 3.56% score in F$_1$-score but underperformed it with 0.75% score in EM-score. Compared with BERT-Base, our model led to a 5.64% performance improvement in EM-score and 3.69% in F$_1$-score. Although our model didn't outperform much with QANet in F$_1$-score (only 0.13%), our model significantly outperformed it with 6.39% score in EM-score."
+    ],
+    [
+      "To further investigate the effects of named entity information and two-stage training mechanism for our model, we apply ablation analysis to see the improvement brought by each of them, where $\\times $ refers to removing that part from our model.",
+      "As demonstrated in Table TABREF25, with named entity information enabled, two-stage training mechanism improved the result by 4.36% in EM-score and 3.8% in F$_1$-score. Without two-stage training mechanism, named entity information led to an improvement by 1.28% in EM-score but it also led to a weak deterioration by 0.12% in F$_1$-score. With both of them enabled, our proposed model achieved a 5.64% score improvement in EM-score and a 3.69% score improvement in F$_1$-score. The experimental results show that both named entity information and two-stage training mechanism are helpful to our model."
+    ],
+    [
+      "There are two methods to integrate named entity information into existing model, we experimentally compare these two integration methods. As named entity recognition has been applied on both pathology report text and query text, there will be two integration here. One is for two named entity information and the other is for contextualized representation and integrated named entity information. For multi-head attention BIBREF30, we set heads number $h = 16$ with 256-dimension hidden vector size for each head.",
+      "From Table TABREF27, we can observe that applying concatenation on both periods achieved the best performance on both EM-score and F$_1$-score. Unfortunately, applying multi-head attention on both period one and period two can not reach convergence in our experiments. This probably because it makes the model too complex to train. The difference on other two methods are the order of concatenation and multi-head attention. Applying multi-head attention on two named entity information $I_{nt}$ and $I_{nq}$ first achieved a better performance with 89.87% in EM-score and 92.88% in F$_1$-score. Applying Concatenation first can only achieve 80.74% in EM-score and 84.42% in F$_1$-score. This is probably due to the processing depth of hidden vectors and dataset size. BERT's output has been modified after many layers but named entity information representation is very close to input. With big amount of parameters in multi-head attention, it requires massive training to find out the optimal parameters. However, our dataset is significantly smaller than what pre-trained BERT uses. This probably can also explain why applying multi-head attention method on both periods can not converge.",
+      "Although Table TABREF27 shows the best integration method is concatenation, multi-head attention still has great potential. Due to the lack of computational resources, our experiment fixed the head number and hidden vector size. However, tuning these hyper parameters may have impact on the result. Tuning integration method and try to utilize larger datasets may give help to improving the performance."
+    ],
+    [
+      "To investigate how shared task and shared model can benefit, we split our dataset by query types, train our proposed model with different datasets and demonstrate their performance on different datasets. Firstly, we investigate the performance on model without two-stage training and named entity information.",
+      "As indicated in Table TABREF30, The model trained by mixed data outperforms 2 of the 3 original tasks in EM-score with 81.55% for proximal resection margin and 86.85% for distal resection margin. The performance on tumor size declined by 1.57% score in EM-score and 3.14% score in F$_1$-score but they were still above 90%. 0.69% and 0.37% score improvement in EM-score was brought by shared model for proximal and distal resection margin prediction. Meanwhile F$_1$-score for those two tasks declined 3.11% and 0.77% score.",
+      "Then we investigate the performance on model with two-stage training and named entity information. In this experiment, pre-training process only use the specific dataset not the mixed data. From Table TABREF31 we can observe that the performance on proximal and distal resection margin achieved the best performance on both EM-score and F$_1$-score. Compared with Table TABREF30, the best performance on proximal resection margin improved by 6.9% in EM-score and 7.94% in F$_1$-score. Meanwhile, the best performance on distal resection margin improved by 5.56% in EM-score and 6.32% in F$_1$-score. Other performances also usually improved a lot. This proves the usefulness of two-stage training and named entity information as well.",
+      "Lastly, we fine tune the model for each task with a pre-trained parameter. Table TABREF32 summarizes the result. (Add some explanations for the Table TABREF32). Comparing Table TABREF32 with Table TABREF31, using mixed-data pre-trained parameters can significantly improve the model performance than task-specific data trained model. Except tumor size, the result was improved by 0.52% score in EM-score, 1.39% score in F$_1$-score for proximal resection margin and 2.6% score in EM-score, 2.96% score in F$_1$-score for distal resection margin. This proves mixed-data pre-trained parameters can lead to a great benefit for specific task. Meanwhile, the model performance on other tasks which are not trained in the final stage was also improved from around 0 to 60 or 70 percent. This proves that there is commonality between different tasks and our proposed QA-CTS task make this learnable. In conclusion, to achieve the best performance for a specific dataset, pre-training the model in multiple datasets and then fine tuning the model on the specific dataset is the best way."
+    ],
+    [
+      "In this paper, we present a question answering based clinical text structuring (QA-CTS) task, which unifies different clinical text structuring tasks and utilize different datasets. A novel model is also proposed to integrate named entity information into a pre-trained language model and adapt it to QA-CTS task. Initially, sequential results of named entity recognition on both paragraph and query texts are integrated together. Contextualized representation on both paragraph and query texts are transformed by a pre-trained language model. Then, the integrated named entity information and contextualized representation are integrated together and fed into a feed forward network for final prediction. Experimental results on real-world dataset demonstrate that our proposed model competes favorably with strong baseline models in all three specific tasks. The shared task and shared model introduced by QA-CTS task has also been proved to be useful for improving the performance on most of the task-specific datasets. In conclusion, the best way to achieve the best performance for a specific dataset is to pre-train the model in multiple datasets and then fine tune it on the specific dataset."
+    ],
+    [
+      "We would like to thank Ting Li and Xizhou Hong (Ruijin Hospital) who have helped us very much in data fetching and data cleansing. This work is supported by the National Key R&D Program of China for \u201cPrecision Medical Research\" (No. 2018YFC0910500)."
+    ]
+  ]
+}
+```

qasper-0026/instruction.md

@@ -0,0 +1,118 @@
+Name of Paper: Question Answering based Clinical Text Structuring Using Pre-trained Language Model
+
+Question: How they introduce domain-specific features into pre-trained language model?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work ::: Clinical Text Structuring",
+    "Related Work ::: Pre-trained Language Model",
+    "Question Answering based Clinical Text Structuring",
+    "The Proposed Model for QA-CTS Task",
+    "The Proposed Model for QA-CTS Task ::: Contextualized Representation of Sentence Text and Query Text",
+    "The Proposed Model for QA-CTS Task ::: Clinical Named Entity Information",
+    "The Proposed Model for QA-CTS Task ::: Integration Method",
+    "The Proposed Model for QA-CTS Task ::: Final Prediction",
+    "The Proposed Model for QA-CTS Task ::: Two-Stage Training Mechanism",
+    "Experimental Studies",
+    "Experimental Studies ::: Dataset and Evaluation Metrics",
+    "Experimental Studies ::: Experimental Settings",
+    "Experimental Studies ::: Comparison with State-of-the-art Methods",
+    "Experimental Studies ::: Ablation Analysis",
+    "Experimental Studies ::: Comparisons Between Two Integration Methods",
+    "Experimental Studies ::: Data Integration Analysis",
+    "Conclusion",
+    "Acknowledgment"
+  ],
+  "paragraphs": [
+    [
+      "Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surgery, or what the specific laboratory test result is, are obtained. It is important to extract structured data from clinical text because bio-medical systems or bio-medical researches greatly rely on structured data but they cannot obtain them directly. In addition, clinical text often contains abundant healthcare information. CTS is able to provide large-scale extracted structured data for enormous down-stream clinical researches.",
+      "However, end-to-end CTS is a very challenging task. Different CTS tasks often have non-uniform output formats, such as specific-class classifications (e.g. tumor stage), strings in the original text (e.g. result for a laboratory test) and inferred values from part of the original text (e.g. calculated tumor size). Researchers have to construct different models for it, which is already costly, and hence it calls for a lot of labeled data for each model. Moreover, labeling necessary amount of data for training neural network requires expensive labor cost. To handle it, researchers turn to some rule-based structuring methods which often have lower labor cost.",
+      "Traditionally, CTS tasks can be addressed by rule and dictionary based methods BIBREF0, BIBREF1, BIBREF2, task-specific end-to-end methods BIBREF3, BIBREF4, BIBREF5, BIBREF6 and pipeline methods BIBREF7, BIBREF8, BIBREF9. Rule and dictionary based methods suffer from costly human-designed extraction rules, while task-specific end-to-end methods have non-uniform output formats and require task-specific training dataset. Pipeline methods break down the entire process into several pieces which improves the performance and generality. However, when the pipeline depth grows, error propagation will have a greater impact on the performance.",
+      "To reduce the pipeline depth and break the barrier of non-uniform output formats, we present a question answering based clinical text structuring (QA-CTS) task (see Fig. FIGREF1). Unlike the traditional CTS task, our QA-CTS task aims to discover the most related text from original paragraph text. For some cases, it is already the final answer in deed (e.g., extracting sub-string). While for other cases, it needs several steps to obtain the final answer, such as entity names conversion and negative words recognition. Our presented QA-CTS task unifies the output format of the traditional CTS task and make the training data shareable, thus enriching the training data. The main contributions of this work can be summarized as follows.",
+      "We first present a question answering based clinical text structuring (QA-CTS) task, which unifies different specific tasks and make dataset shareable. We also propose an effective model to integrate clinical named entity information into pre-trained language model.",
+      "Experimental results show that QA-CTS task leads to significant improvement due to shared dataset. Our proposed model also achieves significantly better performance than the strong baseline methods. In addition, we also show that two-stage training mechanism has a great improvement on QA-CTS task.",
+      "The rest of the paper is organized as follows. We briefly review the related work on clinical text structuring in Section SECREF2. Then, we present question answer based clinical text structuring task in Section SECREF3. In Section SECREF4, we present an effective model for this task. Section SECREF5 is devoted to computational studies and several investigations on the key issues of our proposed model. Finally, conclusions are given in Section SECREF6."
+    ],
+    [
+      "Clinical text structuring is a final problem which is highly related to practical applications. Most of existing studies are case-by-case. Few of them are developed for the general purpose structuring task. These studies can be roughly divided into three categories: rule and dictionary based methods, task-specific end-to-end methods and pipeline methods.",
+      "Rule and dictionary based methods BIBREF0, BIBREF1, BIBREF2 rely extremely on heuristics and handcrafted extraction rules which is more of an art than a science and incurring extensive trial-and-error experiments. Fukuda et al. BIBREF0 identified protein names from biological papers by dictionaries and several features of protein names. Wang et al. BIBREF1 developed some linguistic rules (i.e. normalised/expanded term matching and substring term matching) to map specific terminology to SNOMED CT. Song et al. BIBREF2 proposed a hybrid dictionary-based bio-entity extraction technique and expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. This kind of approach features its interpretability and easy modifiability. However, with the increase of the rule amount, supplementing new rules to existing system will turn to be a rule disaster.",
+      "Task-specific end-to-end methods BIBREF3, BIBREF4 use large amount of data to automatically model the specific task. Topaz et al. BIBREF3 constructed an automated wound information identification model with five output. Tan et al. BIBREF4 identified patients undergoing radical cystectomy for bladder cancer. Although they achieved good performance, none of their models could be used to another task due to output format difference. This makes building a new model for a new task a costly job.",
+      "Pipeline methods BIBREF7, BIBREF8, BIBREF9 break down the entire task into several basic natural language processing tasks. Bill et al. BIBREF7 focused on attributes extraction which mainly relied on dependency parsing and named entity recognition BIBREF10, BIBREF11, BIBREF12. Meanwhile, Fonferko et al. BIBREF9 used more components like noun phrase chunking BIBREF13, BIBREF14, BIBREF15, part-of-speech tagging BIBREF16, BIBREF17, BIBREF18, sentence splitter, named entity linking BIBREF19, BIBREF20, BIBREF21, relation extraction BIBREF22, BIBREF23. This kind of method focus on language itself, so it can handle tasks more general. However, as the depth of pipeline grows, it is obvious that error propagation will be more and more serious. In contrary, using less components to decrease the pipeline depth will lead to a poor performance. So the upper limit of this method depends mainly on the worst component."
+    ],
+    [
+      "Recently, some works focused on pre-trained language representation models to capture language information from text and then utilizing the information to improve the performance of specific natural language processing tasks BIBREF24, BIBREF25, BIBREF26, BIBREF27 which makes language model a shared model to all natural language processing tasks. Radford et al. BIBREF24 proposed a framework for fine-tuning pre-trained language model. Peters et al. BIBREF25 proposed ELMo which concatenates forward and backward language models in a shallow manner. Devlin et al. BIBREF26 used bidirectional Transformers to model deep interactions between the two directions. Yang et al. BIBREF27 replaced the fixed forward or backward factorization order with all possible permutations of the factorization order and avoided using the [MASK] tag which causes pretrain-finetune discrepancy that BERT is subject to.",
+      "The main motivation of introducing pre-trained language model is to solve the shortage of labeled data and polysemy problem. Although polysemy problem is not a common phenomenon in biomedical domain, shortage of labeled data is always a non-trivial problem. Lee et al. BIBREF28 applied BERT on large-scale biomedical unannotated data and achieved improvement on biomedical named entity recognition, relation extraction and question answering. Kim et al. BIBREF29 adapted BioBERT into multi-type named entity recognition and discovered new entities. Both of them demonstrates the usefulness of introducing pre-trained language model into biomedical domain."
+    ],
+    [
+      "Given a sequence of paragraph text $X=<x_1, x_2, ..., x_n>$, clinical text structuring (CTS) can be regarded to extract or generate a key-value pair where key $Q$ is typically a query term such as proximal resection margin and value $V$ is a result of query term $Q$ according to the paragraph text $X$.",
+      "Generally, researchers solve CTS problem in two steps. Firstly, the answer-related text is pick out. And then several steps such as entity names conversion and negative words recognition are deployed to generate the final answer. While final answer varies from task to task, which truly causes non-uniform output formats, finding the answer-related text is a common action among all tasks. Traditional methods regard both the steps as a whole. In this paper, we focus on finding the answer-related substring $Xs = <X_i, X_i+1, X_i+2, ... X_j> (1 <= i < j <= n)$ from paragraph text $X$. For example, given sentence UTF8gkai\u201c\u8fdc\u7aef\u80c3\u5207\u9664\u6807\u672c\uff1a\u5c0f\u5f2f\u957f11.5cm\uff0c\u5927\u5f2f\u957f17.0cm\u3002\u8ddd\u4e0a\u5207\u7aef6.0cm\u3001\u4e0b\u5207\u7aef8.0cm\" (Distal gastrectomy specimen: measuring 11.5cm in length along the lesser curvature, 17.0cm in length along the greater curvature; 6.0cm from the proximal resection margin, and 8.0cm from the distal resection margin) and query UTF8gkai\u201c\u4e0a\u5207\u7f18\u8ddd\u79bb\"(proximal resection margin), the answer should be 6.0cm which is located in original text from index 32 to 37. With such definition, it unifies the output format of CTS tasks and therefore make the training data shareable, in order to reduce the training data quantity requirement.",
+      "Since BERT BIBREF26 has already demonstrated the usefulness of shared model, we suppose extracting commonality of this problem and unifying the output format will make the model more powerful than dedicated model and meanwhile, for a specific clinical task, use the data for other tasks to supplement the training data."
+    ],
+    [
+      "In this section, we present an effective model for the question answering based clinical text structuring (QA-CTS). As shown in Fig. FIGREF8, paragraph text $X$ is first passed to a clinical named entity recognition (CNER) model BIBREF12 to capture named entity information and obtain one-hot CNER output tagging sequence for query text $I_{nq}$ and paragraph text $I_{nt}$ with BIEOS (Begin, Inside, End, Outside, Single) tag scheme. $I_{nq}$ and $I_{nt}$ are then integrated together into $I_n$. Meanwhile, the paragraph text $X$ and query text $Q$ are organized and passed to contextualized representation model which is pre-trained language model BERT BIBREF26 here to obtain the contextualized representation vector $V_s$ of both text and query. Afterwards, $V_s$ and $I_n$ are integrated together and fed into a feed forward network to calculate the start and end index of answer-related text. Here we define this calculation problem as a classification for each word to be the start or end word."
+    ],
+    [
+      "For any clinical free-text paragraph $X$ and query $Q$, contextualized representation is to generate the encoded vector of both of them. Here we use pre-trained language model BERT-base BIBREF26 model to capture contextual information.",
+      "The text input is constructed as `[CLS] $Q$ [SEP] $X$ [SEP]'. For Chinese sentence, each word in this input will be mapped to a pre-trained embedding $e_i$. To tell the model $Q$ and $X$ is two different sentence, a sentence type input is generated which is a binary label sequence to denote what sentence each character in the input belongs to. Positional encoding and mask matrix is also constructed automatically to bring in absolute position information and eliminate the impact of zero padding respectively. Then a hidden vector $V_s$ which contains both query and text information is generated through BERT-base model."
+    ],
+    [
+      "Since BERT is trained on general corpus, its performance on biomedical domain can be improved by introducing biomedical domain-specific features. In this paper, we introduce clinical named entity information into the model.",
+      "The CNER task aims to identify and classify important clinical terms such as diseases, symptoms, treatments, exams, and body parts from Chinese EHRs. It can be regarded as a sequence labeling task. A CNER model typically outputs a sequence of tags. Each character of the original sentence will be tagged a label following a tag scheme. In this paper we recognize the entities by the model of our previous work BIBREF12 but trained on another corpus which has 44 entity types including operations, numbers, unit words, examinations, symptoms, negative words, etc. An illustrative example of named entity information sequence is demonstrated in Table TABREF2. In Table TABREF2, UTF8gkai\u201c\u8fdc\u7aef\u80c3\u5207\u9664\" is tagged as an operation, `11.5' is a number word and `cm' is an unit word. The named entity tag sequence is organized in one-hot type. We denote the sequence for clinical sentence and query term as $I_{nt}$ and $I_{nq}$, respectively."
+    ],
+    [
+      "There are two ways to integrate two named entity information vectors $I_{nt}$ and $I_{nq}$ or hidden contextualized representation $V_s$ and named entity information $I_n$, where $I_n = [I_{nt}; I_{nq}]$. The first one is to concatenate them together because they have sequence output with a common dimension. The second one is to transform them into a new hidden representation. For the concatenation method, the integrated representation is described as follows.",
+      "While for the transformation method, we use multi-head attention BIBREF30 to encode the two vectors. It can be defined as follows where $h$ is the number of heads and $W_o$ is used to projects back the dimension of concatenated matrix.",
+      "$Attention$ denotes the traditional attention and it can be defined as follows.",
+      "where $d_k$ is the length of hidden vector."
+    ],
+    [
+      "The final step is to use integrated representation $H_i$ to predict the start and end index of answer-related text. Here we define this calculation problem as a classification for each word to be the start or end word. We use a feed forward network (FFN) to compress and calculate the score of each word $H_f$ which makes the dimension to $\\left\\langle l_s, 2\\right\\rangle $ where $l_s$ denotes the length of sequence.",
+      "Then we permute the two dimensions for softmax calculation. The calculation process of loss function can be defined as followed.",
+      "where $O_s = softmax(permute(H_f)_0)$ denotes the probability score of each word to be the start word and similarly $O_e = softmax(permute(H_f)_1)$ denotes the end. $y_s$ and $y_e$ denotes the true answer of the output for start word and end word respectively."
+    ],
+    [
+      "Two-stage training mechanism is previously applied on bilinear model in fine-grained visual recognition BIBREF31, BIBREF32, BIBREF33. Two CNNs are deployed in the model. One is trained at first for coarse-graind features while freezing the parameter of the other. Then unfreeze the other one and train the entire model in a low learning rate for fetching fine-grained features.",
+      "Inspired by this and due to the large amount of parameters in BERT model, to speed up the training process, we fine tune the BERT model with new prediction layer first to achieve a better contextualized representation performance. Then we deploy the proposed model and load the fine tuned BERT weights, attach named entity information layers and retrain the model."
+    ],
+    [
+      "In this section, we devote to experimentally evaluating our proposed task and approach. The best results in tables are in bold."
+    ],
+    [
+      "Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. These annotated instances have been partitioned into 1,899 training instances (12,412 sentences) and 815 test instances (5,421 sentences). Each instance has one or several sentences. Detailed statistics of different types of entities are listed in Table TABREF20.",
+      "In the following experiments, two widely-used performance measures (i.e., EM-score BIBREF34 and (macro-averaged) F$_1$-score BIBREF35) are used to evaluate the methods. The Exact Match (EM-score) metric measures the percentage of predictions that match any one of the ground truth answers exactly. The F$_1$-score metric is a looser metric measures the average overlap between the prediction and ground truth answer."
+    ],
+    [
+      "To implement deep neural network models, we utilize the Keras library BIBREF36 with TensorFlow BIBREF37 backend. Each model is run on a single NVIDIA GeForce GTX 1080 Ti GPU. The models are trained by Adam optimization algorithm BIBREF38 whose parameters are the same as the default settings except for learning rate set to $5\\times 10^{-5}$. Batch size is set to 3 or 4 due to the lack of graphical memory. We select BERT-base as the pre-trained language model in this paper. Due to the high cost of pre-training BERT language model, we directly adopt parameters pre-trained by Google in Chinese general corpus. The named entity recognition is applied on both pathology report texts and query texts."
+    ],
+    [
+      "Since BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26. As BERT has two versions: BERT-Base and BERT-Large, due to the lack of computational resource, we can only compare with BERT-Base model instead of BERT-Large. Prediction layer is attached at the end of the original BERT-Base model and we fine tune it on our dataset. In this section, the named entity integration method is chosen to pure concatenation (Concatenate the named entity information on pathology report text and query text first and then concatenate contextualized representation and concatenated named entity information). Comparative results are summarized in Table TABREF23.",
+      "Table TABREF23 indicates that our proposed model achieved the best performance both in EM-score and F$_1$-score with EM-score of 91.84% and F$_1$-score of 93.75%. QANet outperformed BERT-Base with 3.56% score in F$_1$-score but underperformed it with 0.75% score in EM-score. Compared with BERT-Base, our model led to a 5.64% performance improvement in EM-score and 3.69% in F$_1$-score. Although our model didn't outperform much with QANet in F$_1$-score (only 0.13%), our model significantly outperformed it with 6.39% score in EM-score."
+    ],
+    [
+      "To further investigate the effects of named entity information and two-stage training mechanism for our model, we apply ablation analysis to see the improvement brought by each of them, where $\\times $ refers to removing that part from our model.",
+      "As demonstrated in Table TABREF25, with named entity information enabled, two-stage training mechanism improved the result by 4.36% in EM-score and 3.8% in F$_1$-score. Without two-stage training mechanism, named entity information led to an improvement by 1.28% in EM-score but it also led to a weak deterioration by 0.12% in F$_1$-score. With both of them enabled, our proposed model achieved a 5.64% score improvement in EM-score and a 3.69% score improvement in F$_1$-score. The experimental results show that both named entity information and two-stage training mechanism are helpful to our model."
+    ],
+    [
+      "There are two methods to integrate named entity information into existing model, we experimentally compare these two integration methods. As named entity recognition has been applied on both pathology report text and query text, there will be two integration here. One is for two named entity information and the other is for contextualized representation and integrated named entity information. For multi-head attention BIBREF30, we set heads number $h = 16$ with 256-dimension hidden vector size for each head.",
+      "From Table TABREF27, we can observe that applying concatenation on both periods achieved the best performance on both EM-score and F$_1$-score. Unfortunately, applying multi-head attention on both period one and period two can not reach convergence in our experiments. This probably because it makes the model too complex to train. The difference on other two methods are the order of concatenation and multi-head attention. Applying multi-head attention on two named entity information $I_{nt}$ and $I_{nq}$ first achieved a better performance with 89.87% in EM-score and 92.88% in F$_1$-score. Applying Concatenation first can only achieve 80.74% in EM-score and 84.42% in F$_1$-score. This is probably due to the processing depth of hidden vectors and dataset size. BERT's output has been modified after many layers but named entity information representation is very close to input. With big amount of parameters in multi-head attention, it requires massive training to find out the optimal parameters. However, our dataset is significantly smaller than what pre-trained BERT uses. This probably can also explain why applying multi-head attention method on both periods can not converge.",
+      "Although Table TABREF27 shows the best integration method is concatenation, multi-head attention still has great potential. Due to the lack of computational resources, our experiment fixed the head number and hidden vector size. However, tuning these hyper parameters may have impact on the result. Tuning integration method and try to utilize larger datasets may give help to improving the performance."
+    ],
+    [
+      "To investigate how shared task and shared model can benefit, we split our dataset by query types, train our proposed model with different datasets and demonstrate their performance on different datasets. Firstly, we investigate the performance on model without two-stage training and named entity information.",
+      "As indicated in Table TABREF30, The model trained by mixed data outperforms 2 of the 3 original tasks in EM-score with 81.55% for proximal resection margin and 86.85% for distal resection margin. The performance on tumor size declined by 1.57% score in EM-score and 3.14% score in F$_1$-score but they were still above 90%. 0.69% and 0.37% score improvement in EM-score was brought by shared model for proximal and distal resection margin prediction. Meanwhile F$_1$-score for those two tasks declined 3.11% and 0.77% score.",
+      "Then we investigate the performance on model with two-stage training and named entity information. In this experiment, pre-training process only use the specific dataset not the mixed data. From Table TABREF31 we can observe that the performance on proximal and distal resection margin achieved the best performance on both EM-score and F$_1$-score. Compared with Table TABREF30, the best performance on proximal resection margin improved by 6.9% in EM-score and 7.94% in F$_1$-score. Meanwhile, the best performance on distal resection margin improved by 5.56% in EM-score and 6.32% in F$_1$-score. Other performances also usually improved a lot. This proves the usefulness of two-stage training and named entity information as well.",
+      "Lastly, we fine tune the model for each task with a pre-trained parameter. Table TABREF32 summarizes the result. (Add some explanations for the Table TABREF32). Comparing Table TABREF32 with Table TABREF31, using mixed-data pre-trained parameters can significantly improve the model performance than task-specific data trained model. Except tumor size, the result was improved by 0.52% score in EM-score, 1.39% score in F$_1$-score for proximal resection margin and 2.6% score in EM-score, 2.96% score in F$_1$-score for distal resection margin. This proves mixed-data pre-trained parameters can lead to a great benefit for specific task. Meanwhile, the model performance on other tasks which are not trained in the final stage was also improved from around 0 to 60 or 70 percent. This proves that there is commonality between different tasks and our proposed QA-CTS task make this learnable. In conclusion, to achieve the best performance for a specific dataset, pre-training the model in multiple datasets and then fine tuning the model on the specific dataset is the best way."
+    ],
+    [
+      "In this paper, we present a question answering based clinical text structuring (QA-CTS) task, which unifies different clinical text structuring tasks and utilize different datasets. A novel model is also proposed to integrate named entity information into a pre-trained language model and adapt it to QA-CTS task. Initially, sequential results of named entity recognition on both paragraph and query texts are integrated together. Contextualized representation on both paragraph and query texts are transformed by a pre-trained language model. Then, the integrated named entity information and contextualized representation are integrated together and fed into a feed forward network for final prediction. Experimental results on real-world dataset demonstrate that our proposed model competes favorably with strong baseline models in all three specific tasks. The shared task and shared model introduced by QA-CTS task has also been proved to be useful for improving the performance on most of the task-specific datasets. In conclusion, the best way to achieve the best performance for a specific dataset is to pre-train the model in multiple datasets and then fine tune it on the specific dataset."
+    ],
+    [
+      "We would like to thank Ting Li and Xizhou Hong (Ruijin Hospital) who have helped us very much in data fetching and data cleansing. This work is supported by the National Key R&D Program of China for \u201cPrecision Medical Research\" (No. 2018YFC0910500)."
+    ]
+  ]
+}
+```

qasper-0027/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Question Answering based Clinical Text Structuring Using Pre-trained Language Model
+
+Question: How big is QA-CTS task dataset?

qasper-0028/instruction.md

@@ -0,0 +1,118 @@
+Name of Paper: Question Answering based Clinical Text Structuring Using Pre-trained Language Model
+
+Question: How big is dataset of pathology reports collected from Ruijing Hospital?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work ::: Clinical Text Structuring",
+    "Related Work ::: Pre-trained Language Model",
+    "Question Answering based Clinical Text Structuring",
+    "The Proposed Model for QA-CTS Task",
+    "The Proposed Model for QA-CTS Task ::: Contextualized Representation of Sentence Text and Query Text",
+    "The Proposed Model for QA-CTS Task ::: Clinical Named Entity Information",
+    "The Proposed Model for QA-CTS Task ::: Integration Method",
+    "The Proposed Model for QA-CTS Task ::: Final Prediction",
+    "The Proposed Model for QA-CTS Task ::: Two-Stage Training Mechanism",
+    "Experimental Studies",
+    "Experimental Studies ::: Dataset and Evaluation Metrics",
+    "Experimental Studies ::: Experimental Settings",
+    "Experimental Studies ::: Comparison with State-of-the-art Methods",
+    "Experimental Studies ::: Ablation Analysis",
+    "Experimental Studies ::: Comparisons Between Two Integration Methods",
+    "Experimental Studies ::: Data Integration Analysis",
+    "Conclusion",
+    "Acknowledgment"
+  ],
+  "paragraphs": [
+    [
+      "Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surgery, or what the specific laboratory test result is, are obtained. It is important to extract structured data from clinical text because bio-medical systems or bio-medical researches greatly rely on structured data but they cannot obtain them directly. In addition, clinical text often contains abundant healthcare information. CTS is able to provide large-scale extracted structured data for enormous down-stream clinical researches.",
+      "However, end-to-end CTS is a very challenging task. Different CTS tasks often have non-uniform output formats, such as specific-class classifications (e.g. tumor stage), strings in the original text (e.g. result for a laboratory test) and inferred values from part of the original text (e.g. calculated tumor size). Researchers have to construct different models for it, which is already costly, and hence it calls for a lot of labeled data for each model. Moreover, labeling necessary amount of data for training neural network requires expensive labor cost. To handle it, researchers turn to some rule-based structuring methods which often have lower labor cost.",
+      "Traditionally, CTS tasks can be addressed by rule and dictionary based methods BIBREF0, BIBREF1, BIBREF2, task-specific end-to-end methods BIBREF3, BIBREF4, BIBREF5, BIBREF6 and pipeline methods BIBREF7, BIBREF8, BIBREF9. Rule and dictionary based methods suffer from costly human-designed extraction rules, while task-specific end-to-end methods have non-uniform output formats and require task-specific training dataset. Pipeline methods break down the entire process into several pieces which improves the performance and generality. However, when the pipeline depth grows, error propagation will have a greater impact on the performance.",
+      "To reduce the pipeline depth and break the barrier of non-uniform output formats, we present a question answering based clinical text structuring (QA-CTS) task (see Fig. FIGREF1). Unlike the traditional CTS task, our QA-CTS task aims to discover the most related text from original paragraph text. For some cases, it is already the final answer in deed (e.g., extracting sub-string). While for other cases, it needs several steps to obtain the final answer, such as entity names conversion and negative words recognition. Our presented QA-CTS task unifies the output format of the traditional CTS task and make the training data shareable, thus enriching the training data. The main contributions of this work can be summarized as follows.",
+      "We first present a question answering based clinical text structuring (QA-CTS) task, which unifies different specific tasks and make dataset shareable. We also propose an effective model to integrate clinical named entity information into pre-trained language model.",
+      "Experimental results show that QA-CTS task leads to significant improvement due to shared dataset. Our proposed model also achieves significantly better performance than the strong baseline methods. In addition, we also show that two-stage training mechanism has a great improvement on QA-CTS task.",
+      "The rest of the paper is organized as follows. We briefly review the related work on clinical text structuring in Section SECREF2. Then, we present question answer based clinical text structuring task in Section SECREF3. In Section SECREF4, we present an effective model for this task. Section SECREF5 is devoted to computational studies and several investigations on the key issues of our proposed model. Finally, conclusions are given in Section SECREF6."
+    ],
+    [
+      "Clinical text structuring is a final problem which is highly related to practical applications. Most of existing studies are case-by-case. Few of them are developed for the general purpose structuring task. These studies can be roughly divided into three categories: rule and dictionary based methods, task-specific end-to-end methods and pipeline methods.",
+      "Rule and dictionary based methods BIBREF0, BIBREF1, BIBREF2 rely extremely on heuristics and handcrafted extraction rules which is more of an art than a science and incurring extensive trial-and-error experiments. Fukuda et al. BIBREF0 identified protein names from biological papers by dictionaries and several features of protein names. Wang et al. BIBREF1 developed some linguistic rules (i.e. normalised/expanded term matching and substring term matching) to map specific terminology to SNOMED CT. Song et al. BIBREF2 proposed a hybrid dictionary-based bio-entity extraction technique and expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. This kind of approach features its interpretability and easy modifiability. However, with the increase of the rule amount, supplementing new rules to existing system will turn to be a rule disaster.",
+      "Task-specific end-to-end methods BIBREF3, BIBREF4 use large amount of data to automatically model the specific task. Topaz et al. BIBREF3 constructed an automated wound information identification model with five output. Tan et al. BIBREF4 identified patients undergoing radical cystectomy for bladder cancer. Although they achieved good performance, none of their models could be used to another task due to output format difference. This makes building a new model for a new task a costly job.",
+      "Pipeline methods BIBREF7, BIBREF8, BIBREF9 break down the entire task into several basic natural language processing tasks. Bill et al. BIBREF7 focused on attributes extraction which mainly relied on dependency parsing and named entity recognition BIBREF10, BIBREF11, BIBREF12. Meanwhile, Fonferko et al. BIBREF9 used more components like noun phrase chunking BIBREF13, BIBREF14, BIBREF15, part-of-speech tagging BIBREF16, BIBREF17, BIBREF18, sentence splitter, named entity linking BIBREF19, BIBREF20, BIBREF21, relation extraction BIBREF22, BIBREF23. This kind of method focus on language itself, so it can handle tasks more general. However, as the depth of pipeline grows, it is obvious that error propagation will be more and more serious. In contrary, using less components to decrease the pipeline depth will lead to a poor performance. So the upper limit of this method depends mainly on the worst component."
+    ],
+    [
+      "Recently, some works focused on pre-trained language representation models to capture language information from text and then utilizing the information to improve the performance of specific natural language processing tasks BIBREF24, BIBREF25, BIBREF26, BIBREF27 which makes language model a shared model to all natural language processing tasks. Radford et al. BIBREF24 proposed a framework for fine-tuning pre-trained language model. Peters et al. BIBREF25 proposed ELMo which concatenates forward and backward language models in a shallow manner. Devlin et al. BIBREF26 used bidirectional Transformers to model deep interactions between the two directions. Yang et al. BIBREF27 replaced the fixed forward or backward factorization order with all possible permutations of the factorization order and avoided using the [MASK] tag which causes pretrain-finetune discrepancy that BERT is subject to.",
+      "The main motivation of introducing pre-trained language model is to solve the shortage of labeled data and polysemy problem. Although polysemy problem is not a common phenomenon in biomedical domain, shortage of labeled data is always a non-trivial problem. Lee et al. BIBREF28 applied BERT on large-scale biomedical unannotated data and achieved improvement on biomedical named entity recognition, relation extraction and question answering. Kim et al. BIBREF29 adapted BioBERT into multi-type named entity recognition and discovered new entities. Both of them demonstrates the usefulness of introducing pre-trained language model into biomedical domain."
+    ],
+    [
+      "Given a sequence of paragraph text $X=<x_1, x_2, ..., x_n>$, clinical text structuring (CTS) can be regarded to extract or generate a key-value pair where key $Q$ is typically a query term such as proximal resection margin and value $V$ is a result of query term $Q$ according to the paragraph text $X$.",
+      "Generally, researchers solve CTS problem in two steps. Firstly, the answer-related text is pick out. And then several steps such as entity names conversion and negative words recognition are deployed to generate the final answer. While final answer varies from task to task, which truly causes non-uniform output formats, finding the answer-related text is a common action among all tasks. Traditional methods regard both the steps as a whole. In this paper, we focus on finding the answer-related substring $Xs = <X_i, X_i+1, X_i+2, ... X_j> (1 <= i < j <= n)$ from paragraph text $X$. For example, given sentence UTF8gkai\u201c\u8fdc\u7aef\u80c3\u5207\u9664\u6807\u672c\uff1a\u5c0f\u5f2f\u957f11.5cm\uff0c\u5927\u5f2f\u957f17.0cm\u3002\u8ddd\u4e0a\u5207\u7aef6.0cm\u3001\u4e0b\u5207\u7aef8.0cm\" (Distal gastrectomy specimen: measuring 11.5cm in length along the lesser curvature, 17.0cm in length along the greater curvature; 6.0cm from the proximal resection margin, and 8.0cm from the distal resection margin) and query UTF8gkai\u201c\u4e0a\u5207\u7f18\u8ddd\u79bb\"(proximal resection margin), the answer should be 6.0cm which is located in original text from index 32 to 37. With such definition, it unifies the output format of CTS tasks and therefore make the training data shareable, in order to reduce the training data quantity requirement.",
+      "Since BERT BIBREF26 has already demonstrated the usefulness of shared model, we suppose extracting commonality of this problem and unifying the output format will make the model more powerful than dedicated model and meanwhile, for a specific clinical task, use the data for other tasks to supplement the training data."
+    ],
+    [
+      "In this section, we present an effective model for the question answering based clinical text structuring (QA-CTS). As shown in Fig. FIGREF8, paragraph text $X$ is first passed to a clinical named entity recognition (CNER) model BIBREF12 to capture named entity information and obtain one-hot CNER output tagging sequence for query text $I_{nq}$ and paragraph text $I_{nt}$ with BIEOS (Begin, Inside, End, Outside, Single) tag scheme. $I_{nq}$ and $I_{nt}$ are then integrated together into $I_n$. Meanwhile, the paragraph text $X$ and query text $Q$ are organized and passed to contextualized representation model which is pre-trained language model BERT BIBREF26 here to obtain the contextualized representation vector $V_s$ of both text and query. Afterwards, $V_s$ and $I_n$ are integrated together and fed into a feed forward network to calculate the start and end index of answer-related text. Here we define this calculation problem as a classification for each word to be the start or end word."
+    ],
+    [
+      "For any clinical free-text paragraph $X$ and query $Q$, contextualized representation is to generate the encoded vector of both of them. Here we use pre-trained language model BERT-base BIBREF26 model to capture contextual information.",
+      "The text input is constructed as `[CLS] $Q$ [SEP] $X$ [SEP]'. For Chinese sentence, each word in this input will be mapped to a pre-trained embedding $e_i$. To tell the model $Q$ and $X$ is two different sentence, a sentence type input is generated which is a binary label sequence to denote what sentence each character in the input belongs to. Positional encoding and mask matrix is also constructed automatically to bring in absolute position information and eliminate the impact of zero padding respectively. Then a hidden vector $V_s$ which contains both query and text information is generated through BERT-base model."
+    ],
+    [
+      "Since BERT is trained on general corpus, its performance on biomedical domain can be improved by introducing biomedical domain-specific features. In this paper, we introduce clinical named entity information into the model.",
+      "The CNER task aims to identify and classify important clinical terms such as diseases, symptoms, treatments, exams, and body parts from Chinese EHRs. It can be regarded as a sequence labeling task. A CNER model typically outputs a sequence of tags. Each character of the original sentence will be tagged a label following a tag scheme. In this paper we recognize the entities by the model of our previous work BIBREF12 but trained on another corpus which has 44 entity types including operations, numbers, unit words, examinations, symptoms, negative words, etc. An illustrative example of named entity information sequence is demonstrated in Table TABREF2. In Table TABREF2, UTF8gkai\u201c\u8fdc\u7aef\u80c3\u5207\u9664\" is tagged as an operation, `11.5' is a number word and `cm' is an unit word. The named entity tag sequence is organized in one-hot type. We denote the sequence for clinical sentence and query term as $I_{nt}$ and $I_{nq}$, respectively."
+    ],
+    [
+      "There are two ways to integrate two named entity information vectors $I_{nt}$ and $I_{nq}$ or hidden contextualized representation $V_s$ and named entity information $I_n$, where $I_n = [I_{nt}; I_{nq}]$. The first one is to concatenate them together because they have sequence output with a common dimension. The second one is to transform them into a new hidden representation. For the concatenation method, the integrated representation is described as follows.",
+      "While for the transformation method, we use multi-head attention BIBREF30 to encode the two vectors. It can be defined as follows where $h$ is the number of heads and $W_o$ is used to projects back the dimension of concatenated matrix.",
+      "$Attention$ denotes the traditional attention and it can be defined as follows.",
+      "where $d_k$ is the length of hidden vector."
+    ],
+    [
+      "The final step is to use integrated representation $H_i$ to predict the start and end index of answer-related text. Here we define this calculation problem as a classification for each word to be the start or end word. We use a feed forward network (FFN) to compress and calculate the score of each word $H_f$ which makes the dimension to $\\left\\langle l_s, 2\\right\\rangle $ where $l_s$ denotes the length of sequence.",
+      "Then we permute the two dimensions for softmax calculation. The calculation process of loss function can be defined as followed.",
+      "where $O_s = softmax(permute(H_f)_0)$ denotes the probability score of each word to be the start word and similarly $O_e = softmax(permute(H_f)_1)$ denotes the end. $y_s$ and $y_e$ denotes the true answer of the output for start word and end word respectively."
+    ],
+    [
+      "Two-stage training mechanism is previously applied on bilinear model in fine-grained visual recognition BIBREF31, BIBREF32, BIBREF33. Two CNNs are deployed in the model. One is trained at first for coarse-graind features while freezing the parameter of the other. Then unfreeze the other one and train the entire model in a low learning rate for fetching fine-grained features.",
+      "Inspired by this and due to the large amount of parameters in BERT model, to speed up the training process, we fine tune the BERT model with new prediction layer first to achieve a better contextualized representation performance. Then we deploy the proposed model and load the fine tuned BERT weights, attach named entity information layers and retrain the model."
+    ],
+    [
+      "In this section, we devote to experimentally evaluating our proposed task and approach. The best results in tables are in bold."
+    ],
+    [
+      "Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. These annotated instances have been partitioned into 1,899 training instances (12,412 sentences) and 815 test instances (5,421 sentences). Each instance has one or several sentences. Detailed statistics of different types of entities are listed in Table TABREF20.",
+      "In the following experiments, two widely-used performance measures (i.e., EM-score BIBREF34 and (macro-averaged) F$_1$-score BIBREF35) are used to evaluate the methods. The Exact Match (EM-score) metric measures the percentage of predictions that match any one of the ground truth answers exactly. The F$_1$-score metric is a looser metric measures the average overlap between the prediction and ground truth answer."
+    ],
+    [
+      "To implement deep neural network models, we utilize the Keras library BIBREF36 with TensorFlow BIBREF37 backend. Each model is run on a single NVIDIA GeForce GTX 1080 Ti GPU. The models are trained by Adam optimization algorithm BIBREF38 whose parameters are the same as the default settings except for learning rate set to $5\\times 10^{-5}$. Batch size is set to 3 or 4 due to the lack of graphical memory. We select BERT-base as the pre-trained language model in this paper. Due to the high cost of pre-training BERT language model, we directly adopt parameters pre-trained by Google in Chinese general corpus. The named entity recognition is applied on both pathology report texts and query texts."
+    ],
+    [
+      "Since BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26. As BERT has two versions: BERT-Base and BERT-Large, due to the lack of computational resource, we can only compare with BERT-Base model instead of BERT-Large. Prediction layer is attached at the end of the original BERT-Base model and we fine tune it on our dataset. In this section, the named entity integration method is chosen to pure concatenation (Concatenate the named entity information on pathology report text and query text first and then concatenate contextualized representation and concatenated named entity information). Comparative results are summarized in Table TABREF23.",
+      "Table TABREF23 indicates that our proposed model achieved the best performance both in EM-score and F$_1$-score with EM-score of 91.84% and F$_1$-score of 93.75%. QANet outperformed BERT-Base with 3.56% score in F$_1$-score but underperformed it with 0.75% score in EM-score. Compared with BERT-Base, our model led to a 5.64% performance improvement in EM-score and 3.69% in F$_1$-score. Although our model didn't outperform much with QANet in F$_1$-score (only 0.13%), our model significantly outperformed it with 6.39% score in EM-score."
+    ],
+    [
+      "To further investigate the effects of named entity information and two-stage training mechanism for our model, we apply ablation analysis to see the improvement brought by each of them, where $\\times $ refers to removing that part from our model.",
+      "As demonstrated in Table TABREF25, with named entity information enabled, two-stage training mechanism improved the result by 4.36% in EM-score and 3.8% in F$_1$-score. Without two-stage training mechanism, named entity information led to an improvement by 1.28% in EM-score but it also led to a weak deterioration by 0.12% in F$_1$-score. With both of them enabled, our proposed model achieved a 5.64% score improvement in EM-score and a 3.69% score improvement in F$_1$-score. The experimental results show that both named entity information and two-stage training mechanism are helpful to our model."
+    ],
+    [
+      "There are two methods to integrate named entity information into existing model, we experimentally compare these two integration methods. As named entity recognition has been applied on both pathology report text and query text, there will be two integration here. One is for two named entity information and the other is for contextualized representation and integrated named entity information. For multi-head attention BIBREF30, we set heads number $h = 16$ with 256-dimension hidden vector size for each head.",
+      "From Table TABREF27, we can observe that applying concatenation on both periods achieved the best performance on both EM-score and F$_1$-score. Unfortunately, applying multi-head attention on both period one and period two can not reach convergence in our experiments. This probably because it makes the model too complex to train. The difference on other two methods are the order of concatenation and multi-head attention. Applying multi-head attention on two named entity information $I_{nt}$ and $I_{nq}$ first achieved a better performance with 89.87% in EM-score and 92.88% in F$_1$-score. Applying Concatenation first can only achieve 80.74% in EM-score and 84.42% in F$_1$-score. This is probably due to the processing depth of hidden vectors and dataset size. BERT's output has been modified after many layers but named entity information representation is very close to input. With big amount of parameters in multi-head attention, it requires massive training to find out the optimal parameters. However, our dataset is significantly smaller than what pre-trained BERT uses. This probably can also explain why applying multi-head attention method on both periods can not converge.",
+      "Although Table TABREF27 shows the best integration method is concatenation, multi-head attention still has great potential. Due to the lack of computational resources, our experiment fixed the head number and hidden vector size. However, tuning these hyper parameters may have impact on the result. Tuning integration method and try to utilize larger datasets may give help to improving the performance."
+    ],
+    [
+      "To investigate how shared task and shared model can benefit, we split our dataset by query types, train our proposed model with different datasets and demonstrate their performance on different datasets. Firstly, we investigate the performance on model without two-stage training and named entity information.",
+      "As indicated in Table TABREF30, The model trained by mixed data outperforms 2 of the 3 original tasks in EM-score with 81.55% for proximal resection margin and 86.85% for distal resection margin. The performance on tumor size declined by 1.57% score in EM-score and 3.14% score in F$_1$-score but they were still above 90%. 0.69% and 0.37% score improvement in EM-score was brought by shared model for proximal and distal resection margin prediction. Meanwhile F$_1$-score for those two tasks declined 3.11% and 0.77% score.",
+      "Then we investigate the performance on model with two-stage training and named entity information. In this experiment, pre-training process only use the specific dataset not the mixed data. From Table TABREF31 we can observe that the performance on proximal and distal resection margin achieved the best performance on both EM-score and F$_1$-score. Compared with Table TABREF30, the best performance on proximal resection margin improved by 6.9% in EM-score and 7.94% in F$_1$-score. Meanwhile, the best performance on distal resection margin improved by 5.56% in EM-score and 6.32% in F$_1$-score. Other performances also usually improved a lot. This proves the usefulness of two-stage training and named entity information as well.",
+      "Lastly, we fine tune the model for each task with a pre-trained parameter. Table TABREF32 summarizes the result. (Add some explanations for the Table TABREF32). Comparing Table TABREF32 with Table TABREF31, using mixed-data pre-trained parameters can significantly improve the model performance than task-specific data trained model. Except tumor size, the result was improved by 0.52% score in EM-score, 1.39% score in F$_1$-score for proximal resection margin and 2.6% score in EM-score, 2.96% score in F$_1$-score for distal resection margin. This proves mixed-data pre-trained parameters can lead to a great benefit for specific task. Meanwhile, the model performance on other tasks which are not trained in the final stage was also improved from around 0 to 60 or 70 percent. This proves that there is commonality between different tasks and our proposed QA-CTS task make this learnable. In conclusion, to achieve the best performance for a specific dataset, pre-training the model in multiple datasets and then fine tuning the model on the specific dataset is the best way."
+    ],
+    [
+      "In this paper, we present a question answering based clinical text structuring (QA-CTS) task, which unifies different clinical text structuring tasks and utilize different datasets. A novel model is also proposed to integrate named entity information into a pre-trained language model and adapt it to QA-CTS task. Initially, sequential results of named entity recognition on both paragraph and query texts are integrated together. Contextualized representation on both paragraph and query texts are transformed by a pre-trained language model. Then, the integrated named entity information and contextualized representation are integrated together and fed into a feed forward network for final prediction. Experimental results on real-world dataset demonstrate that our proposed model competes favorably with strong baseline models in all three specific tasks. The shared task and shared model introduced by QA-CTS task has also been proved to be useful for improving the performance on most of the task-specific datasets. In conclusion, the best way to achieve the best performance for a specific dataset is to pre-train the model in multiple datasets and then fine tune it on the specific dataset."
+    ],
+    [
+      "We would like to thank Ting Li and Xizhou Hong (Ruijin Hospital) who have helped us very much in data fetching and data cleansing. This work is supported by the National Key R&D Program of China for \u201cPrecision Medical Research\" (No. 2018YFC0910500)."
+    ]
+  ]
+}
+```

qasper-0029/instruction.md

@@ -0,0 +1,118 @@
+Name of Paper: Question Answering based Clinical Text Structuring Using Pre-trained Language Model
+
+Question: What are strong baseline models in specific tasks?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work ::: Clinical Text Structuring",
+    "Related Work ::: Pre-trained Language Model",
+    "Question Answering based Clinical Text Structuring",
+    "The Proposed Model for QA-CTS Task",
+    "The Proposed Model for QA-CTS Task ::: Contextualized Representation of Sentence Text and Query Text",
+    "The Proposed Model for QA-CTS Task ::: Clinical Named Entity Information",
+    "The Proposed Model for QA-CTS Task ::: Integration Method",
+    "The Proposed Model for QA-CTS Task ::: Final Prediction",
+    "The Proposed Model for QA-CTS Task ::: Two-Stage Training Mechanism",
+    "Experimental Studies",
+    "Experimental Studies ::: Dataset and Evaluation Metrics",
+    "Experimental Studies ::: Experimental Settings",
+    "Experimental Studies ::: Comparison with State-of-the-art Methods",
+    "Experimental Studies ::: Ablation Analysis",
+    "Experimental Studies ::: Comparisons Between Two Integration Methods",
+    "Experimental Studies ::: Data Integration Analysis",
+    "Conclusion",
+    "Acknowledgment"
+  ],
+  "paragraphs": [
+    [
+      "Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surgery, or what the specific laboratory test result is, are obtained. It is important to extract structured data from clinical text because bio-medical systems or bio-medical researches greatly rely on structured data but they cannot obtain them directly. In addition, clinical text often contains abundant healthcare information. CTS is able to provide large-scale extracted structured data for enormous down-stream clinical researches.",
+      "However, end-to-end CTS is a very challenging task. Different CTS tasks often have non-uniform output formats, such as specific-class classifications (e.g. tumor stage), strings in the original text (e.g. result for a laboratory test) and inferred values from part of the original text (e.g. calculated tumor size). Researchers have to construct different models for it, which is already costly, and hence it calls for a lot of labeled data for each model. Moreover, labeling necessary amount of data for training neural network requires expensive labor cost. To handle it, researchers turn to some rule-based structuring methods which often have lower labor cost.",
+      "Traditionally, CTS tasks can be addressed by rule and dictionary based methods BIBREF0, BIBREF1, BIBREF2, task-specific end-to-end methods BIBREF3, BIBREF4, BIBREF5, BIBREF6 and pipeline methods BIBREF7, BIBREF8, BIBREF9. Rule and dictionary based methods suffer from costly human-designed extraction rules, while task-specific end-to-end methods have non-uniform output formats and require task-specific training dataset. Pipeline methods break down the entire process into several pieces which improves the performance and generality. However, when the pipeline depth grows, error propagation will have a greater impact on the performance.",
+      "To reduce the pipeline depth and break the barrier of non-uniform output formats, we present a question answering based clinical text structuring (QA-CTS) task (see Fig. FIGREF1). Unlike the traditional CTS task, our QA-CTS task aims to discover the most related text from original paragraph text. For some cases, it is already the final answer in deed (e.g., extracting sub-string). While for other cases, it needs several steps to obtain the final answer, such as entity names conversion and negative words recognition. Our presented QA-CTS task unifies the output format of the traditional CTS task and make the training data shareable, thus enriching the training data. The main contributions of this work can be summarized as follows.",
+      "We first present a question answering based clinical text structuring (QA-CTS) task, which unifies different specific tasks and make dataset shareable. We also propose an effective model to integrate clinical named entity information into pre-trained language model.",
+      "Experimental results show that QA-CTS task leads to significant improvement due to shared dataset. Our proposed model also achieves significantly better performance than the strong baseline methods. In addition, we also show that two-stage training mechanism has a great improvement on QA-CTS task.",
+      "The rest of the paper is organized as follows. We briefly review the related work on clinical text structuring in Section SECREF2. Then, we present question answer based clinical text structuring task in Section SECREF3. In Section SECREF4, we present an effective model for this task. Section SECREF5 is devoted to computational studies and several investigations on the key issues of our proposed model. Finally, conclusions are given in Section SECREF6."
+    ],
+    [
+      "Clinical text structuring is a final problem which is highly related to practical applications. Most of existing studies are case-by-case. Few of them are developed for the general purpose structuring task. These studies can be roughly divided into three categories: rule and dictionary based methods, task-specific end-to-end methods and pipeline methods.",
+      "Rule and dictionary based methods BIBREF0, BIBREF1, BIBREF2 rely extremely on heuristics and handcrafted extraction rules which is more of an art than a science and incurring extensive trial-and-error experiments. Fukuda et al. BIBREF0 identified protein names from biological papers by dictionaries and several features of protein names. Wang et al. BIBREF1 developed some linguistic rules (i.e. normalised/expanded term matching and substring term matching) to map specific terminology to SNOMED CT. Song et al. BIBREF2 proposed a hybrid dictionary-based bio-entity extraction technique and expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. This kind of approach features its interpretability and easy modifiability. However, with the increase of the rule amount, supplementing new rules to existing system will turn to be a rule disaster.",
+      "Task-specific end-to-end methods BIBREF3, BIBREF4 use large amount of data to automatically model the specific task. Topaz et al. BIBREF3 constructed an automated wound information identification model with five output. Tan et al. BIBREF4 identified patients undergoing radical cystectomy for bladder cancer. Although they achieved good performance, none of their models could be used to another task due to output format difference. This makes building a new model for a new task a costly job.",
+      "Pipeline methods BIBREF7, BIBREF8, BIBREF9 break down the entire task into several basic natural language processing tasks. Bill et al. BIBREF7 focused on attributes extraction which mainly relied on dependency parsing and named entity recognition BIBREF10, BIBREF11, BIBREF12. Meanwhile, Fonferko et al. BIBREF9 used more components like noun phrase chunking BIBREF13, BIBREF14, BIBREF15, part-of-speech tagging BIBREF16, BIBREF17, BIBREF18, sentence splitter, named entity linking BIBREF19, BIBREF20, BIBREF21, relation extraction BIBREF22, BIBREF23. This kind of method focus on language itself, so it can handle tasks more general. However, as the depth of pipeline grows, it is obvious that error propagation will be more and more serious. In contrary, using less components to decrease the pipeline depth will lead to a poor performance. So the upper limit of this method depends mainly on the worst component."
+    ],
+    [
+      "Recently, some works focused on pre-trained language representation models to capture language information from text and then utilizing the information to improve the performance of specific natural language processing tasks BIBREF24, BIBREF25, BIBREF26, BIBREF27 which makes language model a shared model to all natural language processing tasks. Radford et al. BIBREF24 proposed a framework for fine-tuning pre-trained language model. Peters et al. BIBREF25 proposed ELMo which concatenates forward and backward language models in a shallow manner. Devlin et al. BIBREF26 used bidirectional Transformers to model deep interactions between the two directions. Yang et al. BIBREF27 replaced the fixed forward or backward factorization order with all possible permutations of the factorization order and avoided using the [MASK] tag which causes pretrain-finetune discrepancy that BERT is subject to.",
+      "The main motivation of introducing pre-trained language model is to solve the shortage of labeled data and polysemy problem. Although polysemy problem is not a common phenomenon in biomedical domain, shortage of labeled data is always a non-trivial problem. Lee et al. BIBREF28 applied BERT on large-scale biomedical unannotated data and achieved improvement on biomedical named entity recognition, relation extraction and question answering. Kim et al. BIBREF29 adapted BioBERT into multi-type named entity recognition and discovered new entities. Both of them demonstrates the usefulness of introducing pre-trained language model into biomedical domain."
+    ],
+    [
+      "Given a sequence of paragraph text $X=<x_1, x_2, ..., x_n>$, clinical text structuring (CTS) can be regarded to extract or generate a key-value pair where key $Q$ is typically a query term such as proximal resection margin and value $V$ is a result of query term $Q$ according to the paragraph text $X$.",
+      "Generally, researchers solve CTS problem in two steps. Firstly, the answer-related text is pick out. And then several steps such as entity names conversion and negative words recognition are deployed to generate the final answer. While final answer varies from task to task, which truly causes non-uniform output formats, finding the answer-related text is a common action among all tasks. Traditional methods regard both the steps as a whole. In this paper, we focus on finding the answer-related substring $Xs = <X_i, X_i+1, X_i+2, ... X_j> (1 <= i < j <= n)$ from paragraph text $X$. For example, given sentence UTF8gkai\u201c\u8fdc\u7aef\u80c3\u5207\u9664\u6807\u672c\uff1a\u5c0f\u5f2f\u957f11.5cm\uff0c\u5927\u5f2f\u957f17.0cm\u3002\u8ddd\u4e0a\u5207\u7aef6.0cm\u3001\u4e0b\u5207\u7aef8.0cm\" (Distal gastrectomy specimen: measuring 11.5cm in length along the lesser curvature, 17.0cm in length along the greater curvature; 6.0cm from the proximal resection margin, and 8.0cm from the distal resection margin) and query UTF8gkai\u201c\u4e0a\u5207\u7f18\u8ddd\u79bb\"(proximal resection margin), the answer should be 6.0cm which is located in original text from index 32 to 37. With such definition, it unifies the output format of CTS tasks and therefore make the training data shareable, in order to reduce the training data quantity requirement.",
+      "Since BERT BIBREF26 has already demonstrated the usefulness of shared model, we suppose extracting commonality of this problem and unifying the output format will make the model more powerful than dedicated model and meanwhile, for a specific clinical task, use the data for other tasks to supplement the training data."
+    ],
+    [
+      "In this section, we present an effective model for the question answering based clinical text structuring (QA-CTS). As shown in Fig. FIGREF8, paragraph text $X$ is first passed to a clinical named entity recognition (CNER) model BIBREF12 to capture named entity information and obtain one-hot CNER output tagging sequence for query text $I_{nq}$ and paragraph text $I_{nt}$ with BIEOS (Begin, Inside, End, Outside, Single) tag scheme. $I_{nq}$ and $I_{nt}$ are then integrated together into $I_n$. Meanwhile, the paragraph text $X$ and query text $Q$ are organized and passed to contextualized representation model which is pre-trained language model BERT BIBREF26 here to obtain the contextualized representation vector $V_s$ of both text and query. Afterwards, $V_s$ and $I_n$ are integrated together and fed into a feed forward network to calculate the start and end index of answer-related text. Here we define this calculation problem as a classification for each word to be the start or end word."
+    ],
+    [
+      "For any clinical free-text paragraph $X$ and query $Q$, contextualized representation is to generate the encoded vector of both of them. Here we use pre-trained language model BERT-base BIBREF26 model to capture contextual information.",
+      "The text input is constructed as `[CLS] $Q$ [SEP] $X$ [SEP]'. For Chinese sentence, each word in this input will be mapped to a pre-trained embedding $e_i$. To tell the model $Q$ and $X$ is two different sentence, a sentence type input is generated which is a binary label sequence to denote what sentence each character in the input belongs to. Positional encoding and mask matrix is also constructed automatically to bring in absolute position information and eliminate the impact of zero padding respectively. Then a hidden vector $V_s$ which contains both query and text information is generated through BERT-base model."
+    ],
+    [
+      "Since BERT is trained on general corpus, its performance on biomedical domain can be improved by introducing biomedical domain-specific features. In this paper, we introduce clinical named entity information into the model.",
+      "The CNER task aims to identify and classify important clinical terms such as diseases, symptoms, treatments, exams, and body parts from Chinese EHRs. It can be regarded as a sequence labeling task. A CNER model typically outputs a sequence of tags. Each character of the original sentence will be tagged a label following a tag scheme. In this paper we recognize the entities by the model of our previous work BIBREF12 but trained on another corpus which has 44 entity types including operations, numbers, unit words, examinations, symptoms, negative words, etc. An illustrative example of named entity information sequence is demonstrated in Table TABREF2. In Table TABREF2, UTF8gkai\u201c\u8fdc\u7aef\u80c3\u5207\u9664\" is tagged as an operation, `11.5' is a number word and `cm' is an unit word. The named entity tag sequence is organized in one-hot type. We denote the sequence for clinical sentence and query term as $I_{nt}$ and $I_{nq}$, respectively."
+    ],
+    [
+      "There are two ways to integrate two named entity information vectors $I_{nt}$ and $I_{nq}$ or hidden contextualized representation $V_s$ and named entity information $I_n$, where $I_n = [I_{nt}; I_{nq}]$. The first one is to concatenate them together because they have sequence output with a common dimension. The second one is to transform them into a new hidden representation. For the concatenation method, the integrated representation is described as follows.",
+      "While for the transformation method, we use multi-head attention BIBREF30 to encode the two vectors. It can be defined as follows where $h$ is the number of heads and $W_o$ is used to projects back the dimension of concatenated matrix.",
+      "$Attention$ denotes the traditional attention and it can be defined as follows.",
+      "where $d_k$ is the length of hidden vector."
+    ],
+    [
+      "The final step is to use integrated representation $H_i$ to predict the start and end index of answer-related text. Here we define this calculation problem as a classification for each word to be the start or end word. We use a feed forward network (FFN) to compress and calculate the score of each word $H_f$ which makes the dimension to $\\left\\langle l_s, 2\\right\\rangle $ where $l_s$ denotes the length of sequence.",
+      "Then we permute the two dimensions for softmax calculation. The calculation process of loss function can be defined as followed.",
+      "where $O_s = softmax(permute(H_f)_0)$ denotes the probability score of each word to be the start word and similarly $O_e = softmax(permute(H_f)_1)$ denotes the end. $y_s$ and $y_e$ denotes the true answer of the output for start word and end word respectively."
+    ],
+    [
+      "Two-stage training mechanism is previously applied on bilinear model in fine-grained visual recognition BIBREF31, BIBREF32, BIBREF33. Two CNNs are deployed in the model. One is trained at first for coarse-graind features while freezing the parameter of the other. Then unfreeze the other one and train the entire model in a low learning rate for fetching fine-grained features.",
+      "Inspired by this and due to the large amount of parameters in BERT model, to speed up the training process, we fine tune the BERT model with new prediction layer first to achieve a better contextualized representation performance. Then we deploy the proposed model and load the fine tuned BERT weights, attach named entity information layers and retrain the model."
+    ],
+    [
+      "In this section, we devote to experimentally evaluating our proposed task and approach. The best results in tables are in bold."
+    ],
+    [
+      "Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. These annotated instances have been partitioned into 1,899 training instances (12,412 sentences) and 815 test instances (5,421 sentences). Each instance has one or several sentences. Detailed statistics of different types of entities are listed in Table TABREF20.",
+      "In the following experiments, two widely-used performance measures (i.e., EM-score BIBREF34 and (macro-averaged) F$_1$-score BIBREF35) are used to evaluate the methods. The Exact Match (EM-score) metric measures the percentage of predictions that match any one of the ground truth answers exactly. The F$_1$-score metric is a looser metric measures the average overlap between the prediction and ground truth answer."
+    ],
+    [
+      "To implement deep neural network models, we utilize the Keras library BIBREF36 with TensorFlow BIBREF37 backend. Each model is run on a single NVIDIA GeForce GTX 1080 Ti GPU. The models are trained by Adam optimization algorithm BIBREF38 whose parameters are the same as the default settings except for learning rate set to $5\\times 10^{-5}$. Batch size is set to 3 or 4 due to the lack of graphical memory. We select BERT-base as the pre-trained language model in this paper. Due to the high cost of pre-training BERT language model, we directly adopt parameters pre-trained by Google in Chinese general corpus. The named entity recognition is applied on both pathology report texts and query texts."
+    ],
+    [
+      "Since BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26. As BERT has two versions: BERT-Base and BERT-Large, due to the lack of computational resource, we can only compare with BERT-Base model instead of BERT-Large. Prediction layer is attached at the end of the original BERT-Base model and we fine tune it on our dataset. In this section, the named entity integration method is chosen to pure concatenation (Concatenate the named entity information on pathology report text and query text first and then concatenate contextualized representation and concatenated named entity information). Comparative results are summarized in Table TABREF23.",
+      "Table TABREF23 indicates that our proposed model achieved the best performance both in EM-score and F$_1$-score with EM-score of 91.84% and F$_1$-score of 93.75%. QANet outperformed BERT-Base with 3.56% score in F$_1$-score but underperformed it with 0.75% score in EM-score. Compared with BERT-Base, our model led to a 5.64% performance improvement in EM-score and 3.69% in F$_1$-score. Although our model didn't outperform much with QANet in F$_1$-score (only 0.13%), our model significantly outperformed it with 6.39% score in EM-score."
+    ],
+    [
+      "To further investigate the effects of named entity information and two-stage training mechanism for our model, we apply ablation analysis to see the improvement brought by each of them, where $\\times $ refers to removing that part from our model.",
+      "As demonstrated in Table TABREF25, with named entity information enabled, two-stage training mechanism improved the result by 4.36% in EM-score and 3.8% in F$_1$-score. Without two-stage training mechanism, named entity information led to an improvement by 1.28% in EM-score but it also led to a weak deterioration by 0.12% in F$_1$-score. With both of them enabled, our proposed model achieved a 5.64% score improvement in EM-score and a 3.69% score improvement in F$_1$-score. The experimental results show that both named entity information and two-stage training mechanism are helpful to our model."
+    ],
+    [
+      "There are two methods to integrate named entity information into existing model, we experimentally compare these two integration methods. As named entity recognition has been applied on both pathology report text and query text, there will be two integration here. One is for two named entity information and the other is for contextualized representation and integrated named entity information. For multi-head attention BIBREF30, we set heads number $h = 16$ with 256-dimension hidden vector size for each head.",
+      "From Table TABREF27, we can observe that applying concatenation on both periods achieved the best performance on both EM-score and F$_1$-score. Unfortunately, applying multi-head attention on both period one and period two can not reach convergence in our experiments. This probably because it makes the model too complex to train. The difference on other two methods are the order of concatenation and multi-head attention. Applying multi-head attention on two named entity information $I_{nt}$ and $I_{nq}$ first achieved a better performance with 89.87% in EM-score and 92.88% in F$_1$-score. Applying Concatenation first can only achieve 80.74% in EM-score and 84.42% in F$_1$-score. This is probably due to the processing depth of hidden vectors and dataset size. BERT's output has been modified after many layers but named entity information representation is very close to input. With big amount of parameters in multi-head attention, it requires massive training to find out the optimal parameters. However, our dataset is significantly smaller than what pre-trained BERT uses. This probably can also explain why applying multi-head attention method on both periods can not converge.",
+      "Although Table TABREF27 shows the best integration method is concatenation, multi-head attention still has great potential. Due to the lack of computational resources, our experiment fixed the head number and hidden vector size. However, tuning these hyper parameters may have impact on the result. Tuning integration method and try to utilize larger datasets may give help to improving the performance."
+    ],
+    [
+      "To investigate how shared task and shared model can benefit, we split our dataset by query types, train our proposed model with different datasets and demonstrate their performance on different datasets. Firstly, we investigate the performance on model without two-stage training and named entity information.",
+      "As indicated in Table TABREF30, The model trained by mixed data outperforms 2 of the 3 original tasks in EM-score with 81.55% for proximal resection margin and 86.85% for distal resection margin. The performance on tumor size declined by 1.57% score in EM-score and 3.14% score in F$_1$-score but they were still above 90%. 0.69% and 0.37% score improvement in EM-score was brought by shared model for proximal and distal resection margin prediction. Meanwhile F$_1$-score for those two tasks declined 3.11% and 0.77% score.",
+      "Then we investigate the performance on model with two-stage training and named entity information. In this experiment, pre-training process only use the specific dataset not the mixed data. From Table TABREF31 we can observe that the performance on proximal and distal resection margin achieved the best performance on both EM-score and F$_1$-score. Compared with Table TABREF30, the best performance on proximal resection margin improved by 6.9% in EM-score and 7.94% in F$_1$-score. Meanwhile, the best performance on distal resection margin improved by 5.56% in EM-score and 6.32% in F$_1$-score. Other performances also usually improved a lot. This proves the usefulness of two-stage training and named entity information as well.",
+      "Lastly, we fine tune the model for each task with a pre-trained parameter. Table TABREF32 summarizes the result. (Add some explanations for the Table TABREF32). Comparing Table TABREF32 with Table TABREF31, using mixed-data pre-trained parameters can significantly improve the model performance than task-specific data trained model. Except tumor size, the result was improved by 0.52% score in EM-score, 1.39% score in F$_1$-score for proximal resection margin and 2.6% score in EM-score, 2.96% score in F$_1$-score for distal resection margin. This proves mixed-data pre-trained parameters can lead to a great benefit for specific task. Meanwhile, the model performance on other tasks which are not trained in the final stage was also improved from around 0 to 60 or 70 percent. This proves that there is commonality between different tasks and our proposed QA-CTS task make this learnable. In conclusion, to achieve the best performance for a specific dataset, pre-training the model in multiple datasets and then fine tuning the model on the specific dataset is the best way."
+    ],
+    [
+      "In this paper, we present a question answering based clinical text structuring (QA-CTS) task, which unifies different clinical text structuring tasks and utilize different datasets. A novel model is also proposed to integrate named entity information into a pre-trained language model and adapt it to QA-CTS task. Initially, sequential results of named entity recognition on both paragraph and query texts are integrated together. Contextualized representation on both paragraph and query texts are transformed by a pre-trained language model. Then, the integrated named entity information and contextualized representation are integrated together and fed into a feed forward network for final prediction. Experimental results on real-world dataset demonstrate that our proposed model competes favorably with strong baseline models in all three specific tasks. The shared task and shared model introduced by QA-CTS task has also been proved to be useful for improving the performance on most of the task-specific datasets. In conclusion, the best way to achieve the best performance for a specific dataset is to pre-train the model in multiple datasets and then fine tune it on the specific dataset."
+    ],
+    [
+      "We would like to thank Ting Li and Xizhou Hong (Ruijin Hospital) who have helped us very much in data fetching and data cleansing. This work is supported by the National Key R&D Program of China for \u201cPrecision Medical Research\" (No. 2018YFC0910500)."
+    ]
+  ]
+}
+```

qasper-0032/instruction.md

@@ -0,0 +1,61 @@
+Name of Paper: Progress and Tradeoffs in Neural Language Models
+
+Question: What is a commonly used evaluation metric for language models?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background and Related Work",
+    "Experimental Setup",
+    "Hyperparameters and Training",
+    "Infrastructure",
+    "Results and Discussion",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Deep learning has unquestionably advanced the state of the art in many natural language processing tasks, from syntactic dependency parsing BIBREF0 to named-entity recognition BIBREF1 to machine translation BIBREF2 . The same certainly applies to language modeling, where recent advances in neural language models (NLMs) have led to dramatically better approaches as measured using standard metrics such as perplexity BIBREF3 , BIBREF4 .",
+      "Specifically focused on language modeling, this paper examines an issue that to our knowledge has not been explored: advances in neural language models have come at a significant cost in terms of increased computational complexity. Computing the probability of a token sequence using non-neural techniques requires a number of phrase lookups and perhaps a few arithmetic operations, whereas model inference with NLMs require large matrix multiplications consuming perhaps millions of floating point operations (FLOPs). These performance tradeoffs are worth discussing.",
+      "In truth, language models exist in a quality\u2013performance tradeoff space. As model quality increases (e.g., lower perplexity), performance as measured in terms of energy consumption, query latency, etc. tends to decrease. For applications primarily running in the cloud\u2014say, machine translation\u2014practitioners often solely optimize for the lowest perplexity. This is because such applications are embarrassingly parallel and hence trivial to scale in a data center environment.",
+      "There are, however, applications of NLMs that require less one-sided optimizations. On mobile devices such as smartphones and tablets, for example, NLMs may be integrated into software keyboards for next-word prediction, allowing much faster text entry. Popular Android apps that enthusiastically tout this technology include SwiftKey and Swype. The greater computational costs of NLMs lead to higher energy usage in model inference, translating into shorter battery life.",
+      "In this paper, we examine the quality\u2013performance tradeoff in the shift from non-neural to neural language models. In particular, we compare Kneser\u2013Ney smoothing, widely accepted as the state of the art prior to NLMs, to the best NLMs today. The decrease in perplexity on standard datasets has been well documented BIBREF3 , but to our knowledge no one has examined the performances tradeoffs. With deployment on a mobile device in mind, we evaluate energy usage and inference latency on a Raspberry Pi (which shares the same ARM architecture as nearly all smartphones today). We find that a 2.5 $\\times $ reduction in perplexity on PTB comes at a staggering cost in terms of performance: inference with NLMs takes 49 $\\times $ longer and requires 32 $\\times $ more energy. Furthermore, we find that impressive reductions in perplexity translate into at best modest improvements in next-word prediction, which is arguable a better metric for evaluating software keyboards on a smartphone. The contribution of this paper is the first known elucidation of this quality\u2013performance tradeoff. Note that we refrain from prescriptive recommendations: whether or not a tradeoff is worthwhile depends on the application. Nevertheless, NLP engineers should arguably keep these tradeoffs in mind when selecting a particular operating point."
+    ],
+    [
+      " BIBREF3 evaluate recent neural language models; however, their focus is not on the computational footprint of each model, but rather the perplexity. To further reduce perplexity, many neural language model extensions exist, such as continuous cache pointer BIBREF5 and mixture of softmaxes BIBREF6 . Since our focus is on comparing \u201ccore\u201d neural and non-neural approaches, we disregard these extra optimizations techniques in all of our models.",
+      "Other work focus on designing lightweight models for resource-efficient inference on mobile devices. BIBREF7 explore LSTMs BIBREF8 with binary weights for language modeling; BIBREF9 examine shallow feedforward neural networks for natural language processing.",
+      "AWD-LSTM. BIBREF4 show that a simple three-layer LSTM, with proper regularization and optimization techniques, can achieve state of the art on various language modeling datasets, surpassing more complex models. Specifically, BIBREF4 apply randomized backpropagation through time, variational dropout, activation regularization, embedding dropout, and temporal activation regularization. A novel scheduler for optimization, non-monotonically triggered ASGD (NT-ASGD) is also introduced. BIBREF4 name their three-layer LSTM model trained with such tricks, \u201cAWD-LSTM.\u201d",
+      "Quasi-Recurrent Neural Networks. Quasi-recurrent neural networks (QRNNs; BIBREF10 ) achieve current state of the art in word-level language modeling BIBREF11 . A quasi-recurrent layer comprises two separate parts: a convolution layer with three weights, and a recurrent pooling layer. Given an input $\\mathbf {X} \\in \\mathbb {R}^{k \\times n}$ , the convolution layer is $\n\\mathbf {Z} = \\tanh (\\mathbf {W}_z \\cdot \\mathbf {X})\\\\\n\\mathbf {F} = \\sigma (\\mathbf {W}_f \\cdot \\mathbf {X})\\\\\n\\mathbf {O} = \\sigma (\\mathbf {W}_o \\cdot \\mathbf {X})\n$ ",
+      "where $\\sigma $ denotes the sigmoid function, $\\cdot $ represents masked convolution across time, and $\\mathbf {W}_{\\lbrace z, f, o\\rbrace } \\in \\mathbb {R}^{m \\times k \\times r}$ are convolution weights with $k$ input channels, $m$ output channels, and a window size of $r$ . In the recurrent pooling layer, the convolution outputs are combined sequentially: $\n\\mathbf {c}_t &= \\mathbf {f}_t \\odot \\mathbf {c}_{t-1} + (1 -\n\\mathbf {f}_t) \\odot \\mathbf {z}_t\\\\\n\\mathbf {h}_t &= \\mathbf {o}_t \\odot \\mathbf {c}_t\n$ ",
+      "Multiple QRNN layers can be stacked for deeper hierarchical representation, with the output $\\mathbf {h}_{1:t}$ being fed as the input into the subsequent layer: In language modeling, a four-layer QRNN is a standard architecture BIBREF11 .",
+      "Perplexity\u2013Recall Scale. Word-level perplexity does not have a strictly monotonic relationship with recall-at- $k$ , the fraction of top $k$ predictions that contain the correct word. A given R@ $k$ imposes a weak minimum perplexity constraint\u2014there are many free parameters that allow for large variability in the perplexity given a certain R@ $k$ . Consider the corpus, \u201cchoo choo train,\u201d with an associated unigram model $P(\\text{``choo''}) = 0.1$ , $P(\\text{``train''}) = 0.9$ , resulting in an R@1 of $1/3$ and perplexity of $4.8$ . Clearly, R@1 $ =1/3$ for all $P(\\text{``choo''}) \\le 0.5$ ; thus, perplexity can drop as low as 2 without affecting recall."
+    ],
+    [
+      "We conducted our experiments on Penn Treebank (PTB; BIBREF12 ) and WikiText-103 (WT103; BIBREF13 ). Preprocessed by BIBREF14 , PTB contains 887K tokens for training, 70K for validation, and 78K for test, with a vocabulary size of 10,000. On the other hand, WT103 comprises 103 million tokens for training, 217K for validation, and 245K for test, spanning a vocabulary of 267K unique tokens.",
+      "For the neural language model, we used a four-layer QRNN BIBREF10 , which achieves state-of-the-art results on a variety of datasets, such as WT103 BIBREF11 and PTB. To compare against more common LSTM architectures, we also evaluated AWD-LSTM BIBREF4 on PTB. For the non-neural approach, we used a standard five-gram model with modified Kneser-Ney smoothing BIBREF15 , as explored in BIBREF16 on PTB. We denote the QRNN models for PTB and WT103 as ptb-qrnn and wt103-qrnn, respectively.",
+      "For each model, we examined word-level perplexity, R@3 in next-word prediction, latency (ms/q), and energy usage (mJ/q). To explore the perplexity\u2013recall relationship, we collected individual perplexity and recall statistics for each sentence in the test set."
+    ],
+    [
+      "The QRNN models followed the exact training procedure and architecture delineated in the official codebase from BIBREF11 . For ptb-qrnn, we trained the model for 550 epochs using NT-ASGD BIBREF4 , then finetuned for 300 epochs using ASGD BIBREF17 , all with a learning rate of 30 throughout. For wt103-qrnn, we followed BIBREF11 and trained the QRNN for 14 epochs, using the Adam optimizer with a learning rate of $10^{-3}$ . We also applied regularization techniques from BIBREF4 ; all the specific hyperparameters are the same as those in the repository. Our model architecture consists of 400-dimensional tied embedding weights BIBREF18 and four QRNN layers, with 1550 hidden units per layer on PTB and 2500 per layer on WT103. Both QRNN models have window sizes of $r=2$ for the first layer and $r=1$ for the rest.",
+      "For the KN-5 model, we trained an off-the-shelf five-gram model using the popular SRILM toolkit BIBREF19 . We did not specify any special hyperparameters."
+    ],
+    [
+      "We trained the QRNNs with PyTorch (0.4.0; commit 1807bac) on a Titan V GPU. To evaluate the models under a resource-constrained environment, we deployed them on a Raspberry Pi 3 (Model B) running Raspbian Stretch (4.9.41-v7+). The Raspberry Pi (RPi) is not only a standard platform, but also a close surrogate to mobile phones, using the same Cortex-A7 in many phones. We then transferred the trained models to the RPi, using the same frameworks for evaluation. We plugged the RPi into a Watts Up Pro meter, a power meter that can be read programatically over USB at a frequency of 1 Hz. For the QRNNs, we used the first 350 words of the test set, and averaged the ms/query and mJ/query. For KN-5, we used the entire test set for evaluation, since the latency was much lower. To adjust for the base power load, we subtracted idle power draw from energy usage.",
+      "For a different perspective, we further evaluated all the models under a desktop environment, using an i7-4790k CPU and Titan V GPU. Because the base power load for powering a desktop is much higher than running neural language models, we collected only latency statistics. We used the entire test set, since the QRNN runs quickly.",
+      "In addition to energy and latency, another consideration for the NLP developer selecting an operating point is the cost of underlying hardware. For our setup, the RPi costs $35 USD, the CPU costs $350 USD, and the GPU costs $3000 USD."
+    ],
+    [
+      "To demonstrate the effectiveness of the QRNN models, we present the results of past and current state-of-the-art neural language models in Table 1 ; we report the Skip- and AWD-LSTM results as seen in the original papers, while we report our QRNN results. Skip LSTM denotes the four-layer Skip LSTM in BIBREF3 . BIBREF20 focus on Hebbian softmax, a model extension technique\u2014Rae-LSTM refers to their base LSTM model without any extensions. In our results, KN-5 refers to the traditional five-gram model with modified Kneser-Ney smoothing, and AWD is shorthand for AWD-LSTM.",
+      "Perplexity\u2013recall scale. In Figure 1 , using KN-5 as the model, we plot the log perplexity (cross entropy) and R@3 error ( $1 - \\text{R@3}$ ) for every sentence in PTB and WT103. The horizontal clusters arise from multiple perplexity points representing the same R@3 value, as explained in Section \"Infrastructure\" . We also observe that the perplexity\u2013recall scale is non-linear\u2014instead, log perplexity appears to have a moderate linear relationship with R@3 error on PTB ( $r=0.85$ ), and an even stronger relationship on WT103 ( $r=0.94$ ). This is partially explained by WT103 having much longer sentences, and thus less noisy statistics.",
+      "From Figure 1 , we find that QRNN models yield strongly linear log perplexity\u2013recall plots as well, where $r=0.88$ and $r=0.93$ for PTB and WT103, respectively. Note that, due to the improved model quality over KN-5, the point clouds are shifted downward compared to Figure 1 . We conclude that log perplexity, or cross entropy, provides a more human-understandable indicator of R@3 than perplexity does. Overall, these findings agree with those from BIBREF21 , which explores the log perplexity\u2013word error rate scale in language modeling for speech recognition.",
+      "Quality\u2013performance tradeoff. In Table 2 , from left to right, we report perplexity results on the validation and test sets, R@3 on test, and finally per-query latency and energy usage. On the RPi, KN-5 is both fast and power-efficient to run, using only about 7 ms/query and 6 mJ/query for PTB (Table 2 , row 1), and 264 ms/q and 229 mJ/q on WT103 (row 5). Taking 220 ms/query and consuming 300 mJ/query, AWD-LSTM and ptb-qrnn are still viable for mobile phones: The modern smartphone holds upwards of 10,000 joules BIBREF22 , and the latency is within usability standards BIBREF23 . Nevertheless, the models are still 49 $\\times $ slower and 32 $\\times $ more power-hungry than KN-5. The wt103-qrnn model is completely unusable on phones, taking over 1.2 seconds per next-word prediction. Neural models achieve perplexity drops of 60\u201380% and R@3 increases of 22\u201334%, but these improvements come at a much higher cost in latency and energy usage.",
+      "In Table 2 (last two columns), the desktop yields very different results: the neural models on PTB (rows 2\u20133) are 9 $\\times $ slower than KN-5, but the absolute latency is only 8 ms/q, which is still much faster than what humans perceive as instantaneous BIBREF23 . If a high-end commodity GPU is available, then the models are only twice as slow as KN-5 is. From row 5, even better results are noted with wt103-qrnn: On the CPU, the QRNN is only 60% slower than KN-5 is, while the model is faster by 11 $\\times $ on a GPU. These results suggest that, if only latency is considered under a commodity desktop environment, the QRNN model is humanly indistinguishable from the KN-5 model, even without using GPU acceleration."
+    ],
+    [
+      "In the present work, we describe and examine the tradeoff space between quality and performance for the task of language modeling. Specifically, we explore the quality\u2013performance tradeoffs between KN-5, a non-neural approach, and AWD-LSTM and QRNN, two neural language models. We find that with decreased perplexity comes vastly increased computational requirements: In one of the NLMs, a perplexity reduction by 2.5 $\\times $ results in a 49 $\\times $ rise in latency and 32 $\\times $ increase in energy usage, when compared to KN-5."
+    ]
+  ]
+}
+```

qasper-0035/instruction.md

@@ -0,0 +1,673 @@
+Name of Paper: Stay On-Topic: Generating Context-specific Fake Restaurant Reviews
+
+Question: How does using NMT ensure generated reviews stay on topic?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background",
+    "System Model",
+    "Attack Model",
+    "Generative Model"
+  ],
+  "paragraphs": [
+    [
+      "Automatically generated fake reviews have only recently become natural enough to fool human readers. Yao et al. BIBREF0 use a deep neural network (a so-called 2-layer LSTM BIBREF1 ) to generate fake reviews, and concluded that these fake reviews look sufficiently genuine to fool native English speakers. They train their model using real restaurant reviews from yelp.com BIBREF2 . Once trained, the model is used to generate reviews character-by-character. Due to the generation methodology, it cannot be easily targeted for a specific context (meaningful side information). Consequently, the review generation process may stray off-topic. For instance, when generating a review for a Japanese restaurant in Las Vegas, the review generation process may include references to an Italian restaurant in Baltimore. The authors of BIBREF0 apply a post-processing step (customization), which replaces food-related words with more suitable ones (sampled from the targeted restaurant). The word replacement strategy has drawbacks: it can miss certain words and replace others independent of their surrounding words, which may alert savvy readers. As an example: when we applied the customization technique described in BIBREF0 to a review for a Japanese restaurant it changed the snippet garlic knots for breakfast with garlic knots for sushi).",
+      "We propose a methodology based on neural machine translation (NMT) that improves the generation process by defining a context for the each generated fake review. Our context is a clear-text sequence of: the review rating, restaurant name, city, state and food tags (e.g. Japanese, Italian). We show that our technique generates review that stay on topic. We can instantiate our basic technique into several variants. We vet them on Amazon Mechanical Turk and find that native English speakers are very poor at recognizing our fake generated reviews. For one variant, the participants' performance is close to random: the class-averaged F-score of detection is INLINEFORM0 (whereas random would be INLINEFORM1 given the 1:6 imbalance in the test). Via a user study with experienced, highly educated participants, we compare this variant (which we will henceforth refer to as NMT-Fake* reviews) with fake reviews generated using the char-LSTM-based technique from BIBREF0 .",
+      "We demonstrate that NMT-Fake* reviews constitute a new category of fake reviews that cannot be detected by classifiers trained only using previously known categories of fake reviews BIBREF0 , BIBREF3 , BIBREF4 . Therefore, NMT-Fake* reviews may go undetected in existing online review sites. To meet this challenge, we develop an effective classifier that detects NMT-Fake* reviews effectively (97% F-score). Our main contributions are:"
+    ],
+    [
+      "Fake reviews User-generated content BIBREF5 is an integral part of the contemporary user experience on the web. Sites like tripadvisor.com, yelp.com and Google Play use user-written reviews to provide rich information that helps other users choose where to spend money and time. User reviews are used for rating services or products, and for providing qualitative opinions. User reviews and ratings may be used to rank services in recommendations. Ratings have an affect on the outwards appearance. Already 8 years ago, researchers estimated that a one-star rating increase affects the business revenue by 5 \u2013 9% on yelp.com BIBREF6 .",
+      "Due to monetary impact of user-generated content, some businesses have relied on so-called crowd-turfing agents BIBREF7 that promise to deliver positive ratings written by workers to a customer in exchange for a monetary compensation. Crowd-turfing ethics are complicated. For example, Amazon community guidelines prohibit buying content relating to promotions, but the act of writing fabricated content is not considered illegal, nor is matching workers to customers BIBREF8 . Year 2015, approximately 20% of online reviews on yelp.com were suspected of being fake BIBREF9 .",
+      "Nowadays, user-generated review sites like yelp.com use filters and fraudulent review detection techniques. These factors have resulted in an increase in the requirements of crowd-turfed reviews provided to review sites, which in turn has led to an increase in the cost of high-quality review. Due to the cost increase, researchers hypothesize the existence of neural network-generated fake reviews. These neural-network-based fake reviews are statistically different from human-written fake reviews, and are not caught by classifiers trained on these BIBREF0 .",
+      "Detecting fake reviews can either be done on an individual level or as a system-wide detection tool (i.e. regulation). Detecting fake online content on a personal level requires knowledge and skills in critical reading. In 2017, the National Literacy Trust assessed that young people in the UK do not have the skillset to differentiate fake news from real news BIBREF10 . For example, 20% of children that use online news sites in age group 12-15 believe that all information on news sites are true.",
+      "Neural Networks Neural networks are function compositions that map input data through INLINEFORM0 subsequent layers: DISPLAYFORM0 ",
+      "where the functions INLINEFORM0 are typically non-linear and chosen by experts partly for known good performance on datasets and partly for simplicity of computational evaluation. Language models (LMs) BIBREF11 are generative probability distributions that assign probabilities to sequences of tokens ( INLINEFORM1 ): DISPLAYFORM0 ",
+      "such that the language model can be used to predict how likely a specific token at time step INLINEFORM0 is, based on the INLINEFORM1 previous tokens. Tokens are typically either words or characters.",
+      "For decades, deep neural networks were thought to be computationally too difficult to train. However, advances in optimization, hardware and the availability of frameworks have shown otherwise BIBREF1 , BIBREF12 . Neural language models (NLMs) have been one of the promising application areas. NLMs are typically various forms of recurrent neural networks (RNNs), which pass through the data sequentially and maintain a memory representation of the past tokens with a hidden context vector. There are many RNN architectures that focus on different ways of updating and maintaining context vectors: Long Short-Term Memory units (LSTM) and Gated Recurrent Units (GRUs) are perhaps most popular. Neural LMs have been used for free-form text generation. In certain application areas, the quality has been high enough to sometimes fool human readers BIBREF0 . Encoder-decoder (seq2seq) models BIBREF13 are architectures of stacked RNNs, which have the ability to generate output sequences based on input sequences. The encoder network reads in a sequence of tokens, and passes it to a decoder network (a LM). In contrast to simpler NLMs, encoder-decoder networks have the ability to use additional context for generating text, which enables more accurate generation of text. Encoder-decoder models are integral in Neural Machine Translation (NMT) BIBREF14 , where the task is to translate a source text from one language to another language. NMT models additionally use beam search strategies to heuristically search the set of possible translations. Training datasets are parallel corpora; large sets of paired sentences in the source and target languages. The application of NMT techniques for online machine translation has significantly improved the quality of translations, bringing it closer to human performance BIBREF15 .",
+      "Neural machine translation models are efficient at mapping one expression to another (one-to-one mapping). Researchers have evaluated these models for conversation generation BIBREF16 , with mixed results. Some researchers attribute poor performance to the use of the negative log likelihood cost function during training, which emphasizes generation of high-confidence phrases rather than diverse phrases BIBREF17 . The results are often generic text, which lacks variation. Li et al. have suggested various augmentations to this, among others suppressing typical responses in the decoder language model to promote response diversity BIBREF17 ."
+    ],
+    [
+      "We discuss the attack model, our generative machine learning method and controlling the generative process in this section."
+    ],
+    [
+      "Wang et al. BIBREF7 described a model of crowd-turfing attacks consisting of three entities: customers who desire to have fake reviews for a particular target (e.g. their restaurant) on a particular platform (e.g. Yelp), agents who offer fake review services to customers, and workers who are orchestrated by the agent to compose and post fake reviews.",
+      "Automated crowd-turfing attacks (ACA) replace workers by a generative model. This has several benefits including better economy and scalability (human workers are more expensive and slower) and reduced detectability (agent can better control the rate at which fake reviews are generated and posted).",
+      "We assume that the agent has access to public reviews on the review platform, by which it can train its generative model. We also assume that it is easy for the agent to create a large number of accounts on the review platform so that account-based detection or rate-limiting techniques are ineffective against fake reviews.",
+      "The quality of the generative model plays a crucial role in the attack. Yao et al. BIBREF0 propose the use of a character-based LSTM as base for generative model. LSTMs are not conditioned to generate reviews for a specific target BIBREF1 , and may mix-up concepts from different contexts during free-form generation. Mixing contextually separate words is one of the key criteria that humans use to identify fake reviews. These may result in violations of known indicators for fake content BIBREF18 . For example, the review content may not match prior expectations nor the information need that the reader has. We improve the attack model by considering a more capable generative model that produces more appropriate reviews: a neural machine translation (NMT) model."
+    ],
+    [
+      "We propose the use of NMT models for fake review generation. The method has several benefits: 1) the ability to learn how to associate context (keywords) to reviews, 2) fast training time, and 3) a high-degree of customization during production time, e.g. introduction of specific waiter or food items names into reviews.",
+      "NMT models are constructions of stacked recurrent neural networks (RNNs). They include an encoder network and a decoder network, which are jointly optimized to produce a translation of one sequence to another. The encoder rolls over the input data in sequence and produces one INLINEFORM0 -dimensional context vector representation for the sentence. The decoder then generates output sequences based on the embedding vector and an attention module, which is taught to associate output words with certain input words. The generation typically continues until a specific EOS (end of sentence) token is encountered. The review length can be controlled in many ways, e.g. by setting the probability of generating the EOS token to zero until the required length is reached.",
+      "NMT models often also include a beam search BIBREF14 , which generates several hypotheses and chooses the best ones amongst them. In our work, we use the greedy beam search technique. We forgo the use of additional beam searches as we found that the quality of the output was already adequate and the translation phase time consumption increases linearly for each beam used.",
+      "We use the Yelp Challenge dataset BIBREF2 for our fake review generation. The dataset (Aug 2017) contains 2.9 million 1 \u20135 star restaurant reviews. We treat all reviews as genuine human-written reviews for the purpose of this work, since wide-scale deployment of machine-generated review attacks are not yet reported (Sep 2017) BIBREF19 . As preprocessing, we remove non-printable (non-ASCII) characters and excessive white-space. We separate punctuation from words. We reserve 15,000 reviews for validation and 3,000 for testing, and the rest we use for training. NMT models require a parallel corpus of source and target sentences, i.e. a large set of (source, target)-pairs. We set up a parallel corpus by constructing (context, review)-pairs from the dataset. Next, we describe how we created our input context.",
+      "The Yelp Challenge dataset includes metadata about restaurants, including their names, food tags, cities and states these restaurants are located in. For each restaurant review, we fetch this metadata and use it as our input context in the NMT model. The corresponding restaurant review is similarly set as the target sentence. This method produced 2.9 million pairs of sentences in our parallel corpus. We show one example of the parallel training corpus in Example 1 below:",
+      "5 Public House Las Vegas NV Gastropubs Restaurants > Excellent",
+      "food and service . Pricey , but well worth it . I would recommend",
+      "the bone marrow and sampler platter for appetizers . \\end{verbatim}",
+      " ",
+      " ",
+      "\\noindent The order {\\textbf{[rating name city state tags]}} is kept constant.",
+      "Training the model conditions it to associate certain sequences of words in the input sentence with others in the output.",
+      " ",
+      "\\subsubsection{Training Settings}",
+      " ",
+      "We train our NMT model on a commodity PC with a i7-4790k CPU (4.00GHz), with 32GB RAM and one NVidia GeForce GTX 980 GPU. Our system can process approximately 1,300 \\textendash 1,500 source tokens/s and approximately 5,730 \\textendash 5,830 output tokens/s. Training one epoch takes in average 72 minutes. The model is trained for 8 epochs, i.e. over night. We call fake review generated by this model \\emph{NMT-Fake reviews}. We only need to train one model to produce reviews of different ratings.",
+      "We use the training settings: adam optimizer \\cite{kingma2014adam} with the suggested learning rate 0.001 \\cite{klein2017opennmt}. For most parts, parameters are at their default values. Notably, the maximum sentence length of input and output is 50 tokens by default.",
+      "We leverage the framework openNMT-py \\cite{klein2017opennmt} to teach the our NMT model.",
+      "We list used openNMT-py commands in Appendix Table~\\ref{table:openNMT-py_commands}.",
+      " ",
+      "\\begin{figure}[t]",
+      "\\begin{center}",
+      " \\begin{tabular}{ | l | }",
+      " \\hline",
+      "Example 2. Greedy NMT \\\\",
+      "Great food, \\underline{great} service, \\underline{great} \\textit{\\textit{beer selection}}. I had the \\textit{Gastropubs burger} and it",
+      "\\\\",
+      "was delicious. The \\underline{\\textit{beer selection}} was also \\underline{great}. \\\\",
+      "\\\\",
+      "Example 3. NMT-Fake* \\\\",
+      "I love this restaurant. Great food, great service. It's \\textit{a little pricy} but worth\\\\",
+      "it for the \\textit{quality} of the \\textit{beer} and atmosphere you can see in \\textit{Vegas}",
+      "\\\\",
+      " \\hline",
+      " \\end{tabular}",
+      " \\label{table:output_comparison}",
+      "\\end{center}",
+      "\\caption{Na\\\"{i}ve text generation with NMT vs. generation using our NTM model. Repetitive patterns are \\underline{underlined}. Contextual words are \\emph{italicized}. Both examples here are generated based on the context given in Example~1.}",
+      "\\label{fig:comparison}",
+      "\\end{figure}",
+      " ",
+      "\\subsection{Controlling generation of fake reviews}",
+      "\\label{sec:generating}",
+      " ",
+      "Greedy NMT beam searches are practical in many NMT cases. However, the results are simply repetitive, when naively applied to fake review generation (See Example~2 in Figure~\\ref{fig:comparison}).",
+      "The NMT model produces many \\emph{high-confidence} word predictions, which are repetitive and obviously fake. We calculated that in fact, 43\\% of the generated sentences started with the phrase ``Great food''. The lack of diversity in greedy use of NMTs for text generation is clear.",
+      " ",
+      " ",
+      "\\begin{algorithm}[!b]",
+      " \\KwData{Desired review context $C_\\mathrm{input}$ (given as cleartext), NMT model}",
+      " \\KwResult{Generated review $out$ for input context $C_\\mathrm{input}$}",
+      "set $b=0.3$, $\\lambda=-5$, $\\alpha=\\frac{2}{3}$, $p_\\mathrm{typo}$, $p_\\mathrm{spell}$ \\\\",
+      "$\\log p \\leftarrow \\text{NMT.decode(NMT.encode(}C_\\mathrm{input}\\text{))}$ \\\\",
+      "out $\\leftarrow$ [~] \\\\",
+      "$i \\leftarrow 0$ \\\\",
+      "$\\log p \\leftarrow \\text{Augment}(\\log p$, $b$, $\\lambda$, $1$, $[~]$, 0)~~~~~~~~~~~~~~~ |~random penalty~\\\\",
+      "\\While{$i=0$ or $o_i$ not EOS}{",
+      "$\\log \\Tilde{p} \\leftarrow \\text{Augment}(\\log p$, $b$, $\\lambda$, $\\alpha$, $o_i$, $i$)~~~~~~~~~~~ |~start \\& memory penalty~\\\\",
+      "$o_i \\leftarrow$ \\text{NMT.beam}($\\log \\Tilde{p}$, out) \\\\",
+      "out.append($o_i$) \\\\",
+      "$i \\leftarrow i+1$",
+      "}\\text{return}~$\\text{Obfuscate}$(out,~$p_\\mathrm{typo}$,~$p_\\mathrm{spell}$)",
+      "\\caption{Generation of NMT-Fake* reviews.}",
+      "\\label{alg:base}",
+      "\\end{algorithm}",
+      " ",
+      "In this work, we describe how we succeeded in creating more diverse and less repetitive generated reviews, such as Example 3 in Figure~\\ref{fig:comparison}.",
+      "We outline pseudocode for our methodology of generating fake reviews in Algorithm~\\ref{alg:base}. There are several parameters in our algorithm.",
+      "The details of the algorithm will be shown later.",
+      "We modify the openNMT-py translation phase by changing log-probabilities before passing them to the beam search.",
+      "We notice that reviews generated with openNMT-py contain almost no language errors. As an optional post-processing step, we obfuscate reviews by introducing natural typos/misspellings randomly. In the next sections, we describe how we succeeded in generating more natural sentences from our NMT model, i.e. generating reviews like Example~3 instead of reviews like Example~2.",
+      " ",
+      "\\subsubsection{Variation in word content}",
+      " ",
+      "Example 2 in Figure~\\ref{fig:comparison} repeats commonly occurring words given for a specific context (e.g. \\textit{great, food, service, beer, selection, burger} for Example~1). Generic review generation can be avoided by decreasing probabilities (log-likelihoods \\cite{murphy2012machine}) of the generators LM, the decoder.",
+      "We constrain the generation of sentences by randomly \\emph{imposing penalties to words}.",
+      "We tried several forms of added randomness, and found that adding constant penalties to a \\emph{random subset} of the target words resulted in the most natural sentence flow. We call these penalties \\emph{Bernoulli penalties}, since the random variables are chosen as either 1 or 0 (on or off).",
+      " ",
+      " ",
+      "\\paragraph{Bernoulli penalties to language model}",
+      "To avoid generic sentences components, we augment the default language model $p(\\cdot)$ of the decoder by",
+      " ",
+      "\\begin{equation}",
+      "\\log \\Tilde{p}(t_k) = \\log p(t_k | t_i, \\dots, t_1) + \\lambda q,",
+      "\\end{equation}",
+      " ",
+      "where $q \\in R^{V}$ is a vector of Bernoulli-distributed random values that obtain values $1$ with probability $b$ and value $0$ with probability $1-b_i$, and $\\lambda < 0$. Parameter $b$ controls how much of the vocabulary is forgotten and $\\lambda$ is a soft penalty of including ``forgotten'' words in a review.",
+      "$\\lambda q_k$ emphasizes sentence forming with non-penalized words. The randomness is reset at the start of generating a new review.",
+      "Using Bernoulli penalties in the language model, we can ``forget'' a certain proportion of words and essentially ``force'' the creation of less typical sentences. We will test the effect of these two parameters, the Bernoulli probability $b$ and log-likelihood penalty of including ``forgotten'' words $\\lambda$, with a user study in Section~\\ref{sec:varying}.",
+      " ",
+      "\\paragraph{Start penalty}",
+      "We introduce start penalties to avoid generic sentence starts (e.g. ``Great food, great service''). Inspired by \\cite{li2016diversity}, we add a random start penalty $\\lambda s^\\mathrm{i}$, to our language model, which decreases monotonically for each generated token. We set $\\alpha \\leftarrow 0.66$ as it's effect decreases by 90\\% every 5 words generated.",
+      " ",
+      "\\paragraph{Penalty for reusing words}",
+      "Bernoulli penalties do not prevent excessive use of certain words in a sentence (such as \\textit{great} in Example~2).",
+      "To avoid excessive reuse of words, we included a memory penalty for previously used words in each translation.",
+      "Concretely, we add the penalty $\\lambda$ to each word that has been generated by the greedy search.",
+      " ",
+      "\\subsubsection{Improving sentence coherence}",
+      "\\label{sec:grammar}",
+      "We visually analyzed reviews after applying these penalties to our NMT model. While the models were clearly diverse, they were \\emph{incoherent}: the introduction of random penalties had degraded the grammaticality of the sentences. Amongst others, the use of punctuation was erratic, and pronouns were used semantically wrongly (e.g. \\emph{he}, \\emph{she} might be replaced, as could ``and''/``but''). To improve the authenticity of our reviews, we added several \\emph{grammar-based rules}.",
+      " ",
+      "English language has several classes of words which are important for the natural flow of sentences.",
+      "We built a list of common pronouns (e.g. I, them, our), conjunctions (e.g. and, thus, if), punctuation (e.g. ,/.,..), and apply only half memory penalties for these words. We found that this change made the reviews more coherent. The pseudocode for this and the previous step is shown in Algorithm~\\ref{alg:aug}.",
+      "The combined effect of grammar-based rules and LM augmentation is visible in Example~3, Figure~\\ref{fig:comparison}.",
+      " ",
+      "\\begin{algorithm}[!t]",
+      " \\KwData{Initial log LM $\\log p$, Bernoulli probability $b$, soft-penalty $\\lambda$, monotonic factor $\\alpha$, last generated token $o_i$, grammar rules set $G$}",
+      " \\KwResult{Augmented log LM $\\log \\Tilde{p}$}",
+      "\\begin{algorithmic}[1]",
+      "\\Procedure {Augment}{$\\log p$, $b$, $\\lambda$, $\\alpha$, $o_i$, $i$}{ \\\\",
+      "generate $P_{\\mathrm{1:N}} \\leftarrow Bernoulli(b)$~~~~~~~~~~~~~~~|~$\\text{One value} \\in \\{0,1\\}~\\text{per token}$~ \\\\",
+      "$I \\leftarrow P>0$ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~|~Select positive indices~\\\\",
+      "$\\log \\Tilde{p} \\leftarrow$ $\\text{Discount}$($\\log p$, $I$, $\\lambda \\cdot \\alpha^i$,$G$) ~~~~~~ |~start penalty~\\\\",
+      "$\\log \\Tilde{p} \\leftarrow$ $\\text{Discount}$($\\log \\Tilde{p}$, $[o_i]$, $\\lambda$,$G$) ~~~~~~~~~ |~memory penalty~\\\\",
+      "\\textbf{return}~$\\log \\Tilde{p}$",
+      "}",
+      "\\EndProcedure",
+      "\\\\",
+      "\\Procedure {Discount}{$\\log p$, $I$, $\\lambda$, $G$}{",
+      "\\State{\\For{$i \\in I$}{",
+      "\\eIf{$o_i \\in G$}{",
+      "$\\log p_{i} \\leftarrow \\log p_{i} + \\lambda/2$",
+      "}{",
+      "$\\log p_{i} \\leftarrow \\log p_{i} + \\lambda$}",
+      "}\\textbf{return}~$\\log p$",
+      "\\EndProcedure",
+      "}}",
+      "\\end{algorithmic}",
+      "\\caption{Pseudocode for augmenting language model. }",
+      "\\label{alg:aug}",
+      "\\end{algorithm}",
+      " ",
+      "\\subsubsection{Human-like errors}",
+      "\\label{sec:obfuscation}",
+      "We notice that our NMT model produces reviews without grammar mistakes.",
+      "This is unlike real human writers, whose sentences contain two types of language mistakes 1) \\emph{typos} that are caused by mistakes in the human motoric input, and 2) \\emph{common spelling mistakes}.",
+      "We scraped a list of common English language spelling mistakes from Oxford dictionary\\footnote{\\url{https://en.oxforddictionaries.com/spelling/common-misspellings}} and created 80 rules for randomly \\emph{re-introducing spelling mistakes}.",
+      "Similarly, typos are randomly reintroduced based on the weighted edit distance\\footnote{\\url{https://pypi.python.org/pypi/weighted-levenshtein/0.1}}, such that typos resulting in real English words with small perturbations are emphasized.",
+      "We use autocorrection tools\\footnote{\\url{https://pypi.python.org/pypi/autocorrect/0.1.0}} for finding these words.",
+      "We call these augmentations \\emph{obfuscations}, since they aim to confound the reader to think a human has written them. We omit the pseudocode description for brevity.",
+      " ",
+      "\\subsection{Experiment: Varying generation parameters in our NMT model}",
+      "\\label{sec:varying}",
+      " ",
+      "Parameters $b$ and $\\lambda$ control different aspects in fake reviews.",
+      "We show six different examples of generated fake reviews in Table~\\ref{table:categories}.",
+      "Here, the largest differences occur with increasing values of $b$: visibly, the restaurant reviews become more extreme.",
+      "This occurs because a large portion of vocabulary is ``forgotten''. Reviews with $b \\geq 0.7$ contain more rare word combinations, e.g. ``!!!!!'' as punctuation, and they occasionally break grammaticality (''experience was awesome'').",
+      "Reviews with lower $b$ are more generic: they contain safe word combinations like ``Great place, good service'' that occur in many reviews. Parameter $\\lambda$'s is more subtle: it affects how random review starts are and to a degree, the discontinuation between statements within the review.",
+      "We conducted an Amazon Mechanical Turk (MTurk) survey in order to determine what kind of NMT-Fake reviews are convincing to native English speakers. We describe the survey and results in the next section.",
+      " ",
+      " ",
+      "\\begin{table}[!b]",
+      "\\caption{Six different parametrizations of our NMT reviews and one example for each. The context is ``5 P~.~F~.~Chang ' s Scottsdale AZ'' in all examples.}",
+      "\\begin{center}",
+      " \\begin{tabular}{ | l | l | }",
+      " \\hline",
+      " $(b, \\lambda)$ & Example review for context \\\\ \\hline",
+      " \\hline",
+      " $(0.3, -3)$ & I love this location! Great service, great food and the best drinks in Scottsdale. \\\\",
+      " & The staff is very friendly and always remembers u when we come in\\\\\\hline",
+      " $(0.3, -5)$ & Love love the food here! I always go for lunch. They have a great menu and \\\\",
+      " & they make it fresh to order. Great place, good service and nice staff\\\\\\hline",
+      " $(0.5, -4)$ & I love their chicken lettuce wraps and fried rice!! The service is good, they are\\\\",
+      " & always so polite. They have great happy hour specials and they have a lot\\\\",
+      " & of options.\\\\\\hline",
+      " $(0.7, -3)$ & Great place to go with friends! They always make sure your dining \\\\",
+      " & experience was awesome.\\\\ \\hline",
+      " $(0.7, -5)$ & Still haven't ordered an entree before but today we tried them once..\\\\",
+      " & both of us love this restaurant....\\\\\\hline",
+      " $(0.9, -4)$ & AMAZING!!!!! Food was awesome with excellent service. Loved the lettuce \\\\",
+      " & wraps. Great drinks and wine! Can't wait to go back so soon!!\\\\ \\hline",
+      " \\end{tabular}",
+      " \\label{table:categories}",
+      "\\end{center}",
+      "\\end{table}",
+      " ",
+      "\\subsubsection{MTurk study}",
+      "\\label{sec:amt}",
+      "We created 20 jobs, each with 100 questions, and requested master workers in MTurk to complete the jobs.",
+      "We randomly generated each survey for the participants. Each review had a 50\\% chance to be real or fake. The fake ones further were chosen among six (6) categories of fake reviews (Table~\\ref{table:categories}).",
+      "The restaurant and the city was given as contextual information to the participants. Our aim was to use this survey to understand how well English-speakers react to different parametrizations of NMT-Fake reviews.",
+      "Table~\\ref{table:amt_pop} in Appendix summarizes the statistics for respondents in the survey. All participants were native English speakers from America. The base rate (50\\%) was revealed to the participants prior to the study.",
+      " ",
+      "We first investigated overall detection of any NMT-Fake reviews (1,006 fake reviews and 994 real reviews). We found that the participants had big difficulties in detecting our fake reviews. In average, the reviews were detected with class-averaged \\emph{F-score of only 56\\%}, with 53\\% F-score for fake review detection and 59\\% F-score for real review detection. The results are very close to \\emph{random detection}, where precision, recall and F-score would each be 50\\%. Results are recorded in Table~\\ref{table:MTurk_super}. Overall, the fake review generation is very successful, since human detection rate across categories is close to random.",
+      " ",
+      "\\begin{table}[t]",
+      "\\caption{Effectiveness of Mechanical Turkers in distinguishing human-written reviews from fake reviews generated by our NMT model (all variants).}",
+      "\\begin{center}",
+      " \\begin{tabular}{ | c | c |c |c | c | }",
+      " \\hline",
+      " \\multicolumn{5}{|c|}{Classification report}",
+      " \\\\ \\hline",
+      " Review Type & Precision & Recall & F-score & Support \\\\ \\hline",
+      " \\hline",
+      " Human & 55\\% & 63\\% & 59\\% & 994\\\\",
+      " NMT-Fake & 57\\% & 50\\% & 53\\% & 1006 \\\\",
+      " \\hline",
+      " \\end{tabular}",
+      " \\label{table:MTurk_super}",
+      "\\end{center}",
+      "\\end{table}",
+      " ",
+      "We noticed some variation in the detection of different fake review categories. The respondents in our MTurk survey had most difficulties recognizing reviews of category $(b=0.3, \\lambda=-5)$, where true positive rate was $40.4\\%$, while the true negative rate of the real class was $62.7\\%$. The precision were $16\\%$ and $86\\%$, respectively. The class-averaged F-score is $47.6\\%$, which is close to random. Detailed classification reports are shown in Table~\\ref{table:MTurk_sub} in Appendix. Our MTurk-study shows that \\emph{our NMT-Fake reviews pose a significant threat to review systems}, since \\emph{ordinary native English-speakers have very big difficulties in separating real reviews from fake reviews}. We use the review category $(b=0.3, \\lambda=-5)$ for future user tests in this paper, since MTurk participants had most difficulties detecting these reviews. We refer to this category as NMT-Fake* in this paper.",
+      " ",
+      "\\section{Evaluation}",
+      "\\graphicspath{ {figures/}}",
+      " ",
+      "We evaluate our fake reviews by first comparing them statistically to previously proposed types of fake reviews, and proceed with a user study with experienced participants. We demonstrate the statistical difference to existing fake review types \\cite{yao2017automated,mukherjee2013yelp,rayana2015collective} by training classifiers to detect previous types and investigate classification performance.",
+      " ",
+      "\\subsection{Replication of state-of-the-art model: LSTM}",
+      "\\label{sec:repl}",
+      " ",
+      "Yao et al. \\cite{yao2017automated} presented the current state-of-the-art generative model for fake reviews. The model is trained over the Yelp Challenge dataset using a two-layer character-based LSTM model.",
+      "We requested the authors of \\cite{yao2017automated} for access to their LSTM model or a fake review dataset generated by their model. Unfortunately they were not able to share either of these with us. We therefore replicated their model as closely as we could, based on their paper and e-mail correspondence\\footnote{We are committed to sharing our code with bonafide researchers for the sake of reproducibility.}.",
+      " ",
+      "We used the same graphics card (GeForce GTX) and trained using the same framework (torch-RNN in lua). We downloaded the reviews from Yelp Challenge and preprocessed the data to only contain printable ASCII characters, and filtered out non-restaurant reviews. We trained the model for approximately 72 hours. We post-processed the reviews using the customization methodology described in \\cite{yao2017automated} and email correspondence. We call fake reviews generated by this model LSTM-Fake reviews.",
+      " ",
+      "\\subsection{Similarity to existing fake reviews}",
+      "\\label{sec:automated}",
+      " ",
+      "We now want to understand how NMT-Fake* reviews compare to a) LSTM fake reviews and b) human-generated fake reviews. We do this by comparing the statistical similarity between these classes.",
+      " ",
+      "For `a' (Figure~\\ref{fig:lstm}), we use the Yelp Challenge dataset. We trained a classifier using 5,000 random reviews from the Yelp Challenge dataset (``human'') and 5,000 fake reviews generated by LSTM-Fake. Yao et al. \\cite{yao2017automated} found that character features are essential in identifying LSTM-Fake reviews. Consequently, we use character features (n-grams up to 3).",
+      " ",
+      "For `b' (Figure~\\ref{fig:shill}),we the ``Yelp Shills'' dataset (combination of YelpZip \\cite{mukherjee2013yelp}, YelpNYC \\cite{mukherjee2013yelp}, YelpChi \\cite{rayana2015collective}). This dataset labels entries that are identified as fraudulent by Yelp's filtering mechanism (''shill reviews'')\\footnote{Note that shill reviews are probably generated by human shills \\cite{zhao2017news}.}. The rest are treated as genuine reviews from human users (''genuine''). We use 100,000 reviews from each category to train a classifier. We use features from the commercial psychometric tool LIWC2015 \\cite{pennebaker2015development} to generated features.",
+      " ",
+      "In both cases, we use AdaBoost (with 200 shallow decision trees) for training. For testing each classifier, we use a held out test set of 1,000 reviews from both classes in each case. In addition, we test 1,000 NMT-Fake* reviews. Figures~\\ref{fig:lstm} and~\\ref{fig:shill} show the results. The classification threshold of 50\\% is marked with a dashed line.",
+      " ",
+      "\\begin{figure}",
+      " \\begin{subfigure}[b]{0.5\\columnwidth}",
+      " \\includegraphics[width=\\columnwidth]{figures/lstm.png}",
+      " \\caption{Human--LSTM reviews.}",
+      " \\label{fig:lstm}",
+      " \\end{subfigure}",
+      " \\begin{subfigure}[b]{0.5\\columnwidth}",
+      " \\includegraphics[width=\\columnwidth]{figures/distribution_shill.png}",
+      " \\caption{Genuine--Shill reviews.}",
+      " \\label{fig:shill}",
+      " \\end{subfigure}",
+      " \\caption{",
+      " Histogram comparison of NMT-Fake* reviews with LSTM-Fake reviews and human-generated (\\emph{genuine} and \\emph{shill}) reviews. Figure~\\ref{fig:lstm} shows that a classifier trained to distinguish ``human'' vs. LSTM-Fake cannot distinguish ``human'' vs NMT-Fake* reviews. Figure~\\ref{fig:shill} shows NMT-Fake* reviews are more similar to \\emph{genuine} reviews than \\emph{shill} reviews.",
+      " }",
+      " \\label{fig:statistical_similarity}",
+      "\\end{figure}",
+      " ",
+      "We can see that our new generated reviews do not share strong attributes with previous known categories of fake reviews. If anything, our fake reviews are more similar to genuine reviews than previous fake reviews. We thus conjecture that our NMT-Fake* fake reviews present a category of fake reviews that may go undetected on online review sites.",
+      " ",
+      " ",
+      "\\subsection{Comparative user study}",
+      "\\label{sec:comparison}",
+      "We wanted to evaluate the effectiveness of fake reviews againsttech-savvy users who understand and know to expect machine-generated fake reviews. We conducted a user study with 20 participants, all with computer science education and at least one university degree. Participant demographics are shown in Table~\\ref{table:amt_pop} in the Appendix. Each participant first attended a training session where they were asked to label reviews (fake and genuine) and could later compare them to the correct answers -- we call these participants \\emph{experienced participants}.",
+      "No personal data was collected during the user study.",
+      " ",
+      "Each person was given two randomly selected sets of 30 of reviews (a total of 60 reviews per person) with reviews containing 10 \\textendash 50 words each.",
+      "Each set contained 26 (87\\%) real reviews from Yelp and 4 (13\\%) machine-generated reviews,",
+      "numbers chosen based on suspicious review prevalence on Yelp~\\cite{mukherjee2013yelp,rayana2015collective}.",
+      "One set contained machine-generated reviews from one of the two models (NMT ($b=0.3, \\lambda=-5$) or LSTM),",
+      "and the other set reviews from the other in randomized order. The number of fake reviews was revealed to each participant in the study description. Each participant was requested to mark four (4) reviews as fake.",
+      " ",
+      "Each review targeted a real restaurant. A screenshot of that restaurant's Yelp page was shown to each participant prior to the study. Each participant evaluated reviews for one specific, randomly selected, restaurant. An example of the first page of the user study is shown in Figure~\\ref{fig:screenshot} in Appendix.",
+      " ",
+      "\\begin{figure}[!ht]",
+      "\\centering",
+      "\\includegraphics[width=.7\\columnwidth]{detection2.png}",
+      "\\caption{Violin plots of detection rate in comparative study. Mean and standard deviations for number of detected fakes are $0.8\\pm0.7$ for NMT-Fake* and $2.5\\pm1.0$ for LSTM-Fake. $n=20$. A sample of random detection is shown as comparison.}",
+      "\\label{fig:aalto}",
+      "\\end{figure}",
+      " ",
+      " ",
+      "Figure~\\ref{fig:aalto} shows the distribution of detected reviews of both types. A hypothetical random detector is shown for comparison.",
+      "NMT-Fake* reviews are significantly more difficult to detect for our experienced participants. In average, detection rate (recall) is $20\\%$ for NMT-Fake* reviews, compared to $61\\%$ for LSTM-based reviews.",
+      "The precision (and F-score) is the same as the recall in our study, since participants labeled 4 fakes in each set of 30 reviews \\cite{murphy2012machine}.",
+      "The distribution of the detection across participants is shown in Figure~\\ref{fig:aalto}. \\emph{The difference is statistically significant with confidence level $99\\%$} (Welch's t-test).",
+      "We compared the detection rate of NMT-Fake* reviews to a random detector, and find that \\emph{our participants detection rate of NMT-Fake* reviews is not statistically different from random predictions with 95\\% confidence level} (Welch's t-test).",
+      " ",
+      " ",
+      "\\section{Defenses}",
+      " ",
+      "\\label{sec:detection}",
+      " ",
+      "We developed an AdaBoost-based classifier to detect our new fake reviews, consisting of 200 shallow decision trees (depth 2). The features we used are recorded in Table~\\ref{table:features_adaboost} (Appendix).",
+      "We used word-level features based on spaCy-tokenization \\cite{honnibal-johnson:2015:EMNLP} and constructed n-gram representation of POS-tags and dependency tree tags. We added readability features from NLTK~\\cite{bird2004nltk}.",
+      " ",
+      "\\begin{figure}[ht]",
+      "\\centering",
+      "\\includegraphics[width=.7\\columnwidth]{obf_score_fair_2.png}",
+      "\\caption{",
+      "Adaboost-based classification of NMT-Fake and human-written reviews.",
+      "Effect of varying $b$ and $\\lambda$ in fake review generation.",
+      "The variant native speakers had most difficulties detecting is well detectable by AdaBoost (97\\%).}",
+      "\\label{fig:adaboost_matrix_b_lambda}",
+      "\\end{figure}",
+      " ",
+      " ",
+      "Figure~\\ref{fig:adaboost_matrix_b_lambda} shows our AdaBoost classifier's class-averaged F-score at detecting different kind of fake reviews. The classifier is very effective in detecting reviews that humans have difficulties detecting. For example, the fake reviews MTurk users had most difficulty detecting ($b=0.3, \\lambda=-5$) are detected with an excellent 97\\% F-score.",
+      "The most important features for the classification were counts for frequently occurring words in fake reviews (such as punctuation, pronouns, articles) as well as the readability feature ``Automated Readability Index''. We thus conclude that while NMT-Fake reviews are difficult to detect for humans, they can be well detected with the right tools.",
+      " ",
+      "\\section{Related Work}",
+      " ",
+      "Kumar and Shah~\\cite{kumar2018false} survey and categorize false information research. Automatically generated fake reviews are a form of \\emph{opinion-based false information}, where the creator of the review may influence reader's opinions or decisions.",
+      "Yao et al. \\cite{yao2017automated} presented their study on machine-generated fake reviews. Contrary to us, they investigated character-level language models, without specifying a specific context before generation. We leverage existing NMT tools to encode a specific context to the restaurant before generating reviews.",
+      "Supporting our study, Everett et al~\\cite{Everett2016Automated} found that security researchers were less likely to be fooled by Markov chain-generated Reddit comments compared to ordinary Internet users.",
+      " ",
+      "Diversification of NMT model outputs has been studied in \\cite{li2016diversity}. The authors proposed the use of a penalty to commonly occurring sentences (\\emph{n-grams}) in order to emphasize maximum mutual information-based generation.",
+      "The authors investigated the use of NMT models in chatbot systems.",
+      "We found that unigram penalties to random tokens (Algorithm~\\ref{alg:aug}) was easy to implement and produced sufficiently diverse responses.",
+      " ",
+      "\\section {Discussion and Future Work}",
+      " ",
+      "\\paragraph{What makes NMT-Fake* reviews difficult to detect?} First, NMT models allow the encoding of a relevant context for each review, which narrows down the possible choices of words that the model has to choose from. Our NMT model had a perplexity of approximately $25$, while the model of \\cite{yao2017automated} had a perplexity of approximately $90$ \\footnote{Personal communication with the authors}. Second, the beam search in NMT models narrows down choices to natural-looking sentences. Third, we observed that the NMT model produced \\emph{better structure} in the generated sentences (i.e. a more coherent story).",
+      " ",
+      "\\paragraph{Cost of generating reviews} With our setup, generating one review took less than one second. The cost of generation stems mainly from the overnight training. Assuming an electricity cost of 16 cents / kWh (California) and 8 hours of training, training the NMT model requires approximately 1.30 USD. This is a 90\\% reduction in time compared to the state-of-the-art \\cite{yao2017automated}. Furthermore, it is possible to generate both positive and negative reviews with the same model.",
+      " ",
+      "\\paragraph{Ease of customization} We experimented with inserting specific words into the text by increasing their log likelihoods in the beam search. We noticed that the success depended on the prevalence of the word in the training set. For example, adding a +5 to \\emph{Mike} in the log-likelihood resulted in approximately 10\\% prevalence of this word in the reviews. An attacker can therefore easily insert specific keywords to reviews, which can increase evasion probability.",
+      " ",
+      "\\paragraph{Ease of testing} Our diversification scheme is applicable during \\emph{generation phase}, and does not affect the training setup of the network in any way. Once the NMT model is obtained, it is easy to obtain several different variants of NMT-Fake reviews by varying parameters $b$ and $\\lambda$.",
+      " ",
+      " ",
+      " ",
+      "\\paragraph{Languages} The generation methodology is not per-se language-dependent. The requirement for successful generation is that sufficiently much data exists in the targeted language. However, our language model modifications require some knowledge of that target language's grammar to produce high-quality reviews.",
+      " ",
+      "\\paragraph{Generalizability of detection techniques} Currently, fake reviews are not universally detectable. Our results highlight that it is difficult to claim detection performance on unseen types of fake reviews (Section~\\ref{sec:automated}). We see this an open problem that deserves more attention in fake reviews research.",
+      " ",
+      "\\paragraph{Generalizability to other types of datasets} Our technique can be applied to any dataset, as long as there is sufficient training data for the NMT model. We used approximately 2.9 million reviews for this work.",
+      " ",
+      "\\section{Conclusion}",
+      " ",
+      "In this paper, we showed that neural machine translation models can be used to generate fake reviews that are very effective in deceiving even experienced, tech-savvy users.",
+      "This supports anecdotal evidence \\cite{national2017commission}.",
+      "Our technique is more effective than state-of-the-art \\cite{yao2017automated}.",
+      "We conclude that machine-aided fake review detection is necessary since human users are ineffective in identifying fake reviews.",
+      "We also showed that detectors trained using one type of fake reviews are not effective in identifying other types of fake reviews.",
+      "Robust detection of fake reviews is thus still an open problem.",
+      " ",
+      " ",
+      "\\section*{Acknowledgments}",
+      "We thank Tommi Gr\\\"{o}ndahl for assistance in planning user studies and the",
+      "participants of the user study for their time and feedback. We also thank",
+      "Luiza Sayfullina for comments that improved the manuscript.",
+      "We thank the authors of \\cite{yao2017automated} for answering questions about",
+      "their work.",
+      " ",
+      " ",
+      "\\bibliographystyle{splncs}",
+      "\\begin{thebibliography}{10}",
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+      " ",
+      "\\end{thebibliography}",
+      " ",
+      " ",
+      " ",
+      "\\section*{Appendix}",
+      " ",
+      "We present basic demographics of our MTurk study and the comparative study with experienced users in Table~\\ref{table:amt_pop}.",
+      " ",
+      "\\begin{table}",
+      "\\caption{User study statistics.}",
+      "\\begin{center}",
+      " \\begin{tabular}{ | l | c | c | }",
+      " \\hline",
+      " Quality & Mechanical Turk users & Experienced users\\\\",
+      " \\hline",
+      " Native English Speaker & Yes (20) & Yes (1) No (19) \\\\",
+      " Fluent in English & Yes (20) & Yes (20) \\\\",
+      " Age & 21-40 (17) 41-60 (3) & 21-25 (8) 26-30 (7) 31-35 (4) 41-45 (1)\\\\",
+      " Gender & Male (14) Female (6) & Male (17) Female (3)\\\\",
+      " Highest Education & High School (10) Bachelor (10) & Bachelor (9) Master (6) Ph.D. (5) \\\\",
+      " \\hline",
+      " \\end{tabular}",
+      " \\label{table:amt_pop}",
+      "\\end{center}",
+      "\\end{table}",
+      " ",
+      " ",
+      "Table~\\ref{table:openNMT-py_commands} shows a listing of the openNMT-py commands we used to create our NMT model and to generate fake reviews.",
+      " ",
+      "\\begin{table}[t]",
+      "\\caption{Listing of used openNMT-py commands.}",
+      "\\begin{center}",
+      " \\begin{tabular}{ | l | l | }",
+      " \\hline",
+      " Phase & Bash command \\\\",
+      " \\hline",
+      " Preprocessing & \\begin{lstlisting}[language=bash]",
+      "python preprocess.py -train_src context-train.txt",
+      "-train_tgt reviews-train.txt -valid_src context-val.txt",
+      "-valid_tgt reviews-val.txt -save_data model",
+      "-lower -tgt_words_min_frequency 10",
+      "\\end{lstlisting}",
+      " \\\\ & \\\\",
+      " Training & \\begin{lstlisting}[language=bash]",
+      "python train.py -data model -save_model model -epochs 8",
+      "-gpuid 0 -learning_rate_decay 0.5 -optim adam",
+      "-learning_rate 0.001 -start_decay_at 3\\end{lstlisting}",
+      " \\\\ & \\\\",
+      " Generation & \\begin{lstlisting}[language=bash]",
+      "python translate.py -model model_acc_35.54_ppl_25.68_e8.pt",
+      "-src context-tst.txt -output pred-e8.txt -replace_unk",
+      "-verbose -max_length 50 -gpu 0",
+      " \\end{lstlisting} \\\\",
+      " \\hline",
+      " \\end{tabular}",
+      " \\label{table:openNMT-py_commands}",
+      "\\end{center}",
+      "\\end{table}",
+      " ",
+      " ",
+      "Table~\\ref{table:MTurk_sub} shows the classification performance of Amazon Mechanical Turkers, separated across different categories of NMT-Fake reviews. The category with best performance ($b=0.3, \\lambda=-5$) is denoted as NMT-Fake*.",
+      " ",
+      "\\begin{table}[b]",
+      "\\caption{MTurk study subclass classification reports. Classes are imbalanced in ratio 1:6. Random predictions are $p_\\mathrm{human} = 86\\%$ and $p_\\mathrm{machine} = 14\\%$, with $r_\\mathrm{human} = r_\\mathrm{machine} = 50\\%$. Class-averaged F-scores for random predictions are $42\\%$.}",
+      "\\begin{center}",
+      " \\begin{tabular}{ | c || c |c |c | c | }",
+      " \\hline",
+      " $(b=0.3, \\lambda = -3)$ & Precision & Recall & F-score & Support \\\\ \\hline",
+      " Human & 89\\% & 63\\% & 73\\% & 994\\\\",
+      " NMT-Fake & 15\\% & 45\\% & 22\\% & 146 \\\\",
+      " \\hline",
+      " \\hline",
+      " $(b=0.3, \\lambda = -5)$ & Precision & Recall & F-score & Support \\\\ \\hline",
+      " Human & 86\\% & 63\\% & 73\\% & 994\\\\",
+      " NMT-Fake* & 16\\% & 40\\% & 23\\% & 171 \\\\",
+      " \\hline",
+      " \\hline",
+      " $(b=0.5, \\lambda = -4)$ & Precision & Recall & F-score & Support \\\\ \\hline",
+      " Human & 88\\% & 63\\% & 73\\% & 994\\\\",
+      " NMT-Fake & 21\\% & 55\\% & 30\\% & 181 \\\\",
+      " \\hline",
+      " \\hline",
+      " $(b=0.7, \\lambda = -3)$ & Precision & Recall & F-score & Support \\\\ \\hline",
+      " Human & 88\\% & 63\\% & 73\\% & 994\\\\",
+      " NMT-Fake & 19\\% & 50\\% & 27\\% & 170 \\\\",
+      " \\hline",
+      " \\hline",
+      " $(b=0.7, \\lambda = -5)$ & Precision & Recall & F-score & Support \\\\ \\hline",
+      " Human & 89\\% & 63\\% & 74\\% & 994\\\\",
+      " NMT-Fake & 21\\% & 57\\% & 31\\% & 174 \\\\",
+      " \\hline",
+      " \\hline",
+      " $(b=0.9, \\lambda = -4)$ & Precision & Recall & F-score & Support \\\\ \\hline",
+      " Human & 88\\% & 63\\% & 73\\% & 994\\\\",
+      " NMT-Fake & 18\\% & 50\\% & 27\\% & 164 \\\\",
+      " \\hline",
+      " \\end{tabular}",
+      " \\label{table:MTurk_sub}",
+      "\\end{center}",
+      "\\end{table}",
+      " ",
+      "Figure~\\ref{fig:screenshot} shows screenshots of the first two pages of our user study with experienced participants.",
+      " ",
+      "\\begin{figure}[ht]",
+      "\\centering",
+      "\\includegraphics[width=1.\\columnwidth]{figures/screenshot_7-3.png}",
+      "\\caption{",
+      "Screenshots of the first two pages in the user study. Example 1 is a NMT-Fake* review, the rest are human-written.",
+      "}",
+      "\\label{fig:screenshot}",
+      "\\end{figure}",
+      " ",
+      "Table~\\ref{table:features_adaboost} shows the features used to detect NMT-Fake reviews using the AdaBoost classifier.",
+      " ",
+      "\\begin{table}",
+      "\\caption{Features used in NMT-Fake review detector.}",
+      "\\begin{center}",
+      " \\begin{tabular}{ | l | c | }",
+      " \\hline",
+      " Feature type & Number of features \\\\ \\hline",
+      " \\hline",
+      " Readability features & 13 \\\\ \\hline",
+      " Unique POS tags & $~20$ \\\\ \\hline",
+      " Word unigrams & 22,831 \\\\ \\hline",
+      " 1/2/3/4-grams of simple part-of-speech tags & 54,240 \\\\ \\hline",
+      " 1/2/3-grams of detailed part-of-speech tags & 112,944 \\\\ \\hline",
+      " 1/2/3-grams of syntactic dependency tags & 93,195 \\\\ \\hline",
+      " \\end{tabular}",
+      " \\label{table:features_adaboost}",
+      "\\end{center}",
+      "\\end{table}",
+      " ",
+      "\\end{document}",
+      ""
+    ]
+  ]
+}
+```

qasper-0042/instruction.md

@@ -0,0 +1,108 @@
+Name of Paper: Probabilistic Bias Mitigation in Word Embeddings
+
+Question: How is embedding quality assessed?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background ::: Geometric Bias Mitigation",
+    "Background ::: Geometric Bias Mitigation ::: WEAT",
+    "Background ::: Geometric Bias Mitigation ::: RIPA",
+    "Background ::: Geometric Bias Mitigation ::: Neighborhood Metric",
+    "A Probabilistic Framework for Bias Mitigation",
+    "A Probabilistic Framework for Bias Mitigation ::: Probabilistic Bias Mitigation",
+    "A Probabilistic Framework for Bias Mitigation ::: Nearest Neighbor Bias Mitigation",
+    "Experiments",
+    "Discussion",
+    "Discussion ::: Acknowledgements",
+    "Experiment Notes",
+    "Professions",
+    "WEAT Word Sets"
+  ],
+  "paragraphs": [
+    [
+      "Word embeddings, or vector representations of words, are an important component of Natural Language Processing (NLP) models and necessary for many downstream tasks. However, word embeddings, including embeddings commonly deployed for public use, have been shown to exhibit unwanted societal stereotypes and biases, raising concerns about disparate impact on axes of gender, race, ethnicity, and religion BIBREF0, BIBREF1. The impact of this bias has manifested in a range of downstream tasks, ranging from autocomplete suggestions BIBREF2 to advertisement delivery BIBREF3, increasing the likelihood of amplifying harmful biases through the use of these models.",
+      "The most well-established method thus far for mitigating bias relies on projecting target words onto a bias subspace (such as a gender subspace) and subtracting out the difference between the resulting distances BIBREF0. On the other hand, the most popular metric for measuring bias is the WEAT statistic BIBREF1, which compares the cosine similarities between groups of words. However, WEAT has been recently shown to overestimate bias as a result of implicitly relying on similar frequencies for the target words BIBREF4, and BIBREF5 demonstrated that evidence of bias can still be recovered after geometric bias mitigation by examining the neighborhood of a target word among socially-biased words.",
+      "In response to this, we propose an alternative framework for bias mitigation in word embeddings that approaches this problem from a probabilistic perspective. The motivation for this approach is two-fold. First, most popular word embedding algorithms are probabilistic at their core \u2013 i.e., they are trained (explicitly or implicitly BIBREF6) to minimize some form of word co-occurrence probabilities. Thus, we argue that a framework for measuring and treating bias in these embeddings should take into account, in addition to their geometric aspect, their probabilistic nature too. On the other hand, the issue of bias has also been approached (albeit in different contexts) in the fairness literature, where various intuitive notions of equity such as equalized odds have been formalized through probabilistic criteria. By considering analogous criteria for the word embedding setting, we seek to draw connections between these two bodies of work.",
+      "We present experiments on various bias mitigation benchmarks and show that our framework is comparable to state-of-the-art alternatives according to measures of geometric bias mitigation and that it performs far better according to measures of neighborhood bias. For fair comparison, we focus on mitigating a binary gender bias in pre-trained word embeddings using SGNS (skip-gram with negative-sampling), though we note that this framework and methods could be extended to other types of bias and word embedding algorithms."
+    ],
+    [
+      "Geometric bias mitigation uses the cosine distances between words to both measure and remove gender bias BIBREF0. This method implicitly defines bias as a geometric asymmetry between words when projected onto a subspace, such as the gender subspace constructed from a set of gender pairs such as $\\mathcal {P} = \\lbrace (he,she),(man,woman),(king,queen)...\\rbrace $. The projection of a vector $v$ onto $B$ (the subspace) is defined by $v_B = \\sum _{j=1}^{k} (v \\cdot b_j) b_j$ where a subspace $B$ is defined by k orthogonal unit vectors $B = {b_1,...,b_k}$."
+    ],
+    [
+      "The WEAT statistic BIBREF1 demonstrates the presence of biases in word embeddings with an effect size defined as the mean test statistic across the two word sets:",
+      "Where $s$, the test statistic, is defined as: $s(w,A,B) = mean_{a \\in A} cos(w,a) - mean_{b \\in B} cos(w,a)$, and $X$,$Y$,$A$, and $B$ are groups of words for which the association is measured. Possible values range from $-2$ to 2 depending on the association of the words groups, and a value of zero indicates $X$ and $Y$ are equally associated with $A$ and $B$. See BIBREF4 for further details on WEAT."
+    ],
+    [
+      "The RIPA (relational inner product association) metric was developed as an alternative to WEAT, with the critique that WEAT is likely to overestimate the bias of a target attribute BIBREF4. The RIPA metric formalizes the measure of bias used in geometric bias mitigation as the inner product association of a word vector $v$ with respect to a relation vector $b$. The relation vector is constructed from the first principal component of the differences between gender word pairs. We report the absolute value of the RIPA metric as the value can be positive or negative according to the direction of the bias. A value of zero indicates a lack of bias, and the value is bound by $[-||w||,||w||]$."
+    ],
+    [
+      "The neighborhood bias metric proposed by BIBREF5 quantifies bias as the proportion of male socially-biased words among the $k$ nearest socially-biased male and female neighboring words, whereby biased words are obtained by projecting neutral words onto a gender relation vector. As we only examine the target word among the 1000 most socially-biased words in the vocabulary (500 male and 500 female), a word\u2019s bias is measured as the ratio of its neighborhood of socially-biased male and socially-biased female words, so that a value of 0.5 in this metric would indicate a perfectly unbiased word, and values closer to 0 and 1 indicate stronger bias."
+    ],
+    [
+      "Our objective here is to extend and complement the geometric notions of word embedding bias described in the previous section with an alternative, probabilistic, approach. Intuitively, we seek a notion of equality akin to that of demographic parity in the fairness literature, which requires that a decision or outcome be independent of a protected attribute such as gender. BIBREF7. Similarly, when considering a probabilistic definition of unbiased in word embeddings, we can consider the conditional probabilities of word pairs, ensuring for example that $p(doctor|man) \\approx p(doctor|woman)$, and can extend this probabilistic framework to include the neighborhood of a target word, addressing the potential pitfalls of geometric bias mitigation.",
+      "Conveniently, most word embedding frameworks allow for immediate computation of the conditional probabilities $P(w|c)$. Here, we focus our attention on the Skip-Gram method with Negative Sampling (SGNS) of BIBREF8, although our framework can be equivalently instantiated for most other popular embedding methods, owing to their core similarities BIBREF6, BIBREF9. Leveraging this probabilistic nature, we construct a bias mitigation method in two steps, and examine each step as an independent method as well as the resulting composite method."
+    ],
+    [
+      "This component of our bias mitigation framework seeks to enforce that the probability of prediction or outcome cannot depend on a protected class such as gender. We can formalize this intuitive goal through a loss function that penalizes the discrepancy between the conditional probabilities of a target word (i.e., one that should not be affected by the protected attribute) conditioned on two words describing the protected attribute (e.g., man and woman in the case of gender). That is, for every target word we seek to minimize:",
+      "where $\\mathcal {P} = \\lbrace (he,she),(man,woman),(king,queen), \\dots \\rbrace $ is a set of word pairs characterizing the protected attribute, akin to that used in previous work BIBREF0.",
+      "At this point, the specific form of the objective will depend on the type of word embeddings used. For our expample of SGNS, recall that this algorithm models the conditional probability of a target word given a context word as a function of the inner product of their representations. Though an exact method for calculating the conditional probability includes summing over conditional probability of all the words in the vocabulary, we can use the estimation of log conditional probability proposed by BIBREF8, i.e., $ \\log p(w_O|w_I) \\approx \\log \\sigma ({v^{\\prime }_{wo}}^T v_{wI}) + \\sum _{i=1}^{k} [\\log {\\sigma ({{-v^{\\prime }_{wi}}^T v_{wI}})}] $."
+    ],
+    [
+      "Based on observations by BIBREF5, we extend our method to consider the composition of the neighborhood of socially-gendered words of a target word. We note that bias in a word embedding depends not only on the relationship between a target word and explicitly gendered words like man and woman, but also between a target word and socially-biased male or female words. Bolukbasi et al BIBREF0 proposed a method for eliminating this kind of indirect bias through geometric bias mitigation, but it is shown to be ineffective by the neighborhood metric BIBREF5.",
+      "Instead, we extend our method of bias mitigation to account for this neighborhood effect. Specifically, we examine the conditional probabilities of a target word given the $k/2$ nearest neighbors from the male socially-biased words as well as given the $k/2$ female socially-biased words (in sorted order, from smallest to largest). The groups of socially-biased words are constructed as described in the neighborhood metric. If the word is unbiased according to the neighborhood metric, these probabilities should be comparable. We then use the following as our loss function:",
+      "",
+      "where $m$ and $f$ represent the male and female neighbors sorted by distance to the target word $t$ (we use $L1$ distance)."
+    ],
+    [
+      "We evaluate our framework on fastText embeddings trained on Wikipedia (2017), UMBC webbase corpus and statmt.org news dataset (16B tokens) BIBREF11. For simplicity, only the first 22000 words are used in all embeddings, though preliminary results indicate the findings extend to the full corpus. For our novel methods of mitigating bias, a shallow neural network is used to adjust the embedding. The single layer of the model is an embedding layer with weights initialized to those of the original embedding. For the composite method, these weights are initialized to those of the embedding after probabilistic bias mitigation. A batch of word indices is fed into the model, which are then embedded and for which a loss value is calculated, allowing back-propagation to adjust the embeddings. For each of the models, a fixed number of iterations is used to prevent overfitting, which can eventually hurt performance on the embedding benchmarks (See Figure FIGREF12). We evaluated the embedding after 1000 iterations, and stopped training if performance on a benchmark decreased significantly.",
+      "We construct a list of candidate words to debias, taken from the words used in the WEAT gender bias statistics. Words in this list should be gender neutral, and are related to the topics of career, arts, science, math, family and professions (see appendix). We note that this list can easily be expanded to include a greater proportion of words in the corpus. For example, BIBREF4 suggested a method for identifying inappropriately gendered words using unsupervised learning.",
+      "We compare this method of bias mitigation with the no bias mitigation (\"Orig\"), geometric bias mitigation (\"Geo\"), the two pieces of our method alone (\"Prob\" and \"KNN\") and the composite method (\"KNN+Prob\"). We note that the composite method performs reasonably well according the the RIPA metric, and much better than traditional geometric bias mitigation according to the neighborhood metric, without significant performance loss according to the accepted benchmarks. To our knowledge this is the first bias mitigation method to perform reasonably both on both metrics."
+    ],
+    [
+      "We proposed a simple method of bias mitigation based on this probabilistic notions of fairness, and showed that it leads to promising results in various benchmark bias mitigation tasks. Future work should include considering a more rigorous definition and non-binary of bias and experimenting with various embedding algorithms and network architectures."
+    ],
+    [
+      "The authors would like to thank Tommi Jaakkola for stimulating discussions during the initial stages of this work."
+    ],
+    [
+      "For Equation 4, as described in the original work, in regards to the k sample words $w_i$ is drawn from the corpus using the Unigram distribution raised to the 3/4 power.",
+      "For reference, the most male socially-biased words include words such as:\u2019john\u2019, \u2019jr\u2019, \u2019mlb\u2019, \u2019dick\u2019, \u2019nfl\u2019, \u2019cfl\u2019, \u2019sgt\u2019, \u2019abbot\u2019, \u2019halfback\u2019, \u2019jock\u2019, \u2019mike\u2019, \u2019joseph\u2019,while the most female socially-biased words include words such as:\u2019feminine\u2019, \u2019marital\u2019, \u2019tatiana\u2019, \u2019pregnancy\u2019, \u2019eva\u2019, \u2019pageant\u2019, \u2019distress\u2019, \u2019cristina\u2019, \u2019ida\u2019, \u2019beauty\u2019, \u2019sexuality\u2019,\u2019fertility\u2019"
+    ],
+    [
+      "'accountant', 'acquaintance', 'actor', 'actress', 'administrator', 'adventurer', 'advocate', 'aide', 'alderman', 'ambassador', 'analyst', 'anthropologist', 'archaeologist', 'archbishop', 'architect', 'artist', 'assassin', 'astronaut', 'astronomer', 'athlete', 'attorney', 'author', 'baker', 'banker', 'barber', 'baron', 'barrister', 'bartender', 'biologist', 'bishop', 'bodyguard', 'boss', 'boxer', 'broadcaster', 'broker', 'businessman', 'butcher', 'butler', 'captain', 'caretaker', 'carpenter', 'cartoonist', 'cellist', 'chancellor', 'chaplain', 'character', 'chef', 'chemist', 'choreographer', 'cinematographer', 'citizen', 'cleric', 'clerk', 'coach', 'collector', 'colonel', 'columnist', 'comedian', 'comic', 'commander', 'commentator', 'commissioner', 'composer', 'conductor', 'confesses', 'congressman', 'constable', 'consultant', 'cop', 'correspondent', 'counselor', 'critic', 'crusader', 'curator', 'dad', 'dancer', 'dean', 'dentist', 'deputy', 'detective', 'diplomat', 'director', 'doctor', 'drummer', 'economist', 'editor', 'educator', 'employee', 'entertainer', 'entrepreneur', 'envoy', 'evangelist', 'farmer', 'filmmaker', 'financier', 'fisherman', 'footballer', 'foreman', 'gangster', 'gardener', 'geologist', 'goalkeeper', 'guitarist', 'headmaster', 'historian', 'hooker', 'illustrator', 'industrialist', 'inspector', 'instructor', 'inventor', 'investigator', 'journalist', 'judge', 'jurist', 'landlord', 'lawyer', 'lecturer', 'legislator', 'librarian', 'lieutenant', 'lyricist', 'maestro', 'magician', 'magistrate', 'maid', 'manager', 'marshal', 'mathematician', 'mechanic', 'midfielder', 'minister', 'missionary', 'monk', 'musician', 'nanny', 'narrator', 'naturalist', 'novelist', 'nun', 'nurse', 'observer', 'officer', 'organist', 'painter', 'pastor', 'performer', 'philanthropist', 'philosopher', 'photographer', 'physician', 'physicist', 'pianist', 'planner', 'playwright', 'poet', 'policeman', 'politician', 'preacher', 'president', 'priest', 'principal', 'prisoner', 'professor', 'programmer', 'promoter', 'proprietor', 'prosecutor', 'protagonist', 'provost', 'psychiatrist', 'psychologist', 'rabbi', 'ranger', 'researcher', 'sailor', 'saint', 'salesman', 'saxophonist', 'scholar', 'scientist', 'screenwriter', 'sculptor', 'secretary', 'senator', 'sergeant', 'servant', 'singer', 'skipper', 'sociologist', 'soldier', 'solicitor', 'soloist', 'sportsman', 'statesman', 'steward', 'student', 'substitute', 'superintendent', 'surgeon', 'surveyor', 'swimmer', 'teacher', 'technician', 'teenager', 'therapist', 'trader', 'treasurer', 'trooper', 'trumpeter', 'tutor', 'tycoon', 'violinist', 'vocalist', 'waiter', 'waitress', 'warden', 'warrior', 'worker', 'wrestler', 'writer'"
+    ],
+    [
+      "Words used for WEAT statistic, consisting of baseline bias tests and gender bias tests in the format X vs Y / A vs B",
+      "Flowers vs Insects / Pleasant vs Unpleasant",
+      "X: \"aster\", \"clover\", \"hyacinth\", \"marigold\", \"poppy\", \"azalea\", \"crocus\", \"iris\", \"orchid\", \"rose\", \"bluebell\", \"daffodil\", \"lilac\", \"pansy\", \"tulip\", \"buttercup\", \"daisy\", \"lily\", \"peony\", \"violet\", \"carnation\", \"gladiola\", \"magnolia\", \"petunia\", \"zinnia\"",
+      "Y: \"ant\", \"caterpillar\", \"flea\", \"locust\", \"spider\", \"bedbug\", \"centipede\", \"fly\", \"maggot\", \"tarantula\", \"bee\", \"cockroach\", \"gnat\", \"mosquito\", \"termite\", \"beetle\", \"cricket\", \"hornet\", \"moth\", \"wasp\", \"blackfly\", \"dragonfly\", \"horsefly\", \"roach\", \"weevil\"",
+      "A: \"caress\", \"freedom\", \"health\", \"love\", \"peace\", \"cheer\", \"friend\", \"heaven\", \"loyal\", \"pleasure\", \"diamond\", \"gentle\", \"honest\", \"lucky\", \"rainbow\", \"diploma\", \"gift\", \"honor\", \"miracle\", \"sunrise\", \"family\", \"happy\", \"laughter\", \"paradise\", \"vacation\"",
+      "B: \"abuse\", \"crash\", \"filth\", \"murder\", \"sickness\", \"accident\", \"death\", \"grief\", \"poison\", \"stink\", \"assault\", \"disaster\", \"hatred\", \"pollute\", \"tragedy\", \"divorce\", \"jail\", \"poverty\", \"ugly\", \"cancer\", \"kill\", \"rotten\", \"vomit\", \"agony\", \"prison\"",
+      "Instruments vs Weapons / Pleasant vs Unpleasant:",
+      "X: \"bagpipe\", \"cello\", \"guitar\", \"lute\", \"trombone\", \"banjo\", \"clarinet\", \"harmonica\", \"mandolin\", \"trumpet\", \"bassoon\", \"drum\", \"harp\", \"oboe\", \"tuba\", \"bell\", \"fiddle\", \"harpsichord\", \"piano\", \"viola\", \"bongo\", \"flute\", \"horn\", \"saxophone\", \"violin\"",
+      "Y: \"arrow\", \"club\", \"gun\", \"missile\", \"spear\", \"ax\", \"dagger\", \"harpoon\", \"pistol\", \"sword\", \"blade\", \"dynamite\", \"hatchet\", \"rifle\", \"tank\", \"bomb\", \"firearm\", \"knife\", \"shotgun\", \"teargas\", \"cannon\", \"grenade\", \"mace\", \"slingshot\", \"whip\"",
+      "A: \"caress\", \"freedom\", \"health\", \"love\", \"peace\", \"cheer\", \"friend\", \"heaven\", \"loyal\", \"pleasure\", \"diamond\", \"gentle\", \"honest\", \"lucky\", \"rainbow\", \"diploma\", \"gift\", \"honor\", \"miracle\", \"sunrise\", \"family\", \"happy\", \"laughter\", \"paradise\", \"vacation\"",
+      "B: \"abuse\", \"crash\", \"filth\", \"murder\", \"sickness\", \"accident\", \"death\", \"grief\", \"poison\", \"stink\", \"assault\", \"disaster\", \"hatred\", \"pollute\", \"tragedy\", \"divorce\", \"jail\", \"poverty\", \"ugly\", \"cancer\", \"kill\", \"rotten\", \"vomit\", \"agony\", \"prison\"",
+      "Male vs Female / Career vs Family:",
+      "X: \"brother\", \"father\", \"uncle\", \"grandfather\", \"son\", \"he\", \"his\", \"him\", \"man\", \"himself\", \"men\", \"husband\", \"boy\", \"uncle\", \"nephew\", \"boyfriend\", \"king\", \"actor\"",
+      "Y: \"sister\", \"mother\", \"aunt\", \"grandmother\", \"daughter\", \"she\", \"hers\", \"her\", \"woman\", \"herself\", \"women\", \"wife\", \"aunt\", \"niece\", \"girlfriend\", \"queen\", \"actress\"",
+      "A: \"executive\", \"management\", \"professional\", \"corporation\", \"salary\", \"office\", \"business\", \"career\", \"industry\", \"company\", \"promotion\", \"profession\", \"CEO\", \"manager\", \"coworker\", \"entrepreneur\"",
+      "B: \"home\", \"parents\", \"children\", \"family\", \"cousins\", \"marriage\", \"wedding\", \"relatives\", \"grandparents\", \"grandchildren\", \"nurture\", \"child\", \"toddler\", \"infant\", \"teenager\"",
+      "Math vs Art / Male vs Female:",
+      "X: \"math\", \"algebra\", \"geometry\", \"calculus\", \"equations\", \"computation\", \"numbers\", \"addition\", \"trigonometry\", \"arithmetic\", \"logic\", \"proofs\", \"multiplication\", \"mathematics\"",
+      "Y: \"poetry\", \"art\", \"Shakespeare\", \"dance\", \"literature\", \"novel\", \"symphony\", \"drama\", \"orchestra\", \"music\", \"ballet\", \"arts\", \"creative\", \"sculpture\"",
+      "A: \"brother\", \"father\", \"uncle\", \"grandfather\", \"son\", \"he\", \"his\", \"him\", \"man\", \"himself\", \"men\", \"husband\", \"boy\", \"uncle\", \"nephew\", \"boyfriend\", \"king\", \"actor\"",
+      "B: \"sister\", \"mother\", \"aunt\", \"grandmother\", \"daughter\", \"she\", \"hers\", \"her\", \"woman\", \"herself\", \"women\", \"wife\", \"aunt\", \"niece\", \"girlfriend\", \"queen\", \"actress\"",
+      "Science vs Art / Male8 vs Female8:",
+      "X:\"science\", \"technology\", \"physics\", \"chemistry\", \"Einstein\", \"NASA\", \"experiment\", \"astronomy\", \"biology\", \"aeronautics\", \"mechanics\", \"thermodynamics\"",
+      "Y: \"poetry\", \"art\", \"Shakespeare\", \"dance\", \"literature\", \"novel\", \"symphony\", \"drama\", \"orchestra\", \"music\", \"ballet\", \"arts\", \"creative\", \"sculpture\"",
+      "A: \"brother\", \"father\", \"uncle\", \"grandfather\", \"son\", \"he\", \"his\", \"him\", \"man\", \"himself\", \"men\", \"husband\", \"boy\", \"uncle\", \"nephew\", \"boyfriend\"",
+      "B: \"sister\", \"mother\", \"aunt\", \"grandmother\", \"daughter\", \"she\", \"hers\", \"her\", \"woman\", \"herself\", \"women\", \"wife\", \"aunt\", \"niece\", \"girlfriend\""
+    ]
+  ]
+}
+```

qasper-0043/instruction.md

@@ -0,0 +1,108 @@
+Name of Paper: Probabilistic Bias Mitigation in Word Embeddings
+
+Question: What are the three measures of bias which are reduced in experiments?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Background ::: Geometric Bias Mitigation",
+    "Background ::: Geometric Bias Mitigation ::: WEAT",
+    "Background ::: Geometric Bias Mitigation ::: RIPA",
+    "Background ::: Geometric Bias Mitigation ::: Neighborhood Metric",
+    "A Probabilistic Framework for Bias Mitigation",
+    "A Probabilistic Framework for Bias Mitigation ::: Probabilistic Bias Mitigation",
+    "A Probabilistic Framework for Bias Mitigation ::: Nearest Neighbor Bias Mitigation",
+    "Experiments",
+    "Discussion",
+    "Discussion ::: Acknowledgements",
+    "Experiment Notes",
+    "Professions",
+    "WEAT Word Sets"
+  ],
+  "paragraphs": [
+    [
+      "Word embeddings, or vector representations of words, are an important component of Natural Language Processing (NLP) models and necessary for many downstream tasks. However, word embeddings, including embeddings commonly deployed for public use, have been shown to exhibit unwanted societal stereotypes and biases, raising concerns about disparate impact on axes of gender, race, ethnicity, and religion BIBREF0, BIBREF1. The impact of this bias has manifested in a range of downstream tasks, ranging from autocomplete suggestions BIBREF2 to advertisement delivery BIBREF3, increasing the likelihood of amplifying harmful biases through the use of these models.",
+      "The most well-established method thus far for mitigating bias relies on projecting target words onto a bias subspace (such as a gender subspace) and subtracting out the difference between the resulting distances BIBREF0. On the other hand, the most popular metric for measuring bias is the WEAT statistic BIBREF1, which compares the cosine similarities between groups of words. However, WEAT has been recently shown to overestimate bias as a result of implicitly relying on similar frequencies for the target words BIBREF4, and BIBREF5 demonstrated that evidence of bias can still be recovered after geometric bias mitigation by examining the neighborhood of a target word among socially-biased words.",
+      "In response to this, we propose an alternative framework for bias mitigation in word embeddings that approaches this problem from a probabilistic perspective. The motivation for this approach is two-fold. First, most popular word embedding algorithms are probabilistic at their core \u2013 i.e., they are trained (explicitly or implicitly BIBREF6) to minimize some form of word co-occurrence probabilities. Thus, we argue that a framework for measuring and treating bias in these embeddings should take into account, in addition to their geometric aspect, their probabilistic nature too. On the other hand, the issue of bias has also been approached (albeit in different contexts) in the fairness literature, where various intuitive notions of equity such as equalized odds have been formalized through probabilistic criteria. By considering analogous criteria for the word embedding setting, we seek to draw connections between these two bodies of work.",
+      "We present experiments on various bias mitigation benchmarks and show that our framework is comparable to state-of-the-art alternatives according to measures of geometric bias mitigation and that it performs far better according to measures of neighborhood bias. For fair comparison, we focus on mitigating a binary gender bias in pre-trained word embeddings using SGNS (skip-gram with negative-sampling), though we note that this framework and methods could be extended to other types of bias and word embedding algorithms."
+    ],
+    [
+      "Geometric bias mitigation uses the cosine distances between words to both measure and remove gender bias BIBREF0. This method implicitly defines bias as a geometric asymmetry between words when projected onto a subspace, such as the gender subspace constructed from a set of gender pairs such as $\\mathcal {P} = \\lbrace (he,she),(man,woman),(king,queen)...\\rbrace $. The projection of a vector $v$ onto $B$ (the subspace) is defined by $v_B = \\sum _{j=1}^{k} (v \\cdot b_j) b_j$ where a subspace $B$ is defined by k orthogonal unit vectors $B = {b_1,...,b_k}$."
+    ],
+    [
+      "The WEAT statistic BIBREF1 demonstrates the presence of biases in word embeddings with an effect size defined as the mean test statistic across the two word sets:",
+      "Where $s$, the test statistic, is defined as: $s(w,A,B) = mean_{a \\in A} cos(w,a) - mean_{b \\in B} cos(w,a)$, and $X$,$Y$,$A$, and $B$ are groups of words for which the association is measured. Possible values range from $-2$ to 2 depending on the association of the words groups, and a value of zero indicates $X$ and $Y$ are equally associated with $A$ and $B$. See BIBREF4 for further details on WEAT."
+    ],
+    [
+      "The RIPA (relational inner product association) metric was developed as an alternative to WEAT, with the critique that WEAT is likely to overestimate the bias of a target attribute BIBREF4. The RIPA metric formalizes the measure of bias used in geometric bias mitigation as the inner product association of a word vector $v$ with respect to a relation vector $b$. The relation vector is constructed from the first principal component of the differences between gender word pairs. We report the absolute value of the RIPA metric as the value can be positive or negative according to the direction of the bias. A value of zero indicates a lack of bias, and the value is bound by $[-||w||,||w||]$."
+    ],
+    [
+      "The neighborhood bias metric proposed by BIBREF5 quantifies bias as the proportion of male socially-biased words among the $k$ nearest socially-biased male and female neighboring words, whereby biased words are obtained by projecting neutral words onto a gender relation vector. As we only examine the target word among the 1000 most socially-biased words in the vocabulary (500 male and 500 female), a word\u2019s bias is measured as the ratio of its neighborhood of socially-biased male and socially-biased female words, so that a value of 0.5 in this metric would indicate a perfectly unbiased word, and values closer to 0 and 1 indicate stronger bias."
+    ],
+    [
+      "Our objective here is to extend and complement the geometric notions of word embedding bias described in the previous section with an alternative, probabilistic, approach. Intuitively, we seek a notion of equality akin to that of demographic parity in the fairness literature, which requires that a decision or outcome be independent of a protected attribute such as gender. BIBREF7. Similarly, when considering a probabilistic definition of unbiased in word embeddings, we can consider the conditional probabilities of word pairs, ensuring for example that $p(doctor|man) \\approx p(doctor|woman)$, and can extend this probabilistic framework to include the neighborhood of a target word, addressing the potential pitfalls of geometric bias mitigation.",
+      "Conveniently, most word embedding frameworks allow for immediate computation of the conditional probabilities $P(w|c)$. Here, we focus our attention on the Skip-Gram method with Negative Sampling (SGNS) of BIBREF8, although our framework can be equivalently instantiated for most other popular embedding methods, owing to their core similarities BIBREF6, BIBREF9. Leveraging this probabilistic nature, we construct a bias mitigation method in two steps, and examine each step as an independent method as well as the resulting composite method."
+    ],
+    [
+      "This component of our bias mitigation framework seeks to enforce that the probability of prediction or outcome cannot depend on a protected class such as gender. We can formalize this intuitive goal through a loss function that penalizes the discrepancy between the conditional probabilities of a target word (i.e., one that should not be affected by the protected attribute) conditioned on two words describing the protected attribute (e.g., man and woman in the case of gender). That is, for every target word we seek to minimize:",
+      "where $\\mathcal {P} = \\lbrace (he,she),(man,woman),(king,queen), \\dots \\rbrace $ is a set of word pairs characterizing the protected attribute, akin to that used in previous work BIBREF0.",
+      "At this point, the specific form of the objective will depend on the type of word embeddings used. For our expample of SGNS, recall that this algorithm models the conditional probability of a target word given a context word as a function of the inner product of their representations. Though an exact method for calculating the conditional probability includes summing over conditional probability of all the words in the vocabulary, we can use the estimation of log conditional probability proposed by BIBREF8, i.e., $ \\log p(w_O|w_I) \\approx \\log \\sigma ({v^{\\prime }_{wo}}^T v_{wI}) + \\sum _{i=1}^{k} [\\log {\\sigma ({{-v^{\\prime }_{wi}}^T v_{wI}})}] $."
+    ],
+    [
+      "Based on observations by BIBREF5, we extend our method to consider the composition of the neighborhood of socially-gendered words of a target word. We note that bias in a word embedding depends not only on the relationship between a target word and explicitly gendered words like man and woman, but also between a target word and socially-biased male or female words. Bolukbasi et al BIBREF0 proposed a method for eliminating this kind of indirect bias through geometric bias mitigation, but it is shown to be ineffective by the neighborhood metric BIBREF5.",
+      "Instead, we extend our method of bias mitigation to account for this neighborhood effect. Specifically, we examine the conditional probabilities of a target word given the $k/2$ nearest neighbors from the male socially-biased words as well as given the $k/2$ female socially-biased words (in sorted order, from smallest to largest). The groups of socially-biased words are constructed as described in the neighborhood metric. If the word is unbiased according to the neighborhood metric, these probabilities should be comparable. We then use the following as our loss function:",
+      "",
+      "where $m$ and $f$ represent the male and female neighbors sorted by distance to the target word $t$ (we use $L1$ distance)."
+    ],
+    [
+      "We evaluate our framework on fastText embeddings trained on Wikipedia (2017), UMBC webbase corpus and statmt.org news dataset (16B tokens) BIBREF11. For simplicity, only the first 22000 words are used in all embeddings, though preliminary results indicate the findings extend to the full corpus. For our novel methods of mitigating bias, a shallow neural network is used to adjust the embedding. The single layer of the model is an embedding layer with weights initialized to those of the original embedding. For the composite method, these weights are initialized to those of the embedding after probabilistic bias mitigation. A batch of word indices is fed into the model, which are then embedded and for which a loss value is calculated, allowing back-propagation to adjust the embeddings. For each of the models, a fixed number of iterations is used to prevent overfitting, which can eventually hurt performance on the embedding benchmarks (See Figure FIGREF12). We evaluated the embedding after 1000 iterations, and stopped training if performance on a benchmark decreased significantly.",
+      "We construct a list of candidate words to debias, taken from the words used in the WEAT gender bias statistics. Words in this list should be gender neutral, and are related to the topics of career, arts, science, math, family and professions (see appendix). We note that this list can easily be expanded to include a greater proportion of words in the corpus. For example, BIBREF4 suggested a method for identifying inappropriately gendered words using unsupervised learning.",
+      "We compare this method of bias mitigation with the no bias mitigation (\"Orig\"), geometric bias mitigation (\"Geo\"), the two pieces of our method alone (\"Prob\" and \"KNN\") and the composite method (\"KNN+Prob\"). We note that the composite method performs reasonably well according the the RIPA metric, and much better than traditional geometric bias mitigation according to the neighborhood metric, without significant performance loss according to the accepted benchmarks. To our knowledge this is the first bias mitigation method to perform reasonably both on both metrics."
+    ],
+    [
+      "We proposed a simple method of bias mitigation based on this probabilistic notions of fairness, and showed that it leads to promising results in various benchmark bias mitigation tasks. Future work should include considering a more rigorous definition and non-binary of bias and experimenting with various embedding algorithms and network architectures."
+    ],
+    [
+      "The authors would like to thank Tommi Jaakkola for stimulating discussions during the initial stages of this work."
+    ],
+    [
+      "For Equation 4, as described in the original work, in regards to the k sample words $w_i$ is drawn from the corpus using the Unigram distribution raised to the 3/4 power.",
+      "For reference, the most male socially-biased words include words such as:\u2019john\u2019, \u2019jr\u2019, \u2019mlb\u2019, \u2019dick\u2019, \u2019nfl\u2019, \u2019cfl\u2019, \u2019sgt\u2019, \u2019abbot\u2019, \u2019halfback\u2019, \u2019jock\u2019, \u2019mike\u2019, \u2019joseph\u2019,while the most female socially-biased words include words such as:\u2019feminine\u2019, \u2019marital\u2019, \u2019tatiana\u2019, \u2019pregnancy\u2019, \u2019eva\u2019, \u2019pageant\u2019, \u2019distress\u2019, \u2019cristina\u2019, \u2019ida\u2019, \u2019beauty\u2019, \u2019sexuality\u2019,\u2019fertility\u2019"
+    ],
+    [
+      "'accountant', 'acquaintance', 'actor', 'actress', 'administrator', 'adventurer', 'advocate', 'aide', 'alderman', 'ambassador', 'analyst', 'anthropologist', 'archaeologist', 'archbishop', 'architect', 'artist', 'assassin', 'astronaut', 'astronomer', 'athlete', 'attorney', 'author', 'baker', 'banker', 'barber', 'baron', 'barrister', 'bartender', 'biologist', 'bishop', 'bodyguard', 'boss', 'boxer', 'broadcaster', 'broker', 'businessman', 'butcher', 'butler', 'captain', 'caretaker', 'carpenter', 'cartoonist', 'cellist', 'chancellor', 'chaplain', 'character', 'chef', 'chemist', 'choreographer', 'cinematographer', 'citizen', 'cleric', 'clerk', 'coach', 'collector', 'colonel', 'columnist', 'comedian', 'comic', 'commander', 'commentator', 'commissioner', 'composer', 'conductor', 'confesses', 'congressman', 'constable', 'consultant', 'cop', 'correspondent', 'counselor', 'critic', 'crusader', 'curator', 'dad', 'dancer', 'dean', 'dentist', 'deputy', 'detective', 'diplomat', 'director', 'doctor', 'drummer', 'economist', 'editor', 'educator', 'employee', 'entertainer', 'entrepreneur', 'envoy', 'evangelist', 'farmer', 'filmmaker', 'financier', 'fisherman', 'footballer', 'foreman', 'gangster', 'gardener', 'geologist', 'goalkeeper', 'guitarist', 'headmaster', 'historian', 'hooker', 'illustrator', 'industrialist', 'inspector', 'instructor', 'inventor', 'investigator', 'journalist', 'judge', 'jurist', 'landlord', 'lawyer', 'lecturer', 'legislator', 'librarian', 'lieutenant', 'lyricist', 'maestro', 'magician', 'magistrate', 'maid', 'manager', 'marshal', 'mathematician', 'mechanic', 'midfielder', 'minister', 'missionary', 'monk', 'musician', 'nanny', 'narrator', 'naturalist', 'novelist', 'nun', 'nurse', 'observer', 'officer', 'organist', 'painter', 'pastor', 'performer', 'philanthropist', 'philosopher', 'photographer', 'physician', 'physicist', 'pianist', 'planner', 'playwright', 'poet', 'policeman', 'politician', 'preacher', 'president', 'priest', 'principal', 'prisoner', 'professor', 'programmer', 'promoter', 'proprietor', 'prosecutor', 'protagonist', 'provost', 'psychiatrist', 'psychologist', 'rabbi', 'ranger', 'researcher', 'sailor', 'saint', 'salesman', 'saxophonist', 'scholar', 'scientist', 'screenwriter', 'sculptor', 'secretary', 'senator', 'sergeant', 'servant', 'singer', 'skipper', 'sociologist', 'soldier', 'solicitor', 'soloist', 'sportsman', 'statesman', 'steward', 'student', 'substitute', 'superintendent', 'surgeon', 'surveyor', 'swimmer', 'teacher', 'technician', 'teenager', 'therapist', 'trader', 'treasurer', 'trooper', 'trumpeter', 'tutor', 'tycoon', 'violinist', 'vocalist', 'waiter', 'waitress', 'warden', 'warrior', 'worker', 'wrestler', 'writer'"
+    ],
+    [
+      "Words used for WEAT statistic, consisting of baseline bias tests and gender bias tests in the format X vs Y / A vs B",
+      "Flowers vs Insects / Pleasant vs Unpleasant",
+      "X: \"aster\", \"clover\", \"hyacinth\", \"marigold\", \"poppy\", \"azalea\", \"crocus\", \"iris\", \"orchid\", \"rose\", \"bluebell\", \"daffodil\", \"lilac\", \"pansy\", \"tulip\", \"buttercup\", \"daisy\", \"lily\", \"peony\", \"violet\", \"carnation\", \"gladiola\", \"magnolia\", \"petunia\", \"zinnia\"",
+      "Y: \"ant\", \"caterpillar\", \"flea\", \"locust\", \"spider\", \"bedbug\", \"centipede\", \"fly\", \"maggot\", \"tarantula\", \"bee\", \"cockroach\", \"gnat\", \"mosquito\", \"termite\", \"beetle\", \"cricket\", \"hornet\", \"moth\", \"wasp\", \"blackfly\", \"dragonfly\", \"horsefly\", \"roach\", \"weevil\"",
+      "A: \"caress\", \"freedom\", \"health\", \"love\", \"peace\", \"cheer\", \"friend\", \"heaven\", \"loyal\", \"pleasure\", \"diamond\", \"gentle\", \"honest\", \"lucky\", \"rainbow\", \"diploma\", \"gift\", \"honor\", \"miracle\", \"sunrise\", \"family\", \"happy\", \"laughter\", \"paradise\", \"vacation\"",
+      "B: \"abuse\", \"crash\", \"filth\", \"murder\", \"sickness\", \"accident\", \"death\", \"grief\", \"poison\", \"stink\", \"assault\", \"disaster\", \"hatred\", \"pollute\", \"tragedy\", \"divorce\", \"jail\", \"poverty\", \"ugly\", \"cancer\", \"kill\", \"rotten\", \"vomit\", \"agony\", \"prison\"",
+      "Instruments vs Weapons / Pleasant vs Unpleasant:",
+      "X: \"bagpipe\", \"cello\", \"guitar\", \"lute\", \"trombone\", \"banjo\", \"clarinet\", \"harmonica\", \"mandolin\", \"trumpet\", \"bassoon\", \"drum\", \"harp\", \"oboe\", \"tuba\", \"bell\", \"fiddle\", \"harpsichord\", \"piano\", \"viola\", \"bongo\", \"flute\", \"horn\", \"saxophone\", \"violin\"",
+      "Y: \"arrow\", \"club\", \"gun\", \"missile\", \"spear\", \"ax\", \"dagger\", \"harpoon\", \"pistol\", \"sword\", \"blade\", \"dynamite\", \"hatchet\", \"rifle\", \"tank\", \"bomb\", \"firearm\", \"knife\", \"shotgun\", \"teargas\", \"cannon\", \"grenade\", \"mace\", \"slingshot\", \"whip\"",
+      "A: \"caress\", \"freedom\", \"health\", \"love\", \"peace\", \"cheer\", \"friend\", \"heaven\", \"loyal\", \"pleasure\", \"diamond\", \"gentle\", \"honest\", \"lucky\", \"rainbow\", \"diploma\", \"gift\", \"honor\", \"miracle\", \"sunrise\", \"family\", \"happy\", \"laughter\", \"paradise\", \"vacation\"",
+      "B: \"abuse\", \"crash\", \"filth\", \"murder\", \"sickness\", \"accident\", \"death\", \"grief\", \"poison\", \"stink\", \"assault\", \"disaster\", \"hatred\", \"pollute\", \"tragedy\", \"divorce\", \"jail\", \"poverty\", \"ugly\", \"cancer\", \"kill\", \"rotten\", \"vomit\", \"agony\", \"prison\"",
+      "Male vs Female / Career vs Family:",
+      "X: \"brother\", \"father\", \"uncle\", \"grandfather\", \"son\", \"he\", \"his\", \"him\", \"man\", \"himself\", \"men\", \"husband\", \"boy\", \"uncle\", \"nephew\", \"boyfriend\", \"king\", \"actor\"",
+      "Y: \"sister\", \"mother\", \"aunt\", \"grandmother\", \"daughter\", \"she\", \"hers\", \"her\", \"woman\", \"herself\", \"women\", \"wife\", \"aunt\", \"niece\", \"girlfriend\", \"queen\", \"actress\"",
+      "A: \"executive\", \"management\", \"professional\", \"corporation\", \"salary\", \"office\", \"business\", \"career\", \"industry\", \"company\", \"promotion\", \"profession\", \"CEO\", \"manager\", \"coworker\", \"entrepreneur\"",
+      "B: \"home\", \"parents\", \"children\", \"family\", \"cousins\", \"marriage\", \"wedding\", \"relatives\", \"grandparents\", \"grandchildren\", \"nurture\", \"child\", \"toddler\", \"infant\", \"teenager\"",
+      "Math vs Art / Male vs Female:",
+      "X: \"math\", \"algebra\", \"geometry\", \"calculus\", \"equations\", \"computation\", \"numbers\", \"addition\", \"trigonometry\", \"arithmetic\", \"logic\", \"proofs\", \"multiplication\", \"mathematics\"",
+      "Y: \"poetry\", \"art\", \"Shakespeare\", \"dance\", \"literature\", \"novel\", \"symphony\", \"drama\", \"orchestra\", \"music\", \"ballet\", \"arts\", \"creative\", \"sculpture\"",
+      "A: \"brother\", \"father\", \"uncle\", \"grandfather\", \"son\", \"he\", \"his\", \"him\", \"man\", \"himself\", \"men\", \"husband\", \"boy\", \"uncle\", \"nephew\", \"boyfriend\", \"king\", \"actor\"",
+      "B: \"sister\", \"mother\", \"aunt\", \"grandmother\", \"daughter\", \"she\", \"hers\", \"her\", \"woman\", \"herself\", \"women\", \"wife\", \"aunt\", \"niece\", \"girlfriend\", \"queen\", \"actress\"",
+      "Science vs Art / Male8 vs Female8:",
+      "X:\"science\", \"technology\", \"physics\", \"chemistry\", \"Einstein\", \"NASA\", \"experiment\", \"astronomy\", \"biology\", \"aeronautics\", \"mechanics\", \"thermodynamics\"",
+      "Y: \"poetry\", \"art\", \"Shakespeare\", \"dance\", \"literature\", \"novel\", \"symphony\", \"drama\", \"orchestra\", \"music\", \"ballet\", \"arts\", \"creative\", \"sculpture\"",
+      "A: \"brother\", \"father\", \"uncle\", \"grandfather\", \"son\", \"he\", \"his\", \"him\", \"man\", \"himself\", \"men\", \"husband\", \"boy\", \"uncle\", \"nephew\", \"boyfriend\"",
+      "B: \"sister\", \"mother\", \"aunt\", \"grandmother\", \"daughter\", \"she\", \"hers\", \"her\", \"woman\", \"herself\", \"women\", \"wife\", \"aunt\", \"niece\", \"girlfriend\""
+    ]
+  ]
+}
+```

qasper-0044/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Probabilistic Bias Mitigation in Word Embeddings
+
+Question: What are the probabilistic observations which contribute to the more robust algorithm?

qasper-0045/instruction.md

@@ -0,0 +1,117 @@
+Name of Paper: Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yor\`ub\'a and Twi
+
+Question: What turn out to be more important high volume or high quality data?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Languages under Study ::: Yor\u00f9b\u00e1",
+    "Languages under Study ::: Twi",
+    "Data",
+    "Data ::: Training Corpora",
+    "Data ::: Evaluation Test Sets ::: Yor\u00f9b\u00e1.",
+    "Data ::: Evaluation Test Sets ::: Twi",
+    "Semantic Representations",
+    "Semantic Representations ::: Word Embeddings Architectures",
+    "Semantic Representations ::: Experiments ::: FastText Training and Evaluation",
+    "Semantic Representations ::: Experiments ::: CWE Training and Evaluation",
+    "Semantic Representations ::: Experiments ::: BERT Evaluation on NER Task",
+    "Summary and Discussion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "In recent years, word embeddings BIBREF0, BIBREF1, BIBREF2 have been proven to be very useful for training downstream natural language processing (NLP) tasks. Moreover, contextualized embeddings BIBREF3, BIBREF4 have been shown to further improve the performance of NLP tasks such as named entity recognition, question answering, or text classification when used as word features because they are able to resolve ambiguities of word representations when they appear in different contexts. Different deep learning architectures such as multilingual BERT BIBREF4, LASER BIBREF5 and XLM BIBREF6 have proved successful in the multilingual setting. All these architectures learn the semantic representations from unannotated text, making them cheap given the availability of texts in online multilingual resources such as Wikipedia. However, the evaluation of such resources is usually done for the high-resourced languages, where one has a smorgasbord of tasks and test sets to evaluate on. This is the best-case scenario, languages with tones of data for training that generate high-quality models.",
+      "For low-resourced languages, the evaluation is more difficult and therefore normally ignored simply because of the lack of resources. In these cases, training data is scarce, and the assumption that the capability of deep learning architectures to learn (multilingual) representations in the high-resourced setting holds in the low-resourced one does not need to be true. In this work, we focus on two African languages, Yor\u00f9b\u00e1 and Twi, and carry out several experiments to verify this claim. Just by a simple inspection of the word embeddings trained on Wikipedia by fastText, we see a high number of non-Yor\u00f9b\u00e1 or non-Twi words in the vocabularies. For Twi, the vocabulary has only 935 words, and for Yor\u00f9b\u00e1 we estimate that 135 k out of the 150 k words belong to other languages such as English, French and Arabic.",
+      "In order to improve the semantic representations for these languages, we collect online texts and study the influence of the quality and quantity of the data in the final models. We also examine the most appropriate architecture depending on the characteristics of each language. Finally, we translate test sets and annotate corpora to evaluate the performance of both our models together with fastText and BERT pre-trained embeddings which could not be evaluated otherwise for Yor\u00f9b\u00e1 and Twi. The evaluation is carried out in a word similarity and relatedness task using the wordsim-353 test set, and in a named entity recognition (NER) task where embeddings play a crucial role. Of course, the evaluation of the models in only two tasks is not exhaustive but it is an indication of the quality we can obtain for these two low-resourced languages as compared to others such as English where these evaluations are already available.",
+      "The rest of the paper is organized as follows. Related works are reviewed in Section SECREF2 The two languages under study are described in Section SECREF3. We introduce the corpora and test sets in Section SECREF4. The fifth section explores the different training architectures we consider, and the experiments that are carried out. Finally, discussion and concluding remarks are given in Section SECREF6"
+    ],
+    [
+      "The large amount of freely available text in the internet for multiple languages is facilitating the massive and automatic creation of multilingual resources. The resource par excellence is Wikipedia, an online encyclopedia currently available in 307 languages. Other initiatives such as Common Crawl or the Jehovah\u2019s Witnesses site are also repositories for multilingual data, usually assumed to be noisier than Wikipedia. Word and contextual embeddings have been pre-trained on these data, so that the resources are nowadays at hand for more than 100 languages. Some examples include fastText word embeddings BIBREF2, BIBREF7, MUSE embeddings BIBREF8, BERT multilingual embeddings BIBREF4 and LASER sentence embeddings BIBREF5. In all cases, embeddings are trained either simultaneously for multiple languages, joining high- and low-resource data, or following the same methodology.",
+      "On the other hand, different approaches try to specifically design architectures to learn embeddings in a low-resourced setting. ChaudharyEtAl:2018 follow a transfer learning approach that uses phonemes, lemmas and morphological tags to transfer the knowledge from related high-resource language into the low-resource one. jiangEtal:2018 apply Positive-Unlabeled Learning for word embedding calculations, assuming that unobserved pairs of words in a corpus also convey information, and this is specially important for small corpora.",
+      "In order to assess the quality of word embeddings, word similarity and relatedness tasks are usually used. wordsim-353 BIBREF9 is a collection of 353 pairs annotated with semantic similarity scores in a scale from 0 to 10. Even the problems detected in this dataset BIBREF10, it is widely used by the community. The test set was originally created for English, but the need for comparison with other languages has motivated several translations/adaptations. In hassanMihalcea:2009 the test was translated manually into Spanish, Romanian and Arabic and the scores were adapted to reflect similarities in the new language. The reported correlation between the English scores and the Spanish ones is 0.86. Later, JoubarneInkpen:2011 show indications that the measures of similarity highly correlate across languages. leviantReichart:2015 translated also wordsim-353 into German, Italian and Russian and used crowdsourcing to score the pairs. Finally, jiangEtal:2018 translated with Google Cloud the test set from English into Czech, Danish and Dutch. In our work, native speakers translate wordsim-353 into Yor\u00f9b\u00e1 and Twi, and similarity scores are kept unless the discrepancy with English is big (see Section SECREF11 for details). A similar approach to our work is done for Gujarati in JoshiEtAl:2019."
+    ],
+    [
+      "is a language in the West Africa with over 50 million speakers. It is spoken among other languages in Nigeria, republic of Togo, Benin Republic, Ghana and Sierra Leon. It is also a language of \u00d2r\u00ecs\u00e0 in Cuba, Brazil, and some Caribbean countries. It is one of the three major languages in Nigeria and it is regarded as the third most spoken native African language. There are different dialects of Yor\u00f9b\u00e1 in Nigeria BIBREF11, BIBREF12, BIBREF13. However, in this paper our focus is the standard Yor\u00f9b\u00e1 based upon a report from the 1974 Joint Consultative Committee on Education BIBREF14.",
+      "Standard Yor\u00f9b\u00e1 has 25 letters without the Latin characters c, q, v, x and z. There are 18 consonants (b, d, f, g, gb, j[dz], k, l, m, n, p[kp], r, s, \u1e63, t, w y[j]), 7 oral vowels (a, e, \u1eb9, i, o, \u1ecd, u), five nasal vowels, (an, $ \\underaccent{\\dot{}}{e}$n, in, $ \\underaccent{\\dot{}}{o}$n, un) and syllabic nasals (m\u0300, \u1e3f, \u01f9, \u0144). Yor\u00f9b\u00e1 is a tone language which makes heavy use of lexical tones which are indicated by the use of diacritics. There are three tones in Yor\u00f9b\u00e1 namely low, mid and high which are represented as grave ($\\setminus $), macron ($-$) and acute ($/$) symbols respectively. These tones are applied on vowels and syllabic nasals. Mid tone is usually left unmarked on vowels and every initial or first vowel in a word cannot have a high tone. It is important to note that tone information is needed for correct pronunciation and to have the meaning of a word BIBREF15, BIBREF12, BIBREF14. For example, ow\u00f3 (money), \u1ecdw (broom), \u00f2w\u00f2 (business), w (honour), \u1ecdw (hand), and w (group) are different words with different dots and diacritic combinations. According to Asahiah2014, Standard Yor\u00f9b\u00e1 uses 4 diacritics, 3 are for marking tones while the fourth which is the dot below is used to indicate the open phonetic variants of letter \"e\" and \"o\" and the long variant of \"s\". Also, there are 19 single diacritic letters, 3 are marked with dots below (\u1eb9, \u1ecd, \u1e63) while the rest are either having the grave or acute accent. The four double diacritics are divided between the grave and the acute accent as well.",
+      "As noted in Asahiah2014, most of the Yor\u00f9b\u00e1 texts found in websites or public domain repositories (i) either use the correct Yor\u00f9b\u00e1 orthography or (ii) replace diacritized characters with un-diacritized ones.",
+      "This happens as a result of many factors, but most especially to the unavailability of appropriate input devices for the accurate application of the diacritical marks BIBREF11. This has led to research on restoration models for diacritics BIBREF16, but the problem is not well solved and we find that most Yor\u00f9b\u00e1 text in the public domain today is not well diacritized. Wikipedia is not an exception."
+    ],
+    [
+      "is an Akan language of the Central Tano Branch of the Niger Congo family of languages. It is the most widely spoken of the about 80 indigenous languages in Ghana BIBREF17. It has about 9 million native speakers and about a total of 17\u201318 million Ghanaians have it as either first or second language. There are two mutually intelligible dialects, Asante and Akuapem, and sub-dialectical variants which are mostly unknown to and unnoticed by non-native speakers. It is also mutually intelligible with Fante and to a large extent Bono, another of the Akan languages. It is one of, if not the, easiest to learn to speak of the indigenous Ghanaian languages. The same is however not true when it comes to reading and especially writing. This is due to a number of easily overlooked complexities in the structure of the language. First of all, similarly to Yor\u00f9b\u00e1, Twi is a tonal language but written without diacritics or accents. As a result, words which are pronounced differently and unambiguous in speech tend to be ambiguous in writing. Besides, most of such words fit interchangeably in the same context and some of them can have more than two meanings. A simple example is:",
+      "Me papa aba nti na me ne wo redi no yie no. S wo ara wo nim s me papa ba a, me suban fofor adi.",
+      "This sentence could be translated as",
+      "(i) I'm only treating you nicely because I'm in a good mood. You already know I'm a completely different person when I'm in a good mood.",
+      "(ii) I'm only treating you nicely because my dad is around. You already know I'm a completely different person when my dad comes around.",
+      "Another characteristic of Twi is the fact that a good number of stop words have the same written form as content words. For instance, \u201cna\u201d or \u201cna\u201d could be the words \u201cand, then\u201d, the phrase \u201cand then\u201d or the word \u201cmother\u201d. This kind of ambiguity has consequences in several natural language applications where stop words are removed from text.",
+      "Finally, we want to point out that words can also be written with or without prefixes. An example is this same na and na which happen to be the same word with an omissible prefix across its multiple senses. For some words, the prefix characters are mostly used when the word begins a sentence and omitted in the middle. This however depends on the author/speaker. For the word embeddings calculation, this implies that one would have different embeddings for the same word found in different contexts."
+    ],
+    [
+      "We collect clean and noisy corpora for Yor\u00f9b\u00e1 and Twi in order to quantify the effect of noise on the quality of the embeddings, where noisy has a different meaning depending on the language as it will be explained in the next subsections."
+    ],
+    [
+      "For Yor\u00f9b\u00e1, we use several corpora collected by the Niger-Volta Language Technologies Institute with texts from different sources, including the Lagos-NWU conversational speech corpus, fully-diacritized Yor\u00f9b\u00e1 language websites and an online Bible. The largest source with clean data is the JW300 corpus. We also created our own small-sized corpus by web-crawling three Yor\u00f9b\u00e1 language websites (Al\u00e0kw\u00e9, r Yor\u00f9b\u00e1 and \u00c8d\u00e8 Yor\u00f9b\u00e1 R\u1eb9w in Table TABREF7), some Yoruba Tweets with full diacritics and also news corpora (BBC Yor\u00f9b\u00e1 and VON Yor\u00f9b\u00e1) with poor diacritics which we use to introduce noise. By noisy corpus, we refer to texts with incorrect diacritics (e.g in BBC Yor\u00f9b\u00e1), removal of tonal symbols (e.g in VON Yor\u00f9b\u00e1) and removal of all diacritics/under-dots (e.g some articles in Yor\u00f9b\u00e1 Wikipedia). Furthermore, we got two manually typed fully-diacritized Yor\u00f9b\u00e1 literature (\u00ccr\u00ecnk\u00e8rind\u00f2 n\u00edn\u00fa igb\u00f3 el\u00e9gb\u00e8je and Igb\u00f3 Ol\u00f3d\u00f9mar\u00e8) both written by Daniel Orowole Olorunfemi Fagunwa a popular Yor\u00f9b\u00e1 author. The number of tokens available from each source, the link to the original source and the quality of the data is summarised in Table TABREF7.",
+      "The gathering of clean data in Twi is more difficult. We use as the base text as it has been shown that the Bible is the most available resource for low and endangered languages BIBREF18. This is the cleanest of all the text we could obtain. In addition, we use the available (and small) Wikipedia dumps which are quite noisy, i.e. Wikipedia contains a good number of English words, spelling errors and Twi sentences formulated in a non-natural way (formulated as L2 speakers would speak Twi as compared to native speakers). Lastly, we added text crawled from jw and the JW300 Twi corpus. Notice that the Bible text, is mainly written in the Asante dialect whilst the last, Jehovah's Witnesses, was written mainly in the Akuapem dialect. The Wikipedia text is a mixture of the two dialects. This introduces a lot of noise into the embeddings as the spelling of most words differs especially at the end of the words due to the mixture of dialects. The JW300 Twi corpus also contains mixed dialects but is mainly Akuampem. In this case, the noise comes also from spelling errors and the uncommon addition of diacritics which are not standardised on certain vowels. Figures for Twi corpora are summarised in the bottom block of Table TABREF7."
+    ],
+    [
+      "One of the contribution of this work is the introduction of the wordsim-353 word pairs dataset for Yor\u00f9b\u00e1. All the 353 word pairs were translated from English to Yor\u00f9b\u00e1 by 3 native speakers. The set is composed of 446 unique English words, 348 of which can be expressed as one-word translation in Yor\u00f9b\u00e1 (e.g. book translates to \u00ecw\u00e9). In 61 cases (most countries and locations but also other content words) translations are transliterations (e.g. Doctor is d\u00f3k\u00edt\u00e0 and cucumber k\u00f9k\u00famb\u00e0.). 98 words were translated by short phrases instead of single words. This mostly affects words from science and technology (e.g. keyboard translates to p\u00e1t\u00e1k\u00f3 \u00ectw\u00e9 \u2014literally meaning typing board\u2014, laboratory translates to \u00ecy\u00e0r\u00e1 \u00ec\u1e63\u00e8w\u00e1d\u00ec\u00ed \u2014research room\u2014, and ecology translates to \u00ecm n\u00edpa \u00e0y\u00edk\u00e1 while psychology translates to \u00ecm n\u00edpa d\u00e1). Finally, 6 terms have the same form in English and Yor\u00f9b\u00e1 therefore they are retained like that in the dataset (e.g. Jazz, Rock and acronyms such as FBI or OPEC).",
+      "We also annotate the Global Voices Yor\u00f9b\u00e1 corpus to test the performance of our trained Yor\u00f9b\u00e1 BERT embeddings on the named entity recognition task. The corpus consists of 25 k tokens which we annotate with four named entity types: DATE, location (LOC), organization (ORG) and personal names (PER). Any other token that does not belong to the four named entities is tagged with \"O\". The dataset is further split into training (70%), development (10%) and test (20%) partitions. Table TABREF12 shows the number of named entities per type and partition."
+    ],
+    [
+      "Just like Yor\u00f9b\u00e1, the wordsim-353 word pairs dataset was translated for Twi. Out of the 353 word pairs, 274 were used in this case. The remaining 79 pairs contain words that translate into longer phrases.",
+      "The number of words that can be translated by a single token is higher than for Yor\u00f9b\u00e1. Within the 274 pairs, there are 351 unique English words which translated to 310 unique Twi words. 298 of the 310 Twi words are single word translations, 4 transliterations and 16 are used as is.",
+      "Even if JoubarneInkpen:2011 showed indications that semantic similarity has a high correlation across languages, different nuances between words are captured differently by languages. For instance, both money and currency in English translate into sika in Twi (and other 32 English words which translate to 14 Twi words belong to this category) and drink in English is translated as Nsa or nom depending on the part of speech (noun for the former, verb for the latter). 17 English words fall into this category. In translating these, we picked the translation that best suits the context (other word in the pair). In two cases, the correlation is not fulfilled at all: soap\u2013opera and star\u2013movies are not related in the Twi language and the score has been modified accordingly."
+    ],
+    [
+      "In this section, we describe the architectures used for learning word embeddings for the Twi and Yor\u00f9b\u00e1 languages. Also, we discuss the quality of the embeddings as measured by the correlation with human judgements on the translated wordSim-353 test sets and by the F1 score in a NER task."
+    ],
+    [
+      "Modeling sub-word units has recently become a popular way to address out-of-vocabulary word problem in NLP especially in word representation learning BIBREF19, BIBREF2, BIBREF4. A sub-word unit can be a character, character $n$-grams, or heuristically learned Byte Pair Encodings (BPE) which work very well in practice especially for morphologically rich languages. Here, we consider two word embedding models that make use of character-level information together with word information: Character Word Embedding (CWE) BIBREF20 and fastText BIBREF2. Both of them are extensions of the Word2Vec architectures BIBREF0 that model sub-word units, character embeddings in the case of CWE and character $n$-grams for fastText.",
+      "CWE was introduced in 2015 to model the embeddings of characters jointly with words in order to address the issues of character ambiguities and non-compositional words especially in the Chinese language. A word or character embedding is learned in CWE using either CBOW or skipgram architectures, and then the final word embedding is computed by adding the character embeddings to the word itself:",
+      "where $w_j$ is the word embedding of $x_j$, $N_j$ is the number of characters in $x_j$, and $c_k$ is the embedding of the $k$-th character $c_k$ in $x_j$.",
+      "Similarly, in 2017 fastText was introduced as an extension to skipgram in order to take into account morphology and improve the representation of rare words. In this case the embedding of a word also includes the embeddings of its character $n$-grams:",
+      "where $w_j$ is the word embedding of $x_j$, $G_j$ is the number of character $n$-grams in $x_j$ and $g_k$ is the embedding of the $k$-th $n$-gram.",
+      "cwe also proposed three alternatives to learn multiple embeddings per character and resolve ambiguities: (i) position-based character embeddings where each character has different embeddings depending on the position it appears in a word, i.e., beginning, middle or end (ii) cluster-based character embeddings where a character can have $K$ different cluster embeddings, and (iii) position-based cluster embeddings (CWE-LP) where for each position $K$ different embeddings are learned. We use the latter in our experiments with CWE but no positional embeddings are used with fastText.",
+      "Finally, we consider a contextualized embedding architecture, BERT BIBREF4. BERT is a masked language model based on the highly efficient and parallelizable Transformer architecture BIBREF21 known to produce very rich contextualized representations for downstream NLP tasks.",
+      "The architecture is trained by jointly conditioning on both left and right contexts in all the transformer layers using two unsupervised objectives: Masked LM and Next-sentence prediction. The representation of a word is therefore learned according to the context it is found in.",
+      "Training contextual embeddings needs of huge amounts of corpora which are not available for low-resourced languages such as Yor\u00f9b\u00e1 and Twi. However, Google provided pre-trained multilingual embeddings for 102 languages including Yor\u00f9b\u00e1 (but not Twi)."
+    ],
+    [
+      "As a first experiment, we compare the quality of fastText embeddings trained on (high-quality) curated data and (low-quality) massively extracted data for Twi and Yor\u00f9b\u00e1 languages.",
+      "Facebook released pre-trained word embeddings using fastText for 294 languages trained on Wikipedia BIBREF2 (F1 in tables) and for 157 languages trained on Wikipedia and Common Crawl BIBREF7 (F2). For Yor\u00f9b\u00e1, both versions are available but only embeddings trained on Wikipedia are available for Twi. We consider these embeddings the result of training on what we call massively-extracted corpora. Notice that training settings for both embeddings are not exactly the same, and differences in performance might come both from corpus size/quality but also from the background model. The 294-languages version is trained using skipgram, in dimension 300, with character $n$-grams of length 5, a window of size 5 and 5 negatives. The 157-languages version is trained using CBOW with position-weights, in dimension 300, with character $n$-grams of length 5, a window of size 5 and 10 negatives.",
+      "We want to compare the performance of these embeddings with the equivalent models that can be obtained by training on the different sources verified by native speakers of Twi and Yor\u00f9b\u00e1; what we call curated corpora and has been described in Section SECREF4 For the comparison, we define 3 datasets according to the quality and quantity of textual data used for training: (i) Curated Small Dataset (clean), C1, about 1.6 million tokens for Yor\u00f9b\u00e1 and over 735 k tokens for Twi. The clean text for Twi is the Bible and for Yoruba all texts marked under the C1 column in Table TABREF7. (ii) In Curated Small Dataset (clean + noisy), C2, we add noise to the clean corpus (Wikipedia articles for Twi, and BBC Yor\u00f9b\u00e1 news articles for Yor\u00f9b\u00e1). This increases the number of training tokens for Twi to 742 k tokens and Yor\u00f9b\u00e1 to about 2 million tokens. (iii) Curated Large Dataset, C3 consists of all available texts we are able to crawl and source out for, either clean or noisy. The addition of JW300 BIBREF22 texts increases the vocabulary to more than 10 k tokens in both languages.",
+      "We train our fastText systems using a skipgram model with an embedding size of 300 dimensions, context window size of 5, 10 negatives and $n$-grams ranging from 3 to 6 characters similarly to the pre-trained models for both languages. Best results are obtained with minimum word count of 3.",
+      "Table TABREF15 shows the Spearman correlation between human judgements and cosine similarity scores on the wordSim-353 test set. Notice that pre-trained embeddings on Wikipedia show a very low correlation with humans on the similarity task for both languages ($\\rho $=$0.14$) and their performance is even lower when Common Crawl is also considered ($\\rho $=$0.07$ for Yor\u00f9b\u00e1). An important reason for the low performance is the limited vocabulary. The pre-trained Twi model has only 935 tokens. For Yor\u00f9b\u00e1, things are apparently better with more than 150 k tokens when both Wikipedia and Common Crawl are used but correlation is even lower. An inspection of the pre-trained embeddings indicates that over 135 k words belong to other languages mostly English, French and Arabic.",
+      "If we focus only on Wikipedia, we see that many texts are without diacritics in Yor\u00f9b\u00e1 and often make use of mixed dialects and English sentences in Twi.",
+      "The Spearman $\\rho $ correlation for fastText models on the curated small dataset (clean), C1, improves the baselines by a large margin ($\\rho =0.354$ for Twi and 0.322 for Yor\u00f9b\u00e1) even with a small dataset. The improvement could be justified just by the larger vocabulary in Twi, but in the case of Yor\u00f9b\u00e1 the enhancement is there with almost half of the vocabulary size. We found out that adding some noisy texts (C2 dataset) slightly improves the correlation for Twi language but not for the Yor\u00f9b\u00e1 language. The Twi language benefits from Wikipedia articles because its inclusion doubles the vocabulary and reduces the bias of the model towards religious texts. However, for Yor\u00f9b\u00e1, noisy texts often ignore diacritics or tonal marks which increases the vocabulary size at the cost of an increment in the ambiguity too. As a result, the correlation is slightly hurt. One would expect that training with more data would improve the quality of the embeddings, but we found out with the results obtained with the C3 dataset, that only high-quality data helps. The addition of JW300 boosts the vocabulary in both cases, but whereas for Twi the corpus mixes dialects and is noisy, for Yor\u00f9b\u00e1 it is very clean and with full diacritics. Consequently, the best embeddings for Yor\u00f9b\u00e1 are obtained when training with the C3 dataset, whereas for Twi, C2 is the best option. In both cases, the curated embeddings improve the correlation with human judgements on the similarity task a $\\Delta \\rho =+0.25$ or, equivalently, by an increment on $\\rho $ of 170% (Twi) and 180% (Yor\u00f9b\u00e1)."
+    ],
+    [
+      "The huge ambiguity in the written Twi language motivates the exploration of different approaches to word embedding estimations. In this work, we compare the standard fastText methodology to include sub-word information with the character-enhanced approach with position-based clustered embeddings (CWE-LP as introduced in Section SECREF17). With the latter, we expect to specifically address the ambiguity present in a language that does not translate the different oral tones on vowels into the written language.",
+      "The character-enhanced word embeddings are trained using a skipgram architecture with cluster-based embeddings and an embedding size of 300 dimensions, context window-size of 5, and 5 negative samples. In this case, the best performance is obtained with a minimum word count of 1, and that increases the effective vocabulary that is used for training the embeddings with respect to the fastText experiments reported in Table TABREF15.",
+      "We repeat the same experiments as with fastText and summarise them in Table TABREF16. If we compare the relative numbers for the three datasets (C1, C2 and C3) we observe the same trends as before: the performance of the embeddings in the similarity task improves with the vocabulary size when the training data can be considered clean, but the performance diminishes when the data is noisy.",
+      "According to the results, CWE is specially beneficial for Twi but not always for Yor\u00f9b\u00e1. Clean Yor\u00f9b\u00e1 text, does not have the ambiguity issues at character-level, therefore the $n$-gram approximation works better when enough clean data is used ($\\rho ^{C3}_{CWE}=0.354$ vs. $\\rho ^{C3}_{fastText}=0.391$) but it does not when too much noisy data (no diacritics, therefore character-level information would be needed) is used ($\\rho ^{C2}_{CWE}=0.345$ vs. $\\rho ^{C2}_{fastText}=0.302$). For Twi, the character-level information reinforces the benefits of clean data and the best correlation with human judgements is reached with CWE embeddings ($\\rho ^{C2}_{CWE}=0.437$ vs. $\\rho ^{C2}_{fastText}=0.388$)."
+    ],
+    [
+      "In order to go beyond the similarity task using static word vectors, we also investigate the quality of the multilingual BERT embeddings by fine-tuning a named entity recognition task on the Yor\u00f9b\u00e1 Global Voices corpus.",
+      "One of the major advantages of pre-trained BERT embeddings is that fine-tuning of the model on downstream NLP tasks is typically computationally inexpensive, often with few number of epochs. However, the data the embeddings are trained on has the same limitations as that used in massive word embeddings. Fine-tuning involves replacing the last layer of BERT used optimizing the masked LM with a task-dependent linear classifier or any other deep learning architecture, and training all the model parameters end-to-end. For the NER task, we obtain the token-level representation from BERT and train a linear classifier for sequence tagging.",
+      "Similar to our observations with non-contextualized embeddings, we find out that fine-tuning the pre-trained multilingual-uncased BERT for 4 epochs on the NER task gives an F1 score of 0. If we do the same experiment in English, F1 is 58.1 after 4 epochs.",
+      "That shows how pre-trained embeddings by themselves do not perform well in downstream tasks on low-resource languages. To address this problem for Yor\u00f9b\u00e1, we fine-tune BERT representations on the Yor\u00f9b\u00e1 corpus in two ways: (i) using the multilingual vocabulary, and (ii) using only Yor\u00f9b\u00e1 vocabulary. In both cases diacritics are ignored to be consistent with the base model training.",
+      "As expected, the fine-tuning of the pre-trained BERT on the Yor\u00f9b\u00e1 corpus in the two configurations generates better representations than the base model. These models are able to achieve a better performance on the NER task with an average F1 score of over 47% (see Table TABREF26 for the comparative). The fine-tuned BERT model with only Yor\u00f9b\u00e1 vocabulary further increases by more than 4% in F1 score obtained with the tuning that uses the multilingual vocabulary. Although we do not have enough data to train BERT from scratch, we observe that fine-tuning BERT on a limited amount of monolingual data of a low-resource language helps to improve the quality of the embeddings. The same observation holds true for high-resource languages like German and French BIBREF23."
+    ],
+    [
+      "In this paper, we present curated word and contextual embeddings for Yor\u00f9b\u00e1 and Twi. For this purpose, we gather and select corpora and study the most appropriate techniques for the languages. We also create test sets for the evaluation of the word embeddings within a word similarity task (wordsim353) and the contextual embeddings within a NER task. Corpora, embeddings and test sets are available in github.",
+      "In our analysis, we show how massively generated embeddings perform poorly for low-resourced languages as compared to the performance for high-resourced ones. This is due both to the quantity but also the quality of the data used. While the Pearson $\\rho $ correlation for English obtained with fastText embeddings trained on Wikipedia (WP) and Common Crawl (CC) are $\\rho _{WP}$=$0.67$ and $\\rho _{WP+CC}$=$0.78$, the equivalent ones for Yor\u00f9b\u00e1 are $\\rho _{WP}$=$0.14$ and $\\rho _{WP+CC}$=$0.07$. For Twi, only embeddings with Wikipedia are available ($\\rho _{WP}$=$0.14$). By carefully gathering high-quality data and optimising the models to the characteristics of each language, we deliver embeddings with correlations of $\\rho $=$0.39$ (Yor\u00f9b\u00e1) and $\\rho $=$0.44$ (Twi) on the same test set, still far from the high-resourced models, but representing an improvement over $170\\%$ on the task.",
+      "In a low-resourced setting, the data quality, processing and model selection is more critical than in a high-resourced scenario. We show how the characteristics of a language (such as diacritization in our case) should be taken into account in order to choose the relevant data and model to use. As an example, Twi word embeddings are significantly better when training on 742 k selected tokens than on 16 million noisy tokens, and when using a model that takes into account single character information (CWE-LP) instead of $n$-gram information (fastText).",
+      "Finally, we want to note that, even within a corpus, the quality of the data might depend on the language. Wikipedia is usually used as a high-quality freely available multilingual corpus as compared to noisier data such as Common Crawl. However, for the two languages under study, Wikipedia resulted to have too much noise: interference from other languages, text clearly written by non-native speakers, lack of diacritics and mixture of dialects. The JW300 corpus on the other hand, has been rated as high-quality by our native Yor\u00f9b\u00e1 speakers, but as noisy by our native Twi speakers. In both cases, experiments confirm the conclusions."
+    ],
+    [
+      "The authors thank Dr. Clement Odoje of the Department of Linguistics and African Languages, University of Ibadan, Nigeria and Ol\u00f3y\u00e8 Gb\u00e9mis\u00f3y\u00e8 \u00c0rd\u00e8\u00f3 for helping us with the Yor\u00f9b\u00e1 translation of the WordSim-353 word pairs and Dr. Felix Y. Adu-Gyamfi and Ps. Isaac Sarfo for helping with the Twi translation. We also thank the members of the Niger-Volta Language Technologies Institute for providing us with clean Yor\u00f9b\u00e1 corpus",
+      "The project on which this paper is based was partially funded by the German Federal Ministry of Education and Research under the funding code 01IW17001 (Deeplee). Responsibility for the content of this publication is with the authors."
+    ]
+  ]
+}
+```

qasper-0051/instruction.md

@@ -0,0 +1,98 @@
+Name of Paper: Citation Data of Czech Apex Courts
+
+Question: Did they experiment on this dataset?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related work ::: Legal Citation Analysis",
+    "Related work ::: Reference Recognition",
+    "Related work ::: Data Availability",
+    "Related work ::: Document Segmentation",
+    "Methodology",
+    "Methodology ::: Dataset and models ::: CzCDC 1.0 dataset",
+    "Methodology ::: Dataset and models ::: Reference recognition model",
+    "Methodology ::: Dataset and models ::: Text segmentation model",
+    "Methodology ::: Pipeline",
+    "Results",
+    "Discussion",
+    "Conclusion",
+    "Acknowledgment"
+  ],
+  "paragraphs": [
+    [
+      "Analysis of the way court decisions refer to each other provides us with important insights into the decision-making process at courts. This is true both for the common law courts and for their counterparts in the countries belonging to the continental legal system. Citation data can be used for both qualitative and quantitative studies, casting light in the behavior of specific judges through document analysis or allowing complex studies into changing the nature of courts in transforming countries.",
+      "That being said, it is still difficult to create sufficiently large citation datasets to allow a complex research. In the case of the Czech Republic, it was difficult to obtain a relevant dataset of the court decisions of the apex courts (Supreme Court, Supreme Administrative Court and Constitutional Court). Due to its size, it is nearly impossible to extract the references manually. One has to reach out for an automation of such task. However, study of court decisions displayed many different ways that courts use to cite even decisions of their own, not to mention the decisions of other courts.The great diversity in citations led us to the use of means of the natural language processing for the recognition and the extraction of the citation data from court decisions of the Czech apex courts.",
+      "In this paper, we describe the tool ultimately used for the extraction of the references from the court decisions, together with a subsequent way of manual processing of the raw data to achieve a higher-quality dataset. Section SECREF2 maps the related work in the area of legal citation analysis (SectionSECREF1), reference recognition (Section SECREF2), text segmentation (Section SECREF4), and data availability (Section SECREF3). Section SECREF3 describes the method we used for the citation extraction, listing the individual models and the way we have combined these models into the NLP pipeline. Section SECREF4 presents results in the terms of evaluation of the performance of our pipeline, the statistics of the raw data, further manual processing and statistics of the final citation dataset. Section SECREF5 discusses limitations of our work and outlines the possible future development. Section SECREF6 concludes this paper."
+    ],
+    [
+      "The legal citation analysis is an emerging phenomenon in the field of the legal theory and the legal empirical research.The legal citation analysis employs tools provided by the field of network analysis.",
+      "In spite of the long-term use of the citations in the legal domain (eg. the use of Shepard's Citations since 1873), interest in the network citation analysis increased significantly when Fowler et al. published the two pivotal works on the case law citations by the Supreme Court of the United States BIBREF0, BIBREF1. Authors used the citation data and network analysis to test the hypotheses about the function of stare decisis the doctrine and other issues of legal precedents. In the continental legal system, this work was followed by Winkels and de Ruyter BIBREF2. Authors adopted similar approach to Fowler to the court decisions of the Dutch Supreme Court. Similar methods were later used by Derl\u00e9n and Lindholm BIBREF3, BIBREF4 and Panagis and \u0160adl BIBREF5 for the citation data of the Court of Justice of the European Union, and by Olsen and K\u00fc\u00e7\u00fcksu for the citation data of the European Court of Human Rights BIBREF6.",
+      "Additionally, a minor part in research in the legal network analysis resulted in the past in practical tools designed to help lawyers conduct the case law research. Kuppevelt and van Dijck built prototypes employing these techniques in the Netherlands BIBREF7. G\u00f6r\u00f6g a Weisz introduced the new legal information retrieval system, Justeus, based on a large database of the legal sources and partly on the network analysis methods. BIBREF8"
+    ],
+    [
+      "The area of reference recognition already contains a large amount of work. It is concerned with recognizing text spans in documents that are referring to other documents. As such, it is a classical topic within the AI & Law literature.",
+      "The extraction of references from the Italian legislation based on regular expressions was reported by Palmirani et al. BIBREF9. The main goal was to bring references under a set of common standards to ensure the interoperability between different legal information systems.",
+      "De Maat et al. BIBREF10 focused on an automated detection of references to legal acts in Dutch language. Their approach consisted of a grammar covering increasingly complex citation patterns.",
+      "Opijnen BIBREF11 aimed for a reference recognition and a reference standardization using regular expressions accounting for multiple the variant of the same reference and multiple vendor-specific identifiers.",
+      "The language specific work by Kr\u00ed\u017e et al. BIBREF12 focused on the detecting and classification references to other court decisions and legal acts. Authors used a statistical recognition (HMM and Perceptron algorithms) and reported F1-measure over 90% averaged over all entities. It is the state-of-art in the automatic recognition of references in the Czech court decisions. Unfortunately, it allows only for the detection of docket numbers and it is unable to recognize court-specific or vendor-specific identifiers in the court decisions.",
+      "Other language specific-work includes our previous reference recognition model presented in BIBREF13. Prediction model is based on conditional random fields and it allows recognition of different constituents which then establish both explicit and implicit case-law and doctrinal references. Parts of this model were used in the pipeline described further within this paper in Section SECREF3."
+    ],
+    [
+      "Large scale quantitative and qualitative studies are often hindered by the unavailability of court data. Access to court decisions is often hindered by different obstacles. In some countries, court decisions are not available at all, while in some other they are accessible only through legal information systems, often proprietary. This effectively restricts the access to court decisions in terms of the bulk data. This issue was already approached by many researchers either through making available selected data for computational linguistics studies or by making available datasets of digitized data for various purposes. Non-exhaustive list of publicly available corpora includes British Law Report Corpus BIBREF14, The Corpus of US Supreme Court Opinions BIBREF15,the HOLJ corpus BIBREF16, the Corpus of Historical English Law Reports, Corpus de Sentencias Penales BIBREF17, Juristisches Referenzkorpus BIBREF18 and many others.",
+      "Language specific work in this area is presented by the publicly available Czech Court Decisions Corpus (CzCDC 1.0) BIBREF19. This corpus contains majority of court decisions of the Czech Supreme Court, the Supreme Administrative Court and the Constitutional Court, hence allowing a large-scale extraction of references to yield representative results. The CzCDC 1.0 was used as a dataset for extraction of the references as is described further within this paper in Section SECREF3. Unfortunately, despite containing 237 723 court decisions issued between 1st January 1993 and 30th September 2018, it is not complete. This fact is reflected in the analysis of the results."
+    ],
+    [
+      "A large volume of legal information is available in unstructured form, which makes processing these data a challenging task \u2013 both for human lawyers and for computers. Schweighofer BIBREF20 called for generic tools allowing a document segmentation to ease the processing of unstructured data by giving them some structure.",
+      "Topic-based segmentation often focuses on the identifying specific sentences that present borderlines of different textual segments.",
+      "The automatic segmentation is not an individual goal \u2013 it always serves as a prerequisite for further tasks requiring structured data. Segmentation is required for the text summarization BIBREF21, BIBREF22, keyword extraction BIBREF23, textual information retrieval BIBREF24, and other applications requiring input in the form of structured data.",
+      "Major part of research is focused on semantic similarity methods.The computing similarity between the parts of text presumes that a decrease of similarity means a topical border of two text segments. This approach was introduced by Hearst BIBREF22 and was used by Choi BIBREF25 and Heinonen BIBREF26 as well.",
+      "Another approach takes word frequencies and presumes a border according to different key words extracted. Reynar BIBREF27 authored graphical method based on statistics called dotplotting. Similar techniques were used by Ye BIBREF28 or Saravanan BIBREF29. Bommarito et al. BIBREF30 introduced a Python library combining different features including pre-trained models to the use for automatic legal text segmentation. Li BIBREF31 included neural network into his method to segment Chinese legal texts.",
+      "\u0160avelka and Ashley BIBREF32 similarly introduced the machine learning based approach for the segmentation of US court decisions texts into seven different parts. Authors reached high success rates in recognizing especially the Introduction and Analysis parts of the decisions.",
+      "Language specific work includes the model presented by Hara\u0161ta et al. BIBREF33. This work focuses on segmentation of the Czech court decisions into pre-defined topical segments. Parts of this segmentation model were used in the pipeline described further within this paper in Section SECREF3."
+    ],
+    [
+      "In this paper, we present and describe the citation dataset of the Czech top-tier courts. To obtain this dataset, we have processed the court decisions contained in CzCDC 1.0 dataset by the NLP pipeline consisting of the segmentation model introduced in BIBREF33, and parts of the reference recognition model presented in BIBREF13. The process is described in this section."
+    ],
+    [
+      "Novotn\u00e1 and Hara\u0161ta BIBREF19 prepared a dataset of the court decisions of the Czech Supreme Court, the Supreme Administrative Court and the Constitutional Court. The dataset contains 237,723 decisions published between 1st January 1993 and the 30th September 2018. These decisions are organised into three sub-corpora. The sub-corpus of the Supreme Court contains 111,977 decisions, the sub-corpus of the Supreme Administrative Court contains 52,660 decisions and the sub-corpus of the Constitutional Court contains 73,086 decisions. Authors in BIBREF19 assessed that the CzCDC currently contains approximately 91% of all decisions of the Supreme Court, 99,5% of all decisions of the Constitutional Court, and 99,9% of all decisions of the Supreme Administrative Court. As such, it presents the best currently available dataset of the Czech top-tier court decisions."
+    ],
+    [
+      "Hara\u0161ta and \u0160avelka BIBREF13 introduced a reference recognition model trained specifically for the Czech top-tier courts. Moreover, authors made their training data available in the BIBREF34. Given the lack of a single citation standard, references in this work consist of smaller units, because these were identified as more uniform and therefore better suited for the automatic detection. The model was trained using conditional random fields, which is a random field model that is globally conditioned on an observation sequence O. The states of the model correspond to event labels E. Authors used a first-order conditional random fields. Model was trained for each type of the smaller unit independently."
+    ],
+    [
+      "Hara\u0161ta et al. BIBREF33, authors introduced the model for the automatic segmentation of the Czech court decisions into pre-defined multi-paragraph parts. These segments include the Header (introduction of given case), History (procedural history prior the apex court proceeding), Submission/Rejoinder (petition of plaintiff and response of defendant), Argumentation (argumentation of the court hearing the case), Footer (legally required information, such as information about further proceedings), Dissent and Footnotes. The model for automatic segmentation of the text was trained using conditional random fields. The model was trained for each type independently."
+    ],
+    [
+      "In order to obtain the citation data of the Czech apex courts, it was necessary to recognize and extract the references from the CzCDC 1.0. Given that training data for both the reference recognition model BIBREF13, BIBREF34 and the text segmentation model BIBREF33 are publicly available, we were able to conduct extensive error analysis and put together a pipeline to arguably achieve the maximum efficiency in the task. The pipeline described in this part is graphically represented in Figure FIGREF10.",
+      "As the first step, every document in the CzCDC 1.0 was segmented using the text segmentation model. This allowed us to treat different parts of processed court documents differently in the further text processing. Specifically, it allowed us to subject only the specific part of a court decision, in this case the court argumentation, to further the reference recognition and extraction. A textual segment recognised as the court argumentation is then processed further.",
+      "As the second step, parts recognised by the text segmentation model as a court argumentation was processed using the reference recognition model. After carefully studying the evaluation of the model's performance in BIBREF13, we have decided to use only part of the said model. Specifically, we have employed the recognition of the court identifiers, as we consider the rest of the smaller units introduced by Hara\u0161ta and \u0160avelka of a lesser value for our task. Also, deploying only the recognition of the court identifiers allowed us to avoid the problematic parsing of smaller textual units into the references. The text spans recognised as identifiers of court decisions are then processed further.",
+      "At this point, it is necessary to evaluate the performance of the above mentioned part of the pipeline before proceeding further. The evaluation of the performance is summarised in Table TABREF11. It shows that organising the two models into the pipeline boosted the performance of the reference recognition model, leading to a higher F1 measure in the initial recognition of the text spans and their classification.",
+      "Further processing included:",
+      "control and repair of incompletely identified court identifiers (manual);",
+      "identification and sorting of identifiers as belonging to Supreme Court, Supreme Administrative Court or Constitutional Court (rule-based, manual);",
+      "standardisation of different types of court identifiers (rule-based, manual);",
+      "parsing of identifiers with court decisions available in CzCDC 1.0."
+    ],
+    [
+      "Overall, through the process described in Section SECREF3, we have retrieved three datasets of extracted references - one dataset per each of the apex courts. These datasets consist of the individual pairs containing the identification of the decision from which the reference was retrieved, and the identification of the referred documents. As we only extracted references to other judicial decisions, we obtained 471,319 references from Supreme Court decisions, 167,237 references from Supreme Administrative Court decisions and 264,463 references from Constitutional Court Decisions. These are numbers of text spans identified as references prior the further processing described in Section SECREF3.",
+      "These references include all identifiers extracted from the court decisions contained in the CzCDC 1.0. Therefore, this number includes all other court decisions, including lower courts, the Court of Justice of the European Union, the European Court of Human Rights, decisions of other public authorities etc. Therefore, it was necessary to classify these into references referring to decisions of the Supreme Court, Supreme Administrative Court, Constitutional Court and others. These groups then underwent a standardisation - or more precisely a resolution - of different court identifiers used by the Czech courts. Numbers of the references resulting from this step are shown in Table TABREF16.",
+      "Following this step, we linked court identifiers with court decisions contained in the CzCDC 1.0. Given that, the CzCDC 1.0 does not contain all the decisions of the respective courts, we were not able to parse all the references. Numbers of the references resulting from this step are shown in Table TABREF17."
+    ],
+    [
+      "This paper introduced the first dataset of citation data of the three Czech apex courts. Understandably, there are some pitfalls and limitations to our approach.",
+      "As we admitted in the evaluation in Section SECREF9, the models we included in our NLP pipelines are far from perfect. Overall, we were able to achieve a reasonable recall and precision rate, which was further enhanced by several round of manual processing of the resulting data. However, it is safe to say that we did not manage to extract all the references. Similarly, because the CzCDC 1.0 dataset we used does not contain all the decisions of the respective courts, we were not able to parse all court identifiers to the documents these refer to. Therefore, the future work in this area may include further development of the resources we used. The CzCDC 1.0 would benefit from the inclusion of more documents of the Supreme Court, the reference recognition model would benefit from more refined training methods etc.",
+      "That being said, the presented dataset is currently the only available resource of its kind focusing on the Czech court decisions that is freely available to research teams. This significantly reduces the costs necessary to conduct these types of studies involving network analysis, and the similar techniques requiring a large amount of citation data."
+    ],
+    [
+      "In this paper, we have described the process of the creation of the first dataset of citation data of the three Czech apex courts. The dataset is publicly available for download at https://github.com/czech-case-law-relevance/czech-court-citations-dataset."
+    ],
+    [
+      "J.H., and T.N. gratefully acknowledge the support from the Czech Science Foundation under grant no. GA-17-20645S. T.N. also acknowledges the institutional support of the Masaryk University. This paper was presented at CEILI Workshop on Legal Data Analysis held in conjunction with Jurix 2019 in Madrid, Spain."
+    ]
+  ]
+}
+```

qasper-0072/instruction.md

@@ -0,0 +1,104 @@
+Name of Paper: Unsupervised Bilingual Lexicon Induction from Mono-lingual Multimodal Data
+
+Question: Which vision-based approaches does this approach outperform?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Unsupervised Bilingual Lexicon Induction",
+    "Multi-lingual Image Caption Model",
+    "Visual-guided Word Representation",
+    "Word Translation Prediction",
+    "Datasets",
+    "Experimental Setup",
+    "Evaluation of Multi-lingual Image Caption",
+    "Evaluation of Bilingual Lexicon Induction",
+    "Generalization to Diverse Language Pairs",
+    "Conclusion",
+    " Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "The bilingual lexicon induction task aims to automatically build word translation dictionaries across different languages, which is beneficial for various natural language processing tasks such as cross-lingual information retrieval BIBREF0 , multi-lingual sentiment analysis BIBREF1 , machine translation BIBREF2 and so on. Although building bilingual lexicon has achieved success with parallel sentences in resource-rich languages BIBREF2 , the parallel data is insufficient or even unavailable especially for resource-scarce languages and it is expensive to collect. On the contrary, there are abundant multimodal mono-lingual data on the Internet, such as images and their associated tags and descriptions, which motivates researchers to induce bilingual lexicon from these non-parallel data without supervision.",
+      "There are mainly two types of mono-lingual approaches to build bilingual dictionaries in recent works. The first is purely text-based, which explores the structure similarity between different linguistic space. The most popular approach among them is to linearly map source word embedding into the target word embedding space BIBREF3 , BIBREF4 . The second type utilizes vision as bridge to connect different languages BIBREF5 , BIBREF6 , BIBREF7 . It assumes that words correlating to similar images should share similar semantic meanings. So previous vision-based methods search images with multi-lingual words and translate words according to similarities of visual features extracted from the corresponding images. It has been proved that the visual-grounded word representation improves the semantic quality of the words BIBREF8 .",
+      "However, previous vision-based methods suffer from two limitations for bilingual lexicon induction. Firstly, the accurate translation performance is confined to concrete visual-relevant words such as nouns and adjectives as shown in Figure SECREF2 . For words without high-quality visual groundings, previous methods would generate poor translations BIBREF7 . Secondly, previous works extract visual features from the whole image to represent words and thus require object-centered images in order to obtain reliable visual groundings. However, common images usually contain multiple objects or scenes, and the word might only be grounded to part of the image, therefore the global visual features will be quite noisy to represent the word.",
+      "In this paper, we address the two limitations via learning from mono-lingual multimodal data with both sentence and visual context (e.g., image and caption data) to induce bilingual lexicon. Such multimodal data is also easily obtained for different languages on the Internet BIBREF9 . We propose a multi-lingual image caption model trained on multiple mono-lingual image caption data, which is able to induce two types of word representations for different languages in the joint space. The first is the linguistic feature learned from the sentence context with visual semantic constraints, so that it is able to generate more accurate translations for words that are less visual-relevant. The second is the localized visual feature which attends to the local region of the object or scene in the image for the corresponding word, so that the visual representation of words will be more salient than previous global visual features. The two representations are complementary and can be combined to induce better bilingual word translation.",
+      "We carry out experiments on multiple language pairs including German-English, French-English, and Japanese-English. The experimental results show that the proposed multi-lingual caption model not only achieves better caption performance than independent mono-lingual models for data-scarce languages, but also can induce the two types of features, linguistic and visual features, for different languages in joint spaces. Our proposed method consistently outperforms previous state-of-the-art vision-based bilingual word induction approaches on different languages. The contributions of this paper are as follows:"
+    ],
+    [
+      "The early works for bilingual lexicon induction require parallel data in different languages. BIBREF2 systematically investigates various word alignment methods with parallel texts to induce bilingual lexicon. However, the parallel data is scarce or even unavailable for low-resource languages. Therefore, methods with less dependency on the availability of parallel corpora are highly desired.",
+      "There are mainly two types of mono-lingual approaches for bilingual lexicon induction: text-based and vision-based methods. The text-based methods purely exploit the linguistic information to translate words. The initiative works BIBREF10 , BIBREF11 utilize word co-occurrences in different languages as clue for word alignment. With the improvement in word representation based on deep learning, BIBREF3 finds the structure similarity of the deep-learned word embeddings in different languages, and employs a parallel vocabulary to learn a linear mapping from the source to target word embeddings. BIBREF12 improves the translation performance via adding an orthogonality constraint to the mapping. BIBREF13 further introduces a matching mechanism to induce bilingual lexicon with fewer seeds. However, these models require seed lexicon as the start-point to train the bilingual mapping. Recently, BIBREF4 proposes an adversarial learning approach to learn the joint bilingual embedding space without any seed lexicon.",
+      "The vision-based methods exploit images to connect different languages, which assume that words corresponding to similar images are semantically alike. BIBREF5 collects images with labeled words in different languages to learn word translation with image as pivot. BIBREF6 improves the visual-based word translation performance via using more powerful visual representations: the CNN-based BIBREF14 features. The above works mainly focus on the translation of nouns and are limited in the number of collected languages. The recent work BIBREF7 constructs the current largest (with respect to the number of language pairs and types of part-of-speech) multimodal word translation dataset, MMID. They show that concrete words are easiest for vision-based translation methods while others are much less accurate. In our work, we alleviate the limitations of previous vision-based methods via exploring images and their captions rather than images with unstructured tags to connect different languages.",
+      "Image captioning has received more and more research attentions. Most image caption works focus on the English caption generation BIBREF15 , BIBREF16 , while there are limited works considering generating multi-lingual captions. The recent WMT workshop BIBREF17 has proposed a subtask of multi-lingual caption generation, where different strategies such as multi-task captioning and source-to-target translation followed by captioning have been proposed to generate captions in target languages. Our work proposes a multi-lingual image caption model that shares part of the parameters across different languages in order to benefit each other."
+    ],
+    [
+      "Our goal is to induce bilingual lexicon without supervision of parallel sentences or seed word pairs, purely based on the mono-lingual image caption data. In the following, we introduce the multi-lingual image caption model whose objectives for bilingual lexicon induction are two folds: 1) explicitly build multi-lingual word embeddings in the joint linguistic space; 2) implicitly extract the localized visual features for each word in the shared visual space. The former encodes linguistic information of words while the latter encodes the visual-grounded information, which are complementary for bilingual lexicon induction."
+    ],
+    [
+      "Suppose we have mono-lingual image caption datasets INLINEFORM0 in the source language and INLINEFORM1 in the target language. The images INLINEFORM2 in INLINEFORM3 and INLINEFORM4 do not necessarily overlap, but cover overlapped object or scene classes which is the basic assumption of vision-based methods. For notation simplicity, we omit the superscript INLINEFORM5 for the data sample. Each image caption INLINEFORM6 and INLINEFORM7 is composed of word sequences INLINEFORM8 and INLINEFORM9 respectively, where INLINEFORM10 is the sentence length.",
+      "The proposed multi-lingual image caption model aims to generate sentences in different languages to describe the image content, which connects the vision and multi-lingual sentences. Figure FIGREF15 illustrates the framework of the caption model, which consists of three parts: the image encoder, word embedding module and language decoder.",
+      "The image encoder encodes the image into the shared visual space. We apply the Resnet152 BIBREF18 as our encoder INLINEFORM0 , which produces INLINEFORM1 vectors corresponding to different spatial locations in the image: DISPLAYFORM0 ",
+      "where INLINEFORM0 . The parameter INLINEFORM1 of the encoder is shared for different languages in order to encode all the images in the same visual space.",
+      "The word embedding module maps the one-hot word representation in each language into low-dimensional distributional embeddings: DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 is the word embedding matrix for the source and target languages respectively. INLINEFORM2 and INLINEFORM3 are the vocabulary size of the two languages.",
+      "The decoder then generates word step by step conditioning on the encoded image feature and previous generated words. The probability of generating INLINEFORM0 in the source language is as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the hidden state of the decoder at step INLINEFORM1 , which is functioned by LSTM BIBREF19 : DISPLAYFORM0 ",
+      "The INLINEFORM0 is the dynamically located contextual image feature to generate word INLINEFORM1 via attention mechanism, which is the weighted sum of INLINEFORM2 computed by DISPLAYFORM0 DISPLAYFORM1 ",
+      "where INLINEFORM0 is a fully connected neural network. The parameter INLINEFORM1 in the decoder includes all the weights in the LSTM and the attention network INLINEFORM2 .",
+      "Similarly, INLINEFORM0 is the probability of generating INLINEFORM1 in the target language, which shares INLINEFORM2 with the source language. By sharing the same parameters across different languages in the encoder and decoder, both the visual features and the learned word embeddings for different languages are enforced to project in a joint semantic space. To be noted, the proposed multi-lingual parameter sharing strategy is not constrained to the presented image captioning model, but can be applied in various image captioning models such as show-tell model BIBREF15 and so on.",
+      "We use maximum likelihood as objective function to train the multi-lingual caption model, which maximizes the log-probability of the ground-truth captions: DISPLAYFORM0 "
+    ],
+    [
+      "The proposed multi-lingual caption model can induce similarities of words in different languages from two aspects: the linguistic similarity and the visual similarity. In the following, we discuss the two types of similarity and then construct the source and target word representations.",
+      "The linguistic similarity is reflected from the learned word embeddings INLINEFORM0 and INLINEFORM1 in the multi-lingual caption model. As shown in previous works BIBREF20 , word embeddings learned from the language contexts can capture syntactic and semantic regularities in the language. However, if the word embeddings of different languages are trained independently, they are not in the same linguistic space and we cannot compute similarities directly. In our multi-lingual caption model, since images in INLINEFORM2 and INLINEFORM3 share the same visual space, the features of sentence INLINEFORM4 and INLINEFORM5 belonging to similar images are bound to be close in the same space with the visual constraints. Meanwhile, the language decoder is also shared, which enforces the word embeddings across languages into the same semantic space in order to generate similar sentence features. Therefore, INLINEFORM6 and INLINEFORM7 not only encode the linguistic information of different languages but also share the embedding space which enables direct cross-lingual similarity comparison. We refer the linguistic features of source and target words INLINEFORM8 and INLINEFORM9 as INLINEFORM10 and INLINEFORM11 respectively.",
+      "For the visual similarity, the multi-lingual caption model locates the image region to generate each word base on the spatial attention in Eq ( EQREF13 ), which can be used to calculate the localized visual representation of the word. However, since the attention is computed before word generation, the localization performance can be less accurate. It also cannot be generalized to image captioning models without spatial attention. Therefore, inspired by BIBREF21 , where they occlude over regions of the image to observe the change of classification probabilities, we feed different parts of the image to the caption model and investigate the probability changes for each word in the sentence. Algorithm SECREF16 presents the procedure of word localization and the grounded visual feature generation. Please note that such visual-grounding is learned unsupervisedly from the image caption data. Therefore, every word can be represented as a set of grounded visual features (the set size equals to the word occurrence number in the dataset). We refer the localized visual feature set for source word INLINEFORM0 as INLINEFORM1 , for target word INLINEFORM2 as INLINEFORM3 .",
+      "Generating localized visual features. Encoded image features INLINEFORM0 , sentence INLINEFORM1 . Localized visual features for each word INLINEFORM2 each INLINEFORM3 compute INLINEFORM4 according to Eq ( EQREF10 ) INLINEFORM5 INLINEFORM6 INLINEFORM7 "
+    ],
+    [
+      "Since the word representations of the source and target language are in the same space, we could directly compute the similarities across languages. We apply l2-normalization on the word representations and measure with the cosine similarity. For linguistic features, the similarity is measured as: DISPLAYFORM0 ",
+      "However, there are a set of visual features associated with one word, so the visual similarity measurement between two words is required to take two sets of visual features as input. We aggregate the visual features in a single representation and then compute cosine similarity instead of point-wise similarities among two sets: DISPLAYFORM0 ",
+      "The reasons for performing aggregation are two folds. Firstly, the number of visual features is proportional to the word occurrence in our approach instead of fixed numbers as in BIBREF6 , BIBREF7 . So the computation cost for frequent words are much higher. Secondly, the aggregation helps to reduce noise, which is especially important for abstract words. The abstract words such as `event' are more visually diverse, but the overall styles of multiple images can reflect its visual semantics.",
+      "Due to the complementary characteristics of the two features, we combine them to predict the word translation. The translated word for INLINEFORM0 is DISPLAYFORM0 "
+    ],
+    [
+      "For image captioning, we utilize the multi30k BIBREF22 , COCO BIBREF23 and STAIR BIBREF24 datasets. The multi30k dataset contains 30k images and annotations under two tasks. In task 1, each image is annotated with one English description which is then translated into German and French. In task 2, the image is independently annotated with 5 descriptions in English and German respectively. For German and English languages, we utilize annotations in task 2. For the French language, we can only employ French descriptions in task 1, so the training size for French is less than the other two languages. The COCO and STAIR datasets contain the same image set but are independently annotated in English and Japanese. Since the images in the wild for different languages might not overlap, we randomly split the image set into two disjoint parts of equal size. The images in each part only contain the mono-lingual captions. We use Moses SMT Toolkit to tokenize sentences and select words occurring more than five times in our vocabulary for each language. Table TABREF21 summarizes the statistics of caption datasets.",
+      "For bilingual lexicon induction, we use two visual datasets: BERGSMA and MMID. The BERGSMA dataset BIBREF5 consists of 500 German-English word translation pairs. Each word is associated with no more than 20 images. The words in BERGSMA dataset are all nouns. The MMID dataset BIBREF7 covers a larger variety of words and languages, including 9,808 German-English pairs and 9,887 French-English pairs. The source word can be mapped to multiple target words in their dictionary. Each word is associated with no more than 100 retrieved images. Since both these image datasets do not contain Japanese language, we download the Japanese-to-English dictionary online. We select words in each dataset that overlap with our caption vocabulary, which results in 230 German-English pairs in BERGSMA dataset, 1,311 German-English pairs and 1,217 French-English pairs in MMID dataset, and 2,408 Japanese-English pairs."
+    ],
+    [
+      "For the multi-lingual caption model, we set the word embedding size and the hidden size of LSTM as 512. Adam algorithm is applied to optimize the model with learning rate of 0.0001 and batch size of 128. The caption model is trained up to 100 epochs and the best model is selected according to caption performance on the validation set.",
+      "We compare our approach with two baseline vision-based methods proposed in BIBREF6 , BIBREF7 , which measure the similarity of two sets of global visual features for bilingual lexicon induction:",
+      "CNN-mean: taking the similarity score of the averaged feature of the two image sets.",
+      "CNN-avgmax: taking the average of the maximum similarity scores of two image sets.",
+      "We evaluate the word translation performance using MRR (mean-reciprocal rank) as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the groundtruth translated words for source word INLINEFORM1 , and INLINEFORM2 denotes the rank of groundtruth word INLINEFORM3 in the rank list of translation candidates. We also measure the precision at K (P@K) score, which is the proportion of source words whose groundtruth translations rank in the top K words. We set K as 1, 5, 10 and 20."
+    ],
+    [
+      "We first evaluate the captioning performance of the proposed multi-lingual caption model, which serves as the foundation stone for our bilingual lexicon induction method.",
+      "We compare the proposed multi-lingual caption model with the mono-lingual model, which consists of the same model structure, but is trained separately for each language. Table TABREF22 presents the captioning results on the multi30k dataset, where all the languages are from the Latin family. The multi-lingual caption model achieves comparable performance with mono-lingual model for data sufficient languages such as English and German, and significantly outperforms the mono-lingual model for the data-scarce language French with absolute 3.22 gains on the CIDEr metric. For languages with distinctive grammar structures such as English and Japanese, the multi-lingual model is also on par with the mono-lingual model as shown in Table TABREF29 . To be noted, the multi-lingual model contains about twice less of parameters than the independent mono-lingual models, which is more computation efficient.",
+      "We visualize the learned visual groundings from the multi-lingual caption model in Figure FIGREF32 . Though there is certain mistakes such as `musicians' in the bottom image, most of the words are grounded well with correct objects or scenes, and thus can obtain more salient visual features."
+    ],
+    [
+      "We induce the linguistic features and localized visual features from the multi-lingual caption model for word translation from the source to target languages. Table TABREF30 presents the German-to-English word translation performance of the proposed features. In the BERGSMA dataset, the visual features achieve better translation results than the linguistic features while they are inferior to the linguistic features in the MMID dataset. This is because the vocabulary in BERGSMA dataset mainly consists of nouns, but the parts-of-speech is more diverse in the MMID dataset. The visual features contribute most to translate concrete noun words, while the linguistic features are beneficial to other abstract words. The fusion of the two features performs best for word translation, which demonstrates that the two features are complementary with each other.",
+      "We also compare our approach with previous state-of-the-art vision-based methods in Table TABREF30 . Since our visual feature is the averaged representation, it is fair to compare with the CNN-mean baseline method where the only difference lies in the feature rather than similarity measurement. The localized features perform substantially better than the global image features which demonstrate the effectiveness of the attention learned from the caption model. The combination of visual and linguistic features also significantly improves the state-of-the-art visual-based CNN-avgmax method with 11.6% and 6.7% absolute gains on P@1 on the BERGSMA and MMID dataset respectively.",
+      "In Figure FIGREF36 , we present the word translation performance for different POS (part-of-speech) labels. We assign the POS label for words in different languages according to their translations in English. We can see that the previous state-of-the-art vision-based approach contributes mostly to noun words which are most visual-relevant, while generates poor translations for other part-of-speech words. Our approach, however, substantially improves the translation performance for all part-of-speech classes. For concrete words such as nouns and adjectives, the localized visual features produce better representation than previous global visual features; and for other part-of-speech words, the linguistic features, which are learned with sentence context, are effective to complement the visual features. The fusion of the linguistic and localized visual features in our approach leads to significant performance improvement over the state-of-the-art baseline method for all types of POS classes.",
+      "Some correct and incorrect translation examples for different POS classes are shown in Table TABREF34 . The visual-relevant concrete words are easier to translate such as `phone' and `red'. But our approach still generates reasonable results for abstract words such as `area' and functional words such as `for' due to the fusion of visual and sentence contexts.",
+      "We also evaluate the influence of different image captioning structures on the bilingual lexicon induction. We compare our attention model (`attn') with the vanilla show-tell model (`mp') BIBREF15 , which applies mean pooling over spatial image features to generate captions and achieves inferior caption performance to the attention model. Table TABREF35 shows the word translation performance of the two caption models. The attention model with better caption performance also induces better linguistic and localized visual features for bilingual lexicon induction. Nevertheless, the show-tell model still outperforms the previous vision-based methods in Table TABREF30 ."
+    ],
+    [
+      "Beside German-to-English word translation, we expand our approach to other languages including French and Japanese which is more distant from English.",
+      "The French-to-English word translation performance is presented in Table TABREF39 . To be noted, the training data of the French captions is five times less than German captions, which makes French-to-English word translation performance less competitive with German-to-English. But similarly, the fusion of linguistic and visual features achieves the best performance, which has boosted the baseline methods with 4.2% relative gains on the MRR metric and 17.4% relative improvements on the P@20 metric.",
+      "Table TABREF40 shows the Japanese-to-English word translation performance. Since the language structures of Japanese and English are quite different, the linguistic features learned from the multi-lingual caption model are less effective but still can benefit the visual features to improve the translation quality. The results on multiple diverse language pairs further demonstrate the generalization of our approach for different languages."
+    ],
+    [
+      "In this paper, we address the problem of bilingual lexicon induction without reliance on parallel corpora. Based on the experience that we humans can understand words better when they are within the context and can learn word translations with external world (e.g. images) as pivot, we propose a new vision-based approach to induce bilingual lexicon with images and their associated sentences. We build a multi-lingual caption model from multiple mono-lingual multimodal data to map words in different languages into joint spaces. Two types of word representation, linguistic features and localized visual features, are induced from the caption model. The two types of features are complementary for word translation. Experimental results on multiple language pairs demonstrate the effectiveness of our proposed method, which leads to significant performance improvement over the state-of-the-art vision-based approaches for all types of part-of-speech. In the future, we will further expand the vision-pivot approaches for zero-resource machine translation without parallel sentences."
+    ],
+    [
+      "This work was supported by National Natural Science Foundation of China under Grant No. 61772535, National Key Research and Development Plan under Grant No. 2016YFB1001202 and Research Foundation of Beijing Municipal Science & Technology Commission under Grant No. Z181100008918002."
+    ]
+  ]
+}
+```

qasper-0073/instruction.md

@@ -0,0 +1,104 @@
+Name of Paper: Unsupervised Bilingual Lexicon Induction from Mono-lingual Multimodal Data
+
+Question: What baseline is used for the experimental setup?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Unsupervised Bilingual Lexicon Induction",
+    "Multi-lingual Image Caption Model",
+    "Visual-guided Word Representation",
+    "Word Translation Prediction",
+    "Datasets",
+    "Experimental Setup",
+    "Evaluation of Multi-lingual Image Caption",
+    "Evaluation of Bilingual Lexicon Induction",
+    "Generalization to Diverse Language Pairs",
+    "Conclusion",
+    " Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "The bilingual lexicon induction task aims to automatically build word translation dictionaries across different languages, which is beneficial for various natural language processing tasks such as cross-lingual information retrieval BIBREF0 , multi-lingual sentiment analysis BIBREF1 , machine translation BIBREF2 and so on. Although building bilingual lexicon has achieved success with parallel sentences in resource-rich languages BIBREF2 , the parallel data is insufficient or even unavailable especially for resource-scarce languages and it is expensive to collect. On the contrary, there are abundant multimodal mono-lingual data on the Internet, such as images and their associated tags and descriptions, which motivates researchers to induce bilingual lexicon from these non-parallel data without supervision.",
+      "There are mainly two types of mono-lingual approaches to build bilingual dictionaries in recent works. The first is purely text-based, which explores the structure similarity between different linguistic space. The most popular approach among them is to linearly map source word embedding into the target word embedding space BIBREF3 , BIBREF4 . The second type utilizes vision as bridge to connect different languages BIBREF5 , BIBREF6 , BIBREF7 . It assumes that words correlating to similar images should share similar semantic meanings. So previous vision-based methods search images with multi-lingual words and translate words according to similarities of visual features extracted from the corresponding images. It has been proved that the visual-grounded word representation improves the semantic quality of the words BIBREF8 .",
+      "However, previous vision-based methods suffer from two limitations for bilingual lexicon induction. Firstly, the accurate translation performance is confined to concrete visual-relevant words such as nouns and adjectives as shown in Figure SECREF2 . For words without high-quality visual groundings, previous methods would generate poor translations BIBREF7 . Secondly, previous works extract visual features from the whole image to represent words and thus require object-centered images in order to obtain reliable visual groundings. However, common images usually contain multiple objects or scenes, and the word might only be grounded to part of the image, therefore the global visual features will be quite noisy to represent the word.",
+      "In this paper, we address the two limitations via learning from mono-lingual multimodal data with both sentence and visual context (e.g., image and caption data) to induce bilingual lexicon. Such multimodal data is also easily obtained for different languages on the Internet BIBREF9 . We propose a multi-lingual image caption model trained on multiple mono-lingual image caption data, which is able to induce two types of word representations for different languages in the joint space. The first is the linguistic feature learned from the sentence context with visual semantic constraints, so that it is able to generate more accurate translations for words that are less visual-relevant. The second is the localized visual feature which attends to the local region of the object or scene in the image for the corresponding word, so that the visual representation of words will be more salient than previous global visual features. The two representations are complementary and can be combined to induce better bilingual word translation.",
+      "We carry out experiments on multiple language pairs including German-English, French-English, and Japanese-English. The experimental results show that the proposed multi-lingual caption model not only achieves better caption performance than independent mono-lingual models for data-scarce languages, but also can induce the two types of features, linguistic and visual features, for different languages in joint spaces. Our proposed method consistently outperforms previous state-of-the-art vision-based bilingual word induction approaches on different languages. The contributions of this paper are as follows:"
+    ],
+    [
+      "The early works for bilingual lexicon induction require parallel data in different languages. BIBREF2 systematically investigates various word alignment methods with parallel texts to induce bilingual lexicon. However, the parallel data is scarce or even unavailable for low-resource languages. Therefore, methods with less dependency on the availability of parallel corpora are highly desired.",
+      "There are mainly two types of mono-lingual approaches for bilingual lexicon induction: text-based and vision-based methods. The text-based methods purely exploit the linguistic information to translate words. The initiative works BIBREF10 , BIBREF11 utilize word co-occurrences in different languages as clue for word alignment. With the improvement in word representation based on deep learning, BIBREF3 finds the structure similarity of the deep-learned word embeddings in different languages, and employs a parallel vocabulary to learn a linear mapping from the source to target word embeddings. BIBREF12 improves the translation performance via adding an orthogonality constraint to the mapping. BIBREF13 further introduces a matching mechanism to induce bilingual lexicon with fewer seeds. However, these models require seed lexicon as the start-point to train the bilingual mapping. Recently, BIBREF4 proposes an adversarial learning approach to learn the joint bilingual embedding space without any seed lexicon.",
+      "The vision-based methods exploit images to connect different languages, which assume that words corresponding to similar images are semantically alike. BIBREF5 collects images with labeled words in different languages to learn word translation with image as pivot. BIBREF6 improves the visual-based word translation performance via using more powerful visual representations: the CNN-based BIBREF14 features. The above works mainly focus on the translation of nouns and are limited in the number of collected languages. The recent work BIBREF7 constructs the current largest (with respect to the number of language pairs and types of part-of-speech) multimodal word translation dataset, MMID. They show that concrete words are easiest for vision-based translation methods while others are much less accurate. In our work, we alleviate the limitations of previous vision-based methods via exploring images and their captions rather than images with unstructured tags to connect different languages.",
+      "Image captioning has received more and more research attentions. Most image caption works focus on the English caption generation BIBREF15 , BIBREF16 , while there are limited works considering generating multi-lingual captions. The recent WMT workshop BIBREF17 has proposed a subtask of multi-lingual caption generation, where different strategies such as multi-task captioning and source-to-target translation followed by captioning have been proposed to generate captions in target languages. Our work proposes a multi-lingual image caption model that shares part of the parameters across different languages in order to benefit each other."
+    ],
+    [
+      "Our goal is to induce bilingual lexicon without supervision of parallel sentences or seed word pairs, purely based on the mono-lingual image caption data. In the following, we introduce the multi-lingual image caption model whose objectives for bilingual lexicon induction are two folds: 1) explicitly build multi-lingual word embeddings in the joint linguistic space; 2) implicitly extract the localized visual features for each word in the shared visual space. The former encodes linguistic information of words while the latter encodes the visual-grounded information, which are complementary for bilingual lexicon induction."
+    ],
+    [
+      "Suppose we have mono-lingual image caption datasets INLINEFORM0 in the source language and INLINEFORM1 in the target language. The images INLINEFORM2 in INLINEFORM3 and INLINEFORM4 do not necessarily overlap, but cover overlapped object or scene classes which is the basic assumption of vision-based methods. For notation simplicity, we omit the superscript INLINEFORM5 for the data sample. Each image caption INLINEFORM6 and INLINEFORM7 is composed of word sequences INLINEFORM8 and INLINEFORM9 respectively, where INLINEFORM10 is the sentence length.",
+      "The proposed multi-lingual image caption model aims to generate sentences in different languages to describe the image content, which connects the vision and multi-lingual sentences. Figure FIGREF15 illustrates the framework of the caption model, which consists of three parts: the image encoder, word embedding module and language decoder.",
+      "The image encoder encodes the image into the shared visual space. We apply the Resnet152 BIBREF18 as our encoder INLINEFORM0 , which produces INLINEFORM1 vectors corresponding to different spatial locations in the image: DISPLAYFORM0 ",
+      "where INLINEFORM0 . The parameter INLINEFORM1 of the encoder is shared for different languages in order to encode all the images in the same visual space.",
+      "The word embedding module maps the one-hot word representation in each language into low-dimensional distributional embeddings: DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 is the word embedding matrix for the source and target languages respectively. INLINEFORM2 and INLINEFORM3 are the vocabulary size of the two languages.",
+      "The decoder then generates word step by step conditioning on the encoded image feature and previous generated words. The probability of generating INLINEFORM0 in the source language is as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the hidden state of the decoder at step INLINEFORM1 , which is functioned by LSTM BIBREF19 : DISPLAYFORM0 ",
+      "The INLINEFORM0 is the dynamically located contextual image feature to generate word INLINEFORM1 via attention mechanism, which is the weighted sum of INLINEFORM2 computed by DISPLAYFORM0 DISPLAYFORM1 ",
+      "where INLINEFORM0 is a fully connected neural network. The parameter INLINEFORM1 in the decoder includes all the weights in the LSTM and the attention network INLINEFORM2 .",
+      "Similarly, INLINEFORM0 is the probability of generating INLINEFORM1 in the target language, which shares INLINEFORM2 with the source language. By sharing the same parameters across different languages in the encoder and decoder, both the visual features and the learned word embeddings for different languages are enforced to project in a joint semantic space. To be noted, the proposed multi-lingual parameter sharing strategy is not constrained to the presented image captioning model, but can be applied in various image captioning models such as show-tell model BIBREF15 and so on.",
+      "We use maximum likelihood as objective function to train the multi-lingual caption model, which maximizes the log-probability of the ground-truth captions: DISPLAYFORM0 "
+    ],
+    [
+      "The proposed multi-lingual caption model can induce similarities of words in different languages from two aspects: the linguistic similarity and the visual similarity. In the following, we discuss the two types of similarity and then construct the source and target word representations.",
+      "The linguistic similarity is reflected from the learned word embeddings INLINEFORM0 and INLINEFORM1 in the multi-lingual caption model. As shown in previous works BIBREF20 , word embeddings learned from the language contexts can capture syntactic and semantic regularities in the language. However, if the word embeddings of different languages are trained independently, they are not in the same linguistic space and we cannot compute similarities directly. In our multi-lingual caption model, since images in INLINEFORM2 and INLINEFORM3 share the same visual space, the features of sentence INLINEFORM4 and INLINEFORM5 belonging to similar images are bound to be close in the same space with the visual constraints. Meanwhile, the language decoder is also shared, which enforces the word embeddings across languages into the same semantic space in order to generate similar sentence features. Therefore, INLINEFORM6 and INLINEFORM7 not only encode the linguistic information of different languages but also share the embedding space which enables direct cross-lingual similarity comparison. We refer the linguistic features of source and target words INLINEFORM8 and INLINEFORM9 as INLINEFORM10 and INLINEFORM11 respectively.",
+      "For the visual similarity, the multi-lingual caption model locates the image region to generate each word base on the spatial attention in Eq ( EQREF13 ), which can be used to calculate the localized visual representation of the word. However, since the attention is computed before word generation, the localization performance can be less accurate. It also cannot be generalized to image captioning models without spatial attention. Therefore, inspired by BIBREF21 , where they occlude over regions of the image to observe the change of classification probabilities, we feed different parts of the image to the caption model and investigate the probability changes for each word in the sentence. Algorithm SECREF16 presents the procedure of word localization and the grounded visual feature generation. Please note that such visual-grounding is learned unsupervisedly from the image caption data. Therefore, every word can be represented as a set of grounded visual features (the set size equals to the word occurrence number in the dataset). We refer the localized visual feature set for source word INLINEFORM0 as INLINEFORM1 , for target word INLINEFORM2 as INLINEFORM3 .",
+      "Generating localized visual features. Encoded image features INLINEFORM0 , sentence INLINEFORM1 . Localized visual features for each word INLINEFORM2 each INLINEFORM3 compute INLINEFORM4 according to Eq ( EQREF10 ) INLINEFORM5 INLINEFORM6 INLINEFORM7 "
+    ],
+    [
+      "Since the word representations of the source and target language are in the same space, we could directly compute the similarities across languages. We apply l2-normalization on the word representations and measure with the cosine similarity. For linguistic features, the similarity is measured as: DISPLAYFORM0 ",
+      "However, there are a set of visual features associated with one word, so the visual similarity measurement between two words is required to take two sets of visual features as input. We aggregate the visual features in a single representation and then compute cosine similarity instead of point-wise similarities among two sets: DISPLAYFORM0 ",
+      "The reasons for performing aggregation are two folds. Firstly, the number of visual features is proportional to the word occurrence in our approach instead of fixed numbers as in BIBREF6 , BIBREF7 . So the computation cost for frequent words are much higher. Secondly, the aggregation helps to reduce noise, which is especially important for abstract words. The abstract words such as `event' are more visually diverse, but the overall styles of multiple images can reflect its visual semantics.",
+      "Due to the complementary characteristics of the two features, we combine them to predict the word translation. The translated word for INLINEFORM0 is DISPLAYFORM0 "
+    ],
+    [
+      "For image captioning, we utilize the multi30k BIBREF22 , COCO BIBREF23 and STAIR BIBREF24 datasets. The multi30k dataset contains 30k images and annotations under two tasks. In task 1, each image is annotated with one English description which is then translated into German and French. In task 2, the image is independently annotated with 5 descriptions in English and German respectively. For German and English languages, we utilize annotations in task 2. For the French language, we can only employ French descriptions in task 1, so the training size for French is less than the other two languages. The COCO and STAIR datasets contain the same image set but are independently annotated in English and Japanese. Since the images in the wild for different languages might not overlap, we randomly split the image set into two disjoint parts of equal size. The images in each part only contain the mono-lingual captions. We use Moses SMT Toolkit to tokenize sentences and select words occurring more than five times in our vocabulary for each language. Table TABREF21 summarizes the statistics of caption datasets.",
+      "For bilingual lexicon induction, we use two visual datasets: BERGSMA and MMID. The BERGSMA dataset BIBREF5 consists of 500 German-English word translation pairs. Each word is associated with no more than 20 images. The words in BERGSMA dataset are all nouns. The MMID dataset BIBREF7 covers a larger variety of words and languages, including 9,808 German-English pairs and 9,887 French-English pairs. The source word can be mapped to multiple target words in their dictionary. Each word is associated with no more than 100 retrieved images. Since both these image datasets do not contain Japanese language, we download the Japanese-to-English dictionary online. We select words in each dataset that overlap with our caption vocabulary, which results in 230 German-English pairs in BERGSMA dataset, 1,311 German-English pairs and 1,217 French-English pairs in MMID dataset, and 2,408 Japanese-English pairs."
+    ],
+    [
+      "For the multi-lingual caption model, we set the word embedding size and the hidden size of LSTM as 512. Adam algorithm is applied to optimize the model with learning rate of 0.0001 and batch size of 128. The caption model is trained up to 100 epochs and the best model is selected according to caption performance on the validation set.",
+      "We compare our approach with two baseline vision-based methods proposed in BIBREF6 , BIBREF7 , which measure the similarity of two sets of global visual features for bilingual lexicon induction:",
+      "CNN-mean: taking the similarity score of the averaged feature of the two image sets.",
+      "CNN-avgmax: taking the average of the maximum similarity scores of two image sets.",
+      "We evaluate the word translation performance using MRR (mean-reciprocal rank) as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the groundtruth translated words for source word INLINEFORM1 , and INLINEFORM2 denotes the rank of groundtruth word INLINEFORM3 in the rank list of translation candidates. We also measure the precision at K (P@K) score, which is the proportion of source words whose groundtruth translations rank in the top K words. We set K as 1, 5, 10 and 20."
+    ],
+    [
+      "We first evaluate the captioning performance of the proposed multi-lingual caption model, which serves as the foundation stone for our bilingual lexicon induction method.",
+      "We compare the proposed multi-lingual caption model with the mono-lingual model, which consists of the same model structure, but is trained separately for each language. Table TABREF22 presents the captioning results on the multi30k dataset, where all the languages are from the Latin family. The multi-lingual caption model achieves comparable performance with mono-lingual model for data sufficient languages such as English and German, and significantly outperforms the mono-lingual model for the data-scarce language French with absolute 3.22 gains on the CIDEr metric. For languages with distinctive grammar structures such as English and Japanese, the multi-lingual model is also on par with the mono-lingual model as shown in Table TABREF29 . To be noted, the multi-lingual model contains about twice less of parameters than the independent mono-lingual models, which is more computation efficient.",
+      "We visualize the learned visual groundings from the multi-lingual caption model in Figure FIGREF32 . Though there is certain mistakes such as `musicians' in the bottom image, most of the words are grounded well with correct objects or scenes, and thus can obtain more salient visual features."
+    ],
+    [
+      "We induce the linguistic features and localized visual features from the multi-lingual caption model for word translation from the source to target languages. Table TABREF30 presents the German-to-English word translation performance of the proposed features. In the BERGSMA dataset, the visual features achieve better translation results than the linguistic features while they are inferior to the linguistic features in the MMID dataset. This is because the vocabulary in BERGSMA dataset mainly consists of nouns, but the parts-of-speech is more diverse in the MMID dataset. The visual features contribute most to translate concrete noun words, while the linguistic features are beneficial to other abstract words. The fusion of the two features performs best for word translation, which demonstrates that the two features are complementary with each other.",
+      "We also compare our approach with previous state-of-the-art vision-based methods in Table TABREF30 . Since our visual feature is the averaged representation, it is fair to compare with the CNN-mean baseline method where the only difference lies in the feature rather than similarity measurement. The localized features perform substantially better than the global image features which demonstrate the effectiveness of the attention learned from the caption model. The combination of visual and linguistic features also significantly improves the state-of-the-art visual-based CNN-avgmax method with 11.6% and 6.7% absolute gains on P@1 on the BERGSMA and MMID dataset respectively.",
+      "In Figure FIGREF36 , we present the word translation performance for different POS (part-of-speech) labels. We assign the POS label for words in different languages according to their translations in English. We can see that the previous state-of-the-art vision-based approach contributes mostly to noun words which are most visual-relevant, while generates poor translations for other part-of-speech words. Our approach, however, substantially improves the translation performance for all part-of-speech classes. For concrete words such as nouns and adjectives, the localized visual features produce better representation than previous global visual features; and for other part-of-speech words, the linguistic features, which are learned with sentence context, are effective to complement the visual features. The fusion of the linguistic and localized visual features in our approach leads to significant performance improvement over the state-of-the-art baseline method for all types of POS classes.",
+      "Some correct and incorrect translation examples for different POS classes are shown in Table TABREF34 . The visual-relevant concrete words are easier to translate such as `phone' and `red'. But our approach still generates reasonable results for abstract words such as `area' and functional words such as `for' due to the fusion of visual and sentence contexts.",
+      "We also evaluate the influence of different image captioning structures on the bilingual lexicon induction. We compare our attention model (`attn') with the vanilla show-tell model (`mp') BIBREF15 , which applies mean pooling over spatial image features to generate captions and achieves inferior caption performance to the attention model. Table TABREF35 shows the word translation performance of the two caption models. The attention model with better caption performance also induces better linguistic and localized visual features for bilingual lexicon induction. Nevertheless, the show-tell model still outperforms the previous vision-based methods in Table TABREF30 ."
+    ],
+    [
+      "Beside German-to-English word translation, we expand our approach to other languages including French and Japanese which is more distant from English.",
+      "The French-to-English word translation performance is presented in Table TABREF39 . To be noted, the training data of the French captions is five times less than German captions, which makes French-to-English word translation performance less competitive with German-to-English. But similarly, the fusion of linguistic and visual features achieves the best performance, which has boosted the baseline methods with 4.2% relative gains on the MRR metric and 17.4% relative improvements on the P@20 metric.",
+      "Table TABREF40 shows the Japanese-to-English word translation performance. Since the language structures of Japanese and English are quite different, the linguistic features learned from the multi-lingual caption model are less effective but still can benefit the visual features to improve the translation quality. The results on multiple diverse language pairs further demonstrate the generalization of our approach for different languages."
+    ],
+    [
+      "In this paper, we address the problem of bilingual lexicon induction without reliance on parallel corpora. Based on the experience that we humans can understand words better when they are within the context and can learn word translations with external world (e.g. images) as pivot, we propose a new vision-based approach to induce bilingual lexicon with images and their associated sentences. We build a multi-lingual caption model from multiple mono-lingual multimodal data to map words in different languages into joint spaces. Two types of word representation, linguistic features and localized visual features, are induced from the caption model. The two types of features are complementary for word translation. Experimental results on multiple language pairs demonstrate the effectiveness of our proposed method, which leads to significant performance improvement over the state-of-the-art vision-based approaches for all types of part-of-speech. In the future, we will further expand the vision-pivot approaches for zero-resource machine translation without parallel sentences."
+    ],
+    [
+      "This work was supported by National Natural Science Foundation of China under Grant No. 61772535, National Key Research and Development Plan under Grant No. 2016YFB1001202 and Research Foundation of Beijing Municipal Science & Technology Commission under Grant No. Z181100008918002."
+    ]
+  ]
+}
+```

qasper-0074/instruction.md

@@ -0,0 +1,104 @@
+Name of Paper: Unsupervised Bilingual Lexicon Induction from Mono-lingual Multimodal Data
+
+Question: Which languages are used in the multi-lingual caption model?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Unsupervised Bilingual Lexicon Induction",
+    "Multi-lingual Image Caption Model",
+    "Visual-guided Word Representation",
+    "Word Translation Prediction",
+    "Datasets",
+    "Experimental Setup",
+    "Evaluation of Multi-lingual Image Caption",
+    "Evaluation of Bilingual Lexicon Induction",
+    "Generalization to Diverse Language Pairs",
+    "Conclusion",
+    " Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "The bilingual lexicon induction task aims to automatically build word translation dictionaries across different languages, which is beneficial for various natural language processing tasks such as cross-lingual information retrieval BIBREF0 , multi-lingual sentiment analysis BIBREF1 , machine translation BIBREF2 and so on. Although building bilingual lexicon has achieved success with parallel sentences in resource-rich languages BIBREF2 , the parallel data is insufficient or even unavailable especially for resource-scarce languages and it is expensive to collect. On the contrary, there are abundant multimodal mono-lingual data on the Internet, such as images and their associated tags and descriptions, which motivates researchers to induce bilingual lexicon from these non-parallel data without supervision.",
+      "There are mainly two types of mono-lingual approaches to build bilingual dictionaries in recent works. The first is purely text-based, which explores the structure similarity between different linguistic space. The most popular approach among them is to linearly map source word embedding into the target word embedding space BIBREF3 , BIBREF4 . The second type utilizes vision as bridge to connect different languages BIBREF5 , BIBREF6 , BIBREF7 . It assumes that words correlating to similar images should share similar semantic meanings. So previous vision-based methods search images with multi-lingual words and translate words according to similarities of visual features extracted from the corresponding images. It has been proved that the visual-grounded word representation improves the semantic quality of the words BIBREF8 .",
+      "However, previous vision-based methods suffer from two limitations for bilingual lexicon induction. Firstly, the accurate translation performance is confined to concrete visual-relevant words such as nouns and adjectives as shown in Figure SECREF2 . For words without high-quality visual groundings, previous methods would generate poor translations BIBREF7 . Secondly, previous works extract visual features from the whole image to represent words and thus require object-centered images in order to obtain reliable visual groundings. However, common images usually contain multiple objects or scenes, and the word might only be grounded to part of the image, therefore the global visual features will be quite noisy to represent the word.",
+      "In this paper, we address the two limitations via learning from mono-lingual multimodal data with both sentence and visual context (e.g., image and caption data) to induce bilingual lexicon. Such multimodal data is also easily obtained for different languages on the Internet BIBREF9 . We propose a multi-lingual image caption model trained on multiple mono-lingual image caption data, which is able to induce two types of word representations for different languages in the joint space. The first is the linguistic feature learned from the sentence context with visual semantic constraints, so that it is able to generate more accurate translations for words that are less visual-relevant. The second is the localized visual feature which attends to the local region of the object or scene in the image for the corresponding word, so that the visual representation of words will be more salient than previous global visual features. The two representations are complementary and can be combined to induce better bilingual word translation.",
+      "We carry out experiments on multiple language pairs including German-English, French-English, and Japanese-English. The experimental results show that the proposed multi-lingual caption model not only achieves better caption performance than independent mono-lingual models for data-scarce languages, but also can induce the two types of features, linguistic and visual features, for different languages in joint spaces. Our proposed method consistently outperforms previous state-of-the-art vision-based bilingual word induction approaches on different languages. The contributions of this paper are as follows:"
+    ],
+    [
+      "The early works for bilingual lexicon induction require parallel data in different languages. BIBREF2 systematically investigates various word alignment methods with parallel texts to induce bilingual lexicon. However, the parallel data is scarce or even unavailable for low-resource languages. Therefore, methods with less dependency on the availability of parallel corpora are highly desired.",
+      "There are mainly two types of mono-lingual approaches for bilingual lexicon induction: text-based and vision-based methods. The text-based methods purely exploit the linguistic information to translate words. The initiative works BIBREF10 , BIBREF11 utilize word co-occurrences in different languages as clue for word alignment. With the improvement in word representation based on deep learning, BIBREF3 finds the structure similarity of the deep-learned word embeddings in different languages, and employs a parallel vocabulary to learn a linear mapping from the source to target word embeddings. BIBREF12 improves the translation performance via adding an orthogonality constraint to the mapping. BIBREF13 further introduces a matching mechanism to induce bilingual lexicon with fewer seeds. However, these models require seed lexicon as the start-point to train the bilingual mapping. Recently, BIBREF4 proposes an adversarial learning approach to learn the joint bilingual embedding space without any seed lexicon.",
+      "The vision-based methods exploit images to connect different languages, which assume that words corresponding to similar images are semantically alike. BIBREF5 collects images with labeled words in different languages to learn word translation with image as pivot. BIBREF6 improves the visual-based word translation performance via using more powerful visual representations: the CNN-based BIBREF14 features. The above works mainly focus on the translation of nouns and are limited in the number of collected languages. The recent work BIBREF7 constructs the current largest (with respect to the number of language pairs and types of part-of-speech) multimodal word translation dataset, MMID. They show that concrete words are easiest for vision-based translation methods while others are much less accurate. In our work, we alleviate the limitations of previous vision-based methods via exploring images and their captions rather than images with unstructured tags to connect different languages.",
+      "Image captioning has received more and more research attentions. Most image caption works focus on the English caption generation BIBREF15 , BIBREF16 , while there are limited works considering generating multi-lingual captions. The recent WMT workshop BIBREF17 has proposed a subtask of multi-lingual caption generation, where different strategies such as multi-task captioning and source-to-target translation followed by captioning have been proposed to generate captions in target languages. Our work proposes a multi-lingual image caption model that shares part of the parameters across different languages in order to benefit each other."
+    ],
+    [
+      "Our goal is to induce bilingual lexicon without supervision of parallel sentences or seed word pairs, purely based on the mono-lingual image caption data. In the following, we introduce the multi-lingual image caption model whose objectives for bilingual lexicon induction are two folds: 1) explicitly build multi-lingual word embeddings in the joint linguistic space; 2) implicitly extract the localized visual features for each word in the shared visual space. The former encodes linguistic information of words while the latter encodes the visual-grounded information, which are complementary for bilingual lexicon induction."
+    ],
+    [
+      "Suppose we have mono-lingual image caption datasets INLINEFORM0 in the source language and INLINEFORM1 in the target language. The images INLINEFORM2 in INLINEFORM3 and INLINEFORM4 do not necessarily overlap, but cover overlapped object or scene classes which is the basic assumption of vision-based methods. For notation simplicity, we omit the superscript INLINEFORM5 for the data sample. Each image caption INLINEFORM6 and INLINEFORM7 is composed of word sequences INLINEFORM8 and INLINEFORM9 respectively, where INLINEFORM10 is the sentence length.",
+      "The proposed multi-lingual image caption model aims to generate sentences in different languages to describe the image content, which connects the vision and multi-lingual sentences. Figure FIGREF15 illustrates the framework of the caption model, which consists of three parts: the image encoder, word embedding module and language decoder.",
+      "The image encoder encodes the image into the shared visual space. We apply the Resnet152 BIBREF18 as our encoder INLINEFORM0 , which produces INLINEFORM1 vectors corresponding to different spatial locations in the image: DISPLAYFORM0 ",
+      "where INLINEFORM0 . The parameter INLINEFORM1 of the encoder is shared for different languages in order to encode all the images in the same visual space.",
+      "The word embedding module maps the one-hot word representation in each language into low-dimensional distributional embeddings: DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 is the word embedding matrix for the source and target languages respectively. INLINEFORM2 and INLINEFORM3 are the vocabulary size of the two languages.",
+      "The decoder then generates word step by step conditioning on the encoded image feature and previous generated words. The probability of generating INLINEFORM0 in the source language is as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the hidden state of the decoder at step INLINEFORM1 , which is functioned by LSTM BIBREF19 : DISPLAYFORM0 ",
+      "The INLINEFORM0 is the dynamically located contextual image feature to generate word INLINEFORM1 via attention mechanism, which is the weighted sum of INLINEFORM2 computed by DISPLAYFORM0 DISPLAYFORM1 ",
+      "where INLINEFORM0 is a fully connected neural network. The parameter INLINEFORM1 in the decoder includes all the weights in the LSTM and the attention network INLINEFORM2 .",
+      "Similarly, INLINEFORM0 is the probability of generating INLINEFORM1 in the target language, which shares INLINEFORM2 with the source language. By sharing the same parameters across different languages in the encoder and decoder, both the visual features and the learned word embeddings for different languages are enforced to project in a joint semantic space. To be noted, the proposed multi-lingual parameter sharing strategy is not constrained to the presented image captioning model, but can be applied in various image captioning models such as show-tell model BIBREF15 and so on.",
+      "We use maximum likelihood as objective function to train the multi-lingual caption model, which maximizes the log-probability of the ground-truth captions: DISPLAYFORM0 "
+    ],
+    [
+      "The proposed multi-lingual caption model can induce similarities of words in different languages from two aspects: the linguistic similarity and the visual similarity. In the following, we discuss the two types of similarity and then construct the source and target word representations.",
+      "The linguistic similarity is reflected from the learned word embeddings INLINEFORM0 and INLINEFORM1 in the multi-lingual caption model. As shown in previous works BIBREF20 , word embeddings learned from the language contexts can capture syntactic and semantic regularities in the language. However, if the word embeddings of different languages are trained independently, they are not in the same linguistic space and we cannot compute similarities directly. In our multi-lingual caption model, since images in INLINEFORM2 and INLINEFORM3 share the same visual space, the features of sentence INLINEFORM4 and INLINEFORM5 belonging to similar images are bound to be close in the same space with the visual constraints. Meanwhile, the language decoder is also shared, which enforces the word embeddings across languages into the same semantic space in order to generate similar sentence features. Therefore, INLINEFORM6 and INLINEFORM7 not only encode the linguistic information of different languages but also share the embedding space which enables direct cross-lingual similarity comparison. We refer the linguistic features of source and target words INLINEFORM8 and INLINEFORM9 as INLINEFORM10 and INLINEFORM11 respectively.",
+      "For the visual similarity, the multi-lingual caption model locates the image region to generate each word base on the spatial attention in Eq ( EQREF13 ), which can be used to calculate the localized visual representation of the word. However, since the attention is computed before word generation, the localization performance can be less accurate. It also cannot be generalized to image captioning models without spatial attention. Therefore, inspired by BIBREF21 , where they occlude over regions of the image to observe the change of classification probabilities, we feed different parts of the image to the caption model and investigate the probability changes for each word in the sentence. Algorithm SECREF16 presents the procedure of word localization and the grounded visual feature generation. Please note that such visual-grounding is learned unsupervisedly from the image caption data. Therefore, every word can be represented as a set of grounded visual features (the set size equals to the word occurrence number in the dataset). We refer the localized visual feature set for source word INLINEFORM0 as INLINEFORM1 , for target word INLINEFORM2 as INLINEFORM3 .",
+      "Generating localized visual features. Encoded image features INLINEFORM0 , sentence INLINEFORM1 . Localized visual features for each word INLINEFORM2 each INLINEFORM3 compute INLINEFORM4 according to Eq ( EQREF10 ) INLINEFORM5 INLINEFORM6 INLINEFORM7 "
+    ],
+    [
+      "Since the word representations of the source and target language are in the same space, we could directly compute the similarities across languages. We apply l2-normalization on the word representations and measure with the cosine similarity. For linguistic features, the similarity is measured as: DISPLAYFORM0 ",
+      "However, there are a set of visual features associated with one word, so the visual similarity measurement between two words is required to take two sets of visual features as input. We aggregate the visual features in a single representation and then compute cosine similarity instead of point-wise similarities among two sets: DISPLAYFORM0 ",
+      "The reasons for performing aggregation are two folds. Firstly, the number of visual features is proportional to the word occurrence in our approach instead of fixed numbers as in BIBREF6 , BIBREF7 . So the computation cost for frequent words are much higher. Secondly, the aggregation helps to reduce noise, which is especially important for abstract words. The abstract words such as `event' are more visually diverse, but the overall styles of multiple images can reflect its visual semantics.",
+      "Due to the complementary characteristics of the two features, we combine them to predict the word translation. The translated word for INLINEFORM0 is DISPLAYFORM0 "
+    ],
+    [
+      "For image captioning, we utilize the multi30k BIBREF22 , COCO BIBREF23 and STAIR BIBREF24 datasets. The multi30k dataset contains 30k images and annotations under two tasks. In task 1, each image is annotated with one English description which is then translated into German and French. In task 2, the image is independently annotated with 5 descriptions in English and German respectively. For German and English languages, we utilize annotations in task 2. For the French language, we can only employ French descriptions in task 1, so the training size for French is less than the other two languages. The COCO and STAIR datasets contain the same image set but are independently annotated in English and Japanese. Since the images in the wild for different languages might not overlap, we randomly split the image set into two disjoint parts of equal size. The images in each part only contain the mono-lingual captions. We use Moses SMT Toolkit to tokenize sentences and select words occurring more than five times in our vocabulary for each language. Table TABREF21 summarizes the statistics of caption datasets.",
+      "For bilingual lexicon induction, we use two visual datasets: BERGSMA and MMID. The BERGSMA dataset BIBREF5 consists of 500 German-English word translation pairs. Each word is associated with no more than 20 images. The words in BERGSMA dataset are all nouns. The MMID dataset BIBREF7 covers a larger variety of words and languages, including 9,808 German-English pairs and 9,887 French-English pairs. The source word can be mapped to multiple target words in their dictionary. Each word is associated with no more than 100 retrieved images. Since both these image datasets do not contain Japanese language, we download the Japanese-to-English dictionary online. We select words in each dataset that overlap with our caption vocabulary, which results in 230 German-English pairs in BERGSMA dataset, 1,311 German-English pairs and 1,217 French-English pairs in MMID dataset, and 2,408 Japanese-English pairs."
+    ],
+    [
+      "For the multi-lingual caption model, we set the word embedding size and the hidden size of LSTM as 512. Adam algorithm is applied to optimize the model with learning rate of 0.0001 and batch size of 128. The caption model is trained up to 100 epochs and the best model is selected according to caption performance on the validation set.",
+      "We compare our approach with two baseline vision-based methods proposed in BIBREF6 , BIBREF7 , which measure the similarity of two sets of global visual features for bilingual lexicon induction:",
+      "CNN-mean: taking the similarity score of the averaged feature of the two image sets.",
+      "CNN-avgmax: taking the average of the maximum similarity scores of two image sets.",
+      "We evaluate the word translation performance using MRR (mean-reciprocal rank) as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 is the groundtruth translated words for source word INLINEFORM1 , and INLINEFORM2 denotes the rank of groundtruth word INLINEFORM3 in the rank list of translation candidates. We also measure the precision at K (P@K) score, which is the proportion of source words whose groundtruth translations rank in the top K words. We set K as 1, 5, 10 and 20."
+    ],
+    [
+      "We first evaluate the captioning performance of the proposed multi-lingual caption model, which serves as the foundation stone for our bilingual lexicon induction method.",
+      "We compare the proposed multi-lingual caption model with the mono-lingual model, which consists of the same model structure, but is trained separately for each language. Table TABREF22 presents the captioning results on the multi30k dataset, where all the languages are from the Latin family. The multi-lingual caption model achieves comparable performance with mono-lingual model for data sufficient languages such as English and German, and significantly outperforms the mono-lingual model for the data-scarce language French with absolute 3.22 gains on the CIDEr metric. For languages with distinctive grammar structures such as English and Japanese, the multi-lingual model is also on par with the mono-lingual model as shown in Table TABREF29 . To be noted, the multi-lingual model contains about twice less of parameters than the independent mono-lingual models, which is more computation efficient.",
+      "We visualize the learned visual groundings from the multi-lingual caption model in Figure FIGREF32 . Though there is certain mistakes such as `musicians' in the bottom image, most of the words are grounded well with correct objects or scenes, and thus can obtain more salient visual features."
+    ],
+    [
+      "We induce the linguistic features and localized visual features from the multi-lingual caption model for word translation from the source to target languages. Table TABREF30 presents the German-to-English word translation performance of the proposed features. In the BERGSMA dataset, the visual features achieve better translation results than the linguistic features while they are inferior to the linguistic features in the MMID dataset. This is because the vocabulary in BERGSMA dataset mainly consists of nouns, but the parts-of-speech is more diverse in the MMID dataset. The visual features contribute most to translate concrete noun words, while the linguistic features are beneficial to other abstract words. The fusion of the two features performs best for word translation, which demonstrates that the two features are complementary with each other.",
+      "We also compare our approach with previous state-of-the-art vision-based methods in Table TABREF30 . Since our visual feature is the averaged representation, it is fair to compare with the CNN-mean baseline method where the only difference lies in the feature rather than similarity measurement. The localized features perform substantially better than the global image features which demonstrate the effectiveness of the attention learned from the caption model. The combination of visual and linguistic features also significantly improves the state-of-the-art visual-based CNN-avgmax method with 11.6% and 6.7% absolute gains on P@1 on the BERGSMA and MMID dataset respectively.",
+      "In Figure FIGREF36 , we present the word translation performance for different POS (part-of-speech) labels. We assign the POS label for words in different languages according to their translations in English. We can see that the previous state-of-the-art vision-based approach contributes mostly to noun words which are most visual-relevant, while generates poor translations for other part-of-speech words. Our approach, however, substantially improves the translation performance for all part-of-speech classes. For concrete words such as nouns and adjectives, the localized visual features produce better representation than previous global visual features; and for other part-of-speech words, the linguistic features, which are learned with sentence context, are effective to complement the visual features. The fusion of the linguistic and localized visual features in our approach leads to significant performance improvement over the state-of-the-art baseline method for all types of POS classes.",
+      "Some correct and incorrect translation examples for different POS classes are shown in Table TABREF34 . The visual-relevant concrete words are easier to translate such as `phone' and `red'. But our approach still generates reasonable results for abstract words such as `area' and functional words such as `for' due to the fusion of visual and sentence contexts.",
+      "We also evaluate the influence of different image captioning structures on the bilingual lexicon induction. We compare our attention model (`attn') with the vanilla show-tell model (`mp') BIBREF15 , which applies mean pooling over spatial image features to generate captions and achieves inferior caption performance to the attention model. Table TABREF35 shows the word translation performance of the two caption models. The attention model with better caption performance also induces better linguistic and localized visual features for bilingual lexicon induction. Nevertheless, the show-tell model still outperforms the previous vision-based methods in Table TABREF30 ."
+    ],
+    [
+      "Beside German-to-English word translation, we expand our approach to other languages including French and Japanese which is more distant from English.",
+      "The French-to-English word translation performance is presented in Table TABREF39 . To be noted, the training data of the French captions is five times less than German captions, which makes French-to-English word translation performance less competitive with German-to-English. But similarly, the fusion of linguistic and visual features achieves the best performance, which has boosted the baseline methods with 4.2% relative gains on the MRR metric and 17.4% relative improvements on the P@20 metric.",
+      "Table TABREF40 shows the Japanese-to-English word translation performance. Since the language structures of Japanese and English are quite different, the linguistic features learned from the multi-lingual caption model are less effective but still can benefit the visual features to improve the translation quality. The results on multiple diverse language pairs further demonstrate the generalization of our approach for different languages."
+    ],
+    [
+      "In this paper, we address the problem of bilingual lexicon induction without reliance on parallel corpora. Based on the experience that we humans can understand words better when they are within the context and can learn word translations with external world (e.g. images) as pivot, we propose a new vision-based approach to induce bilingual lexicon with images and their associated sentences. We build a multi-lingual caption model from multiple mono-lingual multimodal data to map words in different languages into joint spaces. Two types of word representation, linguistic features and localized visual features, are induced from the caption model. The two types of features are complementary for word translation. Experimental results on multiple language pairs demonstrate the effectiveness of our proposed method, which leads to significant performance improvement over the state-of-the-art vision-based approaches for all types of part-of-speech. In the future, we will further expand the vision-pivot approaches for zero-resource machine translation without parallel sentences."
+    ],
+    [
+      "This work was supported by National Natural Science Foundation of China under Grant No. 61772535, National Key Research and Development Plan under Grant No. 2016YFB1001202 and Research Foundation of Beijing Municipal Science & Technology Commission under Grant No. Z181100008918002."
+    ]
+  ]
+}
+```

qasper-0075/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: AraNet: A Deep Learning Toolkit for Arabic Social Media
+
+Question: Did they experiment on all the tasks?

qasper-0080/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Generative Adversarial Nets for Multiple Text Corpora
+
+Question: Which corpora do they use?

qasper-0081/instruction.md

@@ -0,0 +1,104 @@
+Name of Paper: Stacked DeBERT: All Attention in Incomplete Data for Text Classification
+
+Question: Do they report results only on English datasets?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Proposed model",
+    "Dataset ::: Twitter Sentiment Classification",
+    "Dataset ::: Intent Classification from Text with STT Error",
+    "Experiments ::: Baseline models",
+    "Experiments ::: Baseline models ::: NLU service platforms",
+    "Experiments ::: Baseline models ::: Semantic hashing with classifier",
+    "Experiments ::: Training specifications",
+    "Experiments ::: Training specifications ::: NLU service platforms",
+    "Experiments ::: Training specifications ::: Semantic hashing with classifier",
+    "Experiments ::: Training specifications ::: BERT",
+    "Experiments ::: Training specifications ::: Stacked DeBERT",
+    "Experiments ::: Results on Sentiment Classification from Incorrect Text",
+    "Experiments ::: Results on Intent Classification from Text with STT Error",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Understanding a user's intent and sentiment is of utmost importance for current intelligent chatbots to respond appropriately to human requests. However, current systems are not able to perform to their best capacity when presented with incomplete data, meaning sentences with missing or incorrect words. This scenario is likely to happen when one considers human error done in writing. In fact, it is rather naive to assume that users will always type fully grammatically correct sentences. Panko BIBREF0 goes as far as claiming that human accuracy regarding research paper writing is none when considering the entire document. This has been aggravated with the advent of internet and social networks, which allowed language and modern communication to be been rapidly transformed BIBREF1, BIBREF2. Take Twitter for instance, where information is expected to be readily communicated in short and concise sentences with little to no regard to correct sentence grammar or word spelling BIBREF3.",
+      "Further motivation can be found in Automatic Speech Recognition (ASR) applications, where high error rates prevail and pose an enormous hurdle in the broad adoption of speech technology by users worldwide BIBREF4. This is an important issue to tackle because, in addition to more widespread user adoption, improving Speech-to-Text (STT) accuracy diminishes error propagation to modules using the recognized text. With that in mind, in order for current systems to improve the quality of their services, there is a need for development of robust intelligent systems that are able to understand a user even when faced with incomplete representation in language.",
+      "The advancement of deep neural networks have immensely aided in the development of the Natural Language Processing (NLP) domain. Tasks such as text generation, sentence correction, image captioning and text classification, have been possible via models such as Convolutional Neural Networks and Recurrent Neural Networks BIBREF5, BIBREF6, BIBREF7. More recently, state-of-the-art results have been achieved with attention models, more specifically Transformers BIBREF8. Surprisingly, however, there is currently no research on incomplete text classification in the NLP community. Realizing the need of research in that area, we make it the focus of this paper. In this novel task, the model aims to identify the user's intent or sentiment by analyzing a sentence with missing and/or incorrect words. In the sentiment classification task, the model aims to identify the user's sentiment given a tweet, written in informal language and without regards for sentence correctness.",
+      "Current approaches for Text Classification tasks focus on efficient embedding representations. Kim et al. BIBREF9 use semantically enriched word embeddings to make synonym and antonym word vectors respectively more and less similar in order to improve intent classification performance. Devlin et al. BIBREF10 propose Bidirectional Encoder Representations from Transformers (BERT), a powerful bidirectional language representation model based on Transformers, achieving state-of-the-art results on eleven NLP tasks BIBREF11, including sentiment text classification. Concurrently, Shridhar et al. BIBREF12 also reach state of the art in the intent recognition task using Semantic Hashing for feature representation followed by a neural classifier. All aforementioned approaches are, however, applied to datasets based solely on complete data.",
+      "The incomplete data problem is usually approached as a reconstruction or imputation task and is most often related to missing numbers imputation BIBREF13. Vincent et al. BIBREF14, BIBREF15 propose to reconstruct clean data from its noisy version by mapping the input to meaningful representations. This approach has also been shown to outperform other models, such as predictive mean matching, random forest, Support Vector Machine (SVM) and Multiple imputation by Chained Equations (MICE), at missing data imputation tasks BIBREF16, BIBREF17. Researchers in those two areas have shown that meaningful feature representation of data is of utter importance for high performance achieving methods. We propose a model that combines the power of BERT in the NLP domain and the strength of denoising strategies in incomplete data reconstruction to tackle the tasks of incomplete intent and sentiment classification. This enables the implementation of a novel encoding scheme, more robust to incomplete data, called Stacked Denoising BERT or Stacked DeBERT. Our approach consists of obtaining richer input representations from input tokens by stacking denoising transformers on an embedding layer with vanilla transformers. The embedding layer and vanilla transformers extract intermediate input features from the input tokens, and the denoising transformers are responsible for obtaining richer input representations from them. By improving BERT with stronger denoising abilities, we are able to reconstruct missing and incorrect words' embeddings and improve classification accuracy. To summarize, our contribution is two-fold:",
+      "Novel model architecture that is more robust to incomplete data, including missing or incorrect words in text.",
+      "Proposal of the novel tasks of incomplete intent and sentiment classification from incorrect sentences, and release of corpora related with these tasks.",
+      "The remainder of this paper is organized in four sections, with Section SECREF2 explaining the proposed model. This is followed by Section SECREF3 which includes a detailed description of the dataset used for training and evaluation purposes and how it was obtained. Section SECREF4 covers the baseline models used for comparison, training specifications and experimental results. Finally, Section SECREF5 wraps up this paper with conclusion and future works."
+    ],
+    [
+      "We propose Stacked Denoising BERT (DeBERT) as a novel encoding scheming for the task of incomplete intent classification and sentiment classification from incorrect sentences, such as tweets and text with STT error. The proposed model, illustrated in Fig. FIGREF4, is structured as a stacking of embedding layers and vanilla transformer layers, similarly to the conventional BERT BIBREF10, followed by layers of novel denoising transformers. The main purpose of this model is to improve the robustness and efficiency of BERT when applied to incomplete data by reconstructing hidden embeddings from sentences with missing words. By reconstructing these hidden embeddings, we are able to improve the encoding scheme in BERT.",
+      "The initial part of the model is the conventional BERT, a multi-layer bidirectional Transformer encoder and a powerful language model. During training, BERT is fine-tuned on the incomplete text classification corpus (see Section SECREF3). The first layer pre-processes the input sentence by making it lower-case and by tokenizing it. It also prefixes the sequence of tokens with a special character `[CLS]' and sufixes each sentence with a `[SEP]' character. It is followed by an embedding layer used for input representation, with the final input embedding being a sum of token embedddings, segmentation embeddings and position embeddings. The first one, token embedding layer, uses a vocabulary dictionary to convert each token into a more representative embedding. The segmentation embedding layer indicates which tokens constitute a sentence by signaling either 1 or 0. In our case, since our data are formed of single sentences, the segment is 1 until the first `[SEP]' character appears (indicating segment A) and then it becomes 0 (segment B). The position embedding layer, as the name indicates, adds information related to the token's position in the sentence. This prepares the data to be considered by the layers of vanilla bidirectional transformers, which outputs a hidden embedding that can be used by our novel layers of denoising transformers.",
+      "Although BERT has shown to perform better than other baseline models when handling incomplete data, it is still not enough to completely and efficiently handle such data. Because of that, there is a need for further improvement of the hidden feature vectors obtained from sentences with missing words. With this purpose in mind, we implement a novel encoding scheme consisting of denoising transformers, which is composed of stacks of multilayer perceptrons for the reconstruction of missing words\u2019 embeddings by extracting more abstract and meaningful hidden feature vectors, and bidirectional transformers for improved embedding representation. The embedding reconstruction step is trained on sentence embeddings extracted from incomplete data $h_{inc}$ as input and embeddings corresponding to its complete version $h_{comp}$ as target. Both input and target are obtained after applying the embedding layers and the vanilla transformers, as indicated in Fig. FIGREF4, and have shape $(N_{bs}, 768, 128)$, where $N_{bs}$ is the batch size, 768 is the original BERT embedding size for a single token, and 128 is the maximum sequence length in a sentence.",
+      "The stacks of multilayer perceptrons are structured as two sets of three layers with two hidden layers each. The first set is responsible for compressing the $h_{inc}$ into a latent-space representation, extracting more abstract features into lower dimension vectors $z_1$, $z_2$ and $\\mathbf {z}$ with shape $(N_{bs}, 128, 128)$, $(N_{bs}, 32, 128)$, and $(N_{bs}, 12, 128)$, respectively. This process is shown in Eq. (DISPLAY_FORM5):",
+      "where $f(\\cdot )$ is the parameterized function mapping $h_{inc}$ to the hidden state $\\mathbf {z}$. The second set then respectively reconstructs $z_1$, $z_2$ and $\\mathbf {z}$ into $h_{rec_1}$, $h_{rec_2}$ and $h_{rec}$. This process is shown in Eq. (DISPLAY_FORM6):",
+      "where $g(\\cdot )$ is the parameterized function that reconstructs $\\mathbf {z}$ as $h_{rec}$.",
+      "The reconstructed hidden sentence embedding $h_{rec}$ is compared with the complete hidden sentence embedding $h_{comp}$ through a mean square error loss function, as shown in Eq. (DISPLAY_FORM7):",
+      "After reconstructing the correct hidden embeddings from the incomplete sentences, the correct hidden embeddings are given to bidirectional transformers to generate input representations. The model is then fine-tuned in an end-to-end manner on the incomplete text classification corpus.",
+      "Classification is done with a feedforward network and softmax activation function. Softmax $\\sigma $ is a discrete probability distribution function for $N_C$ classes, with the sum of the classes probability being 1 and the maximum value being the predicted class. The predicted class can be mathematically calculated as in Eq. (DISPLAY_FORM8):",
+      "where $o = W t + b$, the output of the feedforward layer used for classification."
+    ],
+    [
+      "In order to evaluate the performance of our model, we need access to a naturally noisy dataset with real human errors. Poor quality texts obtained from Twitter, called tweets, are then ideal for our task. For this reason, we choose Kaggle's two-class Sentiment140 dataset BIBREF18, which consists of spoken text being used in writing and without strong consideration for grammar or sentence correctness. Thus, it has many mistakes, as specified in Table TABREF11.",
+      "Even though this corpus has incorrect sentences and their emotional labels, they lack their respective corrected sentences, necessary for the training of our model. In order to obtain this missing information, we outsource native English speakers from an unbiased and anonymous platform, called Amazon Mechanical Turk (MTurk) BIBREF19, which is a paid marketplace for Human Intelligence Tasks (HITs). We use this platform to create tasks for native English speakers to format the original incorrect tweets into correct sentences. Some examples are shown in Table TABREF12.",
+      "After obtaining the correct sentences, our two-class dataset has class distribution as shown in Table TABREF14. There are 200 sentences used in the training stage, with 100 belonging to the positive sentiment class and 100 to the negative class, and 50 samples being used in the evaluation stage, with 25 negative and 25 positive. This totals in 300 samples, with incorrect and correct sentences combined. Since our goal is to evaluate the model's performance and robustness in the presence of noise, we only consider incorrect data in the testing phase. Note that BERT is a pre-trained model, meaning that small amounts of data are enough for appropriate fine-tuning."
+    ],
+    [
+      "In the intent classification task, we are presented with a corpus that suffers from the opposite problem of the Twitter sentiment classification corpus. In the intent classification corpus, we have the complete sentences and intent labels but lack their corresponding incomplete sentences, and since our task revolves around text classification in incomplete or incorrect data, it is essential that we obtain this information. To remedy this issue, we apply a Text-to-Speech (TTS) module followed by a Speech-to-Text (STT) module to the complete sentences in order to obtain incomplete sentences with STT error. Due to TTS and STT modules available being imperfect, the resulting sentences have a reasonable level of noise in the form of missing or incorrectly transcribed words. Analysis on this dataset adds value to our work by enabling evaluation of our model's robustness to different rates of data incompleteness.",
+      "The dataset used to evaluate the models' performance is the Chatbot Natural Language Unerstanding (NLU) Evaluation Corpus, introduced by Braun et al. BIBREF20 to test NLU services. It is a publicly available benchmark and is composed of sentences obtained from a German Telegram chatbot used to answer questions about public transport connections. The dataset has two intents, namely Departure Time and Find Connection with 100 train and 106 test samples, shown in Table TABREF18. Even though English is the main language of the benchmark, this dataset contains a few German station and street names.",
+      "The incomplete dataset used for training is composed of lower-cased incomplete data obtained by manipulating the original corpora. The incomplete sentences with STT error are obtained in a 2-step process shown in Fig. FIGREF22. The first step is to apply a TTS module to the available complete sentence. Here, we apply gtts , a Google Text-to-Speech python library, and macsay , a terminal command available in Mac OS as say. The second step consists of applying an STT module to the obtained audio files in order to obtain text containing STT errors. The STT module used here was witai , freely available and maintained by Wit.ai. The mentioned TTS and STT modules were chosen according to code availability and whether it's freely available or has high daily usage limitations.",
+      "Table TABREF24 exemplifies a complete and its respective incomplete sentences with different TTS-STT combinations, thus varying rates of missing and incorrect words. The level of noise in the STT imbued sentences is denoted by a inverted BLEU (iBLEU) score ranging from 0 to 1. The inverted BLEU score is denoted in Eq. (DISPLAY_FORM23):",
+      "where BLEU is a common metric usually used in machine translation tasks BIBREF21. We decide to showcase that instead of regular BLEU because it is more indicative to the amount of noise in the incomplete text, where the higher the iBLEU, the higher the noise."
+    ],
+    [
+      "Besides the already mentioned BERT, the following baseline models are also used for comparison."
+    ],
+    [
+      "We focus on the three following services, where the first two are commercial services and last one is open source with two separate backends: Google Dialogflow (formerly Api.ai) , SAP Conversational AI (formerly Recast.ai) and Rasa (spacy and tensorflow backend) ."
+    ],
+    [
+      "Shridhar et al. BIBREF12 proposed a word embedding method that doesn't suffer from out-of-vocabulary issues. The authors achieve this by using hash tokens in the alphabet instead of a single word, making it vocabulary independent. For classification, classifiers such as Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Random Forest are used. A complete list of classifiers and training specifications are given in Section SECREF31."
+    ],
+    [
+      "The baseline and proposed models are each trained 3 separate times for the incomplete intent classification task: complete data and one for each of the TTS-STT combinations (gtts-witai and macsay-witai). Regarding the sentiment classification from incorrect sentences task, the baseline and proposed models are each trained 3 times: original text, corrected text and incorrect with correct texts. The reported F1 scores are the best accuracies obtained from 10 runs."
+    ],
+    [
+      "No settable training configurations available in the online platforms."
+    ],
+    [
+      "Trained on 3-gram, feature vector size of 768 as to match the BERT embedding size, and 13 classifiers with parameters set as specified in the authors' paper so as to allow comparison: MLP with 3 hidden layers of sizes $[300, 100, 50]$ respectively; Random Forest with 50 estimators or trees; 5-fold Grid Search with Random Forest classifier and estimator $([50, 60, 70]$; Linear Support Vector Classifier with L1 and L2 penalty and tolerance of $10^{-3}$; Regularized linear classifier with Stochastic Gradient Descent (SGD) learning with regularization term $alpha=10^{-4}$ and L1, L2 and Elastic-Net penalty; Nearest Centroid with Euclidian metric, where classification is done by representing each class with a centroid; Bernoulli Naive Bayes with smoothing parameter $alpha=10^{-2}$; K-means clustering with 2 clusters and L2 penalty; and Logistic Regression classifier with L2 penalty, tolerance of $10^{-4}$ and regularization term of $1.0$. Most often, the best performing classifier was MLP."
+    ],
+    [
+      "Conventional BERT is a BERT-base-uncased model, meaning that it has 12 transformer blocks $L$, hidden size $H$ of 768, and 12 self-attention heads $A$. The model is fine-tuned with our dataset on 2 Titan X GPUs for 3 epochs with Adam Optimizer, learning rate of $2*10^{-5}$, maximum sequence length of 128, and warm up proportion of $0.1$. The train batch size is 4 for the Twitter Sentiment Corpus and 8 for the Chatbot Intent Classification Corpus."
+    ],
+    [
+      "Our proposed model is trained in end-to-end manner on 2 Titan X GPUs, with training time depending on the size of the dataset and train batch size. The stack of multilayer perceptrons are trained for 100 and 1,000 epochs with Adam Optimizer, learning rate of $10^{-3}$, weight decay of $10^{-5}$, MSE loss criterion and batch size the same as BERT (4 for the Twitter Sentiment Corpus and 8 for the Chatbot Intent Classification Corpus)."
+    ],
+    [
+      "Experimental results for the Twitter Sentiment Classification task on Kaggle's Sentiment140 Corpus dataset, displayed in Table TABREF37, show that our model has better F1-micros scores, outperforming the baseline models by 6$\\%$ to 8$\\%$. We evaluate our model and baseline models on three versions of the dataset. The first one (Inc) only considers the original data, containing naturally incorrect tweets, and achieves accuracy of 80$\\%$ against BERT's 72$\\%$. The second version (Corr) considers the corrected tweets, and shows higher accuracy given that it is less noisy. In that version, Stacked DeBERT achieves 82$\\%$ accuracy against BERT's 76$\\%$, an improvement of 6$\\%$. In the last case (Inc+Corr), we consider both incorrect and correct tweets as input to the models in hopes of improving performance. However, the accuracy was similar to the first aforementioned version, 80$\\%$ for our model and 74$\\%$ for the second highest performing model. Since the first and last corpus gave similar performances with our model, we conclude that the Twitter dataset does not require complete sentences to be given as training input, in addition to the original naturally incorrect tweets, in order to better model the noisy sentences.",
+      "In addition to the overall F1-score, we also present a confusion matrix, in Fig. FIGREF38, with the per-class F1-scores for BERT and Stacked DeBERT. The normalized confusion matrix plots the predicted labels versus the target/target labels. Similarly to Table TABREF37, we evaluate our model with the original Twitter dataset, the corrected version and both original and corrected tweets. It can be seen that our model is able to improve the overall performance by improving the accuracy of the lower performing classes. In the Inc dataset, the true class 1 in BERT performs with approximately 50%. However, Stacked DeBERT is able to improve that to 72%, although to a cost of a small decrease in performance of class 0. A similar situation happens in the remaining two datasets, with improved accuracy in class 0 from 64% to 84% and 60% to 76% respectively."
+    ],
+    [
+      "Experimental results for the Intent Classification task on the Chatbot NLU Corpus with STT error can be seen in Table TABREF40. When presented with data containing STT error, our model outperforms all baseline models in both combinations of TTS-STT: gtts-witai outperforms the second placing baseline model by 0.94% with F1-score of 97.17%, and macsay-witai outperforms the next highest achieving model by 1.89% with F1-score of 96.23%.",
+      "The table also indicates the level of noise in each dataset with the already mentioned iBLEU score, where 0 means no noise and higher values mean higher quantity of noise. As expected, the models' accuracy degrade with the increase in noise, thus F1-scores of gtts-witai are higher than macsay-witai. However, while the other models decay rapidly in the presence of noise, our model does not only outperform them but does so with a wider margin. This is shown with the increasing robustness curve in Fig. FIGREF41 and can be demonstrated by macsay-witai outperforming the baseline models by twice the gap achieved by gtts-witai.",
+      "Further analysis of the results in Table TABREF40 show that, BERT decay is almost constant with the addition of noise, with the difference between the complete data and gtts-witai being 1.88 and gtts-witai and macsay-witai being 1.89. Whereas in Stacked DeBERT, that difference is 1.89 and 0.94 respectively. This is stronger indication of our model's robustness in the presence of noise.",
+      "Additionally, we also present Fig. FIGREF42 with the normalized confusion matrices for BERT and Stacked DeBERT for sentences containing STT error. Analogously to the Twitter Sentiment Classification task, the per-class F1-scores show that our model is able to improve the overall performance by improving the accuracy of one class while maintaining the high-achieving accuracy of the second one."
+    ],
+    [
+      "In this work, we proposed a novel deep neural network, robust to noisy text in the form of sentences with missing and/or incorrect words, called Stacked DeBERT. The idea was to improve the accuracy performance by improving the representation ability of the model with the implementation of novel denoising transformers. More specifically, our model was able to reconstruct hidden embeddings from their respective incomplete hidden embeddings. Stacked DeBERT was compared against three NLU service platforms and two other machine learning methods, namely BERT and Semantic Hashing with neural classifier. Our model showed better performance when evaluated on F1 scores in both Twitter sentiment and intent text with STT error classification tasks. The per-class F1 score was also evaluated in the form of normalized confusion matrices, showing that our model was able to improve the overall performance by better balancing the accuracy of each class, trading-off small decreases in high achieving class for significant improvements in lower performing ones. In the Chatbot dataset, accuracy improvement was achieved even without trade-off, with the highest achieving classes maintaining their accuracy while the lower achieving class saw improvement. Further evaluation on the F1-scores decay in the presence of noise demonstrated that our model is more robust than the baseline models when considering noisy data, be that in the form of incorrect sentences or sentences with STT error. Not only that, experiments on the Twitter dataset also showed improved accuracy in clean data, with complete sentences. We infer that this is due to our model being able to extract richer data representations from the input data regardless of the completeness of the sentence. For future works, we plan on evaluating the robustness of our model against other types of noise, such as word reordering, word insertion, and spelling mistakes in sentences. In order to improve the performance of our model, further experiments will be done in search for more appropriate hyperparameters and more complex neural classifiers to substitute the last feedforward network layer."
+    ],
+    [
+      "This work was partly supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (2016-0-00564, Development of Intelligent Interaction Technology Based on Context Awareness and Human Intention Understanding) and Korea Evaluation Institute of Industrial Technology (KEIT) grant funded by the Korea government (MOTIE) (50%) and the Technology Innovation Program: Industrial Strategic Technology Development Program (No: 10073162) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea) (50%)."
+    ]
+  ]
+}
+```

qasper-0086/instruction.md

@@ -0,0 +1,104 @@
+Name of Paper: Stacked DeBERT: All Attention in Incomplete Data for Text Classification
+
+Question: By how much do they outperform other models in the sentiment in intent classification tasks?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Proposed model",
+    "Dataset ::: Twitter Sentiment Classification",
+    "Dataset ::: Intent Classification from Text with STT Error",
+    "Experiments ::: Baseline models",
+    "Experiments ::: Baseline models ::: NLU service platforms",
+    "Experiments ::: Baseline models ::: Semantic hashing with classifier",
+    "Experiments ::: Training specifications",
+    "Experiments ::: Training specifications ::: NLU service platforms",
+    "Experiments ::: Training specifications ::: Semantic hashing with classifier",
+    "Experiments ::: Training specifications ::: BERT",
+    "Experiments ::: Training specifications ::: Stacked DeBERT",
+    "Experiments ::: Results on Sentiment Classification from Incorrect Text",
+    "Experiments ::: Results on Intent Classification from Text with STT Error",
+    "Conclusion",
+    "Acknowledgments"
+  ],
+  "paragraphs": [
+    [
+      "Understanding a user's intent and sentiment is of utmost importance for current intelligent chatbots to respond appropriately to human requests. However, current systems are not able to perform to their best capacity when presented with incomplete data, meaning sentences with missing or incorrect words. This scenario is likely to happen when one considers human error done in writing. In fact, it is rather naive to assume that users will always type fully grammatically correct sentences. Panko BIBREF0 goes as far as claiming that human accuracy regarding research paper writing is none when considering the entire document. This has been aggravated with the advent of internet and social networks, which allowed language and modern communication to be been rapidly transformed BIBREF1, BIBREF2. Take Twitter for instance, where information is expected to be readily communicated in short and concise sentences with little to no regard to correct sentence grammar or word spelling BIBREF3.",
+      "Further motivation can be found in Automatic Speech Recognition (ASR) applications, where high error rates prevail and pose an enormous hurdle in the broad adoption of speech technology by users worldwide BIBREF4. This is an important issue to tackle because, in addition to more widespread user adoption, improving Speech-to-Text (STT) accuracy diminishes error propagation to modules using the recognized text. With that in mind, in order for current systems to improve the quality of their services, there is a need for development of robust intelligent systems that are able to understand a user even when faced with incomplete representation in language.",
+      "The advancement of deep neural networks have immensely aided in the development of the Natural Language Processing (NLP) domain. Tasks such as text generation, sentence correction, image captioning and text classification, have been possible via models such as Convolutional Neural Networks and Recurrent Neural Networks BIBREF5, BIBREF6, BIBREF7. More recently, state-of-the-art results have been achieved with attention models, more specifically Transformers BIBREF8. Surprisingly, however, there is currently no research on incomplete text classification in the NLP community. Realizing the need of research in that area, we make it the focus of this paper. In this novel task, the model aims to identify the user's intent or sentiment by analyzing a sentence with missing and/or incorrect words. In the sentiment classification task, the model aims to identify the user's sentiment given a tweet, written in informal language and without regards for sentence correctness.",
+      "Current approaches for Text Classification tasks focus on efficient embedding representations. Kim et al. BIBREF9 use semantically enriched word embeddings to make synonym and antonym word vectors respectively more and less similar in order to improve intent classification performance. Devlin et al. BIBREF10 propose Bidirectional Encoder Representations from Transformers (BERT), a powerful bidirectional language representation model based on Transformers, achieving state-of-the-art results on eleven NLP tasks BIBREF11, including sentiment text classification. Concurrently, Shridhar et al. BIBREF12 also reach state of the art in the intent recognition task using Semantic Hashing for feature representation followed by a neural classifier. All aforementioned approaches are, however, applied to datasets based solely on complete data.",
+      "The incomplete data problem is usually approached as a reconstruction or imputation task and is most often related to missing numbers imputation BIBREF13. Vincent et al. BIBREF14, BIBREF15 propose to reconstruct clean data from its noisy version by mapping the input to meaningful representations. This approach has also been shown to outperform other models, such as predictive mean matching, random forest, Support Vector Machine (SVM) and Multiple imputation by Chained Equations (MICE), at missing data imputation tasks BIBREF16, BIBREF17. Researchers in those two areas have shown that meaningful feature representation of data is of utter importance for high performance achieving methods. We propose a model that combines the power of BERT in the NLP domain and the strength of denoising strategies in incomplete data reconstruction to tackle the tasks of incomplete intent and sentiment classification. This enables the implementation of a novel encoding scheme, more robust to incomplete data, called Stacked Denoising BERT or Stacked DeBERT. Our approach consists of obtaining richer input representations from input tokens by stacking denoising transformers on an embedding layer with vanilla transformers. The embedding layer and vanilla transformers extract intermediate input features from the input tokens, and the denoising transformers are responsible for obtaining richer input representations from them. By improving BERT with stronger denoising abilities, we are able to reconstruct missing and incorrect words' embeddings and improve classification accuracy. To summarize, our contribution is two-fold:",
+      "Novel model architecture that is more robust to incomplete data, including missing or incorrect words in text.",
+      "Proposal of the novel tasks of incomplete intent and sentiment classification from incorrect sentences, and release of corpora related with these tasks.",
+      "The remainder of this paper is organized in four sections, with Section SECREF2 explaining the proposed model. This is followed by Section SECREF3 which includes a detailed description of the dataset used for training and evaluation purposes and how it was obtained. Section SECREF4 covers the baseline models used for comparison, training specifications and experimental results. Finally, Section SECREF5 wraps up this paper with conclusion and future works."
+    ],
+    [
+      "We propose Stacked Denoising BERT (DeBERT) as a novel encoding scheming for the task of incomplete intent classification and sentiment classification from incorrect sentences, such as tweets and text with STT error. The proposed model, illustrated in Fig. FIGREF4, is structured as a stacking of embedding layers and vanilla transformer layers, similarly to the conventional BERT BIBREF10, followed by layers of novel denoising transformers. The main purpose of this model is to improve the robustness and efficiency of BERT when applied to incomplete data by reconstructing hidden embeddings from sentences with missing words. By reconstructing these hidden embeddings, we are able to improve the encoding scheme in BERT.",
+      "The initial part of the model is the conventional BERT, a multi-layer bidirectional Transformer encoder and a powerful language model. During training, BERT is fine-tuned on the incomplete text classification corpus (see Section SECREF3). The first layer pre-processes the input sentence by making it lower-case and by tokenizing it. It also prefixes the sequence of tokens with a special character `[CLS]' and sufixes each sentence with a `[SEP]' character. It is followed by an embedding layer used for input representation, with the final input embedding being a sum of token embedddings, segmentation embeddings and position embeddings. The first one, token embedding layer, uses a vocabulary dictionary to convert each token into a more representative embedding. The segmentation embedding layer indicates which tokens constitute a sentence by signaling either 1 or 0. In our case, since our data are formed of single sentences, the segment is 1 until the first `[SEP]' character appears (indicating segment A) and then it becomes 0 (segment B). The position embedding layer, as the name indicates, adds information related to the token's position in the sentence. This prepares the data to be considered by the layers of vanilla bidirectional transformers, which outputs a hidden embedding that can be used by our novel layers of denoising transformers.",
+      "Although BERT has shown to perform better than other baseline models when handling incomplete data, it is still not enough to completely and efficiently handle such data. Because of that, there is a need for further improvement of the hidden feature vectors obtained from sentences with missing words. With this purpose in mind, we implement a novel encoding scheme consisting of denoising transformers, which is composed of stacks of multilayer perceptrons for the reconstruction of missing words\u2019 embeddings by extracting more abstract and meaningful hidden feature vectors, and bidirectional transformers for improved embedding representation. The embedding reconstruction step is trained on sentence embeddings extracted from incomplete data $h_{inc}$ as input and embeddings corresponding to its complete version $h_{comp}$ as target. Both input and target are obtained after applying the embedding layers and the vanilla transformers, as indicated in Fig. FIGREF4, and have shape $(N_{bs}, 768, 128)$, where $N_{bs}$ is the batch size, 768 is the original BERT embedding size for a single token, and 128 is the maximum sequence length in a sentence.",
+      "The stacks of multilayer perceptrons are structured as two sets of three layers with two hidden layers each. The first set is responsible for compressing the $h_{inc}$ into a latent-space representation, extracting more abstract features into lower dimension vectors $z_1$, $z_2$ and $\\mathbf {z}$ with shape $(N_{bs}, 128, 128)$, $(N_{bs}, 32, 128)$, and $(N_{bs}, 12, 128)$, respectively. This process is shown in Eq. (DISPLAY_FORM5):",
+      "where $f(\\cdot )$ is the parameterized function mapping $h_{inc}$ to the hidden state $\\mathbf {z}$. The second set then respectively reconstructs $z_1$, $z_2$ and $\\mathbf {z}$ into $h_{rec_1}$, $h_{rec_2}$ and $h_{rec}$. This process is shown in Eq. (DISPLAY_FORM6):",
+      "where $g(\\cdot )$ is the parameterized function that reconstructs $\\mathbf {z}$ as $h_{rec}$.",
+      "The reconstructed hidden sentence embedding $h_{rec}$ is compared with the complete hidden sentence embedding $h_{comp}$ through a mean square error loss function, as shown in Eq. (DISPLAY_FORM7):",
+      "After reconstructing the correct hidden embeddings from the incomplete sentences, the correct hidden embeddings are given to bidirectional transformers to generate input representations. The model is then fine-tuned in an end-to-end manner on the incomplete text classification corpus.",
+      "Classification is done with a feedforward network and softmax activation function. Softmax $\\sigma $ is a discrete probability distribution function for $N_C$ classes, with the sum of the classes probability being 1 and the maximum value being the predicted class. The predicted class can be mathematically calculated as in Eq. (DISPLAY_FORM8):",
+      "where $o = W t + b$, the output of the feedforward layer used for classification."
+    ],
+    [
+      "In order to evaluate the performance of our model, we need access to a naturally noisy dataset with real human errors. Poor quality texts obtained from Twitter, called tweets, are then ideal for our task. For this reason, we choose Kaggle's two-class Sentiment140 dataset BIBREF18, which consists of spoken text being used in writing and without strong consideration for grammar or sentence correctness. Thus, it has many mistakes, as specified in Table TABREF11.",
+      "Even though this corpus has incorrect sentences and their emotional labels, they lack their respective corrected sentences, necessary for the training of our model. In order to obtain this missing information, we outsource native English speakers from an unbiased and anonymous platform, called Amazon Mechanical Turk (MTurk) BIBREF19, which is a paid marketplace for Human Intelligence Tasks (HITs). We use this platform to create tasks for native English speakers to format the original incorrect tweets into correct sentences. Some examples are shown in Table TABREF12.",
+      "After obtaining the correct sentences, our two-class dataset has class distribution as shown in Table TABREF14. There are 200 sentences used in the training stage, with 100 belonging to the positive sentiment class and 100 to the negative class, and 50 samples being used in the evaluation stage, with 25 negative and 25 positive. This totals in 300 samples, with incorrect and correct sentences combined. Since our goal is to evaluate the model's performance and robustness in the presence of noise, we only consider incorrect data in the testing phase. Note that BERT is a pre-trained model, meaning that small amounts of data are enough for appropriate fine-tuning."
+    ],
+    [
+      "In the intent classification task, we are presented with a corpus that suffers from the opposite problem of the Twitter sentiment classification corpus. In the intent classification corpus, we have the complete sentences and intent labels but lack their corresponding incomplete sentences, and since our task revolves around text classification in incomplete or incorrect data, it is essential that we obtain this information. To remedy this issue, we apply a Text-to-Speech (TTS) module followed by a Speech-to-Text (STT) module to the complete sentences in order to obtain incomplete sentences with STT error. Due to TTS and STT modules available being imperfect, the resulting sentences have a reasonable level of noise in the form of missing or incorrectly transcribed words. Analysis on this dataset adds value to our work by enabling evaluation of our model's robustness to different rates of data incompleteness.",
+      "The dataset used to evaluate the models' performance is the Chatbot Natural Language Unerstanding (NLU) Evaluation Corpus, introduced by Braun et al. BIBREF20 to test NLU services. It is a publicly available benchmark and is composed of sentences obtained from a German Telegram chatbot used to answer questions about public transport connections. The dataset has two intents, namely Departure Time and Find Connection with 100 train and 106 test samples, shown in Table TABREF18. Even though English is the main language of the benchmark, this dataset contains a few German station and street names.",
+      "The incomplete dataset used for training is composed of lower-cased incomplete data obtained by manipulating the original corpora. The incomplete sentences with STT error are obtained in a 2-step process shown in Fig. FIGREF22. The first step is to apply a TTS module to the available complete sentence. Here, we apply gtts , a Google Text-to-Speech python library, and macsay , a terminal command available in Mac OS as say. The second step consists of applying an STT module to the obtained audio files in order to obtain text containing STT errors. The STT module used here was witai , freely available and maintained by Wit.ai. The mentioned TTS and STT modules were chosen according to code availability and whether it's freely available or has high daily usage limitations.",
+      "Table TABREF24 exemplifies a complete and its respective incomplete sentences with different TTS-STT combinations, thus varying rates of missing and incorrect words. The level of noise in the STT imbued sentences is denoted by a inverted BLEU (iBLEU) score ranging from 0 to 1. The inverted BLEU score is denoted in Eq. (DISPLAY_FORM23):",
+      "where BLEU is a common metric usually used in machine translation tasks BIBREF21. We decide to showcase that instead of regular BLEU because it is more indicative to the amount of noise in the incomplete text, where the higher the iBLEU, the higher the noise."
+    ],
+    [
+      "Besides the already mentioned BERT, the following baseline models are also used for comparison."
+    ],
+    [
+      "We focus on the three following services, where the first two are commercial services and last one is open source with two separate backends: Google Dialogflow (formerly Api.ai) , SAP Conversational AI (formerly Recast.ai) and Rasa (spacy and tensorflow backend) ."
+    ],
+    [
+      "Shridhar et al. BIBREF12 proposed a word embedding method that doesn't suffer from out-of-vocabulary issues. The authors achieve this by using hash tokens in the alphabet instead of a single word, making it vocabulary independent. For classification, classifiers such as Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Random Forest are used. A complete list of classifiers and training specifications are given in Section SECREF31."
+    ],
+    [
+      "The baseline and proposed models are each trained 3 separate times for the incomplete intent classification task: complete data and one for each of the TTS-STT combinations (gtts-witai and macsay-witai). Regarding the sentiment classification from incorrect sentences task, the baseline and proposed models are each trained 3 times: original text, corrected text and incorrect with correct texts. The reported F1 scores are the best accuracies obtained from 10 runs."
+    ],
+    [
+      "No settable training configurations available in the online platforms."
+    ],
+    [
+      "Trained on 3-gram, feature vector size of 768 as to match the BERT embedding size, and 13 classifiers with parameters set as specified in the authors' paper so as to allow comparison: MLP with 3 hidden layers of sizes $[300, 100, 50]$ respectively; Random Forest with 50 estimators or trees; 5-fold Grid Search with Random Forest classifier and estimator $([50, 60, 70]$; Linear Support Vector Classifier with L1 and L2 penalty and tolerance of $10^{-3}$; Regularized linear classifier with Stochastic Gradient Descent (SGD) learning with regularization term $alpha=10^{-4}$ and L1, L2 and Elastic-Net penalty; Nearest Centroid with Euclidian metric, where classification is done by representing each class with a centroid; Bernoulli Naive Bayes with smoothing parameter $alpha=10^{-2}$; K-means clustering with 2 clusters and L2 penalty; and Logistic Regression classifier with L2 penalty, tolerance of $10^{-4}$ and regularization term of $1.0$. Most often, the best performing classifier was MLP."
+    ],
+    [
+      "Conventional BERT is a BERT-base-uncased model, meaning that it has 12 transformer blocks $L$, hidden size $H$ of 768, and 12 self-attention heads $A$. The model is fine-tuned with our dataset on 2 Titan X GPUs for 3 epochs with Adam Optimizer, learning rate of $2*10^{-5}$, maximum sequence length of 128, and warm up proportion of $0.1$. The train batch size is 4 for the Twitter Sentiment Corpus and 8 for the Chatbot Intent Classification Corpus."
+    ],
+    [
+      "Our proposed model is trained in end-to-end manner on 2 Titan X GPUs, with training time depending on the size of the dataset and train batch size. The stack of multilayer perceptrons are trained for 100 and 1,000 epochs with Adam Optimizer, learning rate of $10^{-3}$, weight decay of $10^{-5}$, MSE loss criterion and batch size the same as BERT (4 for the Twitter Sentiment Corpus and 8 for the Chatbot Intent Classification Corpus)."
+    ],
+    [
+      "Experimental results for the Twitter Sentiment Classification task on Kaggle's Sentiment140 Corpus dataset, displayed in Table TABREF37, show that our model has better F1-micros scores, outperforming the baseline models by 6$\\%$ to 8$\\%$. We evaluate our model and baseline models on three versions of the dataset. The first one (Inc) only considers the original data, containing naturally incorrect tweets, and achieves accuracy of 80$\\%$ against BERT's 72$\\%$. The second version (Corr) considers the corrected tweets, and shows higher accuracy given that it is less noisy. In that version, Stacked DeBERT achieves 82$\\%$ accuracy against BERT's 76$\\%$, an improvement of 6$\\%$. In the last case (Inc+Corr), we consider both incorrect and correct tweets as input to the models in hopes of improving performance. However, the accuracy was similar to the first aforementioned version, 80$\\%$ for our model and 74$\\%$ for the second highest performing model. Since the first and last corpus gave similar performances with our model, we conclude that the Twitter dataset does not require complete sentences to be given as training input, in addition to the original naturally incorrect tweets, in order to better model the noisy sentences.",
+      "In addition to the overall F1-score, we also present a confusion matrix, in Fig. FIGREF38, with the per-class F1-scores for BERT and Stacked DeBERT. The normalized confusion matrix plots the predicted labels versus the target/target labels. Similarly to Table TABREF37, we evaluate our model with the original Twitter dataset, the corrected version and both original and corrected tweets. It can be seen that our model is able to improve the overall performance by improving the accuracy of the lower performing classes. In the Inc dataset, the true class 1 in BERT performs with approximately 50%. However, Stacked DeBERT is able to improve that to 72%, although to a cost of a small decrease in performance of class 0. A similar situation happens in the remaining two datasets, with improved accuracy in class 0 from 64% to 84% and 60% to 76% respectively."
+    ],
+    [
+      "Experimental results for the Intent Classification task on the Chatbot NLU Corpus with STT error can be seen in Table TABREF40. When presented with data containing STT error, our model outperforms all baseline models in both combinations of TTS-STT: gtts-witai outperforms the second placing baseline model by 0.94% with F1-score of 97.17%, and macsay-witai outperforms the next highest achieving model by 1.89% with F1-score of 96.23%.",
+      "The table also indicates the level of noise in each dataset with the already mentioned iBLEU score, where 0 means no noise and higher values mean higher quantity of noise. As expected, the models' accuracy degrade with the increase in noise, thus F1-scores of gtts-witai are higher than macsay-witai. However, while the other models decay rapidly in the presence of noise, our model does not only outperform them but does so with a wider margin. This is shown with the increasing robustness curve in Fig. FIGREF41 and can be demonstrated by macsay-witai outperforming the baseline models by twice the gap achieved by gtts-witai.",
+      "Further analysis of the results in Table TABREF40 show that, BERT decay is almost constant with the addition of noise, with the difference between the complete data and gtts-witai being 1.88 and gtts-witai and macsay-witai being 1.89. Whereas in Stacked DeBERT, that difference is 1.89 and 0.94 respectively. This is stronger indication of our model's robustness in the presence of noise.",
+      "Additionally, we also present Fig. FIGREF42 with the normalized confusion matrices for BERT and Stacked DeBERT for sentences containing STT error. Analogously to the Twitter Sentiment Classification task, the per-class F1-scores show that our model is able to improve the overall performance by improving the accuracy of one class while maintaining the high-achieving accuracy of the second one."
+    ],
+    [
+      "In this work, we proposed a novel deep neural network, robust to noisy text in the form of sentences with missing and/or incorrect words, called Stacked DeBERT. The idea was to improve the accuracy performance by improving the representation ability of the model with the implementation of novel denoising transformers. More specifically, our model was able to reconstruct hidden embeddings from their respective incomplete hidden embeddings. Stacked DeBERT was compared against three NLU service platforms and two other machine learning methods, namely BERT and Semantic Hashing with neural classifier. Our model showed better performance when evaluated on F1 scores in both Twitter sentiment and intent text with STT error classification tasks. The per-class F1 score was also evaluated in the form of normalized confusion matrices, showing that our model was able to improve the overall performance by better balancing the accuracy of each class, trading-off small decreases in high achieving class for significant improvements in lower performing ones. In the Chatbot dataset, accuracy improvement was achieved even without trade-off, with the highest achieving classes maintaining their accuracy while the lower achieving class saw improvement. Further evaluation on the F1-scores decay in the presence of noise demonstrated that our model is more robust than the baseline models when considering noisy data, be that in the form of incorrect sentences or sentences with STT error. Not only that, experiments on the Twitter dataset also showed improved accuracy in clean data, with complete sentences. We infer that this is due to our model being able to extract richer data representations from the input data regardless of the completeness of the sentence. For future works, we plan on evaluating the robustness of our model against other types of noise, such as word reordering, word insertion, and spelling mistakes in sentences. In order to improve the performance of our model, further experiments will be done in search for more appropriate hyperparameters and more complex neural classifiers to substitute the last feedforward network layer."
+    ],
+    [
+      "This work was partly supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (2016-0-00564, Development of Intelligent Interaction Technology Based on Context Awareness and Human Intention Understanding) and Korea Evaluation Institute of Industrial Technology (KEIT) grant funded by the Korea government (MOTIE) (50%) and the Technology Innovation Program: Industrial Strategic Technology Development Program (No: 10073162) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea) (50%)."
+    ]
+  ]
+}
+```

qasper-0087/instruction.md

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+Name of Paper: Gunrock: A Social Bot for Complex and Engaging Long Conversations
+
+Question: What is the sample size of people used to measure user satisfaction?

qasper-0088/instruction.md

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+Name of Paper: Gunrock: A Social Bot for Complex and Engaging Long Conversations
+
+Question: What are all the metrics to measure user engagement?

qasper-0089/instruction.md

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+Name of Paper: Gunrock: A Social Bot for Complex and Engaging Long Conversations
+
+Question: What the system designs introduced?

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+Name of Paper: An empirical study on the effectiveness of images in Multimodal Neural Machine Translation
+
+Question: What is the baseline used?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Neural Machine Translation",
+    "Text-based NMT",
+    "Conditional GRU",
+    "Multimodal NMT",
+    "Attention-based Models",
+    "Soft attention",
+    "Hard Stochastic attention",
+    "Local Attention",
+    "Image attention optimization",
+    "Experiments",
+    "Training and model details",
+    "Quantitative results",
+    "Qualitative results",
+    "Conclusion and future work",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "In machine translation, neural networks have attracted a lot of research attention. Recently, the attention-based encoder-decoder framework BIBREF0 , BIBREF1 has been largely adopted. In this approach, Recurrent Neural Networks (RNNs) map source sequences of words to target sequences. The attention mechanism is learned to focus on different parts of the input sentence while decoding. Attention mechanisms have shown to work with other modalities too, like images, where their are able to learn to attend the salient parts of an image, for instance when generating text captions BIBREF2 . For such applications, Convolutional Neural Networks (CNNs) such as Deep Residual BIBREF3 have shown to work best to represent images.",
+      "Multimodal models of texts and images empower new applications such as visual question answering or multimodal caption translation. Also, the grounding of multiple modalities against each other may enable the model to have a better understanding of each modality individually, such as in natural language understanding applications.",
+      "In the field of Machine Translation (MT), the efficient integration of multimodal information still remains a challenging task. It requires combining diverse modality vector representations with each other. These vector representations, also called context vectors, are computed in order the capture the most relevant information in a modality to output the best translation of a sentence.",
+      "To investigate the effectiveness of information obtained from images, a multimodal machine translation shared task BIBREF4 has been addressed to the MT community. The best results of NMT model were those of BIBREF5 huang2016attention who used LSTM fed with global visual features or multiple regional visual features followed by rescoring. Recently, BIBREF6 CalixtoLC17b proposed a doubly-attentive decoder that outperformed this baseline with less data and without rescoring.",
+      "Our paper is structured as follows. In section SECREF2 , we briefly describe our NMT model as well as the conditional GRU activation used in the decoder. We also explain how multi-modalities can be implemented within this framework. In the following sections ( SECREF3 and SECREF4 ), we detail three attention mechanisms and explain how we tweak them to work as well as possible with images. Finally, we report and analyze our results in section SECREF5 then conclude in section SECREF6 ."
+    ],
+    [
+      "In this section, we detail the neural machine translation architecture by BIBREF1 BahdanauCB14, implemented as an attention-based encoder-decoder framework with recurrent neural networks (\u00a7 SECREF2 ). We follow by explaining the conditional GRU layer (\u00a7 SECREF8 ) - the gating mechanism we chose for our RNN - and how the model can be ported to a multimodal version (\u00a7 SECREF13 )."
+    ],
+    [
+      "Given a source sentence INLINEFORM0 , the neural network directly models the conditional probability INLINEFORM1 of its translation INLINEFORM2 . The network consists of one encoder and one decoder with one attention mechanism. The encoder computes a representation INLINEFORM3 for each source sentence and a decoder generates one target word at a time and by decomposing the following conditional probability : DISPLAYFORM0 ",
+      "Each source word INLINEFORM0 and target word INLINEFORM1 are a column index of the embedding matrix INLINEFORM2 and INLINEFORM3 . The encoder is a bi-directional RNN with Gated Recurrent Unit (GRU) layers BIBREF7 , BIBREF8 , where a forward RNN INLINEFORM4 reads the input sequence as it is ordered (from INLINEFORM5 to INLINEFORM6 ) and calculates a sequence of forward hidden states INLINEFORM7 . A backward RNN INLINEFORM8 reads the sequence in the reverse order (from INLINEFORM9 to INLINEFORM10 ), resulting in a sequence of backward hidden states INLINEFORM11 . We obtain an annotation for each word INLINEFORM12 by concatenating the forward and backward hidden state INLINEFORM13 . Each annotation INLINEFORM14 contains the summaries of both the preceding words and the following words. The representation INLINEFORM15 for each source sentence is the sequence of annotations INLINEFORM16 .",
+      "The decoder is an RNN that uses a conditional GRU (cGRU, more details in \u00a7 SECREF8 ) with an attention mechanism to generate a word INLINEFORM0 at each time-step INLINEFORM1 . The cGRU uses it's previous hidden state INLINEFORM2 , the whole sequence of source annotations INLINEFORM3 and the previously decoded symbol INLINEFORM4 in order to update it's hidden state INLINEFORM5 : DISPLAYFORM0 ",
+      "In the process, the cGRU also computes a time-dependent context vector INLINEFORM0 . Both INLINEFORM1 and INLINEFORM2 are further used to decode the next symbol. We use a deep output layer BIBREF9 to compute a vocabulary-sized vector : DISPLAYFORM0 ",
+      "where INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3 are model parameters. We can parameterize the probability of decoding each word INLINEFORM4 as: DISPLAYFORM0 ",
+      "The initial state of the decoder INLINEFORM0 at time-step INLINEFORM1 is initialized by the following equation : DISPLAYFORM0 ",
+      "where INLINEFORM0 is a feedforward network with one hidden layer."
+    ],
+    [
+      "The conditional GRU consists of two stacked GRU activations called INLINEFORM0 and INLINEFORM1 and an attention mechanism INLINEFORM2 in between (called ATT in the footnote paper). At each time-step INLINEFORM3 , REC1 firstly computes a hidden state proposal INLINEFORM4 based on the previous hidden state INLINEFORM5 and the previously emitted word INLINEFORM6 : DISPLAYFORM0 ",
+      " Then, the attention mechanism computes INLINEFORM0 over the source sentence using the annotations sequence INLINEFORM1 and the intermediate hidden state proposal INLINEFORM2 : DISPLAYFORM0 ",
+      "Finally, the second recurrent cell INLINEFORM0 , computes the hidden state INLINEFORM1 of the INLINEFORM2 by looking at the intermediate representation INLINEFORM3 and context vector INLINEFORM4 : DISPLAYFORM0 "
+    ],
+    [
+      "Recently, BIBREF6 CalixtoLC17b proposed a doubly attentive decoder (referred as the \"MNMT\" model in the author's paper) which can be seen as an expansion of the attention-based NMT model proposed in the previous section. Given a sequence of second a modality annotations INLINEFORM0 , we also compute a new context vector based on the same intermediate hidden state proposal INLINEFORM1 : DISPLAYFORM0 ",
+      "This new time-dependent context vector is an additional input to a modified version of REC2 which now computes the final hidden state INLINEFORM0 using the intermediate hidden state proposal INLINEFORM1 and both time-dependent context vectors INLINEFORM2 and INLINEFORM3 : DISPLAYFORM0 ",
+      " The probabilities for the next target word (from equation EQREF5 ) also takes into account the new context vector INLINEFORM0 : DISPLAYFORM0 ",
+      "where INLINEFORM0 is a new trainable parameter.",
+      "In the field of multimodal NMT, the second modality is usually an image computed into feature maps with the help of a CNN. The annotations INLINEFORM0 are spatial features (i.e. each annotation represents features for a specific region in the image) . We follow the same protocol for our experiments and describe it in section SECREF5 ."
+    ],
+    [
+      "We evaluate three models of the image attention mechanism INLINEFORM0 of equation EQREF11 . They have in common the fact that at each time step INLINEFORM1 of the decoding phase, all approaches first take as input the annotation sequence INLINEFORM2 to derive a time-dependent context vector that contain relevant information in the image to help predict the current target word INLINEFORM3 . Even though these models differ in how the time-dependent context vector is derived, they share the same subsequent steps. For each mechanism, we propose two hand-picked illustrations showing where the attention is placed in an image."
+    ],
+    [
+      "Soft attention has firstly been used for syntactic constituency parsing by BIBREF10 NIPS2015Vinyals but has been widely used for translation tasks ever since. One should note that it slightly differs from BIBREF1 BahdanauCB14 where their attention takes as input the previous decoder hidden state instead of the current (intermediate) one as shown in equation EQREF11 . This mechanism has also been successfully investigated for the task of image description generation BIBREF2 where a model generates an image's description in natural language. It has been used in multimodal translation as well BIBREF6 , for which it constitutes a state-of-the-art.",
+      "The idea of the soft attentional model is to consider all the annotations when deriving the context vector INLINEFORM0 . It consists of a single feed-forward network used to compute an expected alignment INLINEFORM1 between modality annotation INLINEFORM2 and the target word to be emitted at the current time step INLINEFORM3 . The inputs are the modality annotations and the intermediate representation of REC1 INLINEFORM4 : DISPLAYFORM0 ",
+      "The vector INLINEFORM0 has length INLINEFORM1 and its INLINEFORM2 -th item contains a score of how much attention should be put on the INLINEFORM3 -th annotation in order to output the best word at time INLINEFORM4 . We compute normalized scores to create an attention mask INLINEFORM5 over annotations: DISPLAYFORM0 ",
+      " Finally, the modality time-dependent context vector INLINEFORM0 is computed as a weighted sum over the annotation vectors (equation ). In the above expressions, INLINEFORM1 , INLINEFORM2 and INLINEFORM3 are trained parameters."
+    ],
+    [
+      "This model is a stochastic and sampling-based process where, at every timestep INLINEFORM0 , we are making a hard choice to attend only one annotation. This corresponds to one spatial location in the image. Hard attention has previously been used in the context of object recognition BIBREF11 , BIBREF12 and later extended to image description generation BIBREF2 . In the context of multimodal NMT, we can follow BIBREF2 icml2015xuc15 because both our models involve the same process on images.",
+      "The mechanism INLINEFORM0 is now a function that returns a sampled intermediate latent variables INLINEFORM1 based upon a multinouilli distribution parameterized by INLINEFORM2 : DISPLAYFORM0 ",
+      "where INLINEFORM0 an indicator one-hot variable which is set to 1 if the INLINEFORM1 -th annotation (out of INLINEFORM2 ) is the one used to compute the context vector INLINEFORM3 : DISPLAYFORM0 ",
+      " Context vector INLINEFORM0 is now seen as the random variable of this distribution. We define the variational lower bound INLINEFORM1 on the marginal log evidence INLINEFORM2 of observing the target sentence INLINEFORM3 given modality annotations INLINEFORM4 . DISPLAYFORM0 ",
+      "The learning rules can be derived by taking derivatives of the above variational free energy INLINEFORM0 with respect to the model parameter INLINEFORM1 : DISPLAYFORM0 ",
+      "In order to propagate a gradient through this process, the summation in equation EQREF26 can then be approximated using Monte Carlo based sampling defined by equation EQREF24 : DISPLAYFORM0 ",
+      "To reduce variance of the estimator in equation EQREF27 , we use a moving average baseline estimated as an accumulated sum of the previous log likelihoods with exponential decay upon seeing the INLINEFORM0 -th mini-batch: DISPLAYFORM0 "
+    ],
+    [
+      "In this section, we propose a local attentional mechanism that chooses to focus only on a small subset of the image annotations. Local Attention has been used for text-based translation BIBREF13 and is inspired by the selective attention model of BIBREF14 gregor15 for image generation. Their approach allows the model to select an image patch of varying location and zoom. Local attention uses instead the same \"zoom\" for all target positions and still achieved good performance. This model can be seen as a trade-off between the soft and hard attentional models. The model picks one patch in the annotation sequence (one spatial location) and selectively focuses on a small window of context around it. Even though an image can't be seen as a temporal sequence, we still hope that the model finds points of interest and selects the useful information around it. This approach has an advantage of being differentiable whereas the stochastic attention requires more complicated techniques such as variance reduction and reinforcement learning to train as shown in section SECREF22 . The soft attention has the drawback to attend the whole image which can be difficult to learn, especially because the number of annotations INLINEFORM0 is usually large (presumably to keep a significant spatial granularity).",
+      "More formally, at every decoding step INLINEFORM0 , the model first generates an aligned position INLINEFORM1 . Context vector INLINEFORM2 is derived as a weighted sum over the annotations within the window INLINEFORM3 where INLINEFORM4 is a fixed model parameter chosen empirically. These selected annotations correspond to a squared region in the attention maps around INLINEFORM7 . The attention mask INLINEFORM8 is of size INLINEFORM9 . The model predicts INLINEFORM10 as an aligned position in the annotation sequence (referred as Predictive alignment (local-m) in the author's paper) according to the following equation: DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 are both trainable model parameters and INLINEFORM2 is the annotation sequence length INLINEFORM3 . Because of the sigmoid, INLINEFORM4 . We use equation EQREF18 and EQREF19 respectively to compute the expected alignment vector INLINEFORM5 and the attention mask INLINEFORM6 . In addition, a Gaussian distribution centered around INLINEFORM7 is placed on the alphas in order to favor annotations near INLINEFORM8 : DISPLAYFORM0 ",
+      "where standard deviation INLINEFORM0 . We obtain context vector INLINEFORM1 by following equation ."
+    ],
+    [
+      "Three optimizations can be added to the attention mechanism regarding the image modality. All lead to a better use of the image by the model and improved the translation scores overall.",
+      "At every decoding step INLINEFORM0 , we compute a gating scalar INLINEFORM1 according to the previous decoder state INLINEFORM2 : DISPLAYFORM0 ",
+      "It is then used to compute the time-dependent image context vector : DISPLAYFORM0 ",
+      " BIBREF2 icml2015xuc15 empirically found it to put more emphasis on the objects in the image descriptions generated with their model.",
+      "We also double the output size of trainable parameters INLINEFORM0 , INLINEFORM1 and INLINEFORM2 in equation EQREF18 when it comes to compute the expected annotations over the image annotation sequence. More formally, given the image annotation sequence INLINEFORM3 , the tree matrices are of size INLINEFORM4 , INLINEFORM5 and INLINEFORM6 respectively. We noticed a better coverage of the objects in the image by the alpha weights.",
+      "Lastly, we use a grounding attention inspired by BIBREF15 delbrouck2017multimodal. The mechanism merge each spatial location INLINEFORM0 in the annotation sequence INLINEFORM1 with the initial decoder state INLINEFORM2 obtained in equation EQREF7 with non-linearity : DISPLAYFORM0 ",
+      " where INLINEFORM0 is INLINEFORM1 function. The new annotations go through a L2 normalization layer followed by two INLINEFORM2 convolutional layers (of size INLINEFORM3 respectively) to obtain INLINEFORM4 weights, one for each spatial location. We normalize the weights with a softmax to obtain a soft attention map INLINEFORM5 . Each annotation INLINEFORM6 is then weighted according to its corresponding INLINEFORM7 : DISPLAYFORM0 ",
+      " This method can be seen as the removal of unnecessary information in the image annotations according to the source sentence. This attention is used on top of the others - before decoding - and is referred as \"grounded image\" in Table TABREF41 ."
+    ],
+    [
+      "For this experiments on Multimodal Machine Translation, we used the Multi30K dataset BIBREF17 which is an extended version of the Flickr30K Entities. For each image, one of the English descriptions was selected and manually translated into German by a professional translator. As training and development data, 29,000 and 1,014 triples are used respectively. A test set of size 1000 is used for metrics evaluation."
+    ],
+    [
+      "All our models are build on top of the nematus framework BIBREF18 . The encoder is a bidirectional RNN with GRU, one 1024D single-layer forward and one 1024D single-layer backward RNN. Word embeddings for source and target language are of 620D and trained jointly with the model. Word embeddings and other non-recurrent matrices are initialized by sampling from a Gaussian INLINEFORM0 , recurrent matrices are random orthogonal and bias vectors are all initialized to zero.",
+      "To create the image annotations used by our decoder, we used a ResNet-50 pre-trained on ImageNet and extracted the features of size INLINEFORM0 at its res4f layer BIBREF3 . In our experiments, our decoder operates on the flattened 196 INLINEFORM1 1024 (i.e INLINEFORM2 ). We also apply dropout with a probability of 0.5 on the embeddings, on the hidden states in the bidirectional RNN in the encoder as well as in the decoder. In the decoder, we also apply dropout on the text annotations INLINEFORM3 , the image features INLINEFORM4 , on both modality context vector and on all components of the deep output layer before the readout operation. We apply dropout using one same mask in all time steps BIBREF19 .",
+      "We also normalize and tokenize English and German descriptions using the Moses tokenizer scripts BIBREF20 . We use the byte pair encoding algorithm on the train set to convert space-separated tokens into subwords BIBREF21 , reducing our vocabulary size to 9226 and 14957 words for English and German respectively.",
+      "All variants of our attention model were trained with ADADELTA BIBREF22 , with mini-batches of size 80 for our monomodal (text-only) NMT model and 40 for our multimodal NMT. We apply early stopping for model selection based on BLEU4 : training is halted if no improvement on the development set is observed for more than 20 epochs. We use the metrics BLEU4 BIBREF23 , METEOR BIBREF24 and TER BIBREF25 to evaluate the quality of our models' translations."
+    ],
+    [
+      "We notice a nice overall progress over BIBREF6 CalixtoLC17b multimodal baseline, especially when using the stochastic attention. With improvements of +1.51 BLEU and -2.2 TER on both precision-oriented metrics, the model shows a strong similarity of the n-grams of our candidate translations with respect to the references. The more recall-oriented metrics METEOR scores are roughly the same across our models which is expected because all attention mechanisms share the same subsequent step at every time-step INLINEFORM0 , i.e. taking into account the attention weights of previous time-step INLINEFORM1 in order to compute the new intermediate hidden state proposal and therefore the new context vector INLINEFORM2 . Again, the largest improvement is given by the hard stochastic attention mechanism (+0.4 METEOR): because it is modeled as a decision process according to the previous choices, this may reinforce the idea of recall. We also remark interesting improvements when using the grounded mechanism, especially for the soft attention. The soft attention may benefit more of the grounded image because of the wide range of spatial locations it looks at, especially compared to the stochastic attention. This motivates us to dig into more complex grounding techniques in order to give the machine a deeper understanding of the modalities.",
+      "Note that even though our baseline NMT model is basically the same as BIBREF6 CalixtoLC17b, our experiments results are slightly better. This is probably due to the different use of dropout and subwords. We also compared our results to BIBREF16 caglayan2016does because our multimodal models are nearly identical with the major exception of the gating scalar (cfr. section SECREF4 ). This motivated some of our qualitative analysis and hesitation towards the current architecture in the next section."
+    ],
+    [
+      "For space-saving and ergonomic reasons, we only discuss about the hard stochastic and soft attention, the latter being a generalization of the local attention.",
+      "As we can see in Figure FIGREF44 , the soft attention model is looking roughly at the same region of the image for every decoding step INLINEFORM0 . Because the words \"hund\"(dog), \"wald\"(forest) or \"weg\"(way) in left image are objects, they benefit from a high gating scalar. As a matter of fact, the attention mechanism has learned to detect the objects within a scene (at every time-step, whichever word we are decoding as shown in the right image) and the gating scalar has learned to decide whether or not we have to look at the picture (or more accurately whether or not we are translating an object). Without this scalar, the translation scores undergo a massive drop (as seen in BIBREF16 caglayan2016does) which means that the attention mechanisms don't really understand the more complex relationships between objects, what is really happening in the scene. Surprisingly, the gating scalar happens to be really low in the stochastic attention mechanism: a significant amount of sentences don't have a summed gating scalar INLINEFORM1 0.10. The model totally discards the image in the translation process.",
+      "It is also worth to mention that we use a ResNet trained on 1.28 million images for a classification tasks. The features used by the attention mechanism are strongly object-oriented and the machine could miss important information for a multimodal translation task. We believe that the robust architecture of both encoders INLINEFORM0 combined with a GRU layer and word-embeddings took care of the right translation for relationships between objects and time-dependencies. Yet, we noticed a common misbehavior for all our multimodal models: if the attention loose track of the objects in the picture and \"gets lost\", the model still takes it into account and somehow overrides the information brought by the text-based annotations. The translation is then totally mislead. We illustrate with an example:",
+      "The monomodal translation has a sentence-level BLEU of 82.16 whilst the soft attention and hard stochastic attention scores are of 16.82 and 34.45 respectively. Figure FIGREF47 shows the attention maps for both mechanism. Nevertheless, one has to concede that the use of images indubitably helps the translation as shown in the score tabular."
+    ],
+    [
+      "We have tried different attention mechanism and tweaks for the image modality. We showed improvements and encouraging results overall on the Flickr30K Entities dataset. Even though we identified some flaws of the current attention mechanisms, we can conclude pretty safely that images are an helpful resource for the machine in a translation task. We are looking forward to try out richer and more suitable features for multimodal translation (ie. dense captioning features). Another interesting approach would be to use visually grounded word embeddings to capture visual notions of semantic relatedness."
+    ],
+    [
+      "This work was partly supported by the Chist-Era project IGLU with contribution from the Belgian Fonds de la Recherche Scientique (FNRS), contract no. R.50.11.15.F, and by the FSO project VCYCLE with contribution from the Belgian Waloon Region, contract no. 1510501."
+    ]
+  ]
+}
+```

qasper-0101/environment/Dockerfile

@@ -0,0 +1,5 @@
+FROM ubuntu:24.04
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y python3 python3-pip && rm -rf /var/lib/apt/lists/*

qasper-0101/instruction.md

@@ -0,0 +1,120 @@
+Name of Paper: An empirical study on the effectiveness of images in Multimodal Neural Machine Translation
+
+Question: Which attention mechanisms do they compare?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Neural Machine Translation",
+    "Text-based NMT",
+    "Conditional GRU",
+    "Multimodal NMT",
+    "Attention-based Models",
+    "Soft attention",
+    "Hard Stochastic attention",
+    "Local Attention",
+    "Image attention optimization",
+    "Experiments",
+    "Training and model details",
+    "Quantitative results",
+    "Qualitative results",
+    "Conclusion and future work",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "In machine translation, neural networks have attracted a lot of research attention. Recently, the attention-based encoder-decoder framework BIBREF0 , BIBREF1 has been largely adopted. In this approach, Recurrent Neural Networks (RNNs) map source sequences of words to target sequences. The attention mechanism is learned to focus on different parts of the input sentence while decoding. Attention mechanisms have shown to work with other modalities too, like images, where their are able to learn to attend the salient parts of an image, for instance when generating text captions BIBREF2 . For such applications, Convolutional Neural Networks (CNNs) such as Deep Residual BIBREF3 have shown to work best to represent images.",
+      "Multimodal models of texts and images empower new applications such as visual question answering or multimodal caption translation. Also, the grounding of multiple modalities against each other may enable the model to have a better understanding of each modality individually, such as in natural language understanding applications.",
+      "In the field of Machine Translation (MT), the efficient integration of multimodal information still remains a challenging task. It requires combining diverse modality vector representations with each other. These vector representations, also called context vectors, are computed in order the capture the most relevant information in a modality to output the best translation of a sentence.",
+      "To investigate the effectiveness of information obtained from images, a multimodal machine translation shared task BIBREF4 has been addressed to the MT community. The best results of NMT model were those of BIBREF5 huang2016attention who used LSTM fed with global visual features or multiple regional visual features followed by rescoring. Recently, BIBREF6 CalixtoLC17b proposed a doubly-attentive decoder that outperformed this baseline with less data and without rescoring.",
+      "Our paper is structured as follows. In section SECREF2 , we briefly describe our NMT model as well as the conditional GRU activation used in the decoder. We also explain how multi-modalities can be implemented within this framework. In the following sections ( SECREF3 and SECREF4 ), we detail three attention mechanisms and explain how we tweak them to work as well as possible with images. Finally, we report and analyze our results in section SECREF5 then conclude in section SECREF6 ."
+    ],
+    [
+      "In this section, we detail the neural machine translation architecture by BIBREF1 BahdanauCB14, implemented as an attention-based encoder-decoder framework with recurrent neural networks (\u00a7 SECREF2 ). We follow by explaining the conditional GRU layer (\u00a7 SECREF8 ) - the gating mechanism we chose for our RNN - and how the model can be ported to a multimodal version (\u00a7 SECREF13 )."
+    ],
+    [
+      "Given a source sentence INLINEFORM0 , the neural network directly models the conditional probability INLINEFORM1 of its translation INLINEFORM2 . The network consists of one encoder and one decoder with one attention mechanism. The encoder computes a representation INLINEFORM3 for each source sentence and a decoder generates one target word at a time and by decomposing the following conditional probability : DISPLAYFORM0 ",
+      "Each source word INLINEFORM0 and target word INLINEFORM1 are a column index of the embedding matrix INLINEFORM2 and INLINEFORM3 . The encoder is a bi-directional RNN with Gated Recurrent Unit (GRU) layers BIBREF7 , BIBREF8 , where a forward RNN INLINEFORM4 reads the input sequence as it is ordered (from INLINEFORM5 to INLINEFORM6 ) and calculates a sequence of forward hidden states INLINEFORM7 . A backward RNN INLINEFORM8 reads the sequence in the reverse order (from INLINEFORM9 to INLINEFORM10 ), resulting in a sequence of backward hidden states INLINEFORM11 . We obtain an annotation for each word INLINEFORM12 by concatenating the forward and backward hidden state INLINEFORM13 . Each annotation INLINEFORM14 contains the summaries of both the preceding words and the following words. The representation INLINEFORM15 for each source sentence is the sequence of annotations INLINEFORM16 .",
+      "The decoder is an RNN that uses a conditional GRU (cGRU, more details in \u00a7 SECREF8 ) with an attention mechanism to generate a word INLINEFORM0 at each time-step INLINEFORM1 . The cGRU uses it's previous hidden state INLINEFORM2 , the whole sequence of source annotations INLINEFORM3 and the previously decoded symbol INLINEFORM4 in order to update it's hidden state INLINEFORM5 : DISPLAYFORM0 ",
+      "In the process, the cGRU also computes a time-dependent context vector INLINEFORM0 . Both INLINEFORM1 and INLINEFORM2 are further used to decode the next symbol. We use a deep output layer BIBREF9 to compute a vocabulary-sized vector : DISPLAYFORM0 ",
+      "where INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3 are model parameters. We can parameterize the probability of decoding each word INLINEFORM4 as: DISPLAYFORM0 ",
+      "The initial state of the decoder INLINEFORM0 at time-step INLINEFORM1 is initialized by the following equation : DISPLAYFORM0 ",
+      "where INLINEFORM0 is a feedforward network with one hidden layer."
+    ],
+    [
+      "The conditional GRU consists of two stacked GRU activations called INLINEFORM0 and INLINEFORM1 and an attention mechanism INLINEFORM2 in between (called ATT in the footnote paper). At each time-step INLINEFORM3 , REC1 firstly computes a hidden state proposal INLINEFORM4 based on the previous hidden state INLINEFORM5 and the previously emitted word INLINEFORM6 : DISPLAYFORM0 ",
+      " Then, the attention mechanism computes INLINEFORM0 over the source sentence using the annotations sequence INLINEFORM1 and the intermediate hidden state proposal INLINEFORM2 : DISPLAYFORM0 ",
+      "Finally, the second recurrent cell INLINEFORM0 , computes the hidden state INLINEFORM1 of the INLINEFORM2 by looking at the intermediate representation INLINEFORM3 and context vector INLINEFORM4 : DISPLAYFORM0 "
+    ],
+    [
+      "Recently, BIBREF6 CalixtoLC17b proposed a doubly attentive decoder (referred as the \"MNMT\" model in the author's paper) which can be seen as an expansion of the attention-based NMT model proposed in the previous section. Given a sequence of second a modality annotations INLINEFORM0 , we also compute a new context vector based on the same intermediate hidden state proposal INLINEFORM1 : DISPLAYFORM0 ",
+      "This new time-dependent context vector is an additional input to a modified version of REC2 which now computes the final hidden state INLINEFORM0 using the intermediate hidden state proposal INLINEFORM1 and both time-dependent context vectors INLINEFORM2 and INLINEFORM3 : DISPLAYFORM0 ",
+      " The probabilities for the next target word (from equation EQREF5 ) also takes into account the new context vector INLINEFORM0 : DISPLAYFORM0 ",
+      "where INLINEFORM0 is a new trainable parameter.",
+      "In the field of multimodal NMT, the second modality is usually an image computed into feature maps with the help of a CNN. The annotations INLINEFORM0 are spatial features (i.e. each annotation represents features for a specific region in the image) . We follow the same protocol for our experiments and describe it in section SECREF5 ."
+    ],
+    [
+      "We evaluate three models of the image attention mechanism INLINEFORM0 of equation EQREF11 . They have in common the fact that at each time step INLINEFORM1 of the decoding phase, all approaches first take as input the annotation sequence INLINEFORM2 to derive a time-dependent context vector that contain relevant information in the image to help predict the current target word INLINEFORM3 . Even though these models differ in how the time-dependent context vector is derived, they share the same subsequent steps. For each mechanism, we propose two hand-picked illustrations showing where the attention is placed in an image."
+    ],
+    [
+      "Soft attention has firstly been used for syntactic constituency parsing by BIBREF10 NIPS2015Vinyals but has been widely used for translation tasks ever since. One should note that it slightly differs from BIBREF1 BahdanauCB14 where their attention takes as input the previous decoder hidden state instead of the current (intermediate) one as shown in equation EQREF11 . This mechanism has also been successfully investigated for the task of image description generation BIBREF2 where a model generates an image's description in natural language. It has been used in multimodal translation as well BIBREF6 , for which it constitutes a state-of-the-art.",
+      "The idea of the soft attentional model is to consider all the annotations when deriving the context vector INLINEFORM0 . It consists of a single feed-forward network used to compute an expected alignment INLINEFORM1 between modality annotation INLINEFORM2 and the target word to be emitted at the current time step INLINEFORM3 . The inputs are the modality annotations and the intermediate representation of REC1 INLINEFORM4 : DISPLAYFORM0 ",
+      "The vector INLINEFORM0 has length INLINEFORM1 and its INLINEFORM2 -th item contains a score of how much attention should be put on the INLINEFORM3 -th annotation in order to output the best word at time INLINEFORM4 . We compute normalized scores to create an attention mask INLINEFORM5 over annotations: DISPLAYFORM0 ",
+      " Finally, the modality time-dependent context vector INLINEFORM0 is computed as a weighted sum over the annotation vectors (equation ). In the above expressions, INLINEFORM1 , INLINEFORM2 and INLINEFORM3 are trained parameters."
+    ],
+    [
+      "This model is a stochastic and sampling-based process where, at every timestep INLINEFORM0 , we are making a hard choice to attend only one annotation. This corresponds to one spatial location in the image. Hard attention has previously been used in the context of object recognition BIBREF11 , BIBREF12 and later extended to image description generation BIBREF2 . In the context of multimodal NMT, we can follow BIBREF2 icml2015xuc15 because both our models involve the same process on images.",
+      "The mechanism INLINEFORM0 is now a function that returns a sampled intermediate latent variables INLINEFORM1 based upon a multinouilli distribution parameterized by INLINEFORM2 : DISPLAYFORM0 ",
+      "where INLINEFORM0 an indicator one-hot variable which is set to 1 if the INLINEFORM1 -th annotation (out of INLINEFORM2 ) is the one used to compute the context vector INLINEFORM3 : DISPLAYFORM0 ",
+      " Context vector INLINEFORM0 is now seen as the random variable of this distribution. We define the variational lower bound INLINEFORM1 on the marginal log evidence INLINEFORM2 of observing the target sentence INLINEFORM3 given modality annotations INLINEFORM4 . DISPLAYFORM0 ",
+      "The learning rules can be derived by taking derivatives of the above variational free energy INLINEFORM0 with respect to the model parameter INLINEFORM1 : DISPLAYFORM0 ",
+      "In order to propagate a gradient through this process, the summation in equation EQREF26 can then be approximated using Monte Carlo based sampling defined by equation EQREF24 : DISPLAYFORM0 ",
+      "To reduce variance of the estimator in equation EQREF27 , we use a moving average baseline estimated as an accumulated sum of the previous log likelihoods with exponential decay upon seeing the INLINEFORM0 -th mini-batch: DISPLAYFORM0 "
+    ],
+    [
+      "In this section, we propose a local attentional mechanism that chooses to focus only on a small subset of the image annotations. Local Attention has been used for text-based translation BIBREF13 and is inspired by the selective attention model of BIBREF14 gregor15 for image generation. Their approach allows the model to select an image patch of varying location and zoom. Local attention uses instead the same \"zoom\" for all target positions and still achieved good performance. This model can be seen as a trade-off between the soft and hard attentional models. The model picks one patch in the annotation sequence (one spatial location) and selectively focuses on a small window of context around it. Even though an image can't be seen as a temporal sequence, we still hope that the model finds points of interest and selects the useful information around it. This approach has an advantage of being differentiable whereas the stochastic attention requires more complicated techniques such as variance reduction and reinforcement learning to train as shown in section SECREF22 . The soft attention has the drawback to attend the whole image which can be difficult to learn, especially because the number of annotations INLINEFORM0 is usually large (presumably to keep a significant spatial granularity).",
+      "More formally, at every decoding step INLINEFORM0 , the model first generates an aligned position INLINEFORM1 . Context vector INLINEFORM2 is derived as a weighted sum over the annotations within the window INLINEFORM3 where INLINEFORM4 is a fixed model parameter chosen empirically. These selected annotations correspond to a squared region in the attention maps around INLINEFORM7 . The attention mask INLINEFORM8 is of size INLINEFORM9 . The model predicts INLINEFORM10 as an aligned position in the annotation sequence (referred as Predictive alignment (local-m) in the author's paper) according to the following equation: DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 are both trainable model parameters and INLINEFORM2 is the annotation sequence length INLINEFORM3 . Because of the sigmoid, INLINEFORM4 . We use equation EQREF18 and EQREF19 respectively to compute the expected alignment vector INLINEFORM5 and the attention mask INLINEFORM6 . In addition, a Gaussian distribution centered around INLINEFORM7 is placed on the alphas in order to favor annotations near INLINEFORM8 : DISPLAYFORM0 ",
+      "where standard deviation INLINEFORM0 . We obtain context vector INLINEFORM1 by following equation ."
+    ],
+    [
+      "Three optimizations can be added to the attention mechanism regarding the image modality. All lead to a better use of the image by the model and improved the translation scores overall.",
+      "At every decoding step INLINEFORM0 , we compute a gating scalar INLINEFORM1 according to the previous decoder state INLINEFORM2 : DISPLAYFORM0 ",
+      "It is then used to compute the time-dependent image context vector : DISPLAYFORM0 ",
+      " BIBREF2 icml2015xuc15 empirically found it to put more emphasis on the objects in the image descriptions generated with their model.",
+      "We also double the output size of trainable parameters INLINEFORM0 , INLINEFORM1 and INLINEFORM2 in equation EQREF18 when it comes to compute the expected annotations over the image annotation sequence. More formally, given the image annotation sequence INLINEFORM3 , the tree matrices are of size INLINEFORM4 , INLINEFORM5 and INLINEFORM6 respectively. We noticed a better coverage of the objects in the image by the alpha weights.",
+      "Lastly, we use a grounding attention inspired by BIBREF15 delbrouck2017multimodal. The mechanism merge each spatial location INLINEFORM0 in the annotation sequence INLINEFORM1 with the initial decoder state INLINEFORM2 obtained in equation EQREF7 with non-linearity : DISPLAYFORM0 ",
+      " where INLINEFORM0 is INLINEFORM1 function. The new annotations go through a L2 normalization layer followed by two INLINEFORM2 convolutional layers (of size INLINEFORM3 respectively) to obtain INLINEFORM4 weights, one for each spatial location. We normalize the weights with a softmax to obtain a soft attention map INLINEFORM5 . Each annotation INLINEFORM6 is then weighted according to its corresponding INLINEFORM7 : DISPLAYFORM0 ",
+      " This method can be seen as the removal of unnecessary information in the image annotations according to the source sentence. This attention is used on top of the others - before decoding - and is referred as \"grounded image\" in Table TABREF41 ."
+    ],
+    [
+      "For this experiments on Multimodal Machine Translation, we used the Multi30K dataset BIBREF17 which is an extended version of the Flickr30K Entities. For each image, one of the English descriptions was selected and manually translated into German by a professional translator. As training and development data, 29,000 and 1,014 triples are used respectively. A test set of size 1000 is used for metrics evaluation."
+    ],
+    [
+      "All our models are build on top of the nematus framework BIBREF18 . The encoder is a bidirectional RNN with GRU, one 1024D single-layer forward and one 1024D single-layer backward RNN. Word embeddings for source and target language are of 620D and trained jointly with the model. Word embeddings and other non-recurrent matrices are initialized by sampling from a Gaussian INLINEFORM0 , recurrent matrices are random orthogonal and bias vectors are all initialized to zero.",
+      "To create the image annotations used by our decoder, we used a ResNet-50 pre-trained on ImageNet and extracted the features of size INLINEFORM0 at its res4f layer BIBREF3 . In our experiments, our decoder operates on the flattened 196 INLINEFORM1 1024 (i.e INLINEFORM2 ). We also apply dropout with a probability of 0.5 on the embeddings, on the hidden states in the bidirectional RNN in the encoder as well as in the decoder. In the decoder, we also apply dropout on the text annotations INLINEFORM3 , the image features INLINEFORM4 , on both modality context vector and on all components of the deep output layer before the readout operation. We apply dropout using one same mask in all time steps BIBREF19 .",
+      "We also normalize and tokenize English and German descriptions using the Moses tokenizer scripts BIBREF20 . We use the byte pair encoding algorithm on the train set to convert space-separated tokens into subwords BIBREF21 , reducing our vocabulary size to 9226 and 14957 words for English and German respectively.",
+      "All variants of our attention model were trained with ADADELTA BIBREF22 , with mini-batches of size 80 for our monomodal (text-only) NMT model and 40 for our multimodal NMT. We apply early stopping for model selection based on BLEU4 : training is halted if no improvement on the development set is observed for more than 20 epochs. We use the metrics BLEU4 BIBREF23 , METEOR BIBREF24 and TER BIBREF25 to evaluate the quality of our models' translations."
+    ],
+    [
+      "We notice a nice overall progress over BIBREF6 CalixtoLC17b multimodal baseline, especially when using the stochastic attention. With improvements of +1.51 BLEU and -2.2 TER on both precision-oriented metrics, the model shows a strong similarity of the n-grams of our candidate translations with respect to the references. The more recall-oriented metrics METEOR scores are roughly the same across our models which is expected because all attention mechanisms share the same subsequent step at every time-step INLINEFORM0 , i.e. taking into account the attention weights of previous time-step INLINEFORM1 in order to compute the new intermediate hidden state proposal and therefore the new context vector INLINEFORM2 . Again, the largest improvement is given by the hard stochastic attention mechanism (+0.4 METEOR): because it is modeled as a decision process according to the previous choices, this may reinforce the idea of recall. We also remark interesting improvements when using the grounded mechanism, especially for the soft attention. The soft attention may benefit more of the grounded image because of the wide range of spatial locations it looks at, especially compared to the stochastic attention. This motivates us to dig into more complex grounding techniques in order to give the machine a deeper understanding of the modalities.",
+      "Note that even though our baseline NMT model is basically the same as BIBREF6 CalixtoLC17b, our experiments results are slightly better. This is probably due to the different use of dropout and subwords. We also compared our results to BIBREF16 caglayan2016does because our multimodal models are nearly identical with the major exception of the gating scalar (cfr. section SECREF4 ). This motivated some of our qualitative analysis and hesitation towards the current architecture in the next section."
+    ],
+    [
+      "For space-saving and ergonomic reasons, we only discuss about the hard stochastic and soft attention, the latter being a generalization of the local attention.",
+      "As we can see in Figure FIGREF44 , the soft attention model is looking roughly at the same region of the image for every decoding step INLINEFORM0 . Because the words \"hund\"(dog), \"wald\"(forest) or \"weg\"(way) in left image are objects, they benefit from a high gating scalar. As a matter of fact, the attention mechanism has learned to detect the objects within a scene (at every time-step, whichever word we are decoding as shown in the right image) and the gating scalar has learned to decide whether or not we have to look at the picture (or more accurately whether or not we are translating an object). Without this scalar, the translation scores undergo a massive drop (as seen in BIBREF16 caglayan2016does) which means that the attention mechanisms don't really understand the more complex relationships between objects, what is really happening in the scene. Surprisingly, the gating scalar happens to be really low in the stochastic attention mechanism: a significant amount of sentences don't have a summed gating scalar INLINEFORM1 0.10. The model totally discards the image in the translation process.",
+      "It is also worth to mention that we use a ResNet trained on 1.28 million images for a classification tasks. The features used by the attention mechanism are strongly object-oriented and the machine could miss important information for a multimodal translation task. We believe that the robust architecture of both encoders INLINEFORM0 combined with a GRU layer and word-embeddings took care of the right translation for relationships between objects and time-dependencies. Yet, we noticed a common misbehavior for all our multimodal models: if the attention loose track of the objects in the picture and \"gets lost\", the model still takes it into account and somehow overrides the information brought by the text-based annotations. The translation is then totally mislead. We illustrate with an example:",
+      "The monomodal translation has a sentence-level BLEU of 82.16 whilst the soft attention and hard stochastic attention scores are of 16.82 and 34.45 respectively. Figure FIGREF47 shows the attention maps for both mechanism. Nevertheless, one has to concede that the use of images indubitably helps the translation as shown in the score tabular."
+    ],
+    [
+      "We have tried different attention mechanism and tweaks for the image modality. We showed improvements and encouraging results overall on the Flickr30K Entities dataset. Even though we identified some flaws of the current attention mechanisms, we can conclude pretty safely that images are an helpful resource for the machine in a translation task. We are looking forward to try out richer and more suitable features for multimodal translation (ie. dense captioning features). Another interesting approach would be to use visually grounded word embeddings to capture visual notions of semantic relatedness."
+    ],
+    [
+      "This work was partly supported by the Chist-Era project IGLU with contribution from the Belgian Fonds de la Recherche Scientique (FNRS), contract no. R.50.11.15.F, and by the FSO project VCYCLE with contribution from the Belgian Waloon Region, contract no. 1510501."
+    ]
+  ]
+}
+```

qasper-0106/environment/Dockerfile

@@ -0,0 +1,5 @@
+FROM ubuntu:24.04
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y python3 python3-pip && rm -rf /var/lib/apt/lists/*

qasper-0106/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Unsupervised Machine Commenting with Neural Variational Topic Model
+
+Question: How many articles did they have?

qasper-0107/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Unsupervised Machine Commenting with Neural Variational Topic Model
+
+Question: What news comment dataset was used?

qasper-0108/environment/Dockerfile

@@ -0,0 +1,5 @@
+FROM ubuntu:24.04
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y python3 python3-pip && rm -rf /var/lib/apt/lists/*

qasper-0108/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Enriching BERT with Knowledge Graph Embeddings for Document Classification
+
+Question: By how much do they outperform standard BERT?

qasper-0109/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Enriching BERT with Knowledge Graph Embeddings for Document Classification
+
+Question: What dataset do they use?

qasper-0130/environment/Dockerfile

@@ -0,0 +1,5 @@
+FROM ubuntu:24.04
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y python3 python3-pip && rm -rf /var/lib/apt/lists/*

qasper-0130/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Improving Spoken Language Understanding By Exploiting ASR N-best Hypotheses
+
+Question: What are the series of simple models?

qasper-0131/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Improving Spoken Language Understanding By Exploiting ASR N-best Hypotheses
+
+Question: Over which datasets/corpora is this work evaluated?

qasper-0136/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Learning Word Embeddings from the Portuguese Twitter Stream: A Study of some Practical Aspects
+
+Question: What intrinsic evaluation metrics are used?

qasper-0137/environment/Dockerfile

@@ -0,0 +1,5 @@
+FROM ubuntu:24.04
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y python3 python3-pip && rm -rf /var/lib/apt/lists/*

qasper-0137/instruction.md

@@ -0,0 +1,99 @@
+Name of Paper: Learning Word Embeddings from the Portuguese Twitter Stream: A Study of some Practical Aspects
+
+Question: What experimental results suggest that using less than 50% of the available training examples might result in overfitting?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Our Neural Word Embedding Model",
+    "Experimental Setup",
+    "Training Data",
+    "Metrics related with the Learning Process",
+    "Tests and Gold-Standard Data for Intrinsic Evaluation",
+    "Results and Analysis",
+    "Intrinsic Evaluation",
+    "Further Analysis regarding Evaluation Metrics",
+    "Conclusions"
+  ],
+  "paragraphs": [
+    [
+      "Word embeddings have great practical importance since they can be used as pre-computed high-density features to ML models, significantly reducing the amount of training data required in a variety of NLP tasks. However, there are several inter-related challenges with computing and consistently distributing word embeddings concerning the:",
+      "Not only the space of possibilities for each of these aspects is large, there are also challenges in performing a consistent large-scale evaluation of the resulting embeddings BIBREF0 . This makes systematic experimentation of alternative word-embedding configurations extremely difficult.",
+      "In this work, we make progress in trying to find good combinations of some of the previous parameters. We focus specifically in the task of computing word embeddings for processing the Portuguese Twitter stream. User-generated content (such as twitter messages) tends to be populated by words that are specific to the medium, and that are constantly being added by users. These dynamics pose challenges to NLP systems, which have difficulties in dealing with out of vocabulary words. Therefore, learning a semantic representation for those words directly from the user-generated stream - and as the words arise - would allow us to keep up with the dynamics of the medium and reduce the cases for which we have no information about the words.",
+      "Starting from our own implementation of a neural word embedding model, which should be seen as a flexible baseline model for further experimentation, our research tries to answer the following practical questions:",
+      "By answering these questions based on a reasonably small sample of Twitter data (5M), we hope to find the best way to proceed and train embeddings for Twitter vocabulary using the much larger amount of Twitter data available (300M), but for which parameter experimentation would be unfeasible. This work can thus be seen as a preparatory study for a subsequent attempt to produce and distribute a large-scale database of embeddings for processing Portuguese Twitter data."
+    ],
+    [
+      "There are several approaches to generating word embeddings. One can build models that explicitly aim at generating word embeddings, such as Word2Vec or GloVe BIBREF1 , BIBREF2 , or one can extract such embeddings as by-products of more general models, which implicitly compute such word embeddings in the process of solving other language tasks.",
+      "Word embeddings methods aim to represent words as real valued continuous vectors in a much lower dimensional space when compared to traditional bag-of-words models. Moreover, this low dimensional space is able to capture lexical and semantic properties of words. Co-occurrence statistics are the fundamental information that allows creating such representations. Two approaches exist for building word embeddings. One creates a low rank approximation of the word co-occurrence matrix, such as in the case of Latent Semantic Analysis BIBREF3 and GloVe BIBREF2 . The other approach consists in extracting internal representations from neural network models of text BIBREF4 , BIBREF5 , BIBREF1 . Levy and Goldberg BIBREF6 showed that the two approaches are closely related.",
+      "Although, word embeddings research go back several decades, it was the recent developments of Deep Learning and the word2vec framework BIBREF1 that captured the attention of the NLP community. Moreover, Mikolov et al. BIBREF7 showed that embeddings trained using word2vec models (CBOW and Skip-gram) exhibit linear structure, allowing analogy questions of the form \u201cman:woman::king:??.\u201d and can boost performance of several text classification tasks.",
+      "One of the issues of recent work in training word embeddings is the variability of experimental setups reported. For instance, in the paper describing GloVe BIBREF2 authors trained their model on five corpora of different sizes and built a vocabulary of 400K most frequent words. Mikolov et al. BIBREF7 trained with 82K vocabulary while Mikolov et al. BIBREF1 was trained with 3M vocabulary. Recently, Arora et al. BIBREF8 proposed a generative model for learning embeddings that tries to explain some theoretical justification for nonlinear models (e.g. word2vec and GloVe) and some hyper parameter choices. Authors evaluated their model using 68K vocabulary.",
+      "SemEval 2016-Task 4: Sentiment Analysis in Twitter organizers report that participants either used general purpose pre-trained word embeddings, or trained from Tweet 2016 dataset or \u201cfrom some sort of dataset\u201d BIBREF9 . However, participants neither report the size of vocabulary used neither the possible effect it might have on the task specific results.",
+      "Recently, Rodrigues et al. BIBREF10 created and distributed the first general purpose embeddings for Portuguese. Word2vec gensim implementation was used and authors report results with different values for the parameters of the framework. Furthermore, authors used experts to translate well established word embeddings test sets for Portuguese language, which they also made publicly available and we use some of those in this work."
+    ],
+    [
+      "The neural word embedding model we use in our experiments is heavily inspired in the one described in BIBREF4 , but ours is one layer deeper and is set to solve a slightly different word prediction task. Given a sequence of 5 words - INLINEFORM0 INLINEFORM1 INLINEFORM2 INLINEFORM3 INLINEFORM4 , the task the model tries to perform is that of predicting the middle word, INLINEFORM5 , based on the two words on the left - INLINEFORM6 INLINEFORM7 - and the two words on the right - INLINEFORM8 INLINEFORM9 : INLINEFORM10 . This should produce embeddings that closely capture distributional similarity, so that words that belong to the same semantic class, or which are synonyms and antonyms of each other, will be embedded in \u201cclose\u201d regions of the embedding hyper-space.",
+      "Our neural model is composed of the following layers:",
+      "All neural activations in the model are sigmoid functions. The model was implemented using the Syntagma library which relies on Keras BIBREF11 for model development, and we train the model using the built-in ADAM BIBREF12 optimizer with the default parameters."
+    ],
+    [
+      "We are interested in assessing two aspects of the word embedding process. On one hand, we wish to evaluate the semantic quality of the produced embeddings. On the other, we want to quantify how much computational power and training data are required to train the embedding model as a function of the size of the vocabulary INLINEFORM0 we try to embed. These aspects have fundamental practical importance for deciding how we should attempt to produce the large-scale database of embeddings we will provide in the future. All resources developed in this work are publicly available.",
+      "Apart from the size of the vocabulary to be processed ( INLINEFORM0 ), the hyperparamaters of the model that we could potentially explore are i) the dimensionality of the input word embeddings and ii) the dimensionality of the output word embeddings. As mentioned before, we set both to 64 bits after performing some quick manual experimentation. Full hyperparameter exploration is left for future work.",
+      "Our experimental testbed comprises a desktop with a nvidia TITAN X (Pascal), Intel Core Quad i7 3770K 3.5Ghz, 32 GB DDR3 RAM and a 180GB SSD drive."
+    ],
+    [
+      "We randomly sampled 5M tweets from a corpus of 300M tweets collected from the Portuguese Twitter community BIBREF13 . The 5M comprise a total of 61.4M words (approx. 12 words per tweets in average). From those 5M tweets we generated a database containing 18.9M distinct 5-grams, along with their frequency counts. In this process, all text was down-cased. To help anonymizing the n-gram information, we substituted all the twitter handles by an artificial token \u201cT_HANDLE\". We also substituted all HTTP links by the token \u201cLINK\". We prepended two special tokens to complete the 5-grams generated from the first two words of the tweet, and we correspondingly appended two other special tokens to complete 5-grams centered around the two last tokens of the tweet.",
+      "Tokenization was perform by trivially separating tokens by blank space. No linguistic pre-processing, such as for example separating punctuation from words, was made. We opted for not doing any pre-processing for not introducing any linguistic bias from another tool (tokenization of user generated content is not a trivial problem). The most direct consequence of not performing any linguistic pre-processing is that of increasing the vocabulary size and diluting token counts. However, in principle, and given enough data, the embedding model should be able to learn the correct embeddings for both actual words (e.g. \u201cronaldo\") and the words that have punctuation attached (e.g. \u201cronaldo!\"). In practice, we believe that this can actually be an advantage for the downstream consumers of the embeddings, since they can also relax the requirements of their own tokenization stage. Overall, the dictionary thus produced contains approximately 1.3M distinct entries. Our dictionary was sorted by frequency, so the words with lowest index correspond to the most common words in the corpus.",
+      "We used the information from the 5-gram database to generate all training data used in the experiments. For a fixed size INLINEFORM0 of the target vocabulary to be embedded (e.g. INLINEFORM1 = 2048), we scanned the database to obtain all possible 5-grams for which all tokens were among the top INLINEFORM2 words of the dictionary (i.e. the top INLINEFORM3 most frequent words in the corpus). Depending on INLINEFORM4 , different numbers of valid training 5-grams were found in the database: the larger INLINEFORM5 the more valid 5-grams would pass the filter. The number of examples collected for each of the values of INLINEFORM6 is shown in Table TABREF16 .",
+      "Since one of the goals of our experiments is to understand the impact of using different amounts of training data, for each size of vocabulary to be embedded INLINEFORM0 we will run experiments training the models using 25%, 50%, 75% and 100% of the data available."
+    ],
+    [
+      "We tracked metrics related to the learning process itself, as a function of the vocabulary size to be embedded INLINEFORM0 and of the fraction of training data used (25%, 50%, 75% and 100%). For all possible configurations, we recorded the values of the training and validation loss (cross entropy) after each epoch. Tracking these metrics serves as a minimalistic sanity check: if the model is not able to solve the word prediction task with some degree of success (e.g. if we observe no substantial decay in the losses) then one should not expect the embeddings to capture any of the distributional information they are supposed to capture."
+    ],
+    [
+      "Using the gold standard data (described below), we performed three types of tests:",
+      "Class Membership Tests: embeddings corresponding two member of the same semantic class (e.g. \u201cMonths of the Year\", \u201cPortuguese Cities\", \u201cSmileys\") should be close, since they are supposed to be found in mostly the same contexts.",
+      "Class Distinction Test: this is the reciprocal of the previous Class Membership test. Embeddings of elements of different classes should be different, since words of different classes ere expected to be found in significantly different contexts.",
+      "Word Equivalence Test: embeddings corresponding to synonyms, antonyms, abbreviations (e.g. \u201cporque\" abbreviated by \u201cpq\") and partial references (e.g. \u201cslb and benfica\") should be almost equal, since both alternatives are supposed to be used be interchangeable in all contexts (either maintaining or inverting the meaning).",
+      "Therefore, in our tests, two words are considered:",
+      "distinct if the cosine of the corresponding embeddings is lower than 0.70 (or 0.80).",
+      "to belong to the same class if the cosine of their embeddings is higher than 0.70 (or 0.80).",
+      "equivalent if the cosine of the embeddings is higher that 0.85 (or 0.95).",
+      "We report results using different thresholds of cosine similarity as we noticed that cosine similarity is skewed to higher values in the embedding space, as observed in related work BIBREF14 , BIBREF15 . We used the following sources of data for testing Class Membership:",
+      "AP+Battig data. This data was collected from the evaluation data provided by BIBREF10 . These correspond to 29 semantic classes.",
+      "Twitter-Class - collected manually by the authors by checking top most frequent words in the dictionary and then expanding the classes. These include the following 6 sets (number of elements in brackets): smileys (13), months (12), countries (6), names (19), surnames (14) Portuguese cities (9).",
+      "For the Class Distinction test, we pair each element of each of the gold standard classes, with all the other elements from other classes (removing duplicate pairs since ordering does not matter), and we generate pairs of words which are supposed belong to different classes. For Word Equivalence test, we manually collected equivalente pairs, focusing on abbreviations that are popular in Twitters (e.g. \u201cqt\" INLINEFORM0 \u201cquanto\" or \u201clx\" INLINEFORM1 \u201clisboa\" and on frequent acronyms (e.g. \u201cslb\" INLINEFORM2 \u201cbenfica\"). In total, we compiled 48 equivalence pairs.",
+      "For all these tests we computed a coverage metric. Our embeddings do not necessarily contain information for all the words contained in each of these tests. So, for all tests, we compute a coverage metric that measures the fraction of the gold-standard pairs that could actually be tested using the different embeddings produced. Then, for all the test pairs actually covered, we obtain the success metrics for each of the 3 tests by computing the ratio of pairs we were able to correctly classified as i) being distinct (cosine INLINEFORM0 0.7 or 0.8), ii) belonging to the same class (cosine INLINEFORM1 0.7 or 0.8), and iii) being equivalent (cosine INLINEFORM2 0.85 or 0.95).",
+      "It is worth making a final comment about the gold standard data. Although we do not expect this gold standard data to be sufficient for a wide-spectrum evaluation of the resulting embeddings, it should be enough for providing us clues regarding areas where the embedding process is capturing enough semantics, and where it is not. These should still provide valuable indications for planning how to produce the much larger database of word embeddings."
+    ],
+    [
+      "We run the training process and performed the corresponding evaluation for 12 combinations of size of vocabulary to be embedded, and the volume of training data available that has been used. Table TABREF27 presents some overall statistics after training for 40 epochs.",
+      "The average time per epoch increases first with the size of the vocabulary to embed INLINEFORM0 (because the model will have more parameters), and then, for each INLINEFORM1 , with the volume of training data. Using our testbed (Section SECREF4 ), the total time of learning in our experiments varied from a minimum of 160 seconds, with INLINEFORM2 = 2048 and 25% of data, to a maximum of 22.5 hours, with INLINEFORM3 = 32768 and using 100% of the training data available (extracted from 5M tweets). These numbers give us an approximate figure of how time consuming it would be to train embeddings from the complete Twitter corpus we have, consisting of 300M tweets.",
+      "We now analyze the learning process itself. We plot the training set loss and validation set loss for the different values of INLINEFORM0 (Figure FIGREF28 left) with 40 epochs and using all the available data. As expected, the loss is reducing after each epoch, with validation loss, although being slightly higher, following the same trend. When using 100% we see no model overfitting. We can also observe that the higher is INLINEFORM1 the higher are the absolute values of the loss sets. This is not surprising because as the number of words to predict becomes higher the problem will tend to become harder. Also, because we keep the dimensionality of the embedding space constant (64 dimensions), it becomes increasingly hard to represent and differentiate larger vocabularies in the same hyper-volume. We believe this is a specially valuable indication for future experiments and for deciding the dimensionality of the final embeddings to distribute.",
+      "On the right side of Figure FIGREF28 we show how the number of training (and validation) examples affects the loss. For a fixed INLINEFORM0 = 32768 we varied the amount of data used for training from 25% to 100%. Three trends are apparent. As we train with more data, we obtain better validation losses. This was expected. The second trend is that by using less than 50% of the data available the model tends to overfit the data, as indicated by the consistent increase in the validation loss after about 15 epochs (check dashed lines in right side of Figure FIGREF28 ). This suggests that for the future we should not try any drastic reduction of the training data to save training time. Finally, when not overfitting, the validation loss seems to stabilize after around 20 epochs. We observed no phase-transition effects (the model seems simple enough for not showing that type of behavior). This indicates we have a practical way of safely deciding when to stop training the model."
+    ],
+    [
+      "Table TABREF30 presents results for the three different tests described in Section SECREF4 . The first (expected) result is that the coverage metrics increase with the size of the vocabulary being embedded, i.e., INLINEFORM0 . Because the Word Equivalence test set was specifically created for evaluating Twitter-based embedding, when embedding INLINEFORM1 = 32768 words we achieve almost 90% test coverage. On the other hand, for the Class Distinction test set - which was created by doing the cross product of the test cases of each class in Class Membership test set - we obtain very low coverage figures. This indicates that it is not always possible to re-use previously compiled gold-standard data, and that it will be important to compile gold-standard data directly from Twitter content if we want to perform a more precise evaluation.",
+      "The effect of varying the cosine similarity decision threshold from 0.70 to 0.80 for Class Membership test shows that the percentage of classified as correct test cases drops significantly. However, the drop is more accentuated when training with only a portion of the available data. The differences of using two alternative thresholds values is even higher in the Word Equivalence test.",
+      "The Word Equivalence test, in which we consider two words equivalent word if the cosine of the embedding vectors is higher than 0.95, revealed to be an extremely demanding test. Nevertheless, for INLINEFORM0 = 32768 the results are far superior, and for a much larger coverage, than for lower INLINEFORM1 . The same happens with the Class Membership test.",
+      "On the other hand, the Class Distinction test shows a different trend for larger values of INLINEFORM0 = 32768 but the coverage for other values of INLINEFORM1 is so low that becomes difficult to hypothesize about the reduced values of True Negatives (TN) percentage obtained for the largest INLINEFORM2 . It would be necessary to confirm this behavior with even larger values of INLINEFORM3 . One might hypothesize that the ability to distinguish between classes requires larger thresholds when INLINEFORM4 is large. Also, we can speculate about the need of increasing the number of dimensions to be able to encapsulate different semantic information for so many words."
+    ],
+    [
+      "Despite already providing interesting practical clues for our goal of trying to embed a larger vocabulary using more of the training data we have available, these results also revealed that the intrinsic evaluation metrics we are using are overly sensitive to their corresponding cosine similarity thresholds. This sensitivity poses serious challenges for further systematic exploration of word embedding architectures and their corresponding hyper-parameters, which was also observed in other recent works BIBREF15 .",
+      "By using these absolute thresholds as criteria for deciding similarity of words, we create a dependency between the evaluation metrics and the geometry of the embedded data. If we see the embedding data as a graph, this means that metrics will change if we apply scaling operations to certain parts of the graph, even if its structure (i.e. relative position of the embedded words) does not change.",
+      "For most practical purposes (including training downstream ML models) absolute distances have little meaning. What is fundamental is that the resulting embeddings are able to capture topological information: similar words should be closer to each other than they are to words that are dissimilar to them (under the various criteria of similarity we care about), independently of the absolute distances involved.",
+      "It is now clear that a key aspect for future work will be developing additional performance metrics based on topological properties. We are in line with recent work BIBREF16 , proposing to shift evaluation from absolute values to more exploratory evaluations focusing on weaknesses and strengths of the embeddings and not so much in generic scores. For example, one metric could consist in checking whether for any given word, all words that are known to belong to the same class are closer than any words belonging to different classes, independently of the actual cosine. Future work will necessarily include developing this type of metrics."
+    ],
+    [
+      "Producing word embeddings from tweets is challenging due to the specificities of the vocabulary in the medium. We implemented a neural word embedding model that embeds words based on n-gram information extracted from a sample of the Portuguese Twitter stream, and which can be seen as a flexible baseline for further experiments in the field. Work reported in this paper is a preliminary study of trying to find parameters for training word embeddings from Twitter and adequate evaluation tests and gold-standard data.",
+      "Results show that using less than 50% of the available training examples for each vocabulary size might result in overfitting. The resulting embeddings obtain an interesting performance on intrinsic evaluation tests when trained a vocabulary containing the 32768 most frequent words in a Twitter sample of relatively small size. Nevertheless, results exhibit a skewness in the cosine similarity scores that should be further explored in future work. More specifically, the Class Distinction test set revealed to be challenging and opens the door to evaluation of not only similarity between words but also dissimilarities between words of different semantic classes without using absolute score values.",
+      "Therefore, a key area of future exploration has to do with better evaluation resources and metrics. We made some initial effort in this front. However, we believe that developing new intrinsic tests, agnostic to absolute values of metrics and concerned with topological aspects of the embedding space, and expanding gold-standard data with cases tailored for user-generated content, is of fundamental importance for the progress of this line of work.",
+      "Furthermore, we plan to make public available word embeddings trained from a large sample of 300M tweets collected from the Portuguese Twitter stream. This will require experimenting producing embeddings with higher dimensionality (to avoid the cosine skewness effect) and training with even larger vocabularies. Also, there is room for experimenting with some of the hyper-parameters of the model itself (e.g. activation functions, dimensions of the layers), which we know have impact on final results."
+    ]
+  ]
+}
+```

qasper-0138/instruction.md

@@ -0,0 +1,110 @@
+Name of Paper: Procedural Reasoning Networks for Understanding Multimodal Procedures
+
+Question: What multimodality is available in the dataset?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Visual Reasoning in RecipeQA",
+    "Procedural Reasoning Networks",
+    "Procedural Reasoning Networks ::: Input Module",
+    "Procedural Reasoning Networks ::: Reasoning Module",
+    "Procedural Reasoning Networks ::: Attention Module",
+    "Procedural Reasoning Networks ::: Modeling Module",
+    "Procedural Reasoning Networks ::: Output Module",
+    "Experiments",
+    "Experiments ::: Entity Extraction",
+    "Experiments ::: Training Details",
+    "Experiments ::: Baselines",
+    "Experiments ::: Results",
+    "Related Work",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "A great deal of commonsense knowledge about the world we live is procedural in nature and involves steps that show ways to achieve specific goals. Understanding and reasoning about procedural texts (e.g. cooking recipes, how-to guides, scientific processes) are very hard for machines as it demands modeling the intrinsic dynamics of the procedures BIBREF0, BIBREF1, BIBREF2. That is, one must be aware of the entities present in the text, infer relations among them and even anticipate changes in the states of the entities after each action. For example, consider the cheeseburger recipe presented in Fig. FIGREF2. The instruction \u201csalt and pepper each patty and cook for 2 to 3 minutes on the first side\u201d in Step 5 entails mixing three basic ingredients, the ground beef, salt and pepper, together and then applying heat to the mix, which in turn causes chemical changes that alter both the appearance and the taste. From a natural language understanding perspective, the main difficulty arises when a model sees the word patty again at a later stage of the recipe. It still corresponds to the same entity, but its form is totally different.",
+      "Over the past few years, many new datasets and approaches have been proposed that address this inherently hard problem BIBREF0, BIBREF1, BIBREF3, BIBREF4. To mitigate the aforementioned challenges, the existing works rely mostly on heavy supervision and focus on predicting the individual state changes of entities at each step. Although these models can accurately learn to make local predictions, they may lack global consistency BIBREF3, BIBREF4, not to mention that building such annotated corpora is very labor-intensive. In this work, we take a different direction and explore the problem from a multimodal standpoint. Our basic motivation, as illustrated in Fig. FIGREF2, is that accompanying images provide complementary cues about causal effects and state changes. For instance, it is quite easy to distinguish raw meat from cooked one in visual domain.",
+      "In particular, we take advantage of recently proposed RecipeQA dataset BIBREF2, a dataset for multimodal comprehension of cooking recipes, and ask whether it is possible to have a model which employs dynamic representations of entities in answering questions that require multimodal understanding of procedures. To this end, inspired from BIBREF5, we propose Procedural Reasoning Networks (PRN) that incorporates entities into the comprehension process and allows to keep track of entities, understand their interactions and accordingly update their states across time. We report that our proposed approach significantly improves upon previously published results on visual reasoning tasks in RecipeQA, which test understanding causal and temporal relations from images and text. We further show that the dynamic entity representations can capture semantics of the state information in the corresponding steps."
+    ],
+    [
+      "In our study, we particularly focus on the visual reasoning tasks of RecipeQA, namely visual cloze, visual coherence, and visual ordering tasks, each of which examines a different reasoning skill. We briefly describe these tasks below.",
+      "Visual Cloze. In the visual cloze task, the question is formed by a sequence of four images from consecutive steps of a recipe where one of them is replaced by a placeholder. A model should select the correct one from a multiple-choice list of four answer candidates to fill in the missing piece. In that regard, the task inherently requires aligning visual and textual information and understanding temporal relationships between the cooking actions and the entities.",
+      "Visual Coherence. The visual coherence task tests the ability to identify the image within a sequence of four images that is inconsistent with the text instructions of a cooking recipe. To succeed in this task, a model should have a clear understanding of the procedure described in the recipe and at the same time connect language and vision.",
+      "Visual Ordering. The visual ordering task is about grasping the temporal flow of visual events with the help of the given recipe text. The questions show a set of four images from the recipe and the task is to sort jumbled images into the correct order. Here, a model needs to infer the temporal relations between the images and align them with the recipe steps."
+    ],
+    [
+      "In the following, we explain our Procedural Reasoning Networks model. Its architecture is based on a bi-directional attention flow (BiDAF) model BIBREF6, but also equipped with an explicit reasoning module that acts on entity-specific relational memory units. Fig. FIGREF4 shows an overview of the network architecture. It consists of five main modules: An input module, an attention module, a reasoning module, a modeling module, and an output module. Note that the question answering tasks we consider here are multimodal in that while the context is a procedural text, the question and the multiple choice answers are composed of images.",
+      "Input Module extracts vector representations of inputs at different levels of granularity by using several different encoders.",
+      "Reasoning Module scans the procedural text and tracks the states of the entities and their relations through a recurrent relational memory core unit BIBREF5.",
+      "Attention Module computes context-aware query vectors and query-aware context vectors as well as query-aware memory vectors.",
+      "Modeling Module employs two multi-layered RNNs to encode previous layers outputs.",
+      "Output Module scores a candidate answer from the given multiple-choice list.",
+      "At a high level, as the model is reading the cooking recipe, it continually updates the internal memory representations of the entities (ingredients) based on the content of each step \u2013 it keeps track of changes in the states of the entities, providing an entity-centric summary of the recipe. The response to a question and a possible answer depends on the representation of the recipe text as well as the last states of the entities. All this happens in a series of implicit relational reasoning steps and there is no need for explicitly encoding the state in terms of a predefined vocabulary."
+    ],
+    [
+      "Let the triple $(\\mathbf {R},\\mathbf {Q},\\mathbf {A})$ be a sample input. Here, $\\mathbf {R}$ denotes the input recipe which contains textual instructions composed of $N$ words in total. $\\mathbf {Q}$ represents the question that consists of a sequence of $M$ images. $\\mathbf {A}$ denotes an answer that is either a single image or a series of $L$ images depending on the reasoning task. In particular, for the visual cloze and the visual coherence type questions, the answer contains a single image ($L=1$) and for the visual ordering task, it includes a sequence.",
+      "We encode the input recipe $\\mathbf {R}$ at character, word, and step levels. Character-level embedding layer uses a convolutional neural network, namely CharCNN model by BIBREF7, which outputs character level embeddings for each word and alleviates the issue of out-of-vocabulary (OOV) words. In word embedding layer, we use a pretrained GloVe model BIBREF8 and extract word-level embeddings. The concatenation of the character and the word embeddings are then fed to a two-layer highway network BIBREF10 to obtain a contextual embedding for each word in the recipe. This results in the matrix $\\mathbf {R}^{\\prime } \\in \\mathbb {R}^{2d \\times N}$.",
+      "On top of these layers, we have another layer that encodes the steps of the recipe in an individual manner. Specifically, we obtain a step-level contextual embedding of the input recipe containing $T$ steps as $\\mathcal {S}=(\\mathbf {s}_1,\\mathbf {s}_2,\\dots ,\\mathbf {s}_T)$ where $\\mathbf {s}_i$ represents the final state of a BiLSTM encoding the $i$-th step of the recipe obtained from the character and word-level embeddings of the tokens exist in the corresponding step.",
+      "We represent both the question $\\mathbf {Q}$ and the answer $\\mathbf {A}$ in terms of visual embeddings. Here, we employ a pretrained ResNet-50 model BIBREF11 trained on ImageNet dataset BIBREF12 and represent each image as a real-valued 2048-d vector using features from the penultimate average-pool layer. Then these embeddings are passed first to a multilayer perceptron (MLP) and then its outputs are fed to a BiLSTM. We then form a matrix $\\mathbf {Q}^{\\prime } \\in \\mathbb {R}^{2d \\times M}$ for the question by concatenating the cell states of the BiLSTM. For the visual ordering task, to represent the sequence of images in the answer with a single vector, we additionally use a BiLSTM and define the answering embedding by the summation of the cell states of the BiLSTM. Finally, for all tasks, these computations produce answer embeddings denoted by $\\mathbf {a} \\in \\mathbb {R}^{2d \\times 1}$."
+    ],
+    [
+      "As mentioned before, comprehending a cooking recipe is mostly about entities (basic ingredients) and actions (cooking activities) described in the recipe instructions. Each action leads to changes in the states of the entities, which usually affects their visual characteristics. A change rarely occurs in isolation; in most cases, the action affects multiple entities at once. Hence, in our reasoning module, we have an explicit memory component implemented with relational memory units BIBREF5. This helps us to keep track of the entities, their state changes and their relations in relation to each other over the course of the recipe (see Fig. FIGREF14). As we will examine in more detail in Section SECREF4, it also greatly improves the interpretability of model outputs.",
+      "Specifically, we set up the memory with a memory matrix $\\mathbf {E} \\in \\mathbb {R}^{d_E \\times K}$ by extracting $K$ entities (ingredients) from the first step of the recipe. We initialize each memory cell $\\mathbf {e}_i$ representing a specific entity by its CharCNN and pre-trained GloVe embeddings. From now on, we will use the terms memory cells and entities interchangeably throughout the paper. Since the input recipe is given in the form of a procedural text decomposed into a number of steps, we update the memory cells after each step, reflecting the state changes happened on the entities. This update procedure is modelled via a relational recurrent neural network (R-RNN), recently proposed by BIBREF5. It is built on a 2-dimensional LSTM model whose matrix of cell states represent our memory matrix $\\mathbf {E}$. Here, each row $i$ of the matrix $\\mathbf {E}$ refers to a specific entity $\\mathbf {e}_i$ and is updated after each recipe step $t$ as follows:",
+      "where $\\mathbf {s}_{t}$ denotes the embedding of recipe step $t$ and $\\mathbf {\\phi }_{i,t}=(\\mathbf {h}_{i,t},\\mathbf {e}_{i,t})$ is the cell state of the R-RNN at step $t$ with $\\mathbf {h}_{i,t}$ and $\\mathbf {e}_{i,t}$ being the $i$-th row of the hidden state of the R-RNN and the dynamic representation of entity $\\mathbf {e}_{i}$ at the step $t$, respectively. The R-RNN model exploits a multi-headed self-attention mechanism BIBREF13 that allows memory cells to interact with each other and attend multiple locations simultaneously during the update phase.",
+      "In Fig. FIGREF14, we illustrate how this interaction takes place in our relational memory module by considering a sample cooking recipe and by presenting how the attention matrix changes throughout the recipe. In particular, the attention matrix at a specific time shows the attention flow from one entity (memory cell) to another along with the attention weights to the corresponding recipe step (offset column). The color intensity shows the magnitude of the attention weights. As can be seen from the figure, the internal representations of the entities are actively updated at each step. Moreover, as argued in BIBREF5, this can be interpreted as a form of relational reasoning as each update on a specific memory cell is operated in relation to others. Here, we should note that it is often difficult to make sense of these attention weights. However, we observe that the attention matrix changes very gradually near the completion of the recipe."
+    ],
+    [
+      "Attention module is in charge of linking the question with the recipe text and the entities present in the recipe. It takes the matrices $\\mathbf {Q^{\\prime }}$ and $\\mathbf {R}^{\\prime }$ from the input module, and $\\mathbf {E}$ from the reasoning module and constructs the question-aware recipe representation $\\mathbf {G}$ and the question-aware entity representation $\\mathbf {Y}$. Following the attention flow mechanism described in BIBREF14, we specifically calculate attentions in four different directions: (1) from question to recipe, (2) from recipe to question, (3) from question to entities, and (4) from entities to question.",
+      "The first two of these attentions require computing a shared affinity matrix $\\mathbf {S}^R \\in \\mathbb {R}^{N \\times M}$ with $\\mathbf {S}^R_{i,j}$ indicating the similarity between $i$-th recipe word and $j$-th image in the question estimated by",
+      "where $\\mathbf {w}^{\\top }_{R}$ is a trainable weight vector, $\\circ $ and $[;]$ denote elementwise multiplication and concatenation operations, respectively.",
+      "Recipe-to-question attention determines the images within the question that is most relevant to each word of the recipe. Let $\\mathbf {\\tilde{Q}} \\in \\mathbb {R}^{2d \\times N}$ represent the recipe-to-question attention matrix with its $i$-th column being given by $ \\mathbf {\\tilde{Q}}_i=\\sum _j \\mathbf {a}_{ij}\\mathbf {Q}^{\\prime }_j$ where the attention weight is computed by $\\mathbf {a}_i=\\operatorname{softmax}(\\mathbf {S}^R_{i}) \\in \\mathbb {R}^M$.",
+      "Question-to-recipe attention signifies the words within the recipe that have the closest similarity to each image in the question, and construct an attended recipe vector given by $ \\tilde{\\mathbf {r}}=\\sum _{i}\\mathbf {b}_i\\mathbf {R}^{\\prime }_i$ with the attention weight is calculated by $\\mathbf {b}=\\operatorname{softmax}(\\operatorname{max}_{\\mathit {col}}(\\mathbf {S}^R)) \\in \\mathbb {R}^{N}$ where $\\operatorname{max}_{\\mathit {col}}$ denotes the maximum function across the column. The question-to-recipe matrix is then obtained by replicating $\\tilde{\\mathbf {r}}$ $N$ times across the column, giving $\\tilde{\\mathbf {R}} \\in \\mathbb {R}^{2d \\times N}$.",
+      "Then, we construct the question aware representation of the input recipe, $\\mathbf {G}$, with its $i$-th column $\\mathbf {G}_i \\in \\mathbb {R}^{8d \\times N}$ denoting the final embedding of $i$-th word given by",
+      "Attentions from question to entities, and from entities to question are computed in a way similar to the ones described above. The only difference is that it uses a different shared affinity matrix to be computed between the memory encoding entities $\\mathbf {E}$ and the question $\\mathbf {Q}^{\\prime }$. These attentions are then used to construct the question aware representation of entities, denoted by $\\mathbf {Y}$, that links and integrates the images in the question and the entities in the input recipe."
+    ],
+    [
+      "Modeling module takes the question-aware representations of the recipe $\\mathbf {G}$ and the entities $\\mathbf {Y}$, and forms their combined vector representation. For this purpose, we first use a two-layer BiLSTM to read the question-aware recipe $\\mathbf {G}$ and to encode the interactions among the words conditioned on the question. For each direction of BiLSTM , we use its hidden state after reading the last token as its output. In the end, we obtain a vector embedding $\\mathbf {c} \\in \\mathbb {R}^{2d \\times 1}$. Similarly, we employ a second BiLSTM, this time, over the entities $\\mathbf {Y}$, which results in another vector embedding $\\mathbf {f} \\in \\mathbb {R}^{2d_E \\times 1}$. Finally, these vector representations are concatenated and then projected to a fixed size representation using $\\mathbf {o}=\\varphi _o(\\left[\\mathbf {c}; \\mathbf {f}\\right]) \\in \\mathbb {R}^{2d \\times 1}$ where $\\varphi _o$ is a multilayer perceptron with $\\operatorname{tanh}$ activation function."
+    ],
+    [
+      "The output module takes the output of the modeling module, encoding vector embeddings of the question-aware recipe and the entities $\\mathbf {Y}$, and the embedding of the answer $\\mathbf {A}$, and returns a similarity score which is used while determining the correct answer. Among all the candidate answer, the one having the highest similarity score is chosen as the correct answer. To train our proposed procedural reasoning network, we employ a hinge ranking loss BIBREF15, similar to the one used in BIBREF2, given below.",
+      "where $\\gamma $ is the margin parameter, $\\mathbf {a}_+$ and $\\mathbf {a}_{-}$ are the correct and the incorrect answers, respectively."
+    ],
+    [
+      "In this section, we describe our experimental setup and then analyze the results of the proposed Procedural Reasoning Networks (PRN) model."
+    ],
+    [
+      "Given a recipe, we automatically extract the entities from the initial step of a recipe by using a dictionary of ingredients. While determining the ingredients, we exploit Recipe1M BIBREF16 and Kaggle What\u2019s Cooking Recipes BIBREF17 datasets, and form our dictionary using the most commonly used ingredients in the training set of RecipeQA. For the cases when no entity can be extracted from the recipe automatically (20 recipes in total), we manually annotate those recipes with the related entities."
+    ],
+    [
+      "In our experiments, we separately trained models on each task, as well as we investigated multi-task learning where a single model is trained to solve all these tasks at once. In total, the PRN architecture consists of $\\sim $12M trainable parameters. We implemented our models in PyTorch BIBREF18 using AllenNLP library BIBREF6. We used Adam optimizer with a learning rate of 1e-4 with an early stopping criteria with the patience set to 10 indicating that the training procedure ends after 10 iterations if the performance would not improve. We considered a batch size of 32 due to our hardware constraints. In the multi-task setting, batches are sampled round-robin from all tasks, where each batch is solely composed of examples from one task. We performed our experiments on a system containing four NVIDIA GTX-1080Ti GPUs, and training a single model took around 2 hours. We employed the same hyperparameters for all the baseline systems. We plan to share our code and model implementation after the review process."
+    ],
+    [
+      "We compare our model with several baseline models as described below. We note that the results of the first two are previously reported in BIBREF2.",
+      "Hasty Student BIBREF2 is a heuristics-based simple model which ignores the recipe and gives an answer by examining only the question and the answer set using distances in the visual feature space.",
+      "Impatient Reader BIBREF19 is a simple neural model that takes its name from the fact that it repeatedly computes attention over the recipe after observing each image in the query.",
+      "BiDAF BIBREF14 is a strong reading comprehension model that employs a bi-directional attention flow mechanism to obtain a question-aware representation and bases its predictions on this representation. Originally, it is a span-selection model from the input context. Here, we adapt it to work in a multimodal setting and answer multiple choice questions instead.",
+      "BiDAF w/ static memory is an extended version of the BiDAF model which resembles our proposed PRN model in that it includes a memory unit for the entities. However, it does not make any updates on the memory cells. That is, it uses the static entity embeeddings initialized with GloVe word vectors. We propose this baseline to test the significance of the use of relational memory updates."
+    ],
+    [
+      "Table TABREF29 presents the quantitative results for the visual reasoning tasks in RecipeQA. In single-task training setting, PRN gives state-of-the-art results compared to other neural models. Moreover, it achieves the best performance on average. These results demonstrate the importance of having a dynamic memory and keeping track of entities extracted from the recipe. In multi-task training setting where a single model is trained to solve all the tasks at once, PRN and BIDAF w/ static memory perform comparably and give much better results than BIDAF. Note that the model performances in the multi-task training setting are worse than single-task performances. We believe that this is due to the nature of the tasks that some are more difficult than the others. We think that the performance could be improved by employing a carefully selected curriculum strategy BIBREF20.",
+      "In Fig. FIGREF28, we illustrate the entity embeddings space by projecting the learned embeddings from the step-by-step memory snapshots through time with t-SNE to 3-d space from 200-d vector space. Color codes denote the categories of the cooking recipes. As can be seen, these step-aware embeddings show clear clustering of these categories. Moreover, within each cluster, the entities are grouped together in terms of their state characteristics. For instance, in the zoomed parts of the figure, chopped and sliced, or stirred and whisked entities are placed close to each other.",
+      "Fig. FIGREF30 demonstrates the entity arithmetics using the learned embeddings from each entity step. Here, we show that the learned embedding from the memory snapshots can effectively capture the contextual information about the entities at each time point in the corresponding step while taking into account of the recipe data. This basic arithmetic operation suggests that the proposed model can successfully capture the semantics of each entity's state in the corresponding step."
+    ],
+    [
+      "In recent years, tracking entities and their state changes have been explored in the literature from a variety of perspectives. In an early work, BIBREF21 proposed a dynamic memory based network which updates entity states using a gating mechanism while reading the text. BIBREF22 presented a more structured memory augmented model which employs memory slots for representing both entities and their relations. BIBREF23 suggested a conceptually similar model in which the pairwise relations between attended memories are utilized to encode the world state. The main difference between our approach and these works is that by utilizing relational memory core units we also allow memories to interact with each other during each update.",
+      "BIBREF24 showed that similar ideas can be used to compile supporting memories in tracking dialogue state. BIBREF25 has shown the importance of coreference signals for reading comprehension task. More recently, BIBREF26 introduced a specialized recurrent layer which uses coreference annotations for improving reading comprehension tasks. On language modeling task, BIBREF27 proposed a language model which can explicitly incorporate entities while dynamically updating their representations for a variety of tasks such as language modeling, coreference resolution, and entity prediction.",
+      "Our work builds upon and contributes to the growing literature on tracking states changes in procedural text. BIBREF0 presented a neural model that can learn to explicitly predict state changes of ingredients at different points in a cooking recipe. BIBREF1 proposed another entity-aware model to track entity states in scientific processes. BIBREF3 demonstrated that the prediction quality can be boosted by including hard and soft constraints to eliminate unlikely or favor probable state changes. In a follow-up work, BIBREF4 exploited the notion of label consistency in training to enforce similar predictions in similar procedural contexts. BIBREF28 proposed a model that dynamically constructs a knowledge graph while reading the procedural text to track the ever-changing entities states. As discussed in the introduction, however, these previous methods use a strong inductive bias and assume that state labels are present during training. In our study, we deliberately focus on unlabeled procedural data and ask the question: Can multimodality help to identify and provide insights to understanding state changes."
+    ],
+    [
+      "We have presented a new neural architecture called Procedural Reasoning Networks (PRN) for multimodal understanding of step-by-step instructions. Our proposed model is based on the successful BiDAF framework but also equipped with an explicit memory unit that provides an implicit mechanism to keep track of the changes in the states of the entities over the course of the procedure. Our experimental analysis on visual reasoning tasks in the RecipeQA dataset shows that the model significantly improves the results of the previous models, indicating that it better understands the procedural text and the accompanying images. Additionally, we carefully analyze our results and find that our approach learns meaningful dynamic representations of entities without any entity-level supervision. Although we achieve state-of-the-art results on RecipeQA, clearly there is still room for improvement compared to human performance. We also believe that the PRN architecture will be of value to other visual and textual sequential reasoning tasks."
+    ],
+    [
+      "We thank the anonymous reviewers and area chairs for their invaluable feedback. This work was supported by TUBA GEBIP fellowship awarded to E. Erdem; and by the MMVC project via an Institutional Links grant (Project No. 217E054) under the Newton-Katip \u00c7elebi Fund partnership funded by the Scientific and Technological Research Council of Turkey (TUBITAK) and the British Council. We also thank NVIDIA Corporation for the donation of GPUs used in this research."
+    ]
+  ]
+}
+```

qasper-0139/environment/Dockerfile

@@ -0,0 +1,5 @@
+FROM ubuntu:24.04
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y python3 python3-pip && rm -rf /var/lib/apt/lists/*