diff --git a/qasper-0002/instruction.md b/qasper-0002/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..7fbc5ab4071817ee95f6c917d048dae0aefa0434 --- /dev/null +++ b/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." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0003/instruction.md b/qasper-0003/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..d33cba34687de1852f60d76f77c0cdd67086eb75 --- /dev/null +++ b/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." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0004/instruction.md b/qasper-0004/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..c5b083f07ac02303bf8734cb026f8bac7821fb84 --- /dev/null +++ b/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? \ No newline at end of file diff --git a/qasper-0005/instruction.md b/qasper-0005/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..bcb2a5ec0f7f41d285baae4c38f02e13d517b9df --- /dev/null +++ b/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." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0010/instruction.md b/qasper-0010/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..ba9272f03a540526b361b586518e06ad8f76c827 --- /dev/null +++ b/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? \ No newline at end of file diff --git a/qasper-0011/instruction.md b/qasper-0011/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..07356e6ba1da6461ffe86973b0a9c6c3f562ed50 --- /dev/null +++ b/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/" + ], + [ + "" + ], + [ + "" + ], + [ + "" + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0016/instruction.md b/qasper-0016/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..34264eecd85da4c49bbc47edd5465c566f9f8a05 --- /dev/null +++ b/qasper-0016/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 temporally dynamic a community is? \ No newline at end of file diff --git a/qasper-0017/instruction.md b/qasper-0017/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..41e92bf3f40bf838bb7d099b9570d4bc9807cc1d --- /dev/null +++ b/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? \ No newline at end of file diff --git a/qasper-0018/instruction.md b/qasper-0018/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..5ff8f9471220fd15048f9863d32c4a765ebd298d --- /dev/null +++ b/qasper-0018/instruction.md @@ -0,0 +1,118 @@ +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 $X=$, 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 $Xs = (1 <= i < j <= n)$ 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)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0019/instruction.md b/qasper-0019/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..b8090de9775a3ad3d293764edb0717f6c221c0eb --- /dev/null +++ b/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 $X=$, 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 $Xs = (1 <= i < j <= n)$ 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)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0020/instruction.md b/qasper-0020/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..7e222dc8d61f39169ae88fd446c40848ce4b8a6b --- /dev/null +++ b/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 $X=$, 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 $Xs = (1 <= i < j <= n)$ 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)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0021/instruction.md b/qasper-0021/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..667f88a6c6675ea8eb52fc4d019f04d2a91726de --- /dev/null +++ b/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 $X=$, 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 $Xs = (1 <= i < j <= n)$ 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)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0026/instruction.md b/qasper-0026/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..7a3bdb8ae500a67c9177acc9469c985380d82dda --- /dev/null +++ b/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 $X=$, 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 $Xs = (1 <= i < j <= n)$ 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)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0027/instruction.md b/qasper-0027/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..3e8f75c8d368432a1abd92bd5778003414b41e6a --- /dev/null +++ b/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? \ No newline at end of file diff --git a/qasper-0028/instruction.md b/qasper-0028/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..8fd321d313bef6fb6d57c850024e9c049c51f54f --- /dev/null +++ b/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 $X=$, 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 $Xs = (1 <= i < j <= n)$ 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)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0029/instruction.md b/qasper-0029/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..9c2927c0064bf7f4273b07703ddb8d7666d77277 --- /dev/null +++ b/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 $X=$, 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 $Xs = (1 <= i < j <= n)$ 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)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0032/instruction.md b/qasper-0032/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..d4f81803b1af8db00a076c799765713866442579 --- /dev/null +++ b/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." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0035/instruction.md b/qasper-0035/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..375a6cff255e672a6fdfe2fd9ee1151053931a6e --- /dev/null +++ b/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}", + " ", + "\\bibitem{yao2017automated}", + "Yao, Y., Viswanath, B., Cryan, J., Zheng, H., Zhao, B.Y.:", + "\\newblock Automated crowdturfing attacks and defenses in online review systems.", + "\\newblock In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and", + " Communications Security, ACM (2017)", + " ", + "\\bibitem{murphy2012machine}", + "Murphy, K.:", + "\\newblock Machine learning: a probabilistic approach.", + "\\newblock Massachusetts Institute of Technology (2012)", + " ", + "\\bibitem{challenge2013yelp}", + "Yelp:", + "\\newblock {Yelp Challenge Dataset} (2013)", + " ", + "\\bibitem{mukherjee2013yelp}", + "Mukherjee, A., Venkataraman, V., Liu, B., Glance, N.:", + "\\newblock What yelp fake review filter might be doing?", + "\\newblock In: Seventh International AAAI Conference on Weblogs and Social Media", + " (ICWSM). 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SAC '16, ACM (2016)", + " ", + "\\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}", + "" + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0042/instruction.md b/qasper-0042/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..7962ca70e4464994a4ee565c6b0c1f4a40bd2cab --- /dev/null +++ b/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\"" + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0043/instruction.md b/qasper-0043/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..4ff3c2d13e011f5bb2f3fba63c63c27274c542f7 --- /dev/null +++ b/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\"" + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0044/instruction.md b/qasper-0044/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..21d991da378e44e0abd9d1f7009d0a259fbacd60 --- /dev/null +++ b/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? \ No newline at end of file diff --git a/qasper-0045/instruction.md b/qasper-0045/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..59a40cc87b406208bdbf6ed2c208b5aea01bd662 --- /dev/null +++ b/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." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0051/instruction.md b/qasper-0051/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..d204ca26cebeedc98b6377a8c35072e4ca3d8f6d --- /dev/null +++ b/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." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0072/instruction.md b/qasper-0072/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..9217daebc7a46a05afd4d6d4f8cf8bee9380e287 --- /dev/null +++ b/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." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0073/instruction.md b/qasper-0073/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..3ce7bc36c96736d26b7887c5e87cb2e3281bd375 --- /dev/null +++ b/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." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0074/instruction.md b/qasper-0074/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..b8fc5cfe967264fbd8a72f71d9034ec0cd3c0c50 --- /dev/null +++ b/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." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0075/instruction.md b/qasper-0075/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..138531d60e49495d0e0d5b5506f77a1acd449cfa --- /dev/null +++ b/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? \ No newline at end of file diff --git a/qasper-0080/instruction.md b/qasper-0080/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..c9aa89f137154fc73565e32d2e5b9c3304d56f99 --- /dev/null +++ b/qasper-0080/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Generative Adversarial Nets for Multiple Text Corpora + +Question: Which corpora do they use? \ No newline at end of file diff --git a/qasper-0081/instruction.md b/qasper-0081/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..ffae997081f16ec32f9382577ea34ffac2413636 --- /dev/null +++ b/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%)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0086/instruction.md b/qasper-0086/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..4cf1639c9f0b46121922c7eb9969397c4e4184bd --- /dev/null +++ b/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%)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0087/instruction.md b/qasper-0087/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..54ff3c47c038ce70bad745539d5fb44e51080d7f --- /dev/null +++ b/qasper-0087/instruction.md @@ -0,0 +1,3 @@ +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? \ No newline at end of file diff --git a/qasper-0088/instruction.md b/qasper-0088/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..f87eafc3a103d90f00945938df08274821651253 --- /dev/null +++ b/qasper-0088/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Gunrock: A Social Bot for Complex and Engaging Long Conversations + +Question: What are all the metrics to measure user engagement? \ No newline at end of file diff --git a/qasper-0089/instruction.md b/qasper-0089/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..3a52f34e0b7279dd0fe774ff935fcbc4f3d3199f --- /dev/null +++ b/qasper-0089/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Gunrock: A Social Bot for Complex and Engaging Long Conversations + +Question: What the system designs introduced? \ No newline at end of file diff --git a/qasper-0100/instruction.md b/qasper-0100/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..3b9fd33785e30974b50475a358cc8e87e312457a --- /dev/null +++ b/qasper-0100/instruction.md @@ -0,0 +1,120 @@ +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." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0101/environment/Dockerfile b/qasper-0101/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/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/* diff --git a/qasper-0101/instruction.md b/qasper-0101/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..c4376228f8594534f84e5b953a013499c7c31928 --- /dev/null +++ b/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." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0106/environment/Dockerfile b/qasper-0106/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/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/* diff --git a/qasper-0106/instruction.md b/qasper-0106/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..fa278fa98f226c66c738fbdc70aaafc42f254e86 --- /dev/null +++ b/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? \ No newline at end of file diff --git a/qasper-0107/instruction.md b/qasper-0107/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..f05a707a6b5ced0a44aa3a1628e5e825c15ccb12 --- /dev/null +++ b/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? \ No newline at end of file diff --git a/qasper-0108/environment/Dockerfile b/qasper-0108/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/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/* diff --git a/qasper-0108/instruction.md b/qasper-0108/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..a104b913f26a5517b7321cadb7e07355b50c4e9d --- /dev/null +++ b/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? \ No newline at end of file diff --git a/qasper-0109/instruction.md b/qasper-0109/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..f2fb14de385b43c419968fd6b10da3530fdb15a7 --- /dev/null +++ b/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? \ No newline at end of file diff --git a/qasper-0130/environment/Dockerfile b/qasper-0130/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/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/* diff --git a/qasper-0130/instruction.md b/qasper-0130/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..31033f75783d56c4ef6a401146d684727517eaec --- /dev/null +++ b/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? \ No newline at end of file diff --git a/qasper-0131/instruction.md b/qasper-0131/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..9de21152f14a763bfe285431e0c47f01867ff4d7 --- /dev/null +++ b/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? \ No newline at end of file diff --git a/qasper-0136/instruction.md b/qasper-0136/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..dea576819328ffdf649e855837a930a0af598ec3 --- /dev/null +++ b/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? \ No newline at end of file diff --git a/qasper-0137/environment/Dockerfile b/qasper-0137/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/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/* diff --git a/qasper-0137/instruction.md b/qasper-0137/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..52da9d9e62eea51733f08dbbbe06107938454655 --- /dev/null +++ b/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." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0138/instruction.md b/qasper-0138/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..154ba903f65070254d5ff3007ef274b7197ad000 --- /dev/null +++ b/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." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0139/environment/Dockerfile b/qasper-0139/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/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/* diff --git a/qasper-0139/instruction.md b/qasper-0139/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..b7bb7c1d0a47c4b3d74e76cb3bfc3e345c84c86d --- /dev/null +++ b/qasper-0139/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Procedural Reasoning Networks for Understanding Multimodal Procedures + +Question: What are previously reported models? \ No newline at end of file diff --git a/qasper-0152/environment/Dockerfile b/qasper-0152/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0152/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/* diff --git a/qasper-0152/instruction.md b/qasper-0152/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..d8e2f461ec7ef5bb3091b247954d19c924c58337 --- /dev/null +++ b/qasper-0152/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Learning Supervised Topic Models for Classification and Regression from Crowds + +Question: what datasets were used? \ No newline at end of file diff --git a/qasper-0153/instruction.md b/qasper-0153/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..95bcee441229e57b7a0c5bdfe4ecbed156cc3a25 --- /dev/null +++ b/qasper-0153/instruction.md @@ -0,0 +1,146 @@ +Name of Paper: CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset + +Question: How was the dataset collected? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Data Collection", + "Data Collection ::: Database Construction", + "Data Collection ::: Goal Generation", + "Data Collection ::: Dialogue Collection", + "Data Collection ::: Dialogue Collection ::: User Side", + "Data Collection ::: Dialogue Collection ::: Wizard Side", + "Data Collection ::: Dialogue Annotation", + "Statistics", + "Corpus Features", + "Benchmark and Analysis", + "Benchmark and Analysis ::: Natural Language Understanding", + "Benchmark and Analysis ::: Dialogue State Tracking", + "Benchmark and Analysis ::: Dialogue Policy Learning", + "Benchmark and Analysis ::: Natural Language Generation", + "Benchmark and Analysis ::: User Simulator", + "Benchmark and Analysis ::: Evaluation with User Simulation", + "Conclusion", + "Acknowledgments" + ], + "paragraphs": [ + [ + "Recently, there have been a variety of task-oriented dialogue models thanks to the prosperity of neural architectures BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5. However, the research is still largely limited by the availability of large-scale high-quality dialogue data. Many corpora have advanced the research of task-oriented dialogue systems, most of which are single domain conversations, including ATIS BIBREF6, DSTC 2 BIBREF7, Frames BIBREF8, KVRET BIBREF9, WOZ 2.0 BIBREF10 and M2M BIBREF11.", + "Despite the significant contributions to the community, these datasets are still limited in size, language variation, or task complexity. Furthermore, there is a gap between existing dialogue corpora and real-life human dialogue data. In real-life conversations, it is natural for humans to transition between different domains or scenarios while still maintaining coherent contexts. Thus, real-life dialogues are much more complicated than those dialogues that are only simulated within a single domain. To address this issue, some multi-domain corpora have been proposed BIBREF12, BIBREF13. The most notable corpus is MultiWOZ BIBREF12, a large-scale multi-domain dataset which consists of crowdsourced human-to-human dialogues. It contains 10K dialogue sessions and 143K utterances for 7 domains, with annotation of system-side dialogue states and dialogue acts. However, the state annotations are noisy BIBREF14, and user-side dialogue acts are missing. The dependency across domains is simply embodied in imposing the same pre-specified constraints on different domains, such as requiring both a hotel and an attraction to locate in the center of the town.", + "In comparison to the abundance of English dialogue data, surprisingly, there is still no widely recognized Chinese task-oriented dialogue corpus. In this paper, we propose CrossWOZ, a large-scale Chinese multi-domain (cross-domain) task-oriented dialogue dataset. An dialogue example is shown in Figure FIGREF1. We compare CrossWOZ to other corpora in Table TABREF5 and TABREF6. Our dataset has the following features comparing to other corpora (particularly MultiWOZ BIBREF12):", + "The dependency between domains is more challenging because the choice in one domain will affect the choices in related domains in CrossWOZ. As shown in Figure FIGREF1 and Table TABREF6, the hotel must be near the attraction chosen by the user in previous turns, which requires more accurate context understanding.", + "It is the first Chinese corpus that contains large-scale multi-domain task-oriented dialogues, consisting of 6K sessions and 102K utterances for 5 domains (attraction, restaurant, hotel, metro, and taxi).", + "Annotation of dialogue states and dialogue acts is provided for both the system side and user side. The annotation of user states enables us to track the conversation from the user's perspective and can empower the development of more elaborate user simulators.", + "In this paper, we present the process of dialogue collection and provide detailed data analysis of the corpus. Statistics show that our cross-domain dialogues are complicated. To facilitate model comparison, benchmark models are provided for different modules in pipelined task-oriented dialogue systems, including natural language understanding, dialogue state tracking, dialogue policy learning, and natural language generation. We also provide a user simulator, which will facilitate the development and evaluation of dialogue models on this corpus. The corpus and the benchmark models are publicly available at https://github.com/thu-coai/CrossWOZ." + ], + [ + "According to whether the dialogue agent is human or machine, we can group the collection methods of existing task-oriented dialogue datasets into three categories. The first one is human-to-human dialogues. One of the earliest and well-known ATIS dataset BIBREF6 used this setting, followed by BIBREF8, BIBREF9, BIBREF10, BIBREF15, BIBREF16 and BIBREF12. Though this setting requires many human efforts, it can collect natural and diverse dialogues. The second one is human-to-machine dialogues, which need a ready dialogue system to converse with humans. The famous Dialogue State Tracking Challenges provided a set of human-to-machine dialogue data BIBREF17, BIBREF7. The performance of the dialogue system will largely influence the quality of dialogue data. The third one is machine-to-machine dialogues. It needs to build both user and system simulators to generate dialogue outlines, then use templates BIBREF3 to generate dialogues or further employ people to paraphrase the dialogues to make them more natural BIBREF11, BIBREF13. It needs much less human effort. However, the complexity and diversity of dialogue policy are limited by the simulators. To explore dialogue policy in multi-domain scenarios, and to collect natural and diverse dialogues, we resort to the human-to-human setting.", + "Most of the existing datasets only involve single domain in one dialogue, except MultiWOZ BIBREF12 and Schema BIBREF13. MultiWOZ dataset has attracted much attention recently, due to its large size and multi-domain characteristics. It is at least one order of magnitude larger than previous datasets, amounting to 8,438 dialogues and 115K turns in the training set. It greatly promotes the research on multi-domain dialogue modeling, such as policy learning BIBREF18, state tracking BIBREF19, and context-to-text generation BIBREF20. Recently the Schema dataset is collected in a machine-to-machine fashion, resulting in 16,142 dialogues and 330K turns for 16 domains in the training set. However, the multi-domain dependency in these two datasets is only embodied in imposing the same pre-specified constraints on different domains, such as requiring a restaurant and an attraction to locate in the same area, or the city of a hotel and the destination of a flight to be the same (Table TABREF6).", + "Table TABREF5 presents a comparison between our dataset with other task-oriented datasets. In comparison to MultiWOZ, our dataset has a comparable scale: 5,012 dialogues and 84K turns in the training set. The average number of domains and turns per dialogue are larger than those of MultiWOZ, which indicates that our task is more complex. The cross-domain dependency in our dataset is natural and challenging. For example, as shown in Table TABREF6, the system needs to recommend a hotel near the attraction chosen by the user in previous turns. Thus, both system recommendation and user selection will dynamically impact the dialogue. We also allow the same domain to appear multiple times in a user goal since a tourist may want to go to more than one attraction.", + "To better track the conversation flow and model user dialogue policy, we provide annotation of user states in addition to system states and dialogue acts. While the system state tracks the dialogue history, the user state is maintained by the user and indicates whether the sub-goals have been completed, which can be used to predict user actions. This information will facilitate the construction of the user simulator.", + "To the best of our knowledge, CrossWOZ is the first large-scale Chinese dataset for task-oriented dialogue systems, which will largely alleviate the shortage of Chinese task-oriented dialogue corpora that are publicly available." + ], + [ + "Our corpus is to simulate scenarios where a traveler seeks tourism information and plans her or his travel in Beijing. Domains include hotel, attraction, restaurant, metro, and taxi. The data collection process is summarized as below:", + "Database Construction: we crawled travel information in Beijing from the Web, including Hotel, Attraction, and Restaurant domains (hereafter we name the three domains as HAR domains). Then, we used the metro information of entities in HAR domains to build the metro database. For the taxi domain, there is no need to store the information. Instead, we can call the API directly if necessary.", + "Goal Generation: a multi-domain goal generator was designed based on the database. The relation across domains is captured in two ways. One is to constrain two targets that locate near each other. The other is to use a taxi or metro to commute between two targets in HAR domains mentioned in the context. To make workers understand the task more easily, we crafted templates to generate natural language descriptions for each structured goal.", + "Dialogue Collection: before the formal data collection starts, we required the workers to make a small number of dialogues and gave them feedback about the dialogue quality. Then, well-trained workers were paired to converse according to the given goals. The workers were also asked to annotate both user states and system states.", + "Dialogue Annotation: we used some rules to automatically annotate dialogue acts according to user states, system states, and dialogue histories. To evaluate the quality of the annotation of dialogue acts and states, three experts were employed to manually annotate dialogue acts and states for 50 dialogues. The results show that our annotations are of high quality. Finally, each dialogue contains a structured goal, a task description, user states, system states, dialogue acts, and utterances." + ], + [ + "We collected 465 attractions, 951 restaurants, and 1,133 hotels in Beijing from the Web. Some statistics are shown in Table TABREF11. There are three types of slots for each entity: common slots such as name and address; binary slots for hotel services such as wake-up call; nearby attractions/restaurants/hotels slots that contain nearby entities in the attraction, restaurant, and hotel domains. Since it is not usual to find another nearby hotel in the hotel domain, we did not collect such information. This nearby relation allows us to generate natural cross-domain goals, such as \"find another attraction near the first one\" and \"find a restaurant near the attraction\". Nearest metro stations of HAR entities form the metro database. In contrast, we provided the pseudo car type and plate number for the taxi domain." + ], + [ + "To avoid generating overly complex goals, each goal has at most five sub-goals. To generate more natural goals, the sub-goals can be of the same domain, such as two attractions near each other. The goal is represented as a list of (sub-goal id, domain, slot, value) tuples, named as semantic tuples. The sub-goal id is used to distinguish sub-goals which may be in the same domain. There are two types of slots: informable slots which are the constraints that the user needs to inform the system, and requestable slots which are the information that the user needs to inquire from the system. As shown in Table TABREF13, besides common informable slots (italic values) whose values are determined before the conversation, we specially design cross-domain informable slots (bold values) whose values refer to other sub-goals. Cross-domain informable slots utilize sub-goal id to connect different sub-goals. Thus the actual constraints vary according to the different contexts instead of being pre-specified. The values of common informable slots are sampled randomly from the database. Based on the informable slots, users are required to gather the values of requestable slots (blank values in Table TABREF13) through conversation.", + "There are four steps in goal generation. First, we generate independent sub-goals in HAR domains. For each domain in HAR domains, with the same probability $\\mathcal {P}$ we generate a sub-goal, while with the probability of $1-\\mathcal {P}$ we do not generate any sub-goal for this domain. Each sub-goal has common informable slots and requestable slots. As shown in Table TABREF15, all slots of HAR domains can be requestable slots, while the slots with an asterisk can be common informable slots.", + "Second, we generate cross-domain sub-goals in HAR domains. For each generated sub-goal (e.g., the attraction sub-goal in Table TABREF13), if its requestable slots contain \"nearby hotels\", we generate an additional sub-goal in the hotel domain (e.g., the hotel sub-goal in Table TABREF13) with the probability of $\\mathcal {P}_{attraction\\rightarrow hotel}$. Of course, the selected hotel must satisfy the nearby relation to the attraction entity. Similarly, we do not generate any additional sub-goal in the hotel domain with the probability of $1-\\mathcal {P}_{attraction\\rightarrow hotel}$. This also works for the attraction and restaurant domains. $\\mathcal {P}_{hotel\\rightarrow hotel}=0$ since we do not allow the user to find the nearby hotels of one hotel.", + "Third, we generate sub-goals in the metro and taxi domains. With the probability of $\\mathcal {P}_{taxi}$, we generate a sub-goal in the taxi domain (e.g., the taxi sub-goal in Table TABREF13) to commute between two entities of HAR domains that are already generated. It is similar for the metro domain and we set $\\mathcal {P}_{metro}=\\mathcal {P}_{taxi}$. All slots in the metro or taxi domain appear in the sub-goals and must be filled. As shown in Table TABREF15, from and to slots are always cross-domain informable slots, while others are always requestable slots.", + "Last, we rearrange the order of the sub-goals to generate more natural and logical user goals. We require that a sub-goal should be followed by its referred sub-goal as immediately as possible.", + "To make the workers aware of this cross-domain feature, we additionally provide a task description for each user goal in natural language, which is generated from the structured goal by hand-crafted templates.", + "Compared with the goals whose constraints are all pre-specified, our goals impose much more dependency between different domains, which will significantly influence the conversation. The exact values of cross-domain informable slots are finally determined according to the dialogue context." + ], + [ + "We developed a specialized website that allows two workers to converse synchronously and make annotations online. On the website, workers are free to choose one of the two roles: tourist (user) or system (wizard). Then, two paired workers are sent to a chatroom. The user needs to accomplish the allocated goal through conversation while the wizard searches the database to provide the necessary information and gives responses. Before the formal data collection, we trained the workers to complete a small number of dialogues by giving them feedback. Finally, 90 well-trained workers are participating in the data collection.", + "In contrast, MultiWOZ BIBREF12 hired more than a thousand workers to converse asynchronously. Each worker received a dialogue context to review and need to respond for only one turn at a time. The collected dialogues may be incoherent because workers may not understand the context correctly and multiple workers contributed to the same dialogue session, possibly leading to more variance in the data quality. For example, some workers expressed two mutually exclusive constraints in two consecutive user turns and failed to eliminate the system's confusion in the next several turns. Compared with MultiWOZ, our synchronous conversation setting may produce more coherent dialogues." + ], + [ + "The user state is the same as the user goal before a conversation starts. At each turn, the user needs to 1) modify the user state according to the system response at the preceding turn, 2) select some semantic tuples in the user state, which indicates the dialogue acts, and 3) compose the utterance according to the selected semantic tuples. In addition to filling the required values and updating cross-domain informable slots with real values in the user state, the user is encouraged to modify the constraints when there is no result under such constraints. The change will also be recorded in the user state. Once the goal is completed (all the values in the user state are filled), the user can terminate the dialogue." + ], + [ + "We regard the database query as the system state, which records the constraints of each domain till the current turn. At each turn, the wizard needs to 1) fill the query according to the previous user response and search the database if necessary, 2) select the retrieved entities, and 3) respond in natural language based on the information of the selected entities. If none of the entities satisfy all the constraints, the wizard will try to relax some of them for a recommendation, resulting in multiple queries. The first query records original user constraints while the last one records the constraints relaxed by the system." + ], + [ + "After collecting the conversation data, we used some rules to annotate dialogue acts automatically. Each utterance can have several dialogue acts. Each dialogue act is a tuple that consists of intent, domain, slot, and value. We pre-define 6 types of intents and use the update of the user state and system state as well as keyword matching to obtain dialogue acts. For the user side, dialogue acts are mainly derived from the selection of semantic tuples that contain the information of domain, slot, and value. For example, if (1, Attraction, fee, free) in Table TABREF13 is selected by the user, then (Inform, Attraction, fee, free) is labelled. If (1, Attraction, name, ) is selected, then (Request, Attraction, name, none) is labelled. If (2, Hotel, name, near (id=1)) is selected, then (Select, Hotel, src_domain, Attraction) is labelled. This intent is specially designed for the \"nearby\" constraint. For the system side, we mainly applied keyword matching to label dialogue acts. Inform intent is derived by matching the system utterance with the information of selected entities. When the wizard selects multiple retrieved entities and recommend them, Recommend intent is labeled. When the wizard expresses that no result satisfies user constraints, NoOffer is labeled. For General intents such as \"goodbye\", \"thanks\" at both user and system sides, keyword matching is applied.", + "We also obtained a binary label for each semantic tuple in the user state, which indicates whether this semantic tuple has been selected to be expressed by the user. This annotation directly illustrates the progress of the conversation.", + "To evaluate the quality of the annotation of dialogue acts and states (both user and system states), three experts were employed to manually annotate dialogue acts and states for the same 50 dialogues (806 utterances), 10 for each goal type (see Section SECREF4). Since dialogue act annotation is not a classification problem, we didn't use Fleiss' kappa to measure the agreement among experts. We used dialogue act F1 and state accuracy to measure the agreement between each two experts' annotations. The average dialogue act F1 is 94.59% and the average state accuracy is 93.55%. We then compared our annotations with each expert's annotations which are regarded as gold standard. The average dialogue act F1 is 95.36% and the average state accuracy is 94.95%, which indicates the high quality of our annotations." + ], + [ + "After removing uncompleted dialogues, we collected 6,012 dialogues in total. The dataset is split randomly for training/validation/test, where the statistics are shown in Table TABREF25. The average number of sub-goals in our dataset is 3.24, which is much larger than that in MultiWOZ (1.80) BIBREF12 and Schema (1.84) BIBREF13. The average number of turns (16.9) is also larger than that in MultiWOZ (13.7). These statistics indicate that our dialogue data are more complex.", + "According to the type of user goal, we group the dialogues in the training set into five categories:", + "417 dialogues have only one sub-goal in HAR domains.", + "1573 dialogues have multiple sub-goals (2$\\sim $3) in HAR domains. However, these sub-goals do not have cross-domain informable slots.", + "691 dialogues have multiple sub-goals in HAR domains and at least one sub-goal in the metro or taxi domain (3$\\sim $5 sub-goals). The sub-goals in HAR domains do not have cross-domain informable slots.", + "1,759 dialogues have multiple sub-goals (2$\\sim $5) in HAR domains with cross-domain informable slots.", + "572 dialogues have multiple sub-goals in HAR domains with cross-domain informable slots and at least one sub-goal in the metro or taxi domain (3$\\sim $5 sub-goals).", + "The data statistics are shown in Table TABREF26. As mentioned in Section SECREF14, we generate independent multi-domain, cross multi-domain, and traffic domain sub-goals one by one. Thus in terms of the task complexity, we have S 0$ is a margin parameter and our purpose is to make the score of the positive target entity $e_t^+$ is at least a margin $\\gamma $ higher than that of negative candidate entity $e_t^-$ .", + "With the local encoder, we obtain the representation of mention context and candidate entities, which will be used as the input into the global encoder and entity selector. In addition, the similarity scores calculated by MLP will be utilized for ranking mentions in the global encoder." + ], + [ + "In the global encoder module, we aim to enforce the topical coherence among the mentions and their target entities. So, we use an LSTM network which is capable of maintaining the long-term memory to encode the ranked mention sequence. What we need to emphasize is that our global encoder just encode the mentions that have been disambiguated by the entity selector which is denoted as $V_{a_t}$ .", + "As mentioned above, the mentions should be sorted according to their contextual information and topical coherence. So, we firstly divide the adjacent mentions into a segment by the order they appear in the document based on the observation that the topical consistency attenuates along with the distance between the mentions. Then, we sort mentions in a segment based on the local similarity and place the mention that has a higher similarity value in the front of the sequence. In Equation 1, we define the local similarity of $m_i$ and its corresponding candidate entity $e_t^i$ . On this basis, we define $\\Psi _{max}(m_i, e_i^a)$ as the the maximum local similarity between the $m_i$ and its candidate set $C_{m_i} = \\lbrace e_i^1, e_i^2,..., e_i^n\\rbrace $ . We use $\\Psi _{max}(m_i, e_i^a)$ as criterion when sorting mentions. For instance, if $\\Psi _{max}(m_i, e_i^a) > \\Psi _{max}(m_j, e_j^b)$ then we place $m_i$ before $m_j$ . Under this circumstances, the mentions in the front positions may not be able to make better use of global consistency, but their target entities have a high degree of similarity to the context words, which allows them to be disambiguated without relying on additional information. In the end, previous selected target entity information is encoded by global encoder and the encoding result will be served as input to the entity selector.", + "Before using entity selector to choose target entities, we pre-trained the global LSTM network. During the training process, we input not only positive samples but also negative ones to the LSTM. By doing this, we can enhance the robustness of the network. In the global encoder module, we adopt the following cross entropy loss function to train the model. ", + "$$L_{global} = -\\frac{1}{n}\\sum _x{\\left[y\\ln {y^{^{\\prime }}} + (1-y)\\ln (1-y^{^{\\prime }})\\right]}$$ (Eq. 12) ", + "Where $y\\in \\lbrace 0,1\\rbrace $ represents the label of the candidate entity. If the candidate entity is correct $y=1$ , otherwise $y=0$ . $y^{^{\\prime }}\\in (0,1)$ indicates the output of our model. After pre-training the global encoder, we start using the entity selector to choose the target entity for each mention and encode these selections." + ], + [ + "In the entity selector module, we choose the target entity from candidate set based on the results of local and global encoder. In the process of sequence disambiguation, each selection result will have an impact on subsequent decisions. Therefore, we transform the choice of the target entity into a reinforcement learning problem and view the entity selector as an agent. In particular, the agent is designed as a policy network which can learn a stochastic policy and prevents the agent from getting stuck at an intermediate state BIBREF12 . Under the guidance of policy, the agent can decide which action (choosing the target entity from the candidate set)should be taken at each state, and receive a delay reward when all the selections are made. In the following part, we first describe the state, action and reward. Then, we detail how to select target entity via a policy network.", + "The result of entity selection is based on the current state information. For time $t$ , the state vector $S_t$ is generated as follows: ", + "$$S_t = V_{m_i}^t\\oplus {V_{e_i}^t}\\oplus {V_{feature}^t}\\oplus {V_{e^*}^{t-1}}$$ (Eq. 15) ", + "Where $\\oplus $ indicates vector concatenation. The $V_{m_i}^t$ and $V_{e_i}^t$ respectively denote the vector of $m_i$ and $e_i$ at time $t$ . For each mention, there are multiple candidate entities correspond to it. With the purpose of comparing the semantic relevance between the mention and each candidate entity at the same time, we copy multiple copies of the mention vector. Formally, we extend $V_{m_i}^t \\in \\mathbb {R}^{1\\times {n}}$ to $V_{m_i}^t{^{\\prime }} \\in \\mathbb {R}^{k\\times {n}}$ and then combine it with $V_{e_i}^t \\in \\mathbb {R}^{k\\times {n}}$ . Since $V_{m_i}^t$ and $V_{m_i}^t$0 are mainly to represent semantic information, we add feature vector $V_{m_i}^t$1 to enrich lexical and statistical features. These features mainly include the popularity of the entity, the edit distance between the entity description and the mention context, the number of identical words in the entity description and the mention context etc. After getting these feature values, we combine them into a vector and add it to the current state. In addition, the global vector $V_{m_i}^t$2 is also added to $V_{m_i}^t$3 . As mentioned in global encoder module, $V_{m_i}^t$4 is the output of global LSTM network at time $V_{m_i}^t$5 , which encodes the mention context and target entity information from $V_{m_i}^t$6 to $V_{m_i}^t$7 . Thus, the state $V_{m_i}^t$8 contains current information and previous decisions, while also covering the semantic representations and a variety of statistical features. Next, the concatenated vector will be fed into the policy network to generate action.", + "According to the status at each time step, we take corresponding action. Specifically, we define the action at time step $t$ is to select the target entity $e_t^*$ for $m_t$ . The size of action space is the number of candidate entities for each mention, where $a_i \\in \\lbrace 0,1,2...k\\rbrace $ indicates the position of the selected entity in the candidate entity list. Clearly, each action is a direct indicator of target entity selection in our model. After completing all the actions in the sequence we will get a delayed reward.", + "The agent takes the reward value as the feedback of its action and learns the policy based on it. Since current selection result has a long-term impact on subsequent decisions, we don't give an immediate reward when taking an action. Instead, a delay reward is given by follows, which can reflect whether the action improves the overall performance or not. ", + "$$R(a_t) = p(a_t)\\sum _{j=t}^{T}p(a_j) + (1 - p(a_t))(\\sum _{j=t}^{T}p(a_j) + t - T)$$ (Eq. 16) ", + "where $p(a_t)\\in \\lbrace 0,1\\rbrace $ indicates whether the current action is correct or not. When the action is correct $p(a_t)=1$ otherwise $p(a_t)=0$ . Hence $\\sum _{j=t}^{T}p(a_j)$ and $\\sum _{j=t}^{T}p(a_j) + t - T$ respectively represent the number of correct and wrong actions from time t to the end of episode. Based on the above definition, our delayed reward can be used to guide the learning of the policy for entity linking.", + "After defining the state, action, and reward, our main challenge becomes to choose an action from the action space. To solve this problem, we sample the value of each action by a policy network $\\pi _{\\Theta }(a|s)$ . The structure of the policy network is shown in Figure 3. The input of the network is the current state, including the mention context representation, candidate entity representation, feature representation, and encoding of the previous decisions. We concatenate these representations and fed them into a multilayer perceptron, for each hidden layer, we generate the output by: ", + "$$h_i(S_t) = Relu(W_i*h_{i-1}(S_t) + b_i)$$ (Eq. 17) ", + "Where $W_i$ and $ b_i$ are the parameters of the $i$ th hidden layer, through the $relu$ activation function we get the $h_i(S_t)$ . After getting the output of the last hidden layer, we feed it into a softmax layer which generates the probability distribution of actions. The probability distribution is generated as follows: ", + "$$\\pi (a|s) = Softmax(W * h_l(S) + b)$$ (Eq. 18) ", + "Where the $W$ and $b$ are the parameters of the softmax layer. For each mention in the sequence, we will take action to select the target entity from its candidate set. After completing all decisions in the episode, each action will get an expected reward and our goal is to maximize the expected total rewards. Formally, the objective function is defined as: ", + "$$\\begin{split}\nJ(\\Theta ) &= \\mathbb {E}_{(s_t, a_t){\\sim }P_\\Theta {(s_t, a_t)}}R(s_1{a_1}...s_L{a_L}) \\\\\n&=\\sum _{t}\\sum _{a}\\pi _{\\Theta }(a|s)R(a_t)\n\\end{split}$$ (Eq. 19) ", + "Where $P_\\Theta {(s_t, a_t)}$ is the state transfer function, $\\pi _{\\Theta }(a|s)$ indicates the probability of taking action $a$ under the state $s$ , $R(a_t)$ is the expected reward of action $a$ at time step $t$ . According to REINFORCE policy gradient algorithm BIBREF13 , we update the policy gradient by the way of equation 9. ", + "$$\\Theta \\leftarrow \\Theta + \\alpha \\sum _{t}R(a_t)\\nabla _{\\Theta }\\log \\pi _{\\Theta }(a|s)$$ (Eq. 20) ", + "As the global encoder and the entity selector are correlated mutually, we train them jointly after pre-training the two networks. The details of the joint learning are presented in Algorithm 1.", + "[t] The Policy Learning for Entity Selector [1] Training data include multiple documents $D = \\lbrace D_1, D_2, ..., D_N\\rbrace $ The target entity for mentions $\\Gamma = \\lbrace T_1, T_2, ..., T_N\\rbrace $ ", + "Initialize the policy network parameter $\\Theta $ , global LSTM network parameter $\\Phi $ ; $D_k$ in $D$ Generate the candidate set for each mention Divide the mentions in $D_k$ into multiple sequences $S = \\lbrace S_1, S_2, ..., S_N\\rbrace $ ; $S_k$ in $S$ Rank the mentions $M = \\lbrace m_1, m_2, ..., m_n\\rbrace $ in $S_k$ based on the local similarity; $\\Phi $0 in $\\Phi $1 Sample the target entity $\\Phi $2 for $\\Phi $3 with $\\Phi $4 ; Input the $\\Phi $5 and $\\Phi $6 to global LSTM network; $\\Phi $7 End of sampling, update parameters Compute delayed reward $\\Phi $8 for each action; Update the parameter $\\Phi $9 of policy network:", + " $\\Theta \\leftarrow \\Theta + \\alpha \\sum _{t}R(a_t)\\nabla _{\\Theta }\\log \\pi _{\\Theta }(a|s)$ ", + "Update the parameter $\\Phi $ in the global LSTM network" + ], + [ + "In order to evaluate the effectiveness of our method, we train the RLEL model and validate it on a series of popular datasets that are also used by BIBREF0 , BIBREF1 . To avoid overfitting with one dataset, we use both AIDA-Train and Wikipedia data in the training set. Furthermore, we compare the RLEL with some baseline methods, where our model achieves the state-of-the-art results. We implement our models in Tensorflow and run experiments on 4 Tesla V100 GPU." + ], + [ + "We conduct experiments on several different types of public datasets including news and encyclopedia corpus. The training set is AIDA-Train and Wikipedia datasets, where AIDA-Train contains 18448 mentions and Wikipedia contains 25995 mentions. In order to compare with the previous methods, we evaluate our model on AIDA-B and other datasets. These datasets are well-known and have been used for the evaluation of most entity linking systems. The statistics of the datasets are shown in Table 1.", + "AIDA-CoNLL BIBREF14 is annotated on Reuters news articles. It contains training (AIDA-Train), validation (AIDA-A) and test (AIDA-B) sets.", + "ACE2004 BIBREF15 is a subset of the ACE2004 Coreference documents.", + "MSNBC BIBREF16 contains top two stories in the ten news categories(Politics, Business, Sports etc.)", + "AQUAINT BIBREF17 is a news corpus from the Xinhua News Service, the New York Times, and the Associated Press.", + "WNED-CWEB BIBREF18 is randomly picked from the FACC1 annotated ClueWeb 2012 dataset.", + "WNED-WIKI BIBREF18 is crawled from Wikipedia pages with its original hyperlink annotation.", + "OURSELF-WIKI is crawled by ourselves from Wikipedia pages.", + "During the training of our RLEL model, we select top K candidate entities for each mention to optimize the memory and run time. In the top K candidate list, we define the recall of correct target entity is $R_t$ . According to our statistics, when K is set to 1, $R_t$ is 0.853, when K is 5, $R_t$ is 0.977, when K increases to 10, $R_t$ is 0.993. Empirically, we choose top 5 candidate entities as the input of our RLEL model. For the entity description, there are lots of redundant information in the wikipedia page, to reduce the impact of noise data, we use TextRank algorithm BIBREF19 to select 15 keywords as description of the entity. Simultaneously, we choose 15 words around mention as its context. In the global LSTM network, when the number of mentions does not reach the set length, we adopt the mention padding strategy. In short, we copy the last mention in the sequence until the number of mentions reaches the set length.", + "We set the dimensions of word embedding and entity embedding to 300, where the word embedding and entity embedding are released by BIBREF20 and BIBREF0 respectively. For parameters of the local LSTM network, the number of LSTM cell units is set to 512, the batch size is 64, and the rank margin $\\gamma $ is 0.1. Similarly, in global LSTM network, the number of LSTM cell units is 700 and the batch size is 16. In the above two LSTM networks, the learning rate is set to 1e-3, the probability of dropout is set to 0.8, and the Adam is utilized as optimizer. In addition, we set the number of MLP layers to 4 and extend the priori feature dimension to 50 in the policy network." + ], + [ + "We compare RLEL with a series of EL systems which report state-of-the-art results on the test datasets. There are various methods including classification model BIBREF17 , rank model BIBREF21 , BIBREF15 and probability graph model BIBREF18 , BIBREF14 , BIBREF22 , BIBREF0 , BIBREF1 . Except that, Cheng $et$ $al.$ BIBREF23 formulate their global decision problem as an Integer Linear Program (ILP) which incorporates the entity-relation inference. Globerson $et$ $al.$ BIBREF24 introduce a multi-focal attention model which allows each candidate to focus on limited mentions, Yamada $et$ $al.$ BIBREF25 propose a word and entity embedding model specifically designed for EL.", + "We use the standard Accuracy, Precision, Recall and F1 at mention level (Micro) as the evaluation metrics: ", + "$$Accuracy = \\frac{|M \\cap M^*|}{|M \\cup M^*|}$$ (Eq. 31) ", + "$$Precision = \\frac{|M \\cap M^*|}{|M|}$$ (Eq. 32) ", + "where $M^*$ is the golden standard set of the linked name mentions, $M$ is the set of linked name mentions outputted by an EL method.", + "Same as previous work, we use in-KB accuracy and micro F1 to evaluate our method. We first test the model on the AIDA-B dataset. From Table 2, we can observe that our model achieves the best result. Previous best results on this dataset are generated by BIBREF0 , BIBREF1 which both built CRF models. They calculate the pairwise scores between all candidate entities. Differently, our model only considers the consistency of the target entities and ignores the relationship between incorrect candidates. The experimental results show that our model can reduce the impact of noise data and improve the accuracy of disambiguation. Apart from experimenting on AIDA-B, we also conduct experiments on several different datasets to verify the generalization performance of our model.", + "From Table 3, we can see that RLEL has achieved relatively good performances on ACE2004, CWEB and WIKI. At the same time, previous models BIBREF0 , BIBREF1 , BIBREF23 achieve better performances on the news datasets such as MSNBC and AQUINT, but their results on encyclopedia datasets such as WIKI are relatively poor. To avoid overfitting with some datasets and improve the robustness of our model, we not only use AIDA-Train but also add Wikipedia data to the training set. In the end, our model achieve the best overall performance.", + "For most existing EL systems, entities with lower frequency are difficult to disambiguate. To gain further insight, we analyze the accuracy of the AIDA-B dataset for situations where gold entities have low popularity. We divide the gold entities according to their pageviews in wikipedia, the statistical disambiguation results are shown in Table 4. Since some pageviews can not be obtained, we only count part of gold entities. The result indicates that our model is still able to work well for low-frequency entities. But for medium-frequency gold entities, our model doesn't work well enough. The most important reason is that other candidate entities corresponding to these medium-frequency gold entities have higher pageviews and local similarities, which makes the model difficult to distinguish." + ], + [ + "To demonstrate the effects of RLEL, we evaluate our model under different conditions. First, we evaluate the effect of sequence length on global decision making. Second, we assess whether sorting the mentions have a positive effect on the results. Third, we analysis the results of not adding globally encoding during entity selection. Last, we compare our RL selection strategy with the greedy choice.", + "A document may contain multiple topics, so we do not add all mentions to a single sequence. In practice, we add some adjacent mentions to the sequence and use reinforcement learning to select entities from beginning to end. To analysis the impact of the number of mentions on joint disambiguation, we experiment with sequences on different lengths. The results on AIDA-B are shown in Figure 4. We can see that when the sequence is too short or too long, the disambiguation results are both very poor. When the sequence length is less than 3, delay reward can't work in reinforcement learning, and when the sequence length reaches 5 or more, noise data may be added. Finally, we choose the 4 adjacent mentions to form a sequence.", + "In this section, we test whether ranking mentions is helpful for entity selections. At first, we directly input them into the global encoder by the order they appear in the text. We record the disambiguation results and compare them with the method which adopts ranking mentions. As shown in Figure 5a, the model with ranking mentions has achieved better performances on most of datasets, indicating that it is effective to place the mention that with a higher local similarity in front of the sequence. It is worth noting that the effect of ranking mentions is not obvious on the MSNBC dataset, the reason is that most of mentions in MSNBC have similar local similarities, the order of disambiguation has little effect on the final result.", + "Most of previous methods mainly use the similarities between entities to correlate each other, but our model associates them by encoding the selected entity information. To assess whether the global encoding contributes to disambiguation rather than add noise, we compare the performance with and without adding the global information. When the global encoding is not added, the current state only contains the mention context representation, candidate entity representation and feature representation, notably, the selected target entity information is not taken into account. From the results in Figure 5b, we can see that the model with global encoding achieves an improvement of 4% accuracy over the method that without global encoding.", + "To illustrate the necessity for adopting the reinforcement learning for entity selection, we compare two entity selection strategies like BIBREF5 . Specifically, we perform entity selection respectively with reinforcement learning and greedy choice. The greedy choice is to select the entity with largest local similarity from candidate set. But the reinforcement learning selection is guided by delay reward, which has a global perspective. In the comparative experiment, we keep the other conditions consistent, just replace the RL selection with a greedy choice. Based on the results in Figure 5c, we can draw a conclusion that our entity selector perform much better than greedy strategies." + ], + [ + "Table 5 shows two entity selection examples by our RLEL model. For multiple mentions appearing in the document, we first sort them according to their local similarities, and select the target entities in order by the reinforcement learning model. From the results of sorting and disambiguation, we can see that our model is able to utilize the topical consistency between mentions and make full use of the selected target entity information." + ], + [ + "The related work can be roughly divided into two groups: entity linking and reinforcement learning." + ], + [ + "Entity linking falls broadly into two major approaches: local and global disambiguation. Early studies use local models to resolve mentions independently, they usually disambiguate mentions based on lexical matching between the mention's surrounding words and the entity profile in the reference KB. Various methods have been proposed to model mention's local context ranging from binary classification BIBREF17 to rank models BIBREF26 , BIBREF27 . In these methods, a large number of hand-designed features are applied. For some marginal mentions that are difficult to extract features, researchers also exploit the data retrieved by search engines BIBREF28 , BIBREF29 or Wikipedia sentences BIBREF30 . However, the feature engineering and search engine methods are both time-consuming and laborious. Recently, with the popularity of deep learning models, representation learning is utilized to automatically find semantic features BIBREF31 , BIBREF32 . The learned entity representations which by jointly modeling textual contexts and knowledge base are effective in combining multiple sources of information. To make full use of the information contained in representations, we also utilize the pre-trained entity embeddings in our model.", + "In recent years, with the assumption that the target entities of all mentions in a document shall be related, many novel global models for joint linking are proposed. Assuming the topical coherence among mentions, authors in BIBREF33 , BIBREF34 construct factor graph models, which represent the mention and candidate entities as variable nodes, and exploit factor nodes to denote a series of features. Two recent studies BIBREF0 , BIBREF1 use fully-connected pairwise Conditional Random Field(CRF) model and exploit loopy belief propagation to estimate the max-marginal probability. Moreover, PageRank or Random Walk BIBREF35 , BIBREF18 , BIBREF7 are utilized to select the target entity for each mention. The above probabilistic models usually need to predefine a lot of features and are difficult to calculate the max-marginal probability as the number of nodes increases. In order to automatically learn features from the data, Cao et al. BIBREF9 applies Graph Convolutional Network to flexibly encode entity graphs. However, the graph-based methods are computationally expensive because there are lots of candidate entity nodes in the graph.", + "To reduce the calculation between candidate entity pairs, Globerson et al. BIBREF24 introduce a coherence model with an attention mechanism, where each mention only focus on a fixed number of mentions. Unfortunately, choosing the number of attention mentions is not easy in practice. Two recent studies BIBREF8 , BIBREF36 finish linking all mentions by scanning the pairs of mentions at most once, they assume each mention only needs to be consistent with one another mention in the document. The limitation of their method is that the consistency information is too sparse, resulting in low confidence. Similar to us, Guo et al. BIBREF18 also sort mentions according to the difficulty of disambiguation, but they did not make full use of the information of previously referred entities for the subsequent entity disambiguation. Nguyen et al. BIBREF2 use the sequence model, but they simply encode the results of the greedy choice, and measure the similarities between the global encoding and the candidate entity representations. Their model does not consider the long-term impact of current decisions on subsequent choices, nor does they add the selected target entity information to the current state to help disambiguation." + ], + [ + "In the last few years, reinforcement learning has emerged as a powerful tool for solving complex sequential decision-making problems. It is well known for its great success in the game field, such as Go BIBREF37 and Atari games BIBREF38 . Recently, reinforcement learning has also been successfully applied to many natural language processing tasks and achieved good performance BIBREF12 , BIBREF39 , BIBREF5 . Feng et al. BIBREF5 used reinforcement learning for relation classification task by filtering out the noisy data from the sentence bag and they achieved huge improvements compared with traditional classifiers. Zhang et al. BIBREF40 applied the reinforcement learning on sentence representation by automatically discovering task-relevant structures. To automatic taxonomy induction from a set of terms, Han et al. BIBREF41 designed an end-to-end reinforcement learning model to determine which term to select and where to place it on the taxonomy, which effectively reduced the error propagation between two phases. Inspired by the above works, we also add reinforcement learning to our framework." + ], + [ + "In this paper we consider entity linking as a sequence decision problem and present a reinforcement learning based model. Our model learns the policy on selecting target entities in a sequential manner and makes decisions based on current state and previous ones. By utilizing the information of previously referred entities, we can take advantage of global consistency to disambiguate mentions. For each selection result in the current state, it also has a long-term impact on subsequent decisions, which allows learned policy strategy has a global view. In experiments, we evaluate our method on AIDA-B and other well-known datasets, the results show that our system outperforms state-of-the-art solutions. In the future, we would like to use reinforcement learning to detect mentions and determine which mention should be firstly disambiguated in the document.", + "This research is supported by the GS501100001809National Key Research and Development Program of China (No. GS5011000018092018YFB1004703), GS501100001809the Beijing Municipal Science and Technology Project under grant (No. GS501100001809", + "Z181100002718004), and GS501100001809the National Natural Science Foundation of China grants(No. GS50110000180961602466)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0163/environment/Dockerfile b/qasper-0163/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0163/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/* diff --git a/qasper-0163/instruction.md b/qasper-0163/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..2255fbb0359172f681a7efcd4ab09ae54ee8a93f --- /dev/null +++ b/qasper-0163/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Joint Entity Linking with Deep Reinforcement Learning + +Question: what are the mentioned cues? \ No newline at end of file diff --git a/qasper-0164/environment/Dockerfile b/qasper-0164/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0164/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/* diff --git a/qasper-0164/instruction.md b/qasper-0164/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..0b9129727b0919eee57059a7d5c81ca4881b0225 --- /dev/null +++ b/qasper-0164/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Classification Betters Regression in Query-based Multi-document Summarisation Techniques for Question Answering: Macquarie University at BioASQ7b + +Question: How did the author's work rank among other submissions on the challenge? \ No newline at end of file diff --git a/qasper-0165/environment/Dockerfile b/qasper-0165/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0165/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/* diff --git a/qasper-0165/instruction.md b/qasper-0165/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..bef043a0c6bbca8ca9e58ba11f1dcf774c6e17a3 --- /dev/null +++ b/qasper-0165/instruction.md @@ -0,0 +1,86 @@ +Name of Paper: Classification Betters Regression in Query-based Multi-document Summarisation Techniques for Question Answering: Macquarie University at BioASQ7b + +Question: What approaches without reinforcement learning have been tried? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Classification vs. Regression Experiments", + "Deep Learning Models", + "Reinforcement Learning", + "Evaluation Correlation Analysis", + "Submitted Runs", + "Conclusions" + ], + "paragraphs": [ + [ + "The BioASQ Challenge includes a question answering task (Phase B, part B) where the aim is to find the \u201cideal answer\u201d \u2014 that is, an answer that would normally be given by a person BIBREF0. This is in contrast with most other question answering challenges where the aim is normally to give an exact answer, usually a fact-based answer or a list. Given that the answer is based on an input that consists of a biomedical question and several relevant PubMed abstracts, the task can be seen as an instance of query-based multi-document summarisation.", + "As in past participation BIBREF1, BIBREF2, we wanted to test the use of deep learning and reinforcement learning approaches for extractive summarisation. In contrast with past years where the training procedure was based on a regression set up, this year we experiment with various classification set ups. The main contributions of this paper are:", + "We compare classification and regression approaches and show that classification produces better results than regression but the quality of the results depends on the approach followed to annotate the data labels.", + "We conduct correlation analysis between various ROUGE evaluation metrics and the human evaluations conducted at BioASQ and show that Precision and F1 correlate better than Recall.", + "Section SECREF2 briefly introduces some related work for context. Section SECREF3 describes our classification and regression experiments. Section SECREF4 details our experiments using deep learning architectures. Section SECREF5 explains the reinforcement learning approaches. Section SECREF6 shows the results of our correlation analysis between ROUGE scores and human annotations. Section SECREF7 lists the specific runs submitted at BioASQ 7b. Finally, Section SECREF8 concludes the paper." + ], + [ + "The BioASQ challenge has organised annual challenges on biomedical semantic indexing and question answering since 2013 BIBREF0. Every year there has been a task about semantic indexing (task a) and another about question answering (task b), and occasionally there have been additional tasks. The tasks defined for 2019 are:", + "Large Scale Online Biomedical Semantic Indexing.", + "Biomedical Semantic QA involving Information Retrieval (IR), Question Answering (QA), and Summarisation.", + "Medical Semantic Indexing in Spanish.", + "BioASQ Task 7b consists of two phases. Phase A provides a biomedical question as an input, and participants are expected to find relevant concepts from designated terminologies and ontologies, relevant articles from PubMed, relevant snippets from the relevant articles, and relevant RDF triples from designated ontologies. Phase B provides a biomedical question and a list of relevant articles and snippets, and participant systems are expected to return the exact answers and the ideal answers. The training data is composed of the test data from all previous years, and amounts to 2,747 samples. There has been considerable research on the use of machine learning approaches for tasks related to text summarisation, especially on single-document summarisation. Abstractive approaches normally use an encoder-decoder architecture and variants of this architecture incorporate attention BIBREF3 and pointer-generator BIBREF4. Recent approaches leveraged the use of pre-trained models BIBREF5. Recent extractive approaches to summarisation incorporate recurrent neural networks that model sequences of sentence extractions BIBREF6 and may incorporate an abstractive component and reinforcement learning during the training stage BIBREF7. But relatively few approaches have been proposed for query-based multi-document summarisation. Table TABREF8 summarises the approaches presented in the proceedings of the 2018 BioASQ challenge." + ], + [ + "Our past participation in BioASQ BIBREF1, BIBREF2 and this paper focus on extractive approaches to summarisation. Our decision to focus on extractive approaches is based on the observation that a relatively large number of sentences from the input snippets has very high ROUGE scores, thus suggesting that human annotators had a general tendency to copy text from the input to generate the target summaries BIBREF1. Our past participating systems used regression approaches using the following framework:", + "Train the regressor to predict the ROUGE-SU4 F1 score of the input sentence.", + "Produce a summary by selecting the top $n$ input sentences.", + "A novelty in the current participation is the introduction of classification approaches using the following framework.", + "Train the classifier to predict the target label (\u201csummary\u201d or \u201cnot summary\u201d) of the input sentence.", + "Produce a summary by selecting all sentences predicted as \u201csummary\u201d.", + "If the total number of sentences selected is less than $n$, select $n$ sentences with higher probability of label \u201csummary\u201d.", + "Introducing a classifier makes labelling the training data not trivial, since the target summaries are human-generated and they do not have a perfect mapping to the input sentences. In addition, some samples have multiple reference summaries. BIBREF11 showed that different data labelling approaches influence the quality of the final summary, and some labelling approaches may lead to better results than using regression. In this paper we experiment with the following labelling approaches:", + ": Label as \u201csummary\u201d all sentences from the input text that have a ROUGE score above a threshold $t$.", + ": Label as \u201csummary\u201d the $m$ input text sentences with highest ROUGE score.", + "As in BIBREF11, The ROUGE score of an input sentence was the ROUGE-SU4 F1 score of the sentence against the set of reference summaries.", + "We conducted cross-validation experiments using various values of $t$ and $m$. Table TABREF26 shows the results for the best values of $t$ and $m$ obtained. The regressor and classifier used Support Vector Regression (SVR) and Support Vector Classification (SVC) respectively. To enable a fair comparison we used the same input features in all systems. These input features combine information from the question and the input sentence and are shown in Fig. FIGREF16. The features are based on BIBREF12, and are the same as in BIBREF1, plus the addition of the position of the input snippet. The best SVC and SVR parameters were determined by grid search.", + "Preliminary experiments showed a relatively high number of cases where the classifier did not classify any of the input sentences as \u201csummary\u201d. To solve this problem, and as mentioned above, the summariser used in Table TABREF26 introduces a backoff step that extracts the $n$ sentences with highest predicted values when the summary has less than $n$ sentences. The value of $n$ is as reported in our prior work and shown in Table TABREF25.", + "The results confirm BIBREF11's finding that classification outperforms regression. However, the actual choice of optimal labelling scheme was different: whereas in BIBREF11 the optimal labelling was based on a labelling threshold of 0.1, our experiments show a better result when using the top 5 sentences as the target summary. The reason for this difference might be the fact that BIBREF11 used all sentences from the abstracts of the relevant PubMed articles, whereas we use only the snippets as the input to our summariser. Consequently, the number of input sentences is now much smaller. We therefore report the results of using the labelling schema of top 5 snippets in all subsequent classifier-based experiments of this paper.", + "barchart=[fill=black!20,draw=black] errorbar=[very thin,draw=black!75] sscale=[very thin,draw=black!75]" + ], + [ + "Based on the findings of Section SECREF3, we apply minimal changes to the deep learning regression models of BIBREF2 to convert them to classification models. In particular, we add a sigmoid activation to the final layer, and use cross-entropy as the loss function. The complete architecture is shown in Fig. FIGREF28.", + "The bottom section of Table TABREF26 shows the results of several variants of the neural architecture. The table includes a neural regressor (NNR) and a neural classifier (NNC). The neural classifier is trained in two set ups: \u201cNNC top 5\u201d uses classification labels as described in Section SECREF3, and \u201cNNC SU4 F1\u201d uses the regression labels, that is, the ROUGE-SU4 F1 scores of each sentence. Of interest is the fact that \u201cNNC SU4 F1\u201d outperforms the neural regressor. We have not explored this further and we presume that the relatively good results are due to the fact that ROUGE values range between 0 and 1, which matches the full range of probability values that can be returned by the sigmoid activation of the classifier final layer.", + "Table TABREF26 also shows the standard deviation across the cross-validation folds. Whereas this standard deviation is fairly large compared with the differences in results, in general the results are compatible with the top part of the table and prior work suggesting that classification-based approaches improve over regression-based approaches." + ], + [ + "We also experiment with the use of reinforcement learning techniques. Again these experiments are based on BIBREF2, who uses REINFORCE to train a global policy. The policy predictor uses a simple feedforward network with a hidden layer.", + "The results reported by BIBREF2 used ROUGE Recall and indicated no improvement with respect to deep learning architectures. Human evaluation results are preferable over ROUGE but these were made available after the publication of the paper. When comparing the ROUGE and human evaluation results (Table TABREF29), we observe an inversion of the results. In particular, the reinforcement learning approaches (RL) of BIBREF2 receive good human evaluation results, and as a matter of fact they are the best of our runs in two of the batches. In contrast, the regression systems (NNR) fare relatively poorly. Section SECREF6 expands on the comparison between the ROUGE and human evaluation scores.", + "Encouraged by the results of Table TABREF29, we decided to continue with our experiments with reinforcement learning. We use the same features as in BIBREF2, namely the length (in number of sentences) of the summary generated so far, plus the $tf.idf$ vectors of the following:", + "Candidate sentence;", + "Entire input to summarise;", + "Summary generated so far;", + "Candidate sentences that are yet to be processed; and", + "Question.", + "The reward used by REINFORCE is the ROUGE value of the summary generated by the system. Since BIBREF2 observed a difference between the ROUGE values of the Python implementation of ROUGE and the original Perl version (partly because the Python implementation does not include ROUGE-SU4), we compare the performance of our system when trained with each of them. Table TABREF35 summarises some of our experiments. We ran the version trained on Python ROUGE once, and the version trained on Perl twice. The two Perl runs have different results, and one of them clearly outperforms the Python run. However, given the differences of results between the two Perl runs we advice to re-run the experiments multiple times and obtain the mean and standard deviation of the runs before concluding whether there is any statistical difference between the results. But it seems that there may be an improvement of the final evaluation results when training on the Perl ROUGE values, presumably because the final evaluation results are measured using the Perl implementation of ROUGE.", + "We have also tested the use of word embeddings instead of $tf.idf$ as input features to the policy model, while keeping the same neural architecture for the policy (one hidden layer using the same number of hidden nodes). In particular, we use the mean of word embeddings using 100 and 200 dimensions. These word embeddings were pre-trained using word2vec on PubMed documents provided by the organisers of BioASQ, as we did for the architectures described in previous sections. The results, not shown in the paper, indicated no major improvement, and re-runs of the experiments showed different results on different runs. Consequently, our submission to BioASQ included the original system using $tf.idf$ as input features in all batches but batch 2, as described in Section SECREF7." + ], + [ + "As mentioned in Section SECREF5, there appears to be a large discrepancy between ROUGE Recall and the human evaluations. This section describes a correlation analysis between human and ROUGE evaluations using the runs of all participants to all previous BioASQ challenges that included human evaluations (Phase B, ideal answers). The human evaluation results were scraped from the BioASQ Results page, and the ROUGE results were kindly provided by the organisers. We compute the correlation of each of the ROUGE metrics (recall, precision, F1 for ROUGE-2 and ROUGE-SU4) against the average of the human scores. The correlation metrics are Pearson, Kendall, and a revised Kendall correlation explained below.", + "The Pearson correlation between two variables is computed as the covariance of the two variables divided by the product of their standard deviations. This correlation is a good indication of a linear relation between the two variables, but may not be very effective when there is non-linear correlation.", + "The Spearman rank correlation and the Kendall rank correlation are two of the most popular among metrics that aim to detect non-linear correlations. The Spearman rank correlation between two variables can be computed as the Pearson correlation between the rank values of the two variables, whereas the Kendall rank correlation measures the ordinal association between the two variables using Equation DISPLAY_FORM36.", + "It is useful to account for the fact that the results are from 28 independent sets (3 batches in BioASQ 1 and 5 batches each year between BioASQ 2 and BioASQ 6). We therefore also compute a revised Kendall rank correlation measure that only considers pairs of variable values within the same set. The revised metric is computed using Equation DISPLAY_FORM37, where $S$ is the list of different sets.", + "Table TABREF38 shows the results of all correlation metrics. Overall, ROUGE-2 and ROUGE-SU4 give similar correlation values but ROUGE-SU4 is marginally better. Among precision, recall and F1, both precision and F1 are similar, but precision gives a better correlation. Recall shows poor correlation, and virtually no correlation when using the revised Kendall measure. For reporting the evaluation of results, it will be therefore more useful to use precision or F1. However, given the small difference between precision and F1, and given that precision may favour short summaries when used as a function to optimise in a machine learning setting (e.g. using reinforcement learning), it may be best to use F1 as the metric to optimise.", + "Fig. FIGREF40 shows the scatterplots of ROUGE-SU4 recall, precision and F1 with respect to the average human evaluation. We observe that the relation between ROUGE and the human evaluations is not linear, and that Precision and F1 have a clear correlation." + ], + [ + "Table TABREF41 shows the results and details of the runs submitted to BioASQ. The table uses ROUGE-SU4 Recall since this is the metric available at the time of writing this paper. However, note that, as explained in Section SECREF6, these results might differ from the final human evaluation results. Therefore we do not comment on the results, other than observing that the \u201cfirst $n$\u201d baseline produces the same results as the neural regressor. As mentioned in Section SECREF3, the labels used for the classification experiments are the 5 sentences with highest ROUGE-SU4 F1 score." + ], + [ + "Macquarie University's participation in BioASQ 7 focused on the task of generating the ideal answers. The runs use query-based extractive techniques and we experiment with classification, regression, and reinforcement learning approaches. At the time of writing there were no human evaluation results, and based on ROUGE-F1 scores under cross-validation on the training data we observed that classification approaches outperform regression approaches. We experimented with several approaches to label the individual sentences for the classifier and observed that the optimal labelling policy for this task differed from prior work.", + "We also observed poor correlation between ROUGE-Recall and human evaluation metrics and suggest to use alternative automatic evaluation metrics with better correlation, such as ROUGE-Precision or ROUGE-F1. Given the nature of precision-based metrics which could bias the system towards returning short summaries, ROUGE-F1 is probably more appropriate when using at development time, for example for the reward function used by a reinforcement learning system.", + "Reinforcement learning gives promising results, especially in human evaluations made on the runs submitted to BioASQ 6b. This year we introduced very small changes to the runs using reinforcement learning, and will aim to explore more complex reinforcement learning strategies and more complex neural models in the policy and value estimators." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0190/environment/Dockerfile b/qasper-0190/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0190/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/* diff --git a/qasper-0190/instruction.md b/qasper-0190/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..363dd0f4a0793bb216728776ccdf4b9473feb174 --- /dev/null +++ b/qasper-0190/instruction.md @@ -0,0 +1,107 @@ +Name of Paper: "Hinglish"Language -- Modeling a Messy Code-Mixed Language + +Question: Does the dataset contain content from various social media platforms? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Introduction ::: Modeling challenges", + "Related Work ::: Transfer learning based approaches", + "Related Work ::: Hybrid models", + "Dataset and Features", + "Dataset and Features ::: Challenges", + "Model Architecture", + "Model Architecture ::: Loss function", + "Model Architecture ::: Models", + "Model Architecture ::: Hyper parameters", + "Results", + "Conclusion and Future work", + "References" + ], + "paragraphs": [ + [ + "Hinglish is a linguistic blend of Hindi (very widely spoken language in India) and English (an associate language of urban areas) and is spoken by upwards of 350 million people in India. While the name is based on the Hindi language, it does not refer exclusively to Hindi, but is used in India, with English words blending with Punjabi, Gujarati, Marathi and Hindi. Sometimes, though rarely, Hinglish is used to refer to Hindi written in English script and mixing with English words or phrases. This makes analyzing the language very interesting. Its rampant usage in social media like Twitter, Facebook, Online blogs and reviews has also led to its usage in delivering hate and abuses in similar platforms. We aim to find such content in the social media focusing on the tweets. Hypothetically, if we can classify such tweets, we might be able to detect them and isolate them for further analysis before it reaches public. This will a great application of AI to the social cause and thus is motivating. An example of a simple, non offensive message written in Hinglish could be:", + "\"Why do you waste your time with . Aapna ghar sambhalta nahi(). Chale dusro ko basane..!!\"", + "The second part of the above sentence is written in Hindi while the first part is in English. Second part calls for an action to a person to bring order to his/her home before trying to settle others." + ], + [ + "From the modeling perspective there are couple of challenges introduced by the language and the labelled dataset. Generally, Hinglish follows largely fuzzy set of rules which evolves and is dependent upon the users preference. It doesn't have any formal definitions and thus the rules of usage are ambiguous. Thus, when used by different users the text produced may differ. Overall the challenges posed by this problem are:", + "Geographical variation: Depending upon the geography of origination, the content may be be highly influenced by the underlying region.", + "Language and phonetics variation: Based on a census in 2001, India has 122 major languages and 1599 other languages. The use of Hindi and English in a code switched setting is highly influenced by these language.", + "No grammar rules: Hinglish has no fixed set of grammar rules. The rules are inspired from both Hindi and English and when mixed with slur and slang produce large variation.", + "Spelling variation: There is no agreement on the spellings of the words which are mixed with English. For example to express love, a code mixed spelling, specially when used social platforms might be pyaar, pyar or pyr.", + "Dataset: Based on some earlier work, only available labelled dataset had 3189 rows of text messages of average length of 116 words and with a range of 1, 1295. Prior work addresses this concern by using Transfer Learning on an architecture learnt on about 14,500 messages with an accuracy of 83.90. We addressed this concern using data augmentation techniques applied on text data." + ], + [ + "Mathur et al. in their paper for detecting offensive tweets proposed a Ternary Trans-CNN model where they train a model architecture comprising of 3 layers of Convolution 1D having filter sizes of 15, 12 and 10 and kernel size of 3 followed by 2 dense fully connected layer of size 64 and 3. The first dense FC layer has ReLU activation while the last Dense layer had Softmax activation. They were able to train this network on a parallel English dataset provided by Davidson et al. The authors were able to achieve Accuracy of 83.9%, Precision of 80.2%, Recall of 69.8%.", + "The approach looked promising given that the dataset was merely 3189 sentences divided into three categories and thus we replicated the experiment but failed to replicate the results. The results were poor than what the original authors achieved. But, most of the model hyper-parameter choices where inspired from this work." + ], + [ + "In another localized setting of Vietnamese language, Nguyen et al. in 2017 proposed a Hybrid multi-channel CNN and LSTM model where they build feature maps for Vietnamese language using CNN to capture shorterm dependencies and LSTM to capture long term dependencies and concatenate both these feature sets to learn a unified set of features on the messages. These concatenated feature vectors are then sent to a few fully connected layers. They achieved an accuracy rate of 87.3% with this architecture." + ], + [ + "We used dataset, HEOT obtained from one of the past studies done by Mathur et al. where they annotated a set of cleaned tweets obtained from twitter for the conversations happening in Indian subcontinent. A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al. This dataset was important to employ Transfer Learning to our task since the number of labeled dataset was very small. Basic summary and examples of the data from the dataset are below:" + ], + [ + "The obtained data set had many challenges and thus a data preparation task was employed to clean the data and make it ready for the deep learning pipeline. The challenges and processes that were applied are stated below:", + "Messy text messages: The tweets had urls, punctuations, username mentions, hastags, emoticons, numbers and lots of special characters. These were all cleaned up in a preprocessing cycle to clean the data.", + "Stop words: Stop words corpus obtained from NLTK was used to eliminate most unproductive words which provide little information about individual tweets.", + "Transliteration: Followed by above two processes, we translated Hinglish tweets into English words using a two phase process", + "Transliteration: In phase I, we used translation API's provided by Google translation services and exposed via a SDK, to transliteration the Hinglish messages to English messages.", + "Translation: After transliteration, words that were specific to Hinglish were translated to English using an Hinglish-English dictionary. By doing this we converted the Hinglish message to and assortment of isolated words being presented in the message in a sequence that can also be represented using word to vector representation.", + "Data augmentation: Given the data set was very small with a high degree of imbalance in the labelled messages for three different classes, we employed a data augmentation technique to boost the learning of the deep network. Following techniques from the paper by Jason et al. was utilized in this setting that really helped during the training phase.Thsi techniques wasnt used in previous studies. The techniques were:", + "Synonym Replacement (SR):Randomly choose n words from the sentence that are not stop words. Replace each of these words with one of its synonyms chosen at random.", + "Random Insertion (RI):Find a random synonym of a random word in the sentence that is not a stop word. Insert that synonym into a random position in the sentence. Do this n times.", + "Random Swap (RS):Randomly choose two words in the sentence and swap their positions. Do this n times.", + "Random Deletion (RD):For each word in the sentence, randomly remove it with probability p.", + "Word Representation: We used word embedding representations by Glove for creating word embedding layers and to obtain the word sequence vector representations of the processed tweets. The pre-trained embedding dimension were one of the hyperparamaters for model. Further more, we introduced another bit flag hyperparameter that determined if to freeze these learnt embedding.", + "Train-test split: The labelled dataset that was available for this task was very limited in number of examples and thus as noted above few data augmentation techniques were applied to boost the learning of the network. Before applying augmentation, a train-test split of 78%-22% was done from the original, cleansed data set. Thus, 700 tweets/messages were held out for testing. All model evaluation were done in on the test set that got generated by this process. The results presented in this report are based on the performance of the model on the test set. The training set of 2489 messages were however sent to an offline pipeline for augmenting the data. The resulting training dataset was thus 7934 messages. the final distribution of messages for training and test was thus below:" + ], + [ + "We tested the performance of various model architectures by running our experiment over 100 times on a CPU based compute which later as migrated to GPU based compute to overcome the slow learning progress. Our universal metric for minimizing was the validation loss and we employed various operational techniques for optimizing on the learning process. These processes and its implementation details will be discussed later but they were learning rate decay, early stopping, model checkpointing and reducing learning rate on plateau." + ], + [ + "For the loss function we chose categorical cross entropy loss in finding the most optimal weights/parameters of the model. Formally this loss function for the model is defined as below:", + "The double sum is over the number of observations and the categories respectively. While the model probability is the probability that the observation i belongs to category c." + ], + [ + "Among the model architectures we experimented with and without data augmentation were:", + "Fully Connected dense networks: Model hyperparameters were inspired from the previous work done by Vo et al and Mathur et al. This was also used as a baseline model but we did not get appreciable performance on such architecture due to FC networks not being able to capture local and long term dependencies.", + "Convolution based architectures: Architecture and hyperparameter choices were chosen from the past study Deon on the subject. We were able to boost the performance as compared to only FC based network but we noticed better performance from architectures that are suitable to sequences such as text messages or any timeseries data.", + "Sequence models: We used SimpleRNN, LSTM, GRU, Bidirectional LSTM model architecture to capture long term dependencies of the messages in determining the class the message or the tweet belonged to.", + "Based on all the experiments we conducted below model had best performance related to metrics - Recall rate, F1 score and Overall accuracy." + ], + [ + "Choice of model parameters were in the above models were inspired from previous work done but then were tuned to the best performance of the Test dataset. Following parameters were considered for tuning.", + "Learning rate: Based on grid search the best performance was achieved when learning rate was set to 0.01. This value was arrived by a grid search on lr parameter.", + "Number of Bidirectional LSTM units: A set of 32, 64, 128 hidden activation units were considered for tuning the model. 128 was a choice made by Vo et al in modeling for Vietnamese language but with our experiments and with a small dataset to avoid overfitting to train dataset, a smaller unit sizes were considered.", + "Embedding dimension: 50, 100 and 200 dimension word representation from Glove word embedding were considered and the best results were obtained with 100d representation, consistent with choices made in the previous work.", + "Transfer learning on Embedding; Another bit flag for training the embedding on the train data or freezing the embedding from Glove was used. It was determined that set of pre-trained weights from Glove was best when it was fine tuned with Hinglish data. It provides evidence that a separate word or sentence level embedding when learnt for Hinglish text analysis will be very useful.", + "Number of dense FC layers.", + "Maximum length of the sequence to be considered: The max length of tweets/message in the dataset was 1265 while average was 116. We determined that choosing 200 resulted in the best performance." + ], + [ + "During our experimentation, it was evident that this is a hard problem especially detecting the hate speech, text in a code- mixed language. The best recall rate of 77 % for hate speech was obtained by a Bidirectional LSTM with 32 units with a recurrent drop out rate of 0.2. Precision wise GRU type of RNN sequence model faired better than other kinds for hate speech detection. On the other hand for detecting offensive and non offensive tweets, fairly satisfactory results were obtained. For offensive tweets, 92 % precision was and recall rate of 88% was obtained with GRU versus BiLSTM based models. Comparatively, Recall of 85 % and precision of 76 % was obtained by again GRU and BiLSTM based models as shown and marked in the results." + ], + [ + "The results of the experiments are encouraging on detective offensive vs non offensive tweets and messages written in Hinglish in social media. The utilization of data augmentation technique in this classification task was one of the vital contributions which led us to surpass results obtained by previous state of the art Hybrid CNN-LSTM based models. However, the results of the model for predicting hateful tweets on the contrary brings forth some shortcomings of the model. The biggest shortcoming on the model based on error analysis indicates less than generalized examples presented by the dataset. We also note that the embedding learnt from the Hinglish data set may be lacking and require extensive training to have competent word representations of Hinglish text. Given this learning's, we identify that creating word embeddings on much larger Hinglish corpora may have significant results. We also hypothesize that considering alternate methods than translation and transliteration may prove beneficial." + ], + [ + "[1] Mathur, Puneet and Sawhney, Ramit and Ayyar, Meghna and Shah, Rajiv, Did you offend me? classification of offensive tweets in hinglish language, Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)", + "[2] Mathur, Puneet and Shah, Rajiv and Sawhney, Ramit and Mahata, Debanjan Detecting offensive tweets in hindi-english code-switched language Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media", + "[3] Vo, Quan-Hoang and Nguyen, Huy-Tien and Le, Bac and Nguyen, Minh-Le Multi-channel LSTM-CNN model for Vietnamese sentiment analysis 2017 9th international conference on knowledge and systems engineering (KSE)", + "[4] Hochreiter, Sepp and Schmidhuber, J\u00fcrgen Long short-term memory Neural computation 1997", + "[5] Sinha, R Mahesh K and Thakur, Anil Multi-channel LSTM-CNN model for Vietnamese sentiment analysis 2017 9th international conference on knowledge and systems engineering (KSE)", + "[6] Pennington, Jeffrey and Socher, Richard and Manning, Christopher Glove: Global vectors for word representation Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)", + "[7] Zhang, Lei and Wang, Shuai and Liu, Bing Deep learning for sentiment analysis: A survey Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery", + "[8] Caruana, Rich and Lawrence, Steve and Giles, C Lee Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping Advances in neural information processing systems", + "[9] Beale, Mark Hudson and Hagan, Martin T and Demuth, Howard B Neural network toolbox user\u2019s guide The MathWorks Incs", + "[10] Chollet, Fran\u00e7ois and others Keras: The python deep learning library Astrophysics Source Code Library", + "[11] Wei, Jason and Zou, Kai EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)" + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0191/environment/Dockerfile b/qasper-0191/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0191/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/* diff --git a/qasper-0191/instruction.md b/qasper-0191/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..b6acd70a1edef337b7501dfdc47392563bba8f33 --- /dev/null +++ b/qasper-0191/instruction.md @@ -0,0 +1,107 @@ +Name of Paper: "Hinglish"Language -- Modeling a Messy Code-Mixed Language + +Question: What dataset is used? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Introduction ::: Modeling challenges", + "Related Work ::: Transfer learning based approaches", + "Related Work ::: Hybrid models", + "Dataset and Features", + "Dataset and Features ::: Challenges", + "Model Architecture", + "Model Architecture ::: Loss function", + "Model Architecture ::: Models", + "Model Architecture ::: Hyper parameters", + "Results", + "Conclusion and Future work", + "References" + ], + "paragraphs": [ + [ + "Hinglish is a linguistic blend of Hindi (very widely spoken language in India) and English (an associate language of urban areas) and is spoken by upwards of 350 million people in India. While the name is based on the Hindi language, it does not refer exclusively to Hindi, but is used in India, with English words blending with Punjabi, Gujarati, Marathi and Hindi. Sometimes, though rarely, Hinglish is used to refer to Hindi written in English script and mixing with English words or phrases. This makes analyzing the language very interesting. Its rampant usage in social media like Twitter, Facebook, Online blogs and reviews has also led to its usage in delivering hate and abuses in similar platforms. We aim to find such content in the social media focusing on the tweets. Hypothetically, if we can classify such tweets, we might be able to detect them and isolate them for further analysis before it reaches public. This will a great application of AI to the social cause and thus is motivating. An example of a simple, non offensive message written in Hinglish could be:", + "\"Why do you waste your time with . Aapna ghar sambhalta nahi(). Chale dusro ko basane..!!\"", + "The second part of the above sentence is written in Hindi while the first part is in English. Second part calls for an action to a person to bring order to his/her home before trying to settle others." + ], + [ + "From the modeling perspective there are couple of challenges introduced by the language and the labelled dataset. Generally, Hinglish follows largely fuzzy set of rules which evolves and is dependent upon the users preference. It doesn't have any formal definitions and thus the rules of usage are ambiguous. Thus, when used by different users the text produced may differ. Overall the challenges posed by this problem are:", + "Geographical variation: Depending upon the geography of origination, the content may be be highly influenced by the underlying region.", + "Language and phonetics variation: Based on a census in 2001, India has 122 major languages and 1599 other languages. The use of Hindi and English in a code switched setting is highly influenced by these language.", + "No grammar rules: Hinglish has no fixed set of grammar rules. The rules are inspired from both Hindi and English and when mixed with slur and slang produce large variation.", + "Spelling variation: There is no agreement on the spellings of the words which are mixed with English. For example to express love, a code mixed spelling, specially when used social platforms might be pyaar, pyar or pyr.", + "Dataset: Based on some earlier work, only available labelled dataset had 3189 rows of text messages of average length of 116 words and with a range of 1, 1295. Prior work addresses this concern by using Transfer Learning on an architecture learnt on about 14,500 messages with an accuracy of 83.90. We addressed this concern using data augmentation techniques applied on text data." + ], + [ + "Mathur et al. in their paper for detecting offensive tweets proposed a Ternary Trans-CNN model where they train a model architecture comprising of 3 layers of Convolution 1D having filter sizes of 15, 12 and 10 and kernel size of 3 followed by 2 dense fully connected layer of size 64 and 3. The first dense FC layer has ReLU activation while the last Dense layer had Softmax activation. They were able to train this network on a parallel English dataset provided by Davidson et al. The authors were able to achieve Accuracy of 83.9%, Precision of 80.2%, Recall of 69.8%.", + "The approach looked promising given that the dataset was merely 3189 sentences divided into three categories and thus we replicated the experiment but failed to replicate the results. The results were poor than what the original authors achieved. But, most of the model hyper-parameter choices where inspired from this work." + ], + [ + "In another localized setting of Vietnamese language, Nguyen et al. in 2017 proposed a Hybrid multi-channel CNN and LSTM model where they build feature maps for Vietnamese language using CNN to capture shorterm dependencies and LSTM to capture long term dependencies and concatenate both these feature sets to learn a unified set of features on the messages. These concatenated feature vectors are then sent to a few fully connected layers. They achieved an accuracy rate of 87.3% with this architecture." + ], + [ + "We used dataset, HEOT obtained from one of the past studies done by Mathur et al. where they annotated a set of cleaned tweets obtained from twitter for the conversations happening in Indian subcontinent. A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al. This dataset was important to employ Transfer Learning to our task since the number of labeled dataset was very small. Basic summary and examples of the data from the dataset are below:" + ], + [ + "The obtained data set had many challenges and thus a data preparation task was employed to clean the data and make it ready for the deep learning pipeline. The challenges and processes that were applied are stated below:", + "Messy text messages: The tweets had urls, punctuations, username mentions, hastags, emoticons, numbers and lots of special characters. These were all cleaned up in a preprocessing cycle to clean the data.", + "Stop words: Stop words corpus obtained from NLTK was used to eliminate most unproductive words which provide little information about individual tweets.", + "Transliteration: Followed by above two processes, we translated Hinglish tweets into English words using a two phase process", + "Transliteration: In phase I, we used translation API's provided by Google translation services and exposed via a SDK, to transliteration the Hinglish messages to English messages.", + "Translation: After transliteration, words that were specific to Hinglish were translated to English using an Hinglish-English dictionary. By doing this we converted the Hinglish message to and assortment of isolated words being presented in the message in a sequence that can also be represented using word to vector representation.", + "Data augmentation: Given the data set was very small with a high degree of imbalance in the labelled messages for three different classes, we employed a data augmentation technique to boost the learning of the deep network. Following techniques from the paper by Jason et al. was utilized in this setting that really helped during the training phase.Thsi techniques wasnt used in previous studies. The techniques were:", + "Synonym Replacement (SR):Randomly choose n words from the sentence that are not stop words. Replace each of these words with one of its synonyms chosen at random.", + "Random Insertion (RI):Find a random synonym of a random word in the sentence that is not a stop word. Insert that synonym into a random position in the sentence. Do this n times.", + "Random Swap (RS):Randomly choose two words in the sentence and swap their positions. Do this n times.", + "Random Deletion (RD):For each word in the sentence, randomly remove it with probability p.", + "Word Representation: We used word embedding representations by Glove for creating word embedding layers and to obtain the word sequence vector representations of the processed tweets. The pre-trained embedding dimension were one of the hyperparamaters for model. Further more, we introduced another bit flag hyperparameter that determined if to freeze these learnt embedding.", + "Train-test split: The labelled dataset that was available for this task was very limited in number of examples and thus as noted above few data augmentation techniques were applied to boost the learning of the network. Before applying augmentation, a train-test split of 78%-22% was done from the original, cleansed data set. Thus, 700 tweets/messages were held out for testing. All model evaluation were done in on the test set that got generated by this process. The results presented in this report are based on the performance of the model on the test set. The training set of 2489 messages were however sent to an offline pipeline for augmenting the data. The resulting training dataset was thus 7934 messages. the final distribution of messages for training and test was thus below:" + ], + [ + "We tested the performance of various model architectures by running our experiment over 100 times on a CPU based compute which later as migrated to GPU based compute to overcome the slow learning progress. Our universal metric for minimizing was the validation loss and we employed various operational techniques for optimizing on the learning process. These processes and its implementation details will be discussed later but they were learning rate decay, early stopping, model checkpointing and reducing learning rate on plateau." + ], + [ + "For the loss function we chose categorical cross entropy loss in finding the most optimal weights/parameters of the model. Formally this loss function for the model is defined as below:", + "The double sum is over the number of observations and the categories respectively. While the model probability is the probability that the observation i belongs to category c." + ], + [ + "Among the model architectures we experimented with and without data augmentation were:", + "Fully Connected dense networks: Model hyperparameters were inspired from the previous work done by Vo et al and Mathur et al. This was also used as a baseline model but we did not get appreciable performance on such architecture due to FC networks not being able to capture local and long term dependencies.", + "Convolution based architectures: Architecture and hyperparameter choices were chosen from the past study Deon on the subject. We were able to boost the performance as compared to only FC based network but we noticed better performance from architectures that are suitable to sequences such as text messages or any timeseries data.", + "Sequence models: We used SimpleRNN, LSTM, GRU, Bidirectional LSTM model architecture to capture long term dependencies of the messages in determining the class the message or the tweet belonged to.", + "Based on all the experiments we conducted below model had best performance related to metrics - Recall rate, F1 score and Overall accuracy." + ], + [ + "Choice of model parameters were in the above models were inspired from previous work done but then were tuned to the best performance of the Test dataset. Following parameters were considered for tuning.", + "Learning rate: Based on grid search the best performance was achieved when learning rate was set to 0.01. This value was arrived by a grid search on lr parameter.", + "Number of Bidirectional LSTM units: A set of 32, 64, 128 hidden activation units were considered for tuning the model. 128 was a choice made by Vo et al in modeling for Vietnamese language but with our experiments and with a small dataset to avoid overfitting to train dataset, a smaller unit sizes were considered.", + "Embedding dimension: 50, 100 and 200 dimension word representation from Glove word embedding were considered and the best results were obtained with 100d representation, consistent with choices made in the previous work.", + "Transfer learning on Embedding; Another bit flag for training the embedding on the train data or freezing the embedding from Glove was used. It was determined that set of pre-trained weights from Glove was best when it was fine tuned with Hinglish data. It provides evidence that a separate word or sentence level embedding when learnt for Hinglish text analysis will be very useful.", + "Number of dense FC layers.", + "Maximum length of the sequence to be considered: The max length of tweets/message in the dataset was 1265 while average was 116. We determined that choosing 200 resulted in the best performance." + ], + [ + "During our experimentation, it was evident that this is a hard problem especially detecting the hate speech, text in a code- mixed language. The best recall rate of 77 % for hate speech was obtained by a Bidirectional LSTM with 32 units with a recurrent drop out rate of 0.2. Precision wise GRU type of RNN sequence model faired better than other kinds for hate speech detection. On the other hand for detecting offensive and non offensive tweets, fairly satisfactory results were obtained. For offensive tweets, 92 % precision was and recall rate of 88% was obtained with GRU versus BiLSTM based models. Comparatively, Recall of 85 % and precision of 76 % was obtained by again GRU and BiLSTM based models as shown and marked in the results." + ], + [ + "The results of the experiments are encouraging on detective offensive vs non offensive tweets and messages written in Hinglish in social media. The utilization of data augmentation technique in this classification task was one of the vital contributions which led us to surpass results obtained by previous state of the art Hybrid CNN-LSTM based models. However, the results of the model for predicting hateful tweets on the contrary brings forth some shortcomings of the model. The biggest shortcoming on the model based on error analysis indicates less than generalized examples presented by the dataset. We also note that the embedding learnt from the Hinglish data set may be lacking and require extensive training to have competent word representations of Hinglish text. Given this learning's, we identify that creating word embeddings on much larger Hinglish corpora may have significant results. We also hypothesize that considering alternate methods than translation and transliteration may prove beneficial." + ], + [ + "[1] Mathur, Puneet and Sawhney, Ramit and Ayyar, Meghna and Shah, Rajiv, Did you offend me? classification of offensive tweets in hinglish language, Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)", + "[2] Mathur, Puneet and Shah, Rajiv and Sawhney, Ramit and Mahata, Debanjan Detecting offensive tweets in hindi-english code-switched language Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media", + "[3] Vo, Quan-Hoang and Nguyen, Huy-Tien and Le, Bac and Nguyen, Minh-Le Multi-channel LSTM-CNN model for Vietnamese sentiment analysis 2017 9th international conference on knowledge and systems engineering (KSE)", + "[4] Hochreiter, Sepp and Schmidhuber, J\u00fcrgen Long short-term memory Neural computation 1997", + "[5] Sinha, R Mahesh K and Thakur, Anil Multi-channel LSTM-CNN model for Vietnamese sentiment analysis 2017 9th international conference on knowledge and systems engineering (KSE)", + "[6] Pennington, Jeffrey and Socher, Richard and Manning, Christopher Glove: Global vectors for word representation Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)", + "[7] Zhang, Lei and Wang, Shuai and Liu, Bing Deep learning for sentiment analysis: A survey Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery", + "[8] Caruana, Rich and Lawrence, Steve and Giles, C Lee Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping Advances in neural information processing systems", + "[9] Beale, Mark Hudson and Hagan, Martin T and Demuth, Howard B Neural network toolbox user\u2019s guide The MathWorks Incs", + "[10] Chollet, Fran\u00e7ois and others Keras: The python deep learning library Astrophysics Source Code Library", + "[11] Wei, Jason and Zou, Kai EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)" + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0196/environment/Dockerfile b/qasper-0196/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0196/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/* diff --git a/qasper-0196/instruction.md b/qasper-0196/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..4eff0761c90b9c33ae66368008ca2ca4fe30de5e --- /dev/null +++ b/qasper-0196/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: CAiRE: An End-to-End Empathetic Chatbot + +Question: What is the performance of their system? \ No newline at end of file diff --git a/qasper-0197/environment/Dockerfile b/qasper-0197/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0197/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/* diff --git a/qasper-0197/instruction.md b/qasper-0197/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..161409e692f7d41a8e2704c24f5243c0bc49b2a7 --- /dev/null +++ b/qasper-0197/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: CAiRE: An End-to-End Empathetic Chatbot + +Question: What evaluation metrics are used? \ No newline at end of file diff --git a/qasper-0198/instruction.md b/qasper-0198/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..54eabd9acd74cb3785023af504041b626f5735db --- /dev/null +++ b/qasper-0198/instruction.md @@ -0,0 +1,39 @@ +Name of Paper: CAiRE: An End-to-End Empathetic Chatbot + +Question: What is the source of the dialogues? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "User Interface", + "Scalable to Multiple Users", + "Generative Conversational Model", + "Active Learning of Ethical Values and Persona", + "Conclusion" + ], + "paragraphs": [ + [ + "Empathetic chatbots are conversational agents that can understand user emotions and respond appropriately. Incorporating empathy into the dialogue system is essential to achieve better human-robot interaction because naturally, humans express and perceive emotion in natural language to increase their sense of social bonding. In the early development stage of such conversational systems, most of the efforts were put into developing hand-crafted rules of engagement. Recently, a modularized empathetic dialogue system, XiaoIce BIBREF0 achieved an impressive number of conversational turns per session, which was even higher than average conversations between humans. Despite the promising results of XiaoIce, this system is designed using a complex architecture with hundreds of independent components, such as Natural Language Understanding and Response Generation modules, using a tremendous amount of labeled data for training each of them.", + "In contrast to such modularized dialogue system, end-to-end systems learn all components as a single model in a fully data-driven manner, and mitigate the lack of labeled data by sharing representations among different modules. In this paper, we build an end-to-end empathetic chatbot by fine-tuning BIBREF1 the Generative Pre-trained Transformer (GPT) BIBREF2 on the PersonaChat dataset BIBREF3 and the Empathetic-Dialogue dataset BIBREF4 . We establish a web-based user interface which allows multiple users to asynchronously chat with CAiRE online. CAiRE can also collect user feedback and continuously improve its response quality and discard undesirable generation behaviors (e.g. unethical responses) via active learning and negative training." + ], + [ + "As shown in Figure FIGREF4 , our user interface is based solely on text inputs. Users can type anything in the input box and get a response immediately from the server. A report button is added at the bottom to allow users to report unethical dialogues, which will then be marked and saved in our back-end server separately. To facilitate the need for teaching our chatbot how to respond properly, we add an edit button next to the response. When the user clicks it, a new input box will appear, and the user can type in the appropriate response they think the chatbot should have replied with." + ], + [ + "Due to the high demand for GPU computations during response generation, the computation cost needs to be well distributed across different GPUs to support multiple users. We adopt several approaches to maximize the utility of GPUs without crashing the system. Firstly, we set up two independent processes in each GTX 1080Ti, where we found the highest GPU utilities to be around 90%, with both processes working stably. Secondly, we employ a load-balancing module to distribute the requests to idle processes based on their working loads. During a stress testing, we simulated users sending requests every 2 seconds, and using 8 GPUs, we were able to support more than 50 concurrent requests." + ], + [ + "We apply the Generative Pre-trained Transformer (GPT) BIBREF2 as our pre-trained language model. GPT is a multi-layer Transformer decoder with a causal self-attention which is pre-trained, unsupervised, on the BooksCorpus dataset. BooksCorpus dataset contains over 7,000 unique unpublished books from a variety of genres. Pre-training on such large contiguous text corpus enables the model to capture long-range dialogue context information. Furthermore, as existing EmpatheticDialogue dataset BIBREF4 is relatively small, fine-tuning only on such dataset will limit the chitchat topic of the model. Hence, we first integrate persona into CAiRE, and pre-train the model on PersonaChat BIBREF3 , following a previous transfer-learning strategy BIBREF1 . This pre-training procedure allows CAiRE to have a more consistent persona, thus improving the engagement and consistency of the model. We refer interested readers to the code repository recently released by HuggingFace. Finally, in order to optimize empathy in CAiRE, we fine-tune this pre-trained model using EmpatheticDialogue dataset to help CAiRE understand users' feeling." + ], + [ + "CAiRE was first presented in ACL 2019 keynote talk \u201cLoquentes Machinea: Technology, Applications, and Ethics of Conversational Systems\", and after that, we have released the chatbot to the public. In one week, we received traffic from more than 500 users, along with several reports of unethical dialogues. According to such feedback, CAiRE does not have any sense of ethical value due to the lack of training data informing of inappropriate behavior. Thus, when users raise some ethically concerning questions, CAiRE may respond without considering ethical implications. For example, a user might ask \u201cWould you kill a human?\", and CAiRE could respond \u201cyes, I want!\". To mitigate this issue, we first incorporate ethical values into CAiRE by customizing the persona of it with sentences such as: \u201cmy name is caire\", \u201ci want to help humans to make a better world\", \u201ci am a good friend of humans\". Then we perform active learning based on the collected user-revised responses. We observe that this approach can greatly reduce unethical responses. As CAiRE gathers more unethical dialogues and their revisions, its performance can be further improved by negative training BIBREF5 and active learning." + ], + [ + "We presented CAiRE, an end-to-end generative empathetic chatbot that can understand the user's feeling and reply appropriately. We built a web interface for our model and have made it accessible to multiple users via a web-link. By further collecting user feedback and improving our model, we can make CAiRE more empathetic in the future, which can be a forward step for end-to-end dialogue models. " + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0199/environment/Dockerfile b/qasper-0199/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0199/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/* diff --git a/qasper-0199/instruction.md b/qasper-0199/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..9b4e82922001f86b7a60891f84182ab107855e0c --- /dev/null +++ b/qasper-0199/instruction.md @@ -0,0 +1,39 @@ +Name of Paper: CAiRE: An End-to-End Empathetic Chatbot + +Question: What pretrained LM is used? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "User Interface", + "Scalable to Multiple Users", + "Generative Conversational Model", + "Active Learning of Ethical Values and Persona", + "Conclusion" + ], + "paragraphs": [ + [ + "Empathetic chatbots are conversational agents that can understand user emotions and respond appropriately. Incorporating empathy into the dialogue system is essential to achieve better human-robot interaction because naturally, humans express and perceive emotion in natural language to increase their sense of social bonding. In the early development stage of such conversational systems, most of the efforts were put into developing hand-crafted rules of engagement. Recently, a modularized empathetic dialogue system, XiaoIce BIBREF0 achieved an impressive number of conversational turns per session, which was even higher than average conversations between humans. Despite the promising results of XiaoIce, this system is designed using a complex architecture with hundreds of independent components, such as Natural Language Understanding and Response Generation modules, using a tremendous amount of labeled data for training each of them.", + "In contrast to such modularized dialogue system, end-to-end systems learn all components as a single model in a fully data-driven manner, and mitigate the lack of labeled data by sharing representations among different modules. In this paper, we build an end-to-end empathetic chatbot by fine-tuning BIBREF1 the Generative Pre-trained Transformer (GPT) BIBREF2 on the PersonaChat dataset BIBREF3 and the Empathetic-Dialogue dataset BIBREF4 . We establish a web-based user interface which allows multiple users to asynchronously chat with CAiRE online. CAiRE can also collect user feedback and continuously improve its response quality and discard undesirable generation behaviors (e.g. unethical responses) via active learning and negative training." + ], + [ + "As shown in Figure FIGREF4 , our user interface is based solely on text inputs. Users can type anything in the input box and get a response immediately from the server. A report button is added at the bottom to allow users to report unethical dialogues, which will then be marked and saved in our back-end server separately. To facilitate the need for teaching our chatbot how to respond properly, we add an edit button next to the response. When the user clicks it, a new input box will appear, and the user can type in the appropriate response they think the chatbot should have replied with." + ], + [ + "Due to the high demand for GPU computations during response generation, the computation cost needs to be well distributed across different GPUs to support multiple users. We adopt several approaches to maximize the utility of GPUs without crashing the system. Firstly, we set up two independent processes in each GTX 1080Ti, where we found the highest GPU utilities to be around 90%, with both processes working stably. Secondly, we employ a load-balancing module to distribute the requests to idle processes based on their working loads. During a stress testing, we simulated users sending requests every 2 seconds, and using 8 GPUs, we were able to support more than 50 concurrent requests." + ], + [ + "We apply the Generative Pre-trained Transformer (GPT) BIBREF2 as our pre-trained language model. GPT is a multi-layer Transformer decoder with a causal self-attention which is pre-trained, unsupervised, on the BooksCorpus dataset. BooksCorpus dataset contains over 7,000 unique unpublished books from a variety of genres. Pre-training on such large contiguous text corpus enables the model to capture long-range dialogue context information. Furthermore, as existing EmpatheticDialogue dataset BIBREF4 is relatively small, fine-tuning only on such dataset will limit the chitchat topic of the model. Hence, we first integrate persona into CAiRE, and pre-train the model on PersonaChat BIBREF3 , following a previous transfer-learning strategy BIBREF1 . This pre-training procedure allows CAiRE to have a more consistent persona, thus improving the engagement and consistency of the model. We refer interested readers to the code repository recently released by HuggingFace. Finally, in order to optimize empathy in CAiRE, we fine-tune this pre-trained model using EmpatheticDialogue dataset to help CAiRE understand users' feeling." + ], + [ + "CAiRE was first presented in ACL 2019 keynote talk \u201cLoquentes Machinea: Technology, Applications, and Ethics of Conversational Systems\", and after that, we have released the chatbot to the public. In one week, we received traffic from more than 500 users, along with several reports of unethical dialogues. According to such feedback, CAiRE does not have any sense of ethical value due to the lack of training data informing of inappropriate behavior. Thus, when users raise some ethically concerning questions, CAiRE may respond without considering ethical implications. For example, a user might ask \u201cWould you kill a human?\", and CAiRE could respond \u201cyes, I want!\". To mitigate this issue, we first incorporate ethical values into CAiRE by customizing the persona of it with sentences such as: \u201cmy name is caire\", \u201ci want to help humans to make a better world\", \u201ci am a good friend of humans\". Then we perform active learning based on the collected user-revised responses. We observe that this approach can greatly reduce unethical responses. As CAiRE gathers more unethical dialogues and their revisions, its performance can be further improved by negative training BIBREF5 and active learning." + ], + [ + "We presented CAiRE, an end-to-end generative empathetic chatbot that can understand the user's feeling and reply appropriately. We built a web interface for our model and have made it accessible to multiple users via a web-link. By further collecting user feedback and improving our model, we can make CAiRE more empathetic in the future, which can be a forward step for end-to-end dialogue models. " + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0200/instruction.md b/qasper-0200/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..bcd33b48c62c38c584adecd5f4e74bad51b684d2 --- /dev/null +++ b/qasper-0200/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness? + +Question: What approaches they propose? \ No newline at end of file diff --git a/qasper-0207/instruction.md b/qasper-0207/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..55c414f1ccc6e8504d20409619a4987fdd36a02a --- /dev/null +++ b/qasper-0207/instruction.md @@ -0,0 +1,75 @@ +Name of Paper: Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference + +Question: Did they compare with gradient-based methods? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Task and Model", + "Visualization of Attention and Gating", + "Attention", + "LSTM Gating Signals", + "Conclusion" + ], + "paragraphs": [ + [ + "Deep learning has achieved tremendous success for many NLP tasks. However, unlike traditional methods that provide optimized weights for human understandable features, the behavior of deep learning models is much harder to interpret. Due to the high dimensionality of word embeddings, and the complex, typically recurrent architectures used for textual data, it is often unclear how and why a deep learning model reaches its decisions.", + "There are a few attempts toward explaining/interpreting deep learning-based models, mostly by visualizing the representation of words and/or hidden states, and their importances (via saliency or erasure) on shallow tasks like sentiment analysis and POS tagging BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . In contrast, we focus on interpreting the gating and attention signals of the intermediate layers of deep models in the challenging task of Natural Language Inference. A key concept in explaining deep models is saliency, which determines what is critical for the final decision of a deep model. So far, saliency has only been used to illustrate the impact of word embeddings. In this paper, we extend this concept to the intermediate layer of deep models to examine the saliency of attention as well as the LSTM gating signals to understand the behavior of these components and their impact on the final decision.", + "We make two main contributions. First, we introduce new strategies for interpreting the behavior of deep models in their intermediate layers, specifically, by examining the saliency of the attention and the gating signals. Second, we provide an extensive analysis of the state-of-the-art model for the NLI task and show that our methods reveal interesting insights not available from traditional methods of inspecting attention and word saliency.", + "In this paper, our focus was on NLI, which is a fundamental NLP task that requires both understanding and reasoning. Furthermore, the state-of-the-art NLI models employ complex neural architectures involving key mechanisms, such as attention and repeated reading, widely seen in successful models for other NLP tasks. As such, we expect our methods to be potentially useful for other natural understanding tasks as well." + ], + [ + "In NLI BIBREF4 , we are given two sentences, a premise and a hypothesis, the goal is to decide the logical relationship (Entailment, Neutral, or Contradiction) between them.", + "Many of the top performing NLI models BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , are variants of the ESIM model BIBREF11 , which we choose to analyze in this paper. ESIM reads the sentences independently using LSTM at first, and then applies attention to align/contrast the sentences. Another round of LSTM reading then produces the final representations, which are compared to make the prediction. Detailed description of ESIM can be found in the Appendix.", + "Using the SNLI BIBREF4 data, we train two variants of ESIM, with dimensionality 50 and 300 respectively, referred to as ESIM-50 and ESIM-300 in the remainder of the paper." + ], + [ + "In this work, we are primarily interested in the internal workings of the NLI model. In particular, we focus on the attention and the gating signals of LSTM readers, and how they contribute to the decisions of the model." + ], + [ + "Attention has been widely used in many NLP tasks BIBREF12 , BIBREF13 , BIBREF14 and is probably one of the most critical parts that affects the inference decisions. Several pieces of prior work in NLI have attempted to visualize the attention layer to provide some understanding of their models BIBREF5 , BIBREF15 . Such visualizations generate a heatmap representing the similarity between the hidden states of the premise and the hypothesis (Eq. 19 of Appendix). Unfortunately the similarities are often the same regardless of the decision.", + "Let us consider the following example, where the same premise \u201cA kid is playing in the garden\u201d, is paired with three different hypotheses:", + "A kid is taking a nap in the garden", + "A kid is having fun in the garden with her family", + "A kid is having fun in the garden", + " Note that the ground truth relationships are Contradiction, Neutral, and Entailment, respectively.", + "The first row of Fig. 1 shows the visualization of normalized attention for the three cases produced by ESIM-50, which makes correct predictions for all of them. As we can see from the figure, the three attention maps are fairly similar despite the completely different decisions. The key issue is that the attention visualization only allows us to see how the model aligns the premise with the hypothesis, but does not show how such alignment impacts the decision. This prompts us to consider the saliency of attention.", + "The concept of saliency was first introduced in vision for visualizing the spatial support on an image for a particular object class BIBREF16 . In NLP, saliency has been used to study the importance of words toward a final decision BIBREF0 .", + "We propose to examine the saliency of attention. Specifically, given a premise-hypothesis pair and the model's decision $y$ , we consider the similarity between a pair of premise and hypothesis hidden states $e_{ij}$ as a variable. The score of the decision $S(y)$ is thus a function of $e_{ij}$ for all $i$ and $j$ . The saliency of $e_{ij}$ is then defined to be $|\\frac{\\partial S(y)}{\\partial {e_{ij}}}|$ .", + "The second row of Fig. 1 presents the attention saliency map for the three examples acquired by the same ESIM-50 model. Interestingly, the saliencies are clearly different across the examples, each highlighting different parts of the alignment. Specifically, for h1, we see the alignment between \u201cis playing\u201d and \u201ctaking a nap\u201d and the alignment of \u201cin a garden\u201d to have the most prominent saliency toward the decision of Contradiction. For h2, the alignment of \u201ckid\u201d and \u201cher family\u201d seems to be the most salient for the decision of Neutral. Finally, for h3, the alignment between \u201cis having fun\u201d and \u201ckid is playing\u201d have the strongest impact toward the decision of Entailment.", + "From this example, we can see that by inspecting the attention saliency, we effectively pinpoint which part of the alignments contribute most critically to the final prediction whereas simply visualizing the attention itself reveals little information.", + "In the previous examples, we study the behavior of the same model on different inputs. Now we use the attention saliency to compare the two different ESIM models: ESIM-50 and ESIM-300.", + "Consider two examples with a shared hypothesis of \u201cA man ordered a book\u201d and premise:", + "John ordered a book from amazon", + "Mary ordered a book from amazon", + " Here ESIM-50 fails to capture the gender connections of the two different names and predicts Neutral for both inputs, whereas ESIM-300 correctly predicts Entailment for the first case and Contradiction for the second.", + "In the first two columns of Fig. 2 (column a and b) we visualize the attention of the two examples for ESIM-50 (left) and ESIM-300 (right) respectively. Although the two models make different predictions, their attention maps appear qualitatively similar.", + "In contrast, columns 3-4 of Fig. 2 (column c and d) present the attention saliency for the two examples by ESIM-50 and ESIM-300 respectively. We see that for both examples, ESIM-50 primarily focused on the alignment of \u201cordered\u201d, whereas ESIM-300 focused more on the alignment of \u201cJohn\u201d and \u201cMary\u201d with \u201cman\u201d. It is interesting to note that ESIM-300 does not appear to learn significantly different similarity values compared to ESIM-50 for the two critical pairs of words (\u201cJohn\u201d, \u201cman\u201d) and (\u201cMary\u201d, \u201cman\u201d) based on the attention map. The saliency map, however, reveals that the two models use these values quite differently, with only ESIM-300 correctly focusing on them." + ], + [ + "LSTM gating signals determine the flow of information. In other words, they indicate how LSTM reads the word sequences and how the information from different parts is captured and combined. LSTM gating signals are rarely analyzed, possibly due to their high dimensionality and complexity. In this work, we consider both the gating signals and their saliency, which is computed as the partial derivative of the score of the final decision with respect to each gating signal.", + "Instead of considering individual dimensions of the gating signals, we aggregate them to consider their norm, both for the signal and for its saliency. Note that ESIM models have two LSTM layers, the first (input) LSTM performs the input encoding and the second (inference) LSTM generates the representation for inference.", + "In Fig. 3 we plot the normalized signal and saliency norms for different gates (input, forget, output) of the Forward input (bottom three rows) and inference (top three rows) LSTMs. These results are produced by the ESIM-50 model for the three examples of Section 3.1, one for each column.", + "From the figure, we first note that the saliency tends to be somewhat consistent across different gates within the same LSTM, suggesting that we can interpret them jointly to identify parts of the sentence important for the model's prediction.", + "Comparing across examples, we see that the saliency curves show pronounced differences across the examples. For instance, the saliency pattern of the Neutral example is significantly different from the other two examples, and heavily concentrated toward the end of the sentence (\u201cwith her family\u201d). Note that without this part of the sentence, the relationship would have been Entailment. The focus (evidenced by its strong saliency and strong gating signal) on this particular part, which presents information not available from the premise, explains the model's decision of Neutral.", + "Comparing the behavior of the input LSTM and the inference LSTM, we observe interesting shifts of focus. In particular, we see that the inference LSTM tends to see much more concentrated saliency over key parts of the sentence, whereas the input LSTM sees more spread of saliency. For example, for the Contradiction example, the input LSTM sees high saliency for both \u201ctaking\u201d and \u201cin\u201d, whereas the inference LSTM primarily focuses on \u201cnap\u201d, which is the key word suggesting a Contradiction. Note that ESIM uses attention between the input and inference LSTM layers to align/contrast the sentences, hence it makes sense that the inference LSTM is more focused on the critical differences between the sentences. This is also observed for the Neutral example as well.", + "It is worth noting that, while revealing similar general trends, the backward LSTM can sometimes focus on different parts of the sentence (e.g., see Fig. 11 of Appendix), suggesting the forward and backward readings provide complementary understanding of the sentence." + ], + [ + "We propose new visualization and interpretation strategies for neural models to understand how and why they work. We demonstrate the effectiveness of the proposed strategies on a complex task (NLI). Our strategies are able to provide interesting insights not achievable by previous explanation techniques. Our future work will extend our study to consider other NLP tasks and models with the goal of producing useful insights for further improving these models. Model In this section we describe the ESIM model. We divide ESIM to three main parts: 1) input encoding, 2) attention, and 3) inference. Figure 4 demonstrates a high-level view of the ESIM framework. Let $u=[u_1, \\cdots , u_n]$ and $v=[v_1, \\cdots , v_m]$ be the given premise with length $n$ and hypothesis with length $m$ respectively, where $u_i, v_j \\in \\mathbb {R}^r$ are word embeddings of $r$ -dimensional vector. The goal is to predict a label $y$ that indicates the logical relationship between premise $u$ and hypothesis $v$ . Below we briefly explain the aforementioned parts. Input Encoding It utilizes a bidirectional LSTM (BiLSTM) for encoding the given premise and hypothesis using Equations 16 and 17 respectively. ", + "$$\\hat{u} \\in \\mathbb {R}^{n \\times 2d}$$ (Eq. ) ", + "$$\\hat{v} \\in \\mathbb {R}^{m \\times 2d}$$ (Eq. ) where $u$ and $v=[v_1, \\cdots , v_m]$0 are the reading sequences of $v=[v_1, \\cdots , v_m]$1 and $v=[v_1, \\cdots , v_m]$2 respectively. Attention It employs a soft alignment method to associate the relevant sub-components between the given premise and hypothesis. Equation 19 (energy function) computes the unnormalized attention weights as the similarity of hidden states of the premise and hypothesis. ", + "$$u$$ (Eq. ) where $v=[v_1, \\cdots , v_m]$3 and $v=[v_1, \\cdots , v_m]$4 are the hidden representations of $v=[v_1, \\cdots , v_m]$5 and $v=[v_1, \\cdots , v_m]$6 respectively which are computed earlier in Equations 16 and 17 . Next, for each word in either premise or hypothesis, the relevant semantics in the other sentence is extracted and composed according to $v=[v_1, \\cdots , v_m]$7 . Equations 20 and 21 provide formal and specific details of this procedure. ", + "$$\\tilde{v}_j$$ (Eq. ) ", + "$$\\hat{u}$$ (Eq. ) where $v=[v_1, \\cdots , v_m]$8 represents the extracted relevant information of $v=[v_1, \\cdots , v_m]$9 by attending to $n$0 while $n$1 represents the extracted relevant information of $n$2 by attending to $n$3 . Next, it passes the enriched information through a projector layer which produce the final output of attention stage. Equations 22 and 23 formally represent this process. ", + "$$p$$ (Eq. ) ", + "$$q$$ (Eq. ) Here $n$4 stands for element-wise product while $n$5 and $n$6 are the trainable weights and biases of the projector layer respectively. $n$7 and $n$8 indicate the output of attention devision for premise and hypothesis respectively. Inference During this phase, it uses another BiLSTM to aggregate the two sequences of computed matching vectors, $n$9 and $m$0 from the attention stage (Equations 27 and 28 ). ", + "$$\\emph {softmax}$$ (Eq. ) ", + "$$\\hat{u} = \\textit {BiLSTM}(u)$$ (Eq. 16) where $m$1 and $m$2 are the reading sequences of $m$3 and $m$4 respectively. Finally the concatenation max and average pooling of $m$5 and $m$6 are pass through a multilayer perceptron (MLP) classifier that includes a hidden layer with $m$7 activation and $m$8 output layer. The model is trained in an end-to-end manner. Attention Study Here we provide more examples on the NLI task which intend to examine specific behavior in this model. Such examples indicate interesting observation that we can analyze them in the future works. Table 1 shows the list of all example. LSTM Gating Signal Finally, Figure 11 depicts the backward LSTM gating signals study. " + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0208/instruction.md b/qasper-0208/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..be67a4a9df39539e3e3b3e6e0d8e9ebc0692483a --- /dev/null +++ b/qasper-0208/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering + +Question: What MC abbreviate for? \ No newline at end of file diff --git a/qasper-0209/instruction.md b/qasper-0209/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..06c6bf061d13660ce6b4278f042e37f0d5c043bb --- /dev/null +++ b/qasper-0209/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering + +Question: how much of improvement the adaptation model can get? \ No newline at end of file diff --git a/qasper-0212/instruction.md b/qasper-0212/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..b9a673cb293b869b74bab6ea0b41b912c297ec95 --- /dev/null +++ b/qasper-0212/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: SUM-QE: a BERT-based Summary Quality Estimation Model + +Question: What are their correlation results? \ No newline at end of file diff --git a/qasper-0213/instruction.md b/qasper-0213/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..bdd4fe453e491d95dea0041e53d16b1510ef91ce --- /dev/null +++ b/qasper-0213/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: SUM-QE: a BERT-based Summary Quality Estimation Model + +Question: What dataset do they use? \ No newline at end of file diff --git a/qasper-0214/instruction.md b/qasper-0214/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..43b4b2db1a6169f8fd53f1cb6ad3cb30ce799df6 --- /dev/null +++ b/qasper-0214/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: SUM-QE: a BERT-based Summary Quality Estimation Model + +Question: What simpler models do they look at? \ No newline at end of file diff --git a/qasper-0215/instruction.md b/qasper-0215/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..af944e2e76667d02a180f5cfd4846393bbfe19d2 --- /dev/null +++ b/qasper-0215/instruction.md @@ -0,0 +1,88 @@ +Name of Paper: SUM-QE: a BERT-based Summary Quality Estimation Model + +Question: What linguistic quality aspects are addressed? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Datasets", + "Methods ::: The Sum-QE Model", + "Methods ::: The Sum-QE Model ::: Single-task (BERT-FT-S-1):", + "Methods ::: The Sum-QE Model ::: Multi-task with one regressor (BERT-FT-M-1):", + "Methods ::: The Sum-QE Model ::: Multi-task with 5 regressors (BERT-FT-M-5):", + "Methods ::: Baselines ::: BiGRU s with attention:", + "Methods ::: Baselines ::: ROUGE:", + "Methods ::: Baselines ::: Language model (LM):", + "Methods ::: Baselines ::: Next sentence prediction:", + "Experiments", + "Results", + "Conclusion and Future Work", + "Acknowledgments" + ], + "paragraphs": [ + [ + "Quality Estimation (QE) is a term used in machine translation (MT) to refer to methods that measure the quality of automatically translated text without relying on human references BIBREF0, BIBREF1. In this study, we address QE for summarization. Our proposed model, Sum-QE, successfully predicts linguistic qualities of summaries that traditional evaluation metrics fail to capture BIBREF2, BIBREF3, BIBREF4, BIBREF5. Sum-QE predictions can be used for system development, to inform users of the quality of automatically produced summaries and other types of generated text, and to select the best among summaries output by multiple systems.", + "Sum-QE relies on the BERT language representation model BIBREF6. We use a pre-trained BERT model adding just a task-specific layer, and fine-tune the entire model on the task of predicting linguistic quality scores manually assigned to summaries. The five criteria addressed are given in Figure FIGREF2. We provide a thorough evaluation on three publicly available summarization datasets from NIST shared tasks, and compare the performance of our model to a wide variety of baseline methods capturing different aspects of linguistic quality. Sum-QE achieves very high correlations with human ratings, showing the ability of BERT to model linguistic qualities that relate to both text content and form." + ], + [ + "Summarization evaluation metrics like Pyramid BIBREF5 and ROUGE BIBREF3, BIBREF2 are recall-oriented; they basically measure the content from a model (reference) summary that is preserved in peer (system generated) summaries. Pyramid requires substantial human effort, even in its more recent versions that involve the use of word embeddings BIBREF8 and a lightweight crowdsourcing scheme BIBREF9. ROUGE is the most commonly used evaluation metric BIBREF10, BIBREF11, BIBREF12. Inspired by BLEU BIBREF4, it relies on common $n$-grams or subsequences between peer and model summaries. Many ROUGE versions are available, but it remains hard to decide which one to use BIBREF13. Being recall-based, ROUGE correlates well with Pyramid but poorly with linguistic qualities of summaries. BIBREF14 proposed a regression model for measuring summary quality without references. The scores of their model correlate well with Pyramid and Responsiveness, but text quality is only addressed indirectly.", + "Quality Estimation is well established in MT BIBREF15, BIBREF0, BIBREF1, BIBREF16, BIBREF17. QE methods provide a quality indicator for translation output at run-time without relying on human references, typically needed by MT evaluation metrics BIBREF4, BIBREF18. QE models for MT make use of large post-edited datasets, and apply machine learning methods to predict post-editing effort scores and quality (good/bad) labels.", + "We apply QE to summarization, focusing on linguistic qualities that reflect the readability and fluency of the generated texts. Since no post-edited datasets \u2013 like the ones used in MT \u2013 are available for summarization, we use instead the ratings assigned by human annotators with respect to a set of linguistic quality criteria. Our proposed models achieve high correlation with human judgments, showing that it is possible to estimate summary quality without human references." + ], + [ + "We use datasets from the NIST DUC-05, DUC-06 and DUC-07 shared tasks BIBREF7, BIBREF19, BIBREF20. Given a question and a cluster of newswire documents, the contestants were asked to generate a 250-word summary answering the question. DUC-05 contains 1,600 summaries (50 questions x 32 systems); in DUC-06, 1,750 summaries are included (50 questions x 35 systems); and DUC-07 has 1,440 summaries (45 questions x 32 systems).", + "The submitted summaries were manually evaluated in terms of content preservation using the Pyramid score, and according to five linguistic quality criteria ($\\mathcal {Q}1, \\dots , \\mathcal {Q}5$), described in Figure FIGREF2, that do not involve comparison with a model summary. Annotators assigned scores on a five-point scale, with 1 and 5 indicating that the summary is bad or good with respect to a specific $\\mathcal {Q}$. The overall score for a contestant with respect to a specific $\\mathcal {Q}$ is the average of the manual scores assigned to the summaries generated by the contestant. Note that the DUC-04 shared task involved seven $\\mathcal {Q}$s, but some of them were found to be highly overlapping and were grouped into five in subsequent years BIBREF20. We address these five criteria and use DUC data from 2005 onwards in our experiments." + ], + [ + "In Sum-QE, each peer summary is converted into a sequence of token embeddings, consumed by an encoder $\\mathcal {E}$ to produce a (dense vector) summary representation $h$. Then, a regressor $\\mathcal {R}$ predicts a quality score $S_{\\mathcal {Q}}$ as an affine transformation of $h$:", + "Non-linear regression could also be used, but a linear (affine) $\\mathcal {R}$ already performs well. We use BERT as our main encoder and fine-tune it in three ways, which leads to three versions of Sum-QE." + ], + [ + "The first version of Sum-QE uses five separate estimators, one per quality score, each having its own encoder $\\mathcal {E}_i$ (a separate BERT instance generating $h_i$) and regressor $\\mathcal {R}_i$ (a separate linear regression layer on top of the corresponding BERT instance):" + ], + [ + "The second version of Sum-QE uses one estimator to predict all five quality scores at once, from a single encoding $h$ of the summary, produced by a single BERT instance. The intuition is that $\\mathcal {E}$ will learn to create richer representations so that $\\mathcal {R}$ (an affine transformation of $h$ with 5 outputs) will be able to predict all quality scores:", + "where $\\mathcal {R}(h)[i]$ is the $i$-th element of the vector returned by $\\mathcal {R}$." + ], + [ + "The third version of Sum-QE is similar to BERT-FT-M-1, but we now use five different linear (affine) regressors, one per quality score:", + "Although BERT-FT-M-5 is mathematically equivalent to BERT-FT-M-1, in practice these two versions of Sum-QE produce different results because of implementation details related to how the losses of the regressors (five or one) are combined." + ], + [ + "This is very similar to Sum-QE but now $\\mathcal {E}$ is a stack of BiGRU s with self-attention BIBREF21, instead of a BERT instance. The final summary representation ($h$) is the sum of the resulting context-aware token embeddings ($h = \\sum _i a_i h_i$) weighted by their self-attention scores ($a_i$). We again have three flavors: one single-task (BiGRU-ATT-S-1) and two multi-task (BiGRU-ATT-M-1 and BiGRU-ATT-M-5)." + ], + [ + "This baseline is the ROUGE version that performs best on each dataset, among the versions considered by BIBREF13. Although ROUGE focuses on surface similarities between peer and reference summaries, we would expect properties like grammaticality, referential clarity and coherence to be captured to some extent by ROUGE versions based on long $n$-grams or longest common subsequences." + ], + [ + "For a peer summary, a reasonable estimate of $\\mathcal {Q}1$ (Grammaticality) is the perplexity returned by a pre-trained language model. We experiment with the pre-trained GPT-2 model BIBREF22, and with the probability estimates that BERT can produce for each token when the token is treated as masked (BERT-FR-LM). Given that the grammaticality of a summary can be corrupted by just a few bad tokens, we compute the perplexity by considering only the $k$ worst (lowest LM probability) tokens of the peer summary, where $k$ is a tuned hyper-parameter." + ], + [ + "BERT training relies on two tasks: predicting masked tokens and next sentence prediction. The latter seems to be aligned with the definitions of $\\mathcal {Q}3$ (Referential Clarity), $\\mathcal {Q}4$ (Focus) and $\\mathcal {Q}5$ (Structure & Coherence). Intuitively, when a sentence follows another with high probability, it should involve clear referential expressions and preserve the focus and local coherence of the text. We, therefore, use a pre-trained BERT model (BERT-FR-NS) to calculate the sentence-level perplexity of each summary:", + "where $p(s_i|s_{i-1})$ is the probability that BERT assigns to the sequence of sentences $\\left< s_{i-1}, s \\right>$, and $n$ is the number of sentences in the peer summary." + ], + [ + "To evaluate our methods for a particular $\\mathcal {Q}$, we calculate the average of the predicted scores for the summaries of each particular contestant, and the average of the corresponding manual scores assigned to the contestant's summaries. We measure the correlation between the two (predicted vs. manual) across all contestants using Spearman's $\\rho $, Kendall's $\\tau $ and Pearson's $r$.", + "We train and test the Sum-QE and BiGRU-ATT versions using a 3-fold procedure. In each fold, we train on two datasets (e.g., DUC-05, DUC-06) and test on the third (e.g., DUC-07). We follow the same procedure with the three BiGRU-based models. Hyper-perameters are tuned on a held out subset from the training set of each fold." + ], + [ + "Table TABREF23 shows Spearman's $\\rho $, Kendall's $\\tau $ and Pearson's $r$ for all datasets and models. The three fine-tuned BERT versions clearly outperform all other methods. Multi-task versions seem to perform better than single-task ones in most cases. Especially for $\\mathcal {Q}4$ and $\\mathcal {Q}5$, which are highly correlated, the multi-task BERT versions achieve the best overall results. BiGRU-ATT also benefits from multi-task learning.", + "The correlation of Sum-QE with human judgments is high or very high BIBREF23 for all $\\mathcal {Q}$s in all datasets, apart from $\\mathcal {Q}2$ in DUC-05 where it is only moderate. Manual scores for $\\mathcal {Q}2$ in DUC-05 are the highest among all $\\mathcal {Q}$s and years (between 4 and 5) and with the smallest standard deviation, as shown in Table TABREF24. Differences among systems are thus small in this respect, and although Sum-QE predicts scores in this range, it struggles to put them in the correct order, as illustrated in Figure FIGREF26.", + "BEST-ROUGE has a negative correlation with the ground-truth scores for $\\mathcal {Q}$2 since it does not account for repetitions. The BiGRU-based models also reach their lowest performance on $\\mathcal {Q}$2 in DUC-05. A possible reason for the higher relative performance of the BERT-based models, which achieve a moderate positive correlation, is that BiGRU captures long-distance relations less effectively than BERT, which utilizes Transformers BIBREF24 and has a larger receptive field. A possible improvement would be a stacked BiGRU, since the states of higher stack layers have a larger receptive field as well.", + "The BERT multi-task versions perform better with highly correlated qualities like $\\mathcal {Q}4$ and $\\mathcal {Q}5$ (as illustrated in Figures 2 to 4 in the supplementary material). However, there is not a clear winner among them. Mathematical equivalence does not lead to deterministic results, especially when random initialization and stochastic learning algorithms are involved. An in-depth exploration of this point would involve further investigation, which will be part of future work." + ], + [ + "We propose a novel Quality Estimation model for summarization which does not require human references to estimate the quality of automatically produced summaries. Sum-QE successfully predicts qualitative aspects of summaries that recall-oriented evaluation metrics fail to approximate. Leveraging powerful BERT representations, it achieves high correlations with human scores for most linguistic qualities rated, on three different datasets. Future work involves extending the Sum-QE model to capture content-related aspects, either in combination with existing evaluation metrics (like Pyramid and ROUGE) or, preferably, by identifying important information in the original text and modelling its preservation in the proposed summaries. This would preserve Sum-QE's independence from human references, a property of central importance in real-life usage scenarios and system development settings.", + "The datasets used in our experiments come from the NIST DUC shared tasks which comprise newswire articles. We believe that Sum-QE could be easily applied to other domains. A small amount of annotated data would be needed for fine-tuning \u2013 especially in domains with specialized vocabulary (e.g., biomedical) \u2013 but the model could also be used out of the box. A concrete estimation of performance in this setting will be part of future work. Also, the model could serve to estimate linguistic qualities other than the ones in the DUC dataset with mininum effort.", + "Finally, Sum-QE could serve to assess the quality of other types of texts, not only summaries. It could thus be applied to other text generation tasks, such as natural language generation and sentence compression." + ], + [ + "We would like to thank the anonymous reviewers for their helpful feedback on this work. The work has been partly supported by the Research Center of the Athens University of Economics and Business, and by the French National Research Agency under project ANR-16-CE33-0013." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0222/instruction.md b/qasper-0222/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..3e58a89642826be55eba630b984f3d08cec1a3d1 --- /dev/null +++ b/qasper-0222/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Machine Translation from Natural Language to Code using Long-Short Term Memory + +Question: Do they compare to other models? \ No newline at end of file diff --git a/qasper-0223/instruction.md b/qasper-0223/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..51acec8b84999fa645b49ef2e2524cf321cd38a3 --- /dev/null +++ b/qasper-0223/instruction.md @@ -0,0 +1,80 @@ +Name of Paper: Machine Translation from Natural Language to Code using Long-Short Term Memory + +Question: What is the architecture of the system? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Problem Description", + "Problem Description ::: Programming Language Diversity", + "Problem Description ::: Human Language Factor", + "Problem Description ::: NLP of statements", + "Proposed Methodology", + "Proposed Methodology ::: Statistical Machine Translation", + "Proposed Methodology ::: Statistical Machine Translation ::: Data Preparation", + "Proposed Methodology ::: Statistical Machine Translation ::: Vocabulary Generation", + "Proposed Methodology ::: Statistical Machine Translation ::: Neural Model Training", + "Result Analysis", + "Conclusion & Future Works", + "Acknowledgment" + ], + "paragraphs": [ + [ + "Removing computer-human language barrier is an inevitable advancement researchers are thriving to achieve for decades. One of the stages of this advancement will be coding through natural human language instead of traditional programming language. On naturalness of computer programming D. Knuth said, \u201cLet us change our traditional attitude to the construction of programs: Instead of imagining that our main task is to instruct a computer what to do, let us concentrate rather on explaining to human beings what we want a computer to do.\u201dBIBREF0. Unfortunately, learning programming language is still necessary to instruct it. Researchers and developers are working to overcome this human-machine language barrier. Multiple branches exists to solve this challenge (i.e. inter-conversion of different programming language to have universally connected programming languages). Automatic code generation through natural language is not a new concept in computer science studies. However, it is difficult to create such tool due to these following three reasons\u2013", + "Programming languages are diverse", + "An individual person expresses logical statements differently than other", + "Natural Language Processing (NLP) of programming statements is challenging since both human and programming language evolve over time", + "In this paper, a neural approach to translate pseudo-code or algorithm like human language expression into programming language code is proposed." + ], + [ + "Code repositories (i.e. Git, SVN) flourished in the last decade producing big data of code allowing data scientists to perform machine learning on these data. In 2017, Allamanis M et al. published a survey in which they presented the state-of-the-art of the research areas where machine learning is changing the way programmers code during software engineering and development process BIBREF1. This paper discusses what are the restricting factors of developing such text-to-code conversion method and what problems need to be solved\u2013" + ], + [ + "According to the sources, there are more than a thousand actively maintained programming languages, which signifies the diversity of these language . These languages were created to achieve different purpose and use different syntaxes. Low-level languages such as assembly languages are easier to express in human language because of the low or no abstraction at all whereas high-level, or Object-Oriented Programing (OOP) languages are more diversified in syntax and expression, which is challenging to bring into a unified human language structure. Nonetheless, portability and transparency between different programming languages also remains a challenge and an open research area. George D. et al. tried to overcome this problem through XML mapping BIBREF2. They tried to convert codes from C++ to Java using XML mapping as an intermediate language. However, the authors encountered challenges to support different features of both languages." + ], + [ + "One of the motivations behind this paper is - as long as it is about programming, there is a finite and small set of expression which is used in human vocabulary. For instance, programmers express a for-loop in a very few specific ways BIBREF3. Variable declaration and value assignment expressions are also limited in nature. Although all codes are executable, human representation through text may not due to the semantic brittleness of code. Since high-level languages have a wide range of syntax, programmers use different linguistic expressions to explain those. For instance, small changes like swapping function arguments can significantly change the meaning of the code. Hence the challenge remains in processing human language to understand it properly which brings us to the next problem-" + ], + [ + "Although there is a finite set of expressions for each programming statements, it is a challenge to extract information from the statements of the code accurately. Semantic analysis of linguistic expression plays an important role in this information extraction. For instance, in case of a loop, what is the initial value? What is the step value? When will the loop terminate?", + "Mihalcea R. et al. has achieved a variable success rate of 70-80% in producing code just from the problem statement expressed in human natural language BIBREF3. They focused solely on the detection of step and loops in their research. Another research group from MIT, Lei et al. use a semantic learning model for text to detect the inputs. The model produces a parser in C++ which can successfully parse more than 70% of the textual description of input BIBREF4. The test dataset and model was initially tested and targeted against ACM-ICPC participants\u00ednputs which contains diverse and sometimes complex input instructions.", + "A recent survey from Allamanis M. et al. presented the state-of-the-art on the area of naturalness of programming BIBREF1. A number of research works have been conducted on text-to-code or code-to-text area in recent years. In 2015, Oda et al. proposed a way to translate each line of Python code into natural language pseudocode using Statistical Machine Learning Technique (SMT) framework BIBREF5 was used. This translation framework was able to - it can successfully translate the code to natural language pseudo coded text in both English and Japanese. In the same year, Chris Q. et al. mapped natural language with simple if-this-then-that logical rules BIBREF6. Tihomir G. and Viktor K. developed an Integrated Development Environment (IDE) integrated code assistant tool anyCode for Java which can search, import and call function just by typing desired functionality through text BIBREF7. They have used model and mapping framework between function signatures and utilized resources like WordNet, Java Corpus, relational mapping to process text online and offline.", + "Recently in 2017, P. Yin and G. Neubig proposed a semantic parser which generates code through its neural model BIBREF8. They formulated a grammatical model which works as a skeleton for neural network training. The grammatical rules are defined based on the various generalized structure of the statements in the programming language." + ], + [ + "The use of machine learning techniques such as SMT proved to be at most 75% successful in converting human text to executable code. BIBREF9. A programming language is just like a language with less vocabulary compared to a typical human language. For instance, the code vocabulary of the training dataset was 8814 (including variable, function, class names), whereas the English vocabulary to express the same code was 13659 in total. Here, programming language is considered just like another human language and widely used SMT techniques have been applied." + ], + [ + "SMT techniques are widely used in Natural Language Processing (NLP). SMT plays a significant role in translation from one language to another, especially in lexical and grammatical rule extraction. In SMT, bilingual grammatical structures are automatically formed by statistical approaches instead of explicitly providing a grammatical model. This reduces months and years of work which requires significant collaboration between bi-lingual linguistics. Here, a neural network based machine translation model is used to translate regular text into programming code." + ], + [ + "SMT techniques require a parallel corpus in thr source and thr target language. A text-code parallel corpus similar to Fig. FIGREF12 is used in training. This parallel corpus has 18805 aligned data in it . In source data, the expression of each line code is written in the English language. In target data, the code is written in Python programming language." + ], + [ + "To train the neural model, the texts should be converted to a computational entity. To do that, two separate vocabulary files are created - one for the source texts and another for the code. Vocabulary generation is done by tokenization of words. Afterwards, the words are put into their contextual vector space using the popular word2vec BIBREF10 method to make the words computational." + ], + [ + "In order to train the translation model between text-to-code an open source Neural Machine Translation (NMT) - OpenNMT implementation is utilized BIBREF11. PyTorch is used as Neural Network coding framework. For training, three types of Recurrent Neural Network (RNN) layers are used \u2013 an encoder layer, a decoder layer and an output layer. These layers together form a LSTM model. LSTM is typically used in seq2seq translation.", + "In Fig. FIGREF13, the neural model architecture is demonstrated. The diagram shows how it takes the source and target text as input and uses it for training. Vector representation of tokenized source and target text are fed into the model. Each token of the source text is passed into an encoder cell. Target text tokens are passed into a decoder cell. Encoder cells are part of the encoder RNN layer and decoder cells are part of the decoder RNN layer. End of the input sequence is marked by a $<$eos$>$ token. Upon getting the $<$eos$>$ token, the final cell state of encoder layer initiate the output layer sequence. At each target cell state, attention is applied with the encoder RNN state and combined with the current hidden state to produce the prediction of next target token. This predictions are then fed back to the target RNN. Attention mechanism helps us to overcome the fixed length restriction of encoder-decoder sequence and allows us to process variable length between input and output sequence. Attention uses encoder state and pass it to the decoder cell to give particular attention to the start of an output layer sequence. The encoder uses an initial state to tell the decoder what it is supposed to generate. Effectively, the decoder learns to generate target tokens, conditioned on the input sequence. Sigmoidal optimization is used to optimize the prediction." + ], + [ + "Training parallel corpus had 18805 lines of annotated code in it. The training model is executed several times with different training parameters. During the final training process, 500 validation data is used to generate the recurrent neural model, which is 3% of the training data. We run the training with epoch value of 10 with a batch size of 64. After finishing the training, the accuracy of the generated model using validation data from the source corpus was 74.40% (Fig. FIGREF17).", + "Although the generated code is incoherent and often predict wrong code token, this is expected because of the limited amount of training data. LSTM generally requires a more extensive set of data (100k+ in such scenario) to build a more accurate model. The incoherence can be resolved by incorporating coding syntax tree model in future. For instance\u2013", + "\"define the method tzname with 2 arguments: self and dt.\"", + "is translated into\u2013", + "def __init__ ( self , regex ) :.", + "The translator is successfully generating the whole codeline automatically but missing the noun part (parameter and function name) part of the syntax." + ], + [ + "The main advantage of translating to a programming language is - it has a concrete and strict lexical and grammatical structure which human languages lack. The aim of this paper was to make the text-to-code framework work for general purpose programming language, primarily Python. In later phase, phrase-based word embedding can be incorporated for improved vocabulary mapping. To get more accurate target code for each line, Abstract Syntax Tree(AST) can be beneficial.", + "The contribution of this research is a machine learning model which can turn the human expression into coding expressions. This paper also discusses available methods which convert natural language to programming language successfully in fixed or tightly bounded linguistic paradigm. Approaching this problem using machine learning will give us the opportunity to explore the possibility of unified programming interface as well in the future." + ], + [ + "We would like to thank Dr. Khandaker Tabin Hasan, Head of the Depertment of Computer Science, American International University-Bangladesh for his inspiration and encouragement in all of our research works. Also, thanks to Future Technology Conference - 2019 committee for partially supporting us to join the conference and one of our colleague - Faheem Abrar, Software Developer for his thorough review and comments on this research work and supporting us by providing fund." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0224/instruction.md b/qasper-0224/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..971904706830d91f753e3dc55f146342a2813ea5 --- /dev/null +++ b/qasper-0224/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Machine Translation from Natural Language to Code using Long-Short Term Memory + +Question: How long are expressions in layman's language? \ No newline at end of file diff --git a/qasper-0225/instruction.md b/qasper-0225/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..aa73e981294c9bc1fd07892822972a90617f2489 --- /dev/null +++ b/qasper-0225/instruction.md @@ -0,0 +1,80 @@ +Name of Paper: Machine Translation from Natural Language to Code using Long-Short Term Memory + +Question: What additional techniques could be incorporated to further improve accuracy? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Problem Description", + "Problem Description ::: Programming Language Diversity", + "Problem Description ::: Human Language Factor", + "Problem Description ::: NLP of statements", + "Proposed Methodology", + "Proposed Methodology ::: Statistical Machine Translation", + "Proposed Methodology ::: Statistical Machine Translation ::: Data Preparation", + "Proposed Methodology ::: Statistical Machine Translation ::: Vocabulary Generation", + "Proposed Methodology ::: Statistical Machine Translation ::: Neural Model Training", + "Result Analysis", + "Conclusion & Future Works", + "Acknowledgment" + ], + "paragraphs": [ + [ + "Removing computer-human language barrier is an inevitable advancement researchers are thriving to achieve for decades. One of the stages of this advancement will be coding through natural human language instead of traditional programming language. On naturalness of computer programming D. Knuth said, \u201cLet us change our traditional attitude to the construction of programs: Instead of imagining that our main task is to instruct a computer what to do, let us concentrate rather on explaining to human beings what we want a computer to do.\u201dBIBREF0. Unfortunately, learning programming language is still necessary to instruct it. Researchers and developers are working to overcome this human-machine language barrier. Multiple branches exists to solve this challenge (i.e. inter-conversion of different programming language to have universally connected programming languages). Automatic code generation through natural language is not a new concept in computer science studies. However, it is difficult to create such tool due to these following three reasons\u2013", + "Programming languages are diverse", + "An individual person expresses logical statements differently than other", + "Natural Language Processing (NLP) of programming statements is challenging since both human and programming language evolve over time", + "In this paper, a neural approach to translate pseudo-code or algorithm like human language expression into programming language code is proposed." + ], + [ + "Code repositories (i.e. Git, SVN) flourished in the last decade producing big data of code allowing data scientists to perform machine learning on these data. In 2017, Allamanis M et al. published a survey in which they presented the state-of-the-art of the research areas where machine learning is changing the way programmers code during software engineering and development process BIBREF1. This paper discusses what are the restricting factors of developing such text-to-code conversion method and what problems need to be solved\u2013" + ], + [ + "According to the sources, there are more than a thousand actively maintained programming languages, which signifies the diversity of these language . These languages were created to achieve different purpose and use different syntaxes. Low-level languages such as assembly languages are easier to express in human language because of the low or no abstraction at all whereas high-level, or Object-Oriented Programing (OOP) languages are more diversified in syntax and expression, which is challenging to bring into a unified human language structure. Nonetheless, portability and transparency between different programming languages also remains a challenge and an open research area. George D. et al. tried to overcome this problem through XML mapping BIBREF2. They tried to convert codes from C++ to Java using XML mapping as an intermediate language. However, the authors encountered challenges to support different features of both languages." + ], + [ + "One of the motivations behind this paper is - as long as it is about programming, there is a finite and small set of expression which is used in human vocabulary. For instance, programmers express a for-loop in a very few specific ways BIBREF3. Variable declaration and value assignment expressions are also limited in nature. Although all codes are executable, human representation through text may not due to the semantic brittleness of code. Since high-level languages have a wide range of syntax, programmers use different linguistic expressions to explain those. For instance, small changes like swapping function arguments can significantly change the meaning of the code. Hence the challenge remains in processing human language to understand it properly which brings us to the next problem-" + ], + [ + "Although there is a finite set of expressions for each programming statements, it is a challenge to extract information from the statements of the code accurately. Semantic analysis of linguistic expression plays an important role in this information extraction. For instance, in case of a loop, what is the initial value? What is the step value? When will the loop terminate?", + "Mihalcea R. et al. has achieved a variable success rate of 70-80% in producing code just from the problem statement expressed in human natural language BIBREF3. They focused solely on the detection of step and loops in their research. Another research group from MIT, Lei et al. use a semantic learning model for text to detect the inputs. The model produces a parser in C++ which can successfully parse more than 70% of the textual description of input BIBREF4. The test dataset and model was initially tested and targeted against ACM-ICPC participants\u00ednputs which contains diverse and sometimes complex input instructions.", + "A recent survey from Allamanis M. et al. presented the state-of-the-art on the area of naturalness of programming BIBREF1. A number of research works have been conducted on text-to-code or code-to-text area in recent years. In 2015, Oda et al. proposed a way to translate each line of Python code into natural language pseudocode using Statistical Machine Learning Technique (SMT) framework BIBREF5 was used. This translation framework was able to - it can successfully translate the code to natural language pseudo coded text in both English and Japanese. In the same year, Chris Q. et al. mapped natural language with simple if-this-then-that logical rules BIBREF6. Tihomir G. and Viktor K. developed an Integrated Development Environment (IDE) integrated code assistant tool anyCode for Java which can search, import and call function just by typing desired functionality through text BIBREF7. They have used model and mapping framework between function signatures and utilized resources like WordNet, Java Corpus, relational mapping to process text online and offline.", + "Recently in 2017, P. Yin and G. Neubig proposed a semantic parser which generates code through its neural model BIBREF8. They formulated a grammatical model which works as a skeleton for neural network training. The grammatical rules are defined based on the various generalized structure of the statements in the programming language." + ], + [ + "The use of machine learning techniques such as SMT proved to be at most 75% successful in converting human text to executable code. BIBREF9. A programming language is just like a language with less vocabulary compared to a typical human language. For instance, the code vocabulary of the training dataset was 8814 (including variable, function, class names), whereas the English vocabulary to express the same code was 13659 in total. Here, programming language is considered just like another human language and widely used SMT techniques have been applied." + ], + [ + "SMT techniques are widely used in Natural Language Processing (NLP). SMT plays a significant role in translation from one language to another, especially in lexical and grammatical rule extraction. In SMT, bilingual grammatical structures are automatically formed by statistical approaches instead of explicitly providing a grammatical model. This reduces months and years of work which requires significant collaboration between bi-lingual linguistics. Here, a neural network based machine translation model is used to translate regular text into programming code." + ], + [ + "SMT techniques require a parallel corpus in thr source and thr target language. A text-code parallel corpus similar to Fig. FIGREF12 is used in training. This parallel corpus has 18805 aligned data in it . In source data, the expression of each line code is written in the English language. In target data, the code is written in Python programming language." + ], + [ + "To train the neural model, the texts should be converted to a computational entity. To do that, two separate vocabulary files are created - one for the source texts and another for the code. Vocabulary generation is done by tokenization of words. Afterwards, the words are put into their contextual vector space using the popular word2vec BIBREF10 method to make the words computational." + ], + [ + "In order to train the translation model between text-to-code an open source Neural Machine Translation (NMT) - OpenNMT implementation is utilized BIBREF11. PyTorch is used as Neural Network coding framework. For training, three types of Recurrent Neural Network (RNN) layers are used \u2013 an encoder layer, a decoder layer and an output layer. These layers together form a LSTM model. LSTM is typically used in seq2seq translation.", + "In Fig. FIGREF13, the neural model architecture is demonstrated. The diagram shows how it takes the source and target text as input and uses it for training. Vector representation of tokenized source and target text are fed into the model. Each token of the source text is passed into an encoder cell. Target text tokens are passed into a decoder cell. Encoder cells are part of the encoder RNN layer and decoder cells are part of the decoder RNN layer. End of the input sequence is marked by a $<$eos$>$ token. Upon getting the $<$eos$>$ token, the final cell state of encoder layer initiate the output layer sequence. At each target cell state, attention is applied with the encoder RNN state and combined with the current hidden state to produce the prediction of next target token. This predictions are then fed back to the target RNN. Attention mechanism helps us to overcome the fixed length restriction of encoder-decoder sequence and allows us to process variable length between input and output sequence. Attention uses encoder state and pass it to the decoder cell to give particular attention to the start of an output layer sequence. The encoder uses an initial state to tell the decoder what it is supposed to generate. Effectively, the decoder learns to generate target tokens, conditioned on the input sequence. Sigmoidal optimization is used to optimize the prediction." + ], + [ + "Training parallel corpus had 18805 lines of annotated code in it. The training model is executed several times with different training parameters. During the final training process, 500 validation data is used to generate the recurrent neural model, which is 3% of the training data. We run the training with epoch value of 10 with a batch size of 64. After finishing the training, the accuracy of the generated model using validation data from the source corpus was 74.40% (Fig. FIGREF17).", + "Although the generated code is incoherent and often predict wrong code token, this is expected because of the limited amount of training data. LSTM generally requires a more extensive set of data (100k+ in such scenario) to build a more accurate model. The incoherence can be resolved by incorporating coding syntax tree model in future. For instance\u2013", + "\"define the method tzname with 2 arguments: self and dt.\"", + "is translated into\u2013", + "def __init__ ( self , regex ) :.", + "The translator is successfully generating the whole codeline automatically but missing the noun part (parameter and function name) part of the syntax." + ], + [ + "The main advantage of translating to a programming language is - it has a concrete and strict lexical and grammatical structure which human languages lack. The aim of this paper was to make the text-to-code framework work for general purpose programming language, primarily Python. In later phase, phrase-based word embedding can be incorporated for improved vocabulary mapping. To get more accurate target code for each line, Abstract Syntax Tree(AST) can be beneficial.", + "The contribution of this research is a machine learning model which can turn the human expression into coding expressions. This paper also discusses available methods which convert natural language to programming language successfully in fixed or tightly bounded linguistic paradigm. Approaching this problem using machine learning will give us the opportunity to explore the possibility of unified programming interface as well in the future." + ], + [ + "We would like to thank Dr. Khandaker Tabin Hasan, Head of the Depertment of Computer Science, American International University-Bangladesh for his inspiration and encouragement in all of our research works. Also, thanks to Future Technology Conference - 2019 committee for partially supporting us to join the conference and one of our colleague - Faheem Abrar, Software Developer for his thorough review and comments on this research work and supporting us by providing fund." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0230/instruction.md b/qasper-0230/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..c07e7502118101843dc359898d4d775451e9f109 --- /dev/null +++ b/qasper-0230/instruction.md @@ -0,0 +1,240 @@ +Name of Paper: A Survey and Taxonomy of Adversarial Neural Networks for Text-to-Image Synthesis + +Question: What is the conclusion of comparison of proposed solution? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Introduction ::: blackTraditional Learning Based Text-to-image Synthesis", + "Introduction ::: GAN Based Text-to-image Synthesis", + "Related Work", + "Preliminaries and Frameworks", + "Preliminaries and Frameworks ::: Generative Adversarial Neural Network", + "Preliminaries and Frameworks ::: cGAN: Conditional GAN", + "Preliminaries and Frameworks ::: Simple GAN Frameworks for Text-to-Image Synthesis", + "Preliminaries and Frameworks ::: Advanced GAN Frameworks for Text-to-Image Synthesis", + "Text-to-Image Synthesis Taxonomy and Categorization", + "Text-to-Image Synthesis Taxonomy and Categorization ::: GAN based Text-to-Image Synthesis Taxonomy", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Semantic Enhancement GANs", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Semantic Enhancement GANs ::: DC-GAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Semantic Enhancement GANs ::: DC-GAN Extensions", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Semantic Enhancement GANs ::: MC-GAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Resolution Enhancement GANs", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Resolution Enhancement GANs ::: StackGAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Resolution Enhancement GANs ::: StackGAN++", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Resolution Enhancement GANs ::: AttnGAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Resolution Enhancement GANs ::: HDGAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Diversity Enhancement GANs", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Diversity Enhancement GANs ::: AC-GAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Diversity Enhancement GANs ::: TAC-GAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Diversity Enhancement GANs ::: Text-SeGAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Diversity Enhancement GANs ::: MirrorGAN and Scene Graph GAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Motion Enhancement GANs", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Motion Enhancement GANs ::: ObamaNet and T2S", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Motion Enhancement GANs ::: T2V", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Motion Enhancement GANs ::: StoryGAN", + "GAN Based Text-to-Image Synthesis Applications, Benchmark, and Evaluation and Comparisons ::: Text-to-image Synthesis Applications", + "GAN Based Text-to-Image Synthesis Applications, Benchmark, and Evaluation and Comparisons ::: Text-to-image Synthesis Benchmark Datasets", + "GAN Based Text-to-Image Synthesis Applications, Benchmark, and Evaluation and Comparisons ::: Text-to-image Synthesis Benchmark Evaluation Metrics", + "GAN Based Text-to-Image Synthesis Applications, Benchmark, and Evaluation and Comparisons ::: GAN Based Text-to-image Synthesis Results Comparison", + "GAN Based Text-to-Image Synthesis Applications, Benchmark, and Evaluation and Comparisons ::: Notable Mentions", + "Conclusion", + "conflict of interest" + ], + "paragraphs": [ + [ + "\u201c (GANs), and the variations that are now being proposed is the most interesting idea in the last 10 years in ML, in my opinion.\u201d (2016)", + "\u2013 Yann LeCun", + "A picture is worth a thousand words! While written text provide efficient, effective, and concise ways for communication, visual content, such as images, is a more comprehensive, accurate, and intelligible method of information sharing and understanding. Generation of images from text descriptions, i.e. text-to-image synthesis, is a complex computer vision and machine learning problem that has seen great progress over recent years. Automatic image generation from natural language may allow users to describe visual elements through visually-rich text descriptions. The ability to do so effectively is highly desirable as it could be used in artificial intelligence applications such as computer-aided design, image editing BIBREF0, BIBREF1, game engines for the development of the next generation of video gamesBIBREF2, and pictorial art generation BIBREF3." + ], + [ + "In the early stages of research, text-to-image synthesis was mainly carried out through a search and supervised learning combined process BIBREF4, as shown in Figure FIGREF4. In order to connect text descriptions to images, one could use correlation between keywords (or keyphrase) & images that identifies informative and \u201cpicturable\u201d text units; then, these units would search for the most likely image parts conditioned on the text, eventually optimizing the picture layout conditioned on both the text and the image parts. Such methods often integrated multiple artificial intelligence key components, including natural language processing, computer vision, computer graphics, and machine learning.", + "The major limitation of the traditional learning based text-to-image synthesis approaches is that they lack the ability to generate new image content; they can only change the characteristics of the given/training images. Alternatively, research in generative models has advanced significantly and delivers solutions to learn from training images and produce new visual content. For example, Attribute2Image BIBREF5 models each image as a composite of foreground and background. In addition, a layered generative model with disentangled latent variables is learned, using a variational auto-encoder, to generate visual content. Because the learning is customized/conditioned by given attributes, the generative models of Attribute2Image can generate images with respect to different attributes, such as gender, hair color, age, etc., as shown in Figure FIGREF5." + ], + [ + "Although generative model based text-to-image synthesis provides much more realistic image synthesis results, the image generation is still conditioned by the limited attributes. In recent years, several papers have been published on the subject of text-to-image synthesis. Most of the contributions from these papers rely on multimodal learning approaches that include generative adversarial networks and deep convolutional decoder networks as their main drivers to generate entrancing images from text BIBREF7, BIBREF8, BIBREF9, BIBREF10, BIBREF11.", + "First introduced by Ian Goodfellow et al. BIBREF9, generative adversarial networks (GANs) consist of two neural networks paired with a discriminator and a generator. These two models compete with one another, with the generator attempting to produce synthetic/fake samples that will fool the discriminator and the discriminator attempting to differentiate between real (genuine) and synthetic samples. Because GANs' adversarial training aims to cause generators to produce images similar to the real (training) images, GANs can naturally be used to generate synthetic images (image synthesis), and this process can even be customized further by using text descriptions to specify the types of images to generate, as shown in Figure FIGREF6.", + "Much like text-to-speech and speech-to-text conversion, there exists a wide variety of problems that text-to-image synthesis could solve in the computer vision field specifically BIBREF8, BIBREF12. Nowadays, researchers are attempting to solve a plethora of computer vision problems with the aid of deep convolutional networks, generative adversarial networks, and a combination of multiple methods, often called multimodal learning methods BIBREF8. For simplicity, multiple learning methods will be referred to as multimodal learning hereafter BIBREF13. Researchers often describe multimodal learning as a method that incorporates characteristics from several methods, algorithms, and ideas. This can include ideas from two or more learning approaches in order to create a robust implementation to solve an uncommon problem or improve a solution BIBREF8, BIBREF14, BIBREF15, BIBREF16, BIBREF17.", + "black In this survey, we focus primarily on reviewing recent works that aim to solve the challenge of text-to-image synthesis using generative adversarial networks (GANs). In order to provide a clear roadmap, we propose a taxonomy to summarize reviewed GANs into four major categories. Our review will elaborate the motivations of methods in each category, analyze typical models, their network architectures, and possible drawbacks for further improvement. The visual abstract of the survey and the list of reviewed GAN frameworks is shown in Figure FIGREF8.", + "black The remainder of the survey is organized as follows. Section 2 presents a brief summary of existing works on subjects similar to that of this paper and highlights the key distinctions making ours unique. Section 3 gives a short introduction to GANs and some preliminary concepts related to image generation, as they are the engines that make text-to-image synthesis possible and are essential building blocks to achieve photo-realistic images from text descriptions. Section 4 proposes a taxonomy to summarize GAN based text-to-image synthesis, discusses models and architectures of novel works focused solely on text-to-image synthesis. This section will also draw key contributions from these works in relation to their applications. Section 5 reviews GAN based text-to-image synthesis benchmarks, performance metrics, and comparisons, including a simple review of GANs for other applications. In section 6, we conclude with a brief summary and outline ideas for future interesting developments in the field of text-to-image synthesis." + ], + [ + "With the growth and success of GANs, deep convolutional decoder networks, and multimodal learning methods, these techniques were some of the first procedures which aimed to solve the challenge of image synthesis. Many engineers and scientists in computer vision and AI have contributed through extensive studies and experiments, with numerous proposals and publications detailing their contributions. Because GANs, introduced by BIBREF9, are emerging research topics, their practical applications to image synthesis are still in their infancy. Recently, many new GAN architectures and designs have been proposed to use GANs for different applications, e.g. using GANs to generate sentimental texts BIBREF18, or using GANs to transform natural images into cartoons BIBREF19.", + "Although GANs are becoming increasingly popular, very few survey papers currently exist to summarize and outline contemporaneous technical innovations and contributions of different GAN architectures BIBREF20, BIBREF21. Survey papers specifically attuned to analyzing different contributions to text-to-image synthesis using GANs are even more scarce. We have thus found two surveys BIBREF6, BIBREF7 on image synthesis using GANs, which are the two most closely related publications to our survey objective. In the following paragraphs, we briefly summarize each of these surveys and point out how our objectives differ from theirs.", + "In BIBREF6, the authors provide an overview of image synthesis using GANs. In this survey, the authors discuss the motivations for research on image synthesis and introduce some background information on the history of GANs, including a section dedicated to core concepts of GANs, namely generators, discriminators, and the min-max game analogy, and some enhancements to the original GAN model, such as conditional GANs, addition of variational auto-encoders, etc.. In this survey, we will carry out a similar review of the background knowledge because the understanding of these preliminary concepts is paramount for the rest of the paper. Three types of approaches for image generation are reviewed, including direct methods (single generator and discriminator), hierarchical methods (two or more generator-discriminator pairs, each with a different goal), and iterative methods (each generator-discriminator pair generates a gradually higher-resolution image). Following the introduction, BIBREF6 discusses methods for text-to-image and image-to-image synthesis, respectively, and also describes several evaluation metrics for synthetic images, including inception scores and Frechet Inception Distance (FID), and explains the significance of the discriminators acting as learned loss functions as opposed to fixed loss functions.", + "Different from the above survey, which has a relatively broad scope in GANs, our objective is heavily focused on text-to-image synthesis. Although this topic, text-to-image synthesis, has indeed been covered in BIBREF6, they did so in a much less detailed fashion, mostly listing the many different works in a time-sequential order. In comparison, we will review several representative methods in the field and outline their models and contributions in detail.", + "Similarly to BIBREF6, the second survey paper BIBREF7 begins with a standard introduction addressing the motivation of image synthesis and the challenges it presents followed by a section dedicated to core concepts of GANs and enhancements to the original GAN model. In addition, the paper covers the review of two types of applications: (1) unconstrained applications of image synthesis such as super-resolution, image inpainting, etc., and (2) constrained image synthesis applications, namely image-to-image, text-to-image, and sketch-to image, and also discusses image and video editing using GANs. Again, the scope of this paper is intrinsically comprehensive, while we focus specifically on text-to-image and go into more detail regarding the contributions of novel state-of-the-art models.", + "Other surveys have been published on related matters, mainly related to the advancements and applications of GANs BIBREF22, BIBREF23, but we have not found any prior works which focus specifically on text-to-image synthesis using GANs. To our knowledge, this is the first paper to do so.", + "black" + ], + [ + "In this section, we first introduce preliminary knowledge of GANs and one of its commonly used variants, conditional GAN (i.e. cGAN), which is the building block for many GAN based text-to-image synthesis models. After that, we briefly separate GAN based text-to-image synthesis into two types, Simple GAN frameworks vs. Advanced GAN frameworks, and discuss why advanced GAN architecture for image synthesis.", + "black Notice that the simple vs. advanced GAN framework separation is rather too brief, our taxonomy in the next section will propose a taxonomy to summarize advanced GAN frameworks into four categories, based on their objective and designs." + ], + [ + "Before moving on to a discussion and analysis of works applying GANs for text-to-image synthesis, there are some preliminary concepts, enhancements of GANs, datasets, and evaluation metrics that are present in some of the works described in the next section and are thus worth introducing.", + "As stated previously, GANs were introduced by Ian Goodfellow et al. BIBREF9 in 2014, and consist of two deep neural networks, a generator and a discriminator, which are trained independently with conflicting goals: The generator aims to generate samples closely related to the original data distribution and fool the discriminator, while the discriminator aims to distinguish between samples from the generator model and samples from the true data distribution by calculating the probability of the sample coming from either source. A conceptual view of the generative adversarial network (GAN) architecture is shown in Figure FIGREF11.", + "The training of GANs is an iterative process that, with each iteration, updates the generator and the discriminator with the goal of each defeating the other. leading each model to become increasingly adept at its specific task until a threshold is reached. This is analogous to a min-max game between the two models, according to the following equation:", + "In Eq. (DISPLAY_FORM10), $x$ denotes a multi-dimensional sample, e.g., an image, and $z$ denotes a multi-dimensional latent space vector, e.g., a multidimensional data point following a predefined distribution function such as that of normal distributions. $D_{\\theta _d}()$ denotes a discriminator function, controlled by parameters $\\theta _d$, which aims to classify a sample into a binary space. $G_{\\theta _g}()$ denotes a generator function, controlled by parameters $\\theta _g$, which aims to generate a sample from some latent space vector. For example, $G_{\\theta _g}(z)$ means using a latent vector $z$ to generate a synthetic/fake image, and $D_{\\theta _d}(x)$ means to classify an image $x$ as binary output (i.e. true/false or 1/0). In the GAN setting, the discriminator $D_{\\theta _d}()$ is learned to distinguish a genuine/true image (labeled as 1) from fake images (labeled as 0). Therefore, given a true image $x$, the ideal output from the discriminator $D_{\\theta _d}(x)$ would be 1. Given a fake image generated from the generator $G_{\\theta _g}(z)$, the ideal prediction from the discriminator $D_{\\theta _d}(G_{\\theta _g}(z))$ would be 0, indicating the sample is a fake image.", + "Following the above definition, the $\\min \\max $ objective function in Eq. (DISPLAY_FORM10) aims to learn parameters for the discriminator ($\\theta _d$) and generator ($\\theta _g$) to reach an optimization goal: The discriminator intends to differentiate true vs. fake images with maximum capability $\\max _{\\theta _d}$ whereas the generator intends to minimize the difference between a fake image vs. a true image $\\min _{\\theta _g}$. In other words, the discriminator sets the characteristics and the generator produces elements, often images, iteratively until it meets the attributes set forth by the discriminator. GANs are often used with images and other visual elements and are notoriously efficient in generating compelling and convincing photorealistic images. Most recently, GANs were used to generate an original painting in an unsupervised fashion BIBREF24. The following sections go into further detail regarding how the generator and discriminator are trained in GANs.", + "Generator - In image synthesis, the generator network can be thought of as a mapping from one representation space (latent space) to another (actual data) BIBREF21. When it comes to image synthesis, all of the images in the data space fall into some distribution in a very complex and high-dimensional feature space. Sampling from such a complex space is very difficult, so GANs instead train a generator to create synthetic images from a much more simple feature space (usually random noise) called the latent space. The generator network performs up-sampling of the latent space and is usually a deep neural network consisting of several convolutional and/or fully connected layers BIBREF21. The generator is trained using gradient descent to update the weights of the generator network with the aim of producing data (in our case, images) that the discriminator classifies as real.", + "Discriminator - The discriminator network can be thought of as a mapping from image data to the probability of the image coming from the real data space, and is also generally a deep neural network consisting of several convolution and/or fully connected layers. However, the discriminator performs down-sampling as opposed to up-sampling. Like the generator, it is trained using gradient descent but its goal is to update the weights so that it is more likely to correctly classify images as real or fake.", + "In GANs, the ideal outcome is for both the generator's and discriminator's cost functions to converge so that the generator produces photo-realistic images that are indistinguishable from real data, and the discriminator at the same time becomes an expert at differentiating between real and synthetic data. This, however, is not possible since a reduction in cost of one model generally leads to an increase in cost of the other. This phenomenon makes training GANs very difficult, and training them simultaneously (both models performing gradient descent in parallel) often leads to a stable orbit where neither model is able to converge. To combat this, the generator and discriminator are often trained independently. In this case, the GAN remains the same, but there are different training stages. In one stage, the weights of the generator are kept constant and gradient descent updates the weights of the discriminator, and in the other stage the weights of the discriminator are kept constant while gradient descent updates the weights of the generator. This is repeated for some number of epochs until a desired low cost for each model is reached BIBREF25." + ], + [ + "Conditional Generative Adversarial Networks (cGAN) are an enhancement of GANs proposed by BIBREF26 shortly after the introduction of GANs by BIBREF9. The objective function of the cGAN is defined in Eq. (DISPLAY_FORM13) which is very similar to the GAN objective function in Eq. (DISPLAY_FORM10) except that the inputs to both discriminator and generator are conditioned by a class label $y$.", + "The main technical innovation of cGAN is that it introduces an additional input or inputs to the original GAN model, allowing the model to be trained on information such as class labels or other conditioning variables as well as the samples themselves, concurrently. Whereas the original GAN was trained only with samples from the data distribution, resulting in the generated sample reflecting the general data distribution, cGAN enables directing the model to generate more tailored outputs.", + "In Figure FIGREF14, the condition vector is the class label (text string) \"Red bird\", which is fed to both the generator and discriminator. It is important, however, that the condition vector is related to the real data. If the model in Figure FIGREF14 was trained with the same set of real data (red birds) but the condition text was \"Yellow fish\", the generator would learn to create images of red birds when conditioned with the text \"Yellow fish\".", + "Note that the condition vector in cGAN can come in many forms, such as texts, not just limited to the class label. Such a unique design provides a direct solution to generate images conditioned by predefined specifications. As a result, cGAN has been used in text-to-image synthesis since the very first day of its invention although modern approaches can deliver much better text-to-image synthesis results.", + "black" + ], + [ + "In order to generate images from text, one simple solution is to employ the conditional GAN (cGAN) designs and add conditions to the training samples, such that the GAN is trained with respect to the underlying conditions. Several pioneer works have followed similar designs for text-to-image synthesis.", + "black An essential disadvantage of using cGAN for text-to-image synthesis is that that it cannot handle complicated textual descriptions for image generation, because cGAN uses labels as conditions to restrict the GAN inputs. If the text inputs have multiple keywords (or long text descriptions) they cannot be used simultaneously to restrict the input. Instead of using text as conditions, another two approaches BIBREF8, BIBREF16 use text as input features, and concatenate such features with other features to train discriminator and generator, as shown in Figure FIGREF15(b) and (c). To ensure text being used as GAN input, a feature embedding or feature representation learning BIBREF29, BIBREF30 function $\\varphi ()$ is often introduced to convert input text as numeric features, which are further concatenated with other features to train GANs.", + "black" + ], + [ + "Motivated by the GAN and conditional GAN (cGAN) design, many GAN based frameworks have been proposed to generate images, with different designs and architectures, such as using multiple discriminators, using progressively trained discriminators, or using hierarchical discriminators. Figure FIGREF17 outlines several advanced GAN frameworks in the literature. In addition to these frameworks, many news designs are being proposed to advance the field with rather sophisticated designs. For example, a recent work BIBREF37 proposes to use a pyramid generator and three independent discriminators, blackeach focusing on a different aspect of the images, to lead the generator towards creating images that are photo-realistic on multiple levels. Another recent publication BIBREF38 proposes to use discriminator to measure semantic relevance between image and text instead of class prediction (like most discriminator in GANs does), resulting a new GAN structure outperforming text conditioned auxiliary classifier (TAC-GAN) BIBREF16 and generating diverse, realistic, and relevant to the input text regardless of class.", + "black In the following section, we will first propose a taxonomy that summarizes advanced GAN frameworks for text-to-image synthesis, and review most recent proposed solutions to the challenge of generating photo-realistic images conditioned on natural language text descriptions using GANs. The solutions we discuss are selected based on relevance and quality of contributions. Many publications exist on the subject of image-generation using GANs, but in this paper we focus specifically on models for text-to-image synthesis, with the review emphasizing on the \u201cmodel\u201d and \u201ccontributions\u201d for text-to-image synthesis. At the end of this section, we also briefly review methods using GANs for other image-synthesis applications.", + "black" + ], + [ + "In this section, we propose a taxonomy to summarize advanced GAN based text-to-image synthesis frameworks, as shown in Figure FIGREF24. The taxonomy organizes GAN frameworks into four categories, including Semantic Enhancement GANs, Resolution Enhancement GANs, Diversity Enhancement GANs, and Motion Enhancement GAGs. Following the proposed taxonomy, each subsection will introduce several typical frameworks and address their techniques of using GANS to solve certain aspects of the text-to-mage synthesis challenges.", + "black" + ], + [ + "Although the ultimate goal of Text-to-Image synthesis is to generate images closely related to the textual descriptions, the relevance of the images to the texts are often validated from different perspectives, due to the inherent diversity of human perceptions. For example, when generating images matching to the description \u201crose flowers\u201d, some users many know the exact type of flowers they like and intend to generate rose flowers with similar colors. Other users, may seek to generate high quality rose flowers with a nice background (e.g. garden). The third group of users may be more interested in generating flowers similar to rose but with different colors and visual appearance, e.g. roses, begonia, and peony. The fourth group of users may want to not only generate flower images, but also use them to form a meaningful action, e.g. a video clip showing flower growth, performing a magic show using those flowers, or telling a love story using the flowers.", + "blackFrom the text-to-Image synthesis point of view, the first group of users intend to precisely control the semantic of the generated images, and their goal is to match the texts and images at the semantic level. The second group of users are more focused on the resolutions and the qualify of the images, in addition to the requirement that the images and texts are semantically related. For the third group of users, their goal is to diversify the output images, such that their images carry diversified visual appearances and are also semantically related. The fourth user group adds a new dimension in image synthesis, and aims to generate sequences of images which are coherent in temporal order, i.e. capture the motion information.", + "black Based on the above descriptions, we categorize GAN based Text-to-Image Synthesis into a taxonomy with four major categories, as shown in Fig. FIGREF24.", + "Semantic Enhancement GANs: Semantic enhancement GANs represent pioneer works of GAN frameworks for text-to-image synthesis. The main focus of the GAN frameworks is to ensure that the generated images are semantically related to the input texts. This objective is mainly achieved by using a neural network to encode texts as dense features, which are further fed to a second network to generate images matching to the texts.", + "Resolution Enhancement GANs: Resolution enhancement GANs mainly focus on generating high qualify images which are semantically matched to the texts. This is mainly achieved through a multi-stage GAN framework, where the outputs from earlier stage GANs are fed to the second (or later) stage GAN to generate better qualify images.", + "Diversity Enhancement GANs: Diversity enhancement GANs intend to diversify the output images, such that the generated images are not only semantically related but also have different types and visual appearance. This objective is mainly achieved through an additional component to estimate semantic relevance between generated images and texts, in order to maximize the output diversity.", + "Motion Enhancement GANs: Motion enhancement GANs intend to add a temporal dimension to the output images, such that they can form meaningful actions with respect to the text descriptions. This goal mainly achieved though a two-step process which first generates images matching to the \u201cactions\u201d of the texts, followed by a mapping or alignment procedure to ensure that images are coherent in the temporal order.", + "black In the following, we will introduce how these GAN frameworks evolve for text-to-image synthesis, and will also review some typical methods of each category.", + "black" + ], + [ + "Semantic relevance is one the of most important criteria of the text-to-image synthesis. For most GNAs discussed in this survey, they are required to generate images semantically related to the text descriptions. However, the semantic relevance is a rather subjective measure, and images are inherently rich in terms of its semantics and interpretations. Therefore, many GANs are further proposed to enhance the text-to-image synthesis from different perspectives. In this subsection, we will review several classical approaches which are commonly served as text-to-image synthesis baseline.", + "black" + ], + [ + "Deep convolution generative adversarial network (DC-GAN) BIBREF8 represents the pioneer work for text-to-image synthesis using GANs. Its main goal is to train a deep convolutional generative adversarial network (DC-GAN) on text features. During this process these text features are encoded by another neural network. This neural network is a hybrid convolutional recurrent network at the character level. Concurrently, both neural networks have also feed-forward inference in the way they condition text features. Generating realistic images automatically from natural language text is the motivation of several of the works proposed in this computer vision field. However, actual artificial intelligence (AI) systems are far from achieving this task BIBREF8, BIBREF39, BIBREF40, BIBREF41, BIBREF42, BIBREF22, BIBREF26. Lately, recurrent neural networks led the way to develop frameworks that learn discriminatively on text features. At the same time, generative adversarial networks (GANs) began recently to show some promise on generating compelling images of a whole host of elements including but not limited to faces, birds, flowers, and non-common images such as room interiorsBIBREF8. DC-GAN is a multimodal learning model that attempts to bridge together both of the above mentioned unsupervised machine learning algorithms, the recurrent neural networks (RNN) and generative adversarial networks (GANs), with the sole purpose of speeding the generation of text-to-image synthesis.", + "black Deep learning shed some light to some of the most sophisticated advances in natural language representation, image synthesis BIBREF7, BIBREF8, BIBREF43, BIBREF35, and classification of generic data BIBREF44. However, a bulk of the latest breakthroughs in deep learning and computer vision were related to supervised learning BIBREF8. Even though natural language and image synthesis were part of several contributions on the supervised side of deep learning, unsupervised learning saw recently a tremendous rise in input from the research community specially on two subproblems: text-based natural language and image synthesis BIBREF45, BIBREF14, BIBREF8, BIBREF46, BIBREF47. These subproblems are typically subdivided as focused research areas. DC-GAN's contributions are mainly driven by these two research areas. In order to generate plausible images from natural language, DC-GAN contributions revolve around developing a straightforward yet effective GAN architecture and training strategy that allows natural text to image synthesis. These contributions are primarily tested on the Caltech-UCSD Birds and Oxford-102 Flowers datasets. Each image in these datasets carry five text descriptions. These text descriptions were created by the research team when setting up the evaluation environment. The DC-GANs model is subsequently trained on several subcategories. Subcategories in this research represent the training and testing sub datasets. The performance shown by these experiments display a promising yet effective way to generate images from textual natural language descriptions BIBREF8.", + "black" + ], + [ + "Following the pioneer DC-GAN framework BIBREF8, many researches propose revised network structures (e.g. different discriminaotrs) in order to improve images with better semantic relevance to the texts. Based on the deep convolutional adversarial network (DC-GAN) network architecture, GAN-CLS with image-text matching discriminator, GAN-INT learned with text manifold interpolation and GAN-INT-CLS which combines both are proposed to find semantic match between text and image. Similar to the DC-GAN architecture, an adaptive loss function (i.e. Perceptual Loss BIBREF48) is proposed for semantic image synthesis which can synthesize a realistic image that not only matches the target text description but also keep the irrelavant features(e.g. background) from source images BIBREF49. Regarding to the Perceptual Losses, three loss functions (i.e. Pixel reconstruction loss, Activation reconstruction loss and Texture reconstruction loss) are proposed in BIBREF50 in which they construct the network architectures based on the DC-GAN, i.e. GAN-INT-CLS-Pixel, GAN-INT-CLS-VGG and GAN-INT-CLS-Gram with respect to three losses. In BIBREF49, a residual transformation unit is added in the network to retain similar structure of the source image.", + "black Following the BIBREF49 and considering the features in early layers address background while foreground is obtained in latter layers in CNN, a pair of discriminators with different architectures (i.e. Paired-D GAN) is proposed to synthesize background and foreground from a source image seperately BIBREF51. Meanwhile, the skip-connection in the generator is employed to more precisely retain background information in the source image.", + "black" + ], + [ + "When synthesising images, most text-to-image synthesis methods consider each output image as one single unit to characterize its semantic relevance to the texts. This is likely problematic because most images naturally consist of two crucial components: foreground and background. Without properly separating these two components, it's hard to characterize the semantics of an image if the whole image is treated as a single unit without proper separation.", + "black In order to enhance the semantic relevance of the images, a multi-conditional GAN (MC-GAN) BIBREF52 is proposed to synthesize a target image by combining the background of a source image and a text-described foreground object which does not exist in the source image. A unique feature of MC-GAN is that it proposes a synthesis block in which the background feature is extracted from the given image without non-linear function (i.e. only using convolution and batch normalization) and the foreground feature is the feature map from the previous layer.", + "black Because MC-GAN is able to properly model the background and foreground of the generated images, a unique strength of MC-GAN is that users are able to provide a base image and MC-GAN is able to preserve the background information of the base image to generate new images. black" + ], + [ + "Due to the fact that training GANs will be much difficult when generating high-resolution images, a two stage GAN (i.e. stackGAN) is proposed in which rough images(i.e. low-resolution images) are generated in stage-I and refined in stage-II. To further improve the quality of generated images, the second version of StackGAN (i.e. Stack++) is proposed to use multi-stage GANs to generate multi-scale images. A color-consistency regularization term is also added into the loss to keep the consistency of images in different scales.", + "black While stackGAN and StackGAN++ are both built on the global sentence vector, AttnGAN is proposed to use attention mechanism (i.e. Deep Attentional Multimodal Similarity Model (DAMSM)) to model the multi-level information (i.e. word level and sentence level) into GANs. In the following, StackGAN, StackGAN++ and AttnGAN will be explained in detail.", + "black Recently, Dynamic Memory Generative Adversarial Network (i.e. DM-GAN)BIBREF53 which uses a dynamic memory component is proposed to focus on refiningthe initial generated image which is the key to the success of generating high quality images." + ], + [ + "In 2017, Zhang et al. proposed a model for generating photo-realistic images from text descriptions called StackGAN (Stacked Generative Adversarial Network) BIBREF33. In their work, they define a two-stage model that uses two cascaded GANs, each corresponding to one of the stages. The stage I GAN takes a text description as input, converts the text description to a text embedding containing several conditioning variables, and generates a low-quality 64x64 image with rough shapes and colors based on the computed conditioning variables. The stage II GAN then takes this low-quality stage I image as well as the same text embedding and uses the conditioning variables to correct and add more detail to the stage I result. The output of stage II is a photorealistic 256$times$256 image that resembles the text description with compelling accuracy.", + "One major contribution of StackGAN is the use of cascaded GANs for text-to-image synthesis through a sketch-refinement process. By conditioning the stage II GAN on the image produced by the stage I GAN and text description, the stage II GAN is able to correct defects in the stage I output, resulting in high-quality 256x256 images. Prior works have utilized \u201cstacked\u201d GANs to separate the image generation process into structure and style BIBREF42, multiple stages each generating lower-level representations from higher-level representations of the previous stage BIBREF35, and multiple stages combined with a laplacian pyramid approach BIBREF54, which was introduced for image compression by P. Burt and E. Adelson in 1983 and uses the differences between consecutive down-samples of an original image to reconstruct the original image from its down-sampled version BIBREF55. However, these works did not use text descriptions to condition their generator models.", + "Conditioning Augmentation is the other major contribution of StackGAN. Prior works transformed the natural language text description into a fixed text embedding containing static conditioning variables which were fed to the generator BIBREF8. StackGAN does this and then creates a Gaussian distribution from the text embedding and randomly selects variables from the Gaussian distribution to add to the set of conditioning variables during training. This encourages robustness by introducing small variations to the original text embedding for a particular training image while keeping the training image that the generated output is compared to the same. The result is that the trained model produces more diverse images in the same distribution when using Conditioning Augmentation than the same model using a fixed text embedding BIBREF33." + ], + [ + "Proposed by the same users as StackGAN, StackGAN++ is also a stacked GAN model, but organizes the generators and discriminators in a \u201ctree-like\u201d structure BIBREF47 with multiple stages. The first stage combines a noise vector and conditioning variables (with Conditional Augmentation introduced in BIBREF33) for input to the first generator, which generates a low-resolution image, 64$\\times $64 by default (this can be changed depending on the desired number of stages). Each following stage uses the result from the previous stage and the conditioning variables to produce gradually higher-resolution images. These stages do not use the noise vector again, as the creators assume that the randomness it introduces is already preserved in the output of the first stage. The final stage produces a 256$\\times $256 high-quality image.", + "StackGAN++ introduces the joint conditional and unconditional approximation in their designs BIBREF47. The discriminators are trained to calculate the loss between the image produced by the generator and the conditioning variables (measuring how accurately the image represents the description) as well as the loss between the image and real images (probability of the image being real or fake). The generators then aim to minimize the sum of these losses, improving the final result." + ], + [ + "Attentional Generative Adversarial Network (AttnGAN) BIBREF10 is very similar, in terms of its structure, to StackGAN++ BIBREF47, discussed in the previous section, but some novel components are added. Like previous works BIBREF56, BIBREF8, BIBREF33, BIBREF47, a text encoder generates a text embedding with conditioning variables based on the overall sentence. Additionally, the text encoder generates a separate text embedding with conditioning variables based on individual words. This process is optimized to produce meaningful variables using a bidirectional recurrent neural network (BRNN), more specifically bidirectional Long Short Term Memory (LSTM) BIBREF57, which, for each word in the description, generates conditions based on the previous word as well as the next word (bidirectional). The first stage of AttnGAN generates a low-resolution image based on the sentence-level text embedding and random noise vector. The output is fed along with the word-level text embedding to an \u201cattention model\u201d, which matches the word-level conditioning variables to regions of the stage I image, producing a word-context matrix. This is then fed to the next stage of the model along with the raw previous stage output. Each consecutive stage works in the same manner, but produces gradually higher-resolution images conditioned on the previous stage.", + "Two major contributions were introduced in AttnGAN: the attentional generative network and the Deep Attentional Multimodal Similarity Model (DAMSM) BIBREF47. The attentional generative network matches specific regions of each stage's output image to conditioning variables from the word-level text embedding. This is a very worthy contribution, allowing each consecutive stage to focus on specific regions of the image independently, adding \u201cattentional\u201d details region by region as opposed to the whole image. The DAMSM is also a key feature introduced by AttnGAN, which is used after the result of the final stage to calculate the similarity between the generated image and the text embedding at both the sentence level and the more fine-grained word level. Table TABREF48 shows scores from different metrics for StackGAN, StackGAN++, AttnGAN, and HDGAN on the CUB, Oxford, and COCO datasets. The table shows that AttnGAN outperforms the other models in terms of IS on the CUB dataset by a small amount and greatly outperforms them on the COCO dataset." + ], + [ + "Hierarchically-nested adversarial network (HDGAN) is a method proposed by BIBREF36, and its main objective is to tackle the difficult problem of dealing with photographic images from semantic text descriptions. These semantic text descriptions are applied on images from diverse datasets. This method introduces adversarial objectives nested inside hierarchically oriented networks BIBREF36. Hierarchical networks helps regularize mid-level manifestations. In addition to regularize mid-level manifestations, it assists the training of the generator in order to capture highly complex still media elements. These elements are captured in statistical order to train the generator based on settings extracted directly from the image. The latter is an ideal scenario. However, this paper aims to incorporate a single-stream architecture. This single-stream architecture functions as the generator that will form an optimum adaptability towards the jointed discriminators. Once jointed discriminators are setup in an optimum manner, the single-stream architecture will then advance generated images to achieve a much higher resolution BIBREF36.", + "The main contributions of the HDGANs include the introduction of a visual-semantic similarity measure BIBREF36. This feature will aid in the evaluation of the consistency of generated images. In addition to checking the consistency of generated images, one of the key objectives of this step is to test the logical consistency of the end product BIBREF36. The end product in this case would be images that are semantically mapped from text-based natural language descriptions to each area on the picture e.g. a wing on a bird or petal on a flower. Deep learning has created a multitude of opportunities and challenges for researchers in the computer vision AI field. Coupled with GAN and multimodal learning architectures, this field has seen tremendous growth BIBREF8, BIBREF39, BIBREF40, BIBREF41, BIBREF42, BIBREF22, BIBREF26. Based on these advancements, HDGANs attempt to further extend some desirable and less common features when generating images from textual natural language BIBREF36. In other words, it takes sentences and treats them as a hierarchical structure. This has some positive and negative implications in most cases. For starters, it makes it more complex to generate compelling images. However, one of the key benefits of this elaborate process is the realism obtained once all processes are completed. In addition, one common feature added to this process is the ability to identify parts of sentences with bounding boxes. If a sentence includes common characteristics of a bird, it will surround the attributes of such bird with bounding boxes. In practice, this should happen if the desired image have other elements such as human faces (e.g. eyes, hair, etc), flowers (e.g. petal size, color, etc), or any other inanimate object (e.g. a table, a mug, etc). Finally, HDGANs evaluated some of its claims on common ideal text-to-image datasets such as CUB, COCO, and Oxford-102 BIBREF8, BIBREF36, BIBREF39, BIBREF40, BIBREF41, BIBREF42, BIBREF22, BIBREF26. These datasets were first utilized on earlier works BIBREF8, and most of them sport modified features such image annotations, labels, or descriptions. The qualitative and quantitative results reported by researchers in this study were far superior of earlier works in this same field of computer vision AI.", + "black" + ], + [ + "In this subsection, we introduce text-to-image synthesis methods which try to maximize the diversity of the output images, based on the text descriptions.", + "black" + ], + [ + "Two issues arise in the traditional GANs BIBREF58 for image synthesis: (1) scalabilirty problem: traditional GANs cannot predict a large number of image categories; and (2) diversity problem: images are often subject to one-to-many mapping, so one image could be labeled as different tags or being described using different texts. To address these problems, GAN conditioned on additional information, e.g. cGAN, is an alternative solution. However, although cGAN and many previously introduced approaches are able to generate images with respect to the text descriptions, they often output images with similar types and visual appearance.", + "black Slightly different from the cGAN, auxiliary classifier GANs (AC-GAN) BIBREF27 proposes to improve the diversity of output images by using an auxiliary classifier to control output images. The overall structure of AC-GAN is shown in Fig. FIGREF15(c). In AC-GAN, every generated image is associated with a class label, in addition to the true/fake label which are commonly used in GAN or cGAN. The discriminator of AC-GAN not only outputs a probability distribution over sources (i.e. whether the image is true or fake), it also output a probability distribution over the class label (i.e. predict which class the image belong to).", + "black By using an auxiliary classifier layer to predict the class of the image, AC-GAN is able to use the predicted class labels of the images to ensure that the output consists of images from different classes, resulting in diversified synthesis images. The results show that AC-GAN can generate images with high diversity.", + "black" + ], + [ + "Building on the AC-GAN, TAC-GAN BIBREF59 is proposed to replace the class information with textual descriptions as the input to perform the task of text to image synthesis. The architecture of TAC-GAN is shown in Fig. FIGREF15(d), which is similar to AC-GAN. Overall, the major difference between TAC-GAN and AC-GAN is that TAC-GAN conditions the generated images on text descriptions instead of on a class label. This design makes TAC-GAN more generic for image synthesis.", + "black For TAC-GAN, it imposes restrictions on generated images in both texts and class labels. The input vector of TAC-GAN's generative network is built based on a noise vector and embedded vector representation of textual descriptions. The discriminator of TAC-GAN is similar to that of the AC-GAN, which not only predicts whether the image is fake or not, but also predicts the label of the images. A minor difference of TAC-GAN's discriminator, compared to that of the AC-GAN, is that it also receives text information as input before performing its classification.", + "black The experiments and validations, on the Oxford-102 flowers dataset, show that the results produced by TAC-GAN are \u201cslightly better\u201d that other approaches, including GAN-INT-CLS and StackGAN.", + "black" + ], + [ + "In order to improve the diversity of the output images, both AC-GAN and TAC-GAN's discriminators predict class labels of the synthesised images. This process likely enforces the semantic diversity of the images, but class labels are inherently restrictive in describing image semantics, and images described by text can be matched to multiple labels. Therefore, instead of predicting images' class labels, an alternative solution is to directly quantify their semantic relevance.", + "black The architecture of Text-SeGAN is shown in Fig. FIGREF15(e). In order to directly quantify semantic relevance, Text-SeGAN BIBREF28 adds a regression layer to estimate the semantic relevance between the image and text instead of a classifier layer of predicting labels. The estimated semantic reference is a fractional value ranging between 0 and 1, with a higher value reflecting better semantic relevance between the image and text. Due to this unique design, an inherent advantage of Text-SeGAN is that the generated images are not limited to certain classes and are semantically matching to the text input.", + "black Experiments and validations, on Oxford-102 flower dataset, show that Text-SeGAN can generate diverse images that are semantically relevant to the input text. In addition, the results of Text-SeGAN show improved inception score compared to other approaches, including GAN-INT-CLS, StackGAN, TAC-GAN, and HDGAN.", + "black" + ], + [ + "Due to the inherent complexity of the visual images, and the diversity of text descriptions (i.e. same words could imply different meanings), it is difficulty to precisely match the texts to the visual images at the semantic levels. For most methods we have discussed so far, they employ a direct text to image generation process, but there is no validation about how generated images comply with the text in a reverse fashion.", + "black To ensure the semantic consistency and diversity, MirrorGAN BIBREF60 employs a mirror structure, which reversely learns from generated images to output texts (an image-to-text process) to further validate whether generated are indeed consistent to the input texts. MirrowGAN includes three modules: a semantic text embedding module (STEM), a global-local collaborative attentive module for cascaded image generation (GLAM), and a semantic text regeneration and alignment module (STREAM). The back to back Text-to-Image (T2I) and Image-to-Text (I2T) are combined to progressively enhance the diversity and semantic consistency of the generated images.", + "black In order to enhance the diversity of the output image, Scene Graph GAN BIBREF61 proposes to use visual scene graphs to describe the layout of the objects, allowing users to precisely specific the relationships between objects in the images. In order to convert the visual scene graph as input for GAN to generate images, this method uses graph convolution to process input graphs. It computes a scene layout by predicting bounding boxes and segmentation masks for objects. After that, it converts the computed layout to an image with a cascaded re\ufb01nement network.", + "black" + ], + [ + "Instead of focusing on generating static images, another line of text-to-image synthesis research focuses on generating videos (i.e. sequences of images) from texts. In this context, the synthesised videos are often useful resources for automated assistance or story telling.", + "black" + ], + [ + "One early/interesting work of motion enhancement GANs is to generate spoofed speech and lip-sync videos (or talking face) of Barack Obama (i.e. ObamaNet) based on text input BIBREF62. This framework is consisted of three parts, i.e. text to speech using \u201cChar2Wav\u201d, mouth shape representation synced to the audio using a time-delayed LSTM and \u201cvideo generation\u201d conditioned on the mouth shape using \u201cU-Net\u201d architecture. Although the results seem promising, ObamaNet only models the mouth region and the videos are not generated from noise which can be regarded as video prediction other than video generation.", + "black Another meaningful trial of using synthesised videos for automated assistance is to translate spoken language (e.g. text) into sign language video sequences (i.e. T2S) BIBREF63. This is often achieved through a two step process: converting texts as meaningful units to generate images, followed by a learning component to arrange images into sequential order for best representation. More specifically, using RNN based machine translation methods, texts are translated into sign language gloss sequences. Then, glosses are mapped to skeletal pose sequences using a lookup-table. To generate videos, a conditional DCGAN with the input of concatenation of latent representation of the image for a base pose and skeletal pose information is built.", + "black" + ], + [ + "In BIBREF64, a text-to-video model (T2V) is proposed based on the cGAN in which the input is the isometric Gaussian noise with the text-gist vector served as the generator. A key component of generating videos from text is to train a conditional generative model to extract both static and dynamic information from text, followed by a hybrid framework combining a Variational Autoencoder (VAE) and a Generative Adversarial Network (GAN).", + "black More specifically, T2V relies on two types of features, static features and dynamic features, to generate videos. Static features, called \u201cgist\u201d are used to sketch text-conditioned background color and object layout structure. Dynamic features, on the other hand, are considered by transforming input text into an image filter which eventually forms the video generator which consists of three entangled neural networks. The text-gist vector is generated by a gist generator which maintains static information (e.g. background) and a text2filter which captures the dynamic information (i.e. actions) in the text to generate videos.", + "black As demonstrated in the paper BIBREF64, the generated videos are semantically related to the texts, but have a rather low quality (e.g. only $64 \\times 64$ resolution).", + "black" + ], + [ + "Different from T2V which generates videos from a single text, StoryGAN aims to produce dynamic scenes consistent of specified texts (i.e. story written in a multi-sentence paragraph) using a sequential GAN model BIBREF65. Story encoder, context encoder, and discriminators are the main components of this model. By using stochastic sampling, the story encoder intends to learn an low-dimensional embedding vector for the whole story to keep the continuity of the story. The context encoder is proposed to capture contextual information during sequential image generation based on a deep RNN. Two discriminators of StoryGAN are image discriminator which evaluates the generated images and story discriminator which ensures the global consistency.", + "black The experiments and comparisons, on CLEVR dataset and Pororo cartoon dataset which are originally used for visual question answering, show that StoryGAN improves the generated video qualify in terms of Structural Similarity Index (SSIM), visual qualify, consistence, and relevance (the last three measure are based on human evaluation)." + ], + [ + "Computer vision applications have strong potential for industries including but not limited to the medical, government, military, entertainment, and online social media fields BIBREF7, BIBREF66, BIBREF67, BIBREF68, BIBREF69, BIBREF70. Text-to-image synthesis is one such application in computer vision AI that has become the main focus in recent years due to its potential for providing beneficial properties and opportunities for a wide range of applicable areas.", + "Text-to-image synthesis is an application byproduct of deep convolutional decoder networks in combination with GANs BIBREF7, BIBREF8, BIBREF10. Deep convolutional networks have contributed to several breakthroughs in image, video, speech, and audio processing. This learning method intends, among other possibilities, to help translate sequential text descriptions to images supplemented by one or many additional methods. Algorithms and methods developed in the computer vision field have allowed researchers in recent years to create realistic images from plain sentences. Advances in the computer vision, deep convolutional nets, and semantic units have shined light and redirected focus to this research area of text-to-image synthesis, having as its prime directive: to aid in the generation of compelling images with as much fidelity to text descriptions as possible.", + "To date, models for generating synthetic images from textual natural language in research laboratories at universities and private companies have yielded compelling images of flowers and birds BIBREF8. Though flowers and birds are the most common objects studied thus far, research has been applied to other classes as well. For example, there have been studies focused solely on human faces BIBREF7, BIBREF8, BIBREF71, BIBREF72.", + "It\u2019s a fascinating time for computer vision AI and deep learning researchers and enthusiasts. The consistent advancement in hardware, software, and contemporaneous development of computer vision AI research disrupts multiple industries. These advances in technology allow for the extraction of several data types from a variety of sources. For example, image data captured from a variety of photo-ready devices, such as smart-phones, and online social media services opened the door to the analysis of large amounts of media datasets BIBREF70. The availability of large media datasets allow new frameworks and algorithms to be proposed and tested on real-world data." + ], + [ + "A summary of some reviewed methods and benchmark datasets used for validation is reported in Table TABREF43. In addition, the performance of different GANs with respect to the benchmark datasets and performance metrics is reported in Table TABREF48.", + "In order to synthesize images from text descriptions, many frameworks have taken a minimalistic approach by creating small and background-less images BIBREF73. In most cases, the experiments were conducted on simple datasets, initially containing images of birds and flowers. BIBREF8 contributed to these data sets by adding corresponding natural language text descriptions to subsets of the CUB, MSCOCO, and Oxford-102 datasets, which facilitated the work on text-to-image synthesis for several papers released more recently.", + "While most deep learning algorithms use MNIST BIBREF74 dataset as the benchmark, there are three main datasets that are commonly used for evaluation of proposed GAN models for text-to-image synthesis: CUB BIBREF75, Oxford BIBREF76, COCO BIBREF77, and CIFAR-10 BIBREF78. CUB BIBREF75 contains 200 birds with matching text descriptions and Oxford BIBREF76 contains 102 categories of flowers with 40-258 images each and matching text descriptions. These datasets contain individual objects, with the text description corresponding to that object, making them relatively simple. COCO BIBREF77 is much more complex, containing 328k images with 91 different object types. CIFAI-10 BIBREF78 dataset consists of 60000 32$times$32 colour images in 10 classes, with 6000 images per class. In contrast to CUB and Oxford, whose images each contain an individual object, COCO\u2019s images may contain multiple objects, each with a label, so there are many labels per image. The total number of labels over the 328k images is 2.5 million BIBREF77." + ], + [ + "Several evaluation metrics are used for judging the images produced by text-to-image GANs. Proposed by BIBREF25, Inception Scores (IS) calculates the entropy (randomness) of the conditional distribution, obtained by applying the Inception Model introduced in BIBREF79, and marginal distribution of a large set of generated images, which should be low and high, respectively, for meaningful images. Low entropy of conditional distribution means that the evaluator is confident that the images came from the data distribution, and high entropy of the marginal distribution means that the set of generated images is diverse, which are both desired features. The IS score is then computed as the KL-divergence between the two entropies. FCN-scores BIBREF2 are computed in a similar manner, relying on the intuition that realistic images generated by a GAN should be able to be classified correctly by a classifier trained on real images of the same distribution. Therefore, if the FCN classifier classifies a set of synthetic images accurately, the image is probably realistic, and the corresponding GAN gets a high FCN score. Frechet Inception Distance (FID) BIBREF80 is the other commonly used evaluation metric, and takes a different approach, actually comparing the generated images to real images in the distribution. A high FID means there is little relationship between statistics of the synthetic and real images and vice versa, so lower FIDs are better.", + "black The performance of different GANs with respect to the benchmark datasets and performance metrics is reported in Table TABREF48. In addition, Figure FIGREF49 further lists the performance of 14 GANs with respect to their Inception Scores (IS)." + ], + [ + "While we gathered all the data we could find on scores for each model on the CUB, Oxford, and COCO datasets using IS, FID, FCN, and human classifiers, we unfortunately were unable to find certain data for AttnGAN and HDGAN (missing in Table TABREF48). The best evaluation we can give for those with missing data is our own opinions by looking at examples of generated images provided in their papers. In this regard, we observed that HDGAN produced relatively better visual results on the CUB and Oxford datasets while AttnGAN produced far more impressive results than the rest on the more complex COCO dataset. This is evidence that the attentional model and DAMSM introduced by AttnGAN are very effective in producing high-quality images. Examples of the best results of birds and plates of vegetables generated by each model are presented in Figures FIGREF50 and FIGREF51, respectively.", + "blackIn terms of inception score (IS), which is the metric that was applied to majority models except DC-GAN, the results in Table TABREF48 show that StackGAN++ only showed slight improvement over its predecessor, StackGAN, for text-to-image synthesis. However, StackGAN++ did introduce a very worthy enhancement for unconditional image generation by organizing the generators and discriminators in a \u201ctree-like\u201d structure. This indicates that revising the structures of the discriminators and/or generators can bring a moderate level of improvement in text-to-image synthesis.", + "blackIn addition, the results in Table TABREF48 also show that DM-GAN BIBREF53 has the best performance, followed by Obj-GAN BIBREF81. Notice that both DM-GAN and Obj-GAN are most recently developed methods in the field (both published in 2019), indicating that research in text to image synthesis is continuously improving the results for better visual perception and interception. Technical wise, DM-GAN BIBREF53 is a model using dynamic memory to refine fuzzy image contents initially generated from the GAN networks. A memory writing gate is used for DM-GAN to select important text information and generate images based on he selected text accordingly. On the other hand, Obj-GAN BIBREF81 focuses on object centered text-to-image synthesis. The proposed framework of Obj-GAN consists of a layout generation, including a bounding box generator and a shape generator, and an object-driven attentive image generator. The designs and advancement of DM-GAN and Obj-GAN indicate that research in text-to-image synthesis is advancing to put more emphasis on the image details and text semantics for better understanding and perception." + ], + [ + "It is worth noting that although this survey mainly focuses on text-to-image synthesis, there have been other applications of GANs in broader image synthesis field that we found fascinating and worth dedicating a small section to. For example, BIBREF72 used Sem-Latent GANs to generate images of faces based on facial attributes, producing impressive results that, at a glance, could be mistaken for real faces. BIBREF82, BIBREF70, and BIBREF83 demonstrated great success in generating text descriptions from images (image captioning) with great accuracy, with BIBREF82 using an attention-based model that automatically learns to focus on salient objects and BIBREF83 using deep visual-semantic alignments. Finally, there is a contribution made by StackGAN++ that was not mentioned in the dedicated section due to its relation to unconditional image generation as opposed to conditional, namely a color-regularization term BIBREF47. This additional term aims to keep the samples generated from the same input at different stages more consistent in color, which resulted in significantly better results for the unconditional model." + ], + [ + "The recent advancement in text-to-image synthesis research opens the door to several compelling methods and architectures. The main objective of text-to-image synthesis initially was to create images from simple labels, and this objective later scaled to natural languages. In this paper, we reviewed novel methods that generate, in our opinion, the most visually-rich and photo-realistic images, from text-based natural language. These generated images often rely on generative adversarial networks (GANs), deep convolutional decoder networks, and multimodal learning methods.", + "blackIn the paper, we first proposed a taxonomy to organize GAN based text-to-image synthesis frameworks into four major groups: semantic enhancement GANs, resolution enhancement GANs, diversity enhancement GANs, and motion enhancement GANs. The taxonomy provides a clear roadmap to show the motivations, architectures, and difference of different methods, and also outlines their evolution timeline and relationships. Following the proposed taxonomy, we reviewed important features of each method and their architectures. We indicated the model definition and key contributions from some advanced GAN framworks, including StackGAN, StackGAN++, AttnGAN, DC-GAN, AC-GAN, TAC-GAN, HDGAN, Text-SeGAn, StoryGAN etc. Many of the solutions surveyed in this paper tackled the highly complex challenge of generating photo-realistic images beyond swatch size samples. In other words, beyond the work of BIBREF8 in which images were generated from text in 64$\\times $64 tiny swatches. Lastly, all methods were evaluated on datasets that included birds, flowers, humans, and other miscellaneous elements. We were also able to allocate some important papers that were as impressive as the papers we finally surveyed. Though, these notable papers have yet to contribute directly or indirectly to the expansion of the vast computer vision AI field. Looking into the future, an excellent extension from the works surveyed in this paper would be to give more independence to the several learning methods (e.g. less human intervention) involved in the studies as well as increasing the size of the output images." + ], + [ + "The authors declare that there is no conflict of interest regarding the publication of this article." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0231/instruction.md b/qasper-0231/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..2c4dd44992208ac69942961d3086df76cc22e0e5 --- /dev/null +++ b/qasper-0231/instruction.md @@ -0,0 +1,240 @@ +Name of Paper: A Survey and Taxonomy of Adversarial Neural Networks for Text-to-Image Synthesis + +Question: What is typical GAN architecture for each text-to-image synhesis group? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Introduction ::: blackTraditional Learning Based Text-to-image Synthesis", + "Introduction ::: GAN Based Text-to-image Synthesis", + "Related Work", + "Preliminaries and Frameworks", + "Preliminaries and Frameworks ::: Generative Adversarial Neural Network", + "Preliminaries and Frameworks ::: cGAN: Conditional GAN", + "Preliminaries and Frameworks ::: Simple GAN Frameworks for Text-to-Image Synthesis", + "Preliminaries and Frameworks ::: Advanced GAN Frameworks for Text-to-Image Synthesis", + "Text-to-Image Synthesis Taxonomy and Categorization", + "Text-to-Image Synthesis Taxonomy and Categorization ::: GAN based Text-to-Image Synthesis Taxonomy", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Semantic Enhancement GANs", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Semantic Enhancement GANs ::: DC-GAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Semantic Enhancement GANs ::: DC-GAN Extensions", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Semantic Enhancement GANs ::: MC-GAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Resolution Enhancement GANs", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Resolution Enhancement GANs ::: StackGAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Resolution Enhancement GANs ::: StackGAN++", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Resolution Enhancement GANs ::: AttnGAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Resolution Enhancement GANs ::: HDGAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Diversity Enhancement GANs", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Diversity Enhancement GANs ::: AC-GAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Diversity Enhancement GANs ::: TAC-GAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Diversity Enhancement GANs ::: Text-SeGAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Diversity Enhancement GANs ::: MirrorGAN and Scene Graph GAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Motion Enhancement GANs", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Motion Enhancement GANs ::: ObamaNet and T2S", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Motion Enhancement GANs ::: T2V", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Motion Enhancement GANs ::: StoryGAN", + "GAN Based Text-to-Image Synthesis Applications, Benchmark, and Evaluation and Comparisons ::: Text-to-image Synthesis Applications", + "GAN Based Text-to-Image Synthesis Applications, Benchmark, and Evaluation and Comparisons ::: Text-to-image Synthesis Benchmark Datasets", + "GAN Based Text-to-Image Synthesis Applications, Benchmark, and Evaluation and Comparisons ::: Text-to-image Synthesis Benchmark Evaluation Metrics", + "GAN Based Text-to-Image Synthesis Applications, Benchmark, and Evaluation and Comparisons ::: GAN Based Text-to-image Synthesis Results Comparison", + "GAN Based Text-to-Image Synthesis Applications, Benchmark, and Evaluation and Comparisons ::: Notable Mentions", + "Conclusion", + "conflict of interest" + ], + "paragraphs": [ + [ + "\u201c (GANs), and the variations that are now being proposed is the most interesting idea in the last 10 years in ML, in my opinion.\u201d (2016)", + "\u2013 Yann LeCun", + "A picture is worth a thousand words! While written text provide efficient, effective, and concise ways for communication, visual content, such as images, is a more comprehensive, accurate, and intelligible method of information sharing and understanding. Generation of images from text descriptions, i.e. text-to-image synthesis, is a complex computer vision and machine learning problem that has seen great progress over recent years. Automatic image generation from natural language may allow users to describe visual elements through visually-rich text descriptions. The ability to do so effectively is highly desirable as it could be used in artificial intelligence applications such as computer-aided design, image editing BIBREF0, BIBREF1, game engines for the development of the next generation of video gamesBIBREF2, and pictorial art generation BIBREF3." + ], + [ + "In the early stages of research, text-to-image synthesis was mainly carried out through a search and supervised learning combined process BIBREF4, as shown in Figure FIGREF4. In order to connect text descriptions to images, one could use correlation between keywords (or keyphrase) & images that identifies informative and \u201cpicturable\u201d text units; then, these units would search for the most likely image parts conditioned on the text, eventually optimizing the picture layout conditioned on both the text and the image parts. Such methods often integrated multiple artificial intelligence key components, including natural language processing, computer vision, computer graphics, and machine learning.", + "The major limitation of the traditional learning based text-to-image synthesis approaches is that they lack the ability to generate new image content; they can only change the characteristics of the given/training images. Alternatively, research in generative models has advanced significantly and delivers solutions to learn from training images and produce new visual content. For example, Attribute2Image BIBREF5 models each image as a composite of foreground and background. In addition, a layered generative model with disentangled latent variables is learned, using a variational auto-encoder, to generate visual content. Because the learning is customized/conditioned by given attributes, the generative models of Attribute2Image can generate images with respect to different attributes, such as gender, hair color, age, etc., as shown in Figure FIGREF5." + ], + [ + "Although generative model based text-to-image synthesis provides much more realistic image synthesis results, the image generation is still conditioned by the limited attributes. In recent years, several papers have been published on the subject of text-to-image synthesis. Most of the contributions from these papers rely on multimodal learning approaches that include generative adversarial networks and deep convolutional decoder networks as their main drivers to generate entrancing images from text BIBREF7, BIBREF8, BIBREF9, BIBREF10, BIBREF11.", + "First introduced by Ian Goodfellow et al. BIBREF9, generative adversarial networks (GANs) consist of two neural networks paired with a discriminator and a generator. These two models compete with one another, with the generator attempting to produce synthetic/fake samples that will fool the discriminator and the discriminator attempting to differentiate between real (genuine) and synthetic samples. Because GANs' adversarial training aims to cause generators to produce images similar to the real (training) images, GANs can naturally be used to generate synthetic images (image synthesis), and this process can even be customized further by using text descriptions to specify the types of images to generate, as shown in Figure FIGREF6.", + "Much like text-to-speech and speech-to-text conversion, there exists a wide variety of problems that text-to-image synthesis could solve in the computer vision field specifically BIBREF8, BIBREF12. Nowadays, researchers are attempting to solve a plethora of computer vision problems with the aid of deep convolutional networks, generative adversarial networks, and a combination of multiple methods, often called multimodal learning methods BIBREF8. For simplicity, multiple learning methods will be referred to as multimodal learning hereafter BIBREF13. Researchers often describe multimodal learning as a method that incorporates characteristics from several methods, algorithms, and ideas. This can include ideas from two or more learning approaches in order to create a robust implementation to solve an uncommon problem or improve a solution BIBREF8, BIBREF14, BIBREF15, BIBREF16, BIBREF17.", + "black In this survey, we focus primarily on reviewing recent works that aim to solve the challenge of text-to-image synthesis using generative adversarial networks (GANs). In order to provide a clear roadmap, we propose a taxonomy to summarize reviewed GANs into four major categories. Our review will elaborate the motivations of methods in each category, analyze typical models, their network architectures, and possible drawbacks for further improvement. The visual abstract of the survey and the list of reviewed GAN frameworks is shown in Figure FIGREF8.", + "black The remainder of the survey is organized as follows. Section 2 presents a brief summary of existing works on subjects similar to that of this paper and highlights the key distinctions making ours unique. Section 3 gives a short introduction to GANs and some preliminary concepts related to image generation, as they are the engines that make text-to-image synthesis possible and are essential building blocks to achieve photo-realistic images from text descriptions. Section 4 proposes a taxonomy to summarize GAN based text-to-image synthesis, discusses models and architectures of novel works focused solely on text-to-image synthesis. This section will also draw key contributions from these works in relation to their applications. Section 5 reviews GAN based text-to-image synthesis benchmarks, performance metrics, and comparisons, including a simple review of GANs for other applications. In section 6, we conclude with a brief summary and outline ideas for future interesting developments in the field of text-to-image synthesis." + ], + [ + "With the growth and success of GANs, deep convolutional decoder networks, and multimodal learning methods, these techniques were some of the first procedures which aimed to solve the challenge of image synthesis. Many engineers and scientists in computer vision and AI have contributed through extensive studies and experiments, with numerous proposals and publications detailing their contributions. Because GANs, introduced by BIBREF9, are emerging research topics, their practical applications to image synthesis are still in their infancy. Recently, many new GAN architectures and designs have been proposed to use GANs for different applications, e.g. using GANs to generate sentimental texts BIBREF18, or using GANs to transform natural images into cartoons BIBREF19.", + "Although GANs are becoming increasingly popular, very few survey papers currently exist to summarize and outline contemporaneous technical innovations and contributions of different GAN architectures BIBREF20, BIBREF21. Survey papers specifically attuned to analyzing different contributions to text-to-image synthesis using GANs are even more scarce. We have thus found two surveys BIBREF6, BIBREF7 on image synthesis using GANs, which are the two most closely related publications to our survey objective. In the following paragraphs, we briefly summarize each of these surveys and point out how our objectives differ from theirs.", + "In BIBREF6, the authors provide an overview of image synthesis using GANs. In this survey, the authors discuss the motivations for research on image synthesis and introduce some background information on the history of GANs, including a section dedicated to core concepts of GANs, namely generators, discriminators, and the min-max game analogy, and some enhancements to the original GAN model, such as conditional GANs, addition of variational auto-encoders, etc.. In this survey, we will carry out a similar review of the background knowledge because the understanding of these preliminary concepts is paramount for the rest of the paper. Three types of approaches for image generation are reviewed, including direct methods (single generator and discriminator), hierarchical methods (two or more generator-discriminator pairs, each with a different goal), and iterative methods (each generator-discriminator pair generates a gradually higher-resolution image). Following the introduction, BIBREF6 discusses methods for text-to-image and image-to-image synthesis, respectively, and also describes several evaluation metrics for synthetic images, including inception scores and Frechet Inception Distance (FID), and explains the significance of the discriminators acting as learned loss functions as opposed to fixed loss functions.", + "Different from the above survey, which has a relatively broad scope in GANs, our objective is heavily focused on text-to-image synthesis. Although this topic, text-to-image synthesis, has indeed been covered in BIBREF6, they did so in a much less detailed fashion, mostly listing the many different works in a time-sequential order. In comparison, we will review several representative methods in the field and outline their models and contributions in detail.", + "Similarly to BIBREF6, the second survey paper BIBREF7 begins with a standard introduction addressing the motivation of image synthesis and the challenges it presents followed by a section dedicated to core concepts of GANs and enhancements to the original GAN model. In addition, the paper covers the review of two types of applications: (1) unconstrained applications of image synthesis such as super-resolution, image inpainting, etc., and (2) constrained image synthesis applications, namely image-to-image, text-to-image, and sketch-to image, and also discusses image and video editing using GANs. Again, the scope of this paper is intrinsically comprehensive, while we focus specifically on text-to-image and go into more detail regarding the contributions of novel state-of-the-art models.", + "Other surveys have been published on related matters, mainly related to the advancements and applications of GANs BIBREF22, BIBREF23, but we have not found any prior works which focus specifically on text-to-image synthesis using GANs. To our knowledge, this is the first paper to do so.", + "black" + ], + [ + "In this section, we first introduce preliminary knowledge of GANs and one of its commonly used variants, conditional GAN (i.e. cGAN), which is the building block for many GAN based text-to-image synthesis models. After that, we briefly separate GAN based text-to-image synthesis into two types, Simple GAN frameworks vs. Advanced GAN frameworks, and discuss why advanced GAN architecture for image synthesis.", + "black Notice that the simple vs. advanced GAN framework separation is rather too brief, our taxonomy in the next section will propose a taxonomy to summarize advanced GAN frameworks into four categories, based on their objective and designs." + ], + [ + "Before moving on to a discussion and analysis of works applying GANs for text-to-image synthesis, there are some preliminary concepts, enhancements of GANs, datasets, and evaluation metrics that are present in some of the works described in the next section and are thus worth introducing.", + "As stated previously, GANs were introduced by Ian Goodfellow et al. BIBREF9 in 2014, and consist of two deep neural networks, a generator and a discriminator, which are trained independently with conflicting goals: The generator aims to generate samples closely related to the original data distribution and fool the discriminator, while the discriminator aims to distinguish between samples from the generator model and samples from the true data distribution by calculating the probability of the sample coming from either source. A conceptual view of the generative adversarial network (GAN) architecture is shown in Figure FIGREF11.", + "The training of GANs is an iterative process that, with each iteration, updates the generator and the discriminator with the goal of each defeating the other. leading each model to become increasingly adept at its specific task until a threshold is reached. This is analogous to a min-max game between the two models, according to the following equation:", + "In Eq. (DISPLAY_FORM10), $x$ denotes a multi-dimensional sample, e.g., an image, and $z$ denotes a multi-dimensional latent space vector, e.g., a multidimensional data point following a predefined distribution function such as that of normal distributions. $D_{\\theta _d}()$ denotes a discriminator function, controlled by parameters $\\theta _d$, which aims to classify a sample into a binary space. $G_{\\theta _g}()$ denotes a generator function, controlled by parameters $\\theta _g$, which aims to generate a sample from some latent space vector. For example, $G_{\\theta _g}(z)$ means using a latent vector $z$ to generate a synthetic/fake image, and $D_{\\theta _d}(x)$ means to classify an image $x$ as binary output (i.e. true/false or 1/0). In the GAN setting, the discriminator $D_{\\theta _d}()$ is learned to distinguish a genuine/true image (labeled as 1) from fake images (labeled as 0). Therefore, given a true image $x$, the ideal output from the discriminator $D_{\\theta _d}(x)$ would be 1. Given a fake image generated from the generator $G_{\\theta _g}(z)$, the ideal prediction from the discriminator $D_{\\theta _d}(G_{\\theta _g}(z))$ would be 0, indicating the sample is a fake image.", + "Following the above definition, the $\\min \\max $ objective function in Eq. (DISPLAY_FORM10) aims to learn parameters for the discriminator ($\\theta _d$) and generator ($\\theta _g$) to reach an optimization goal: The discriminator intends to differentiate true vs. fake images with maximum capability $\\max _{\\theta _d}$ whereas the generator intends to minimize the difference between a fake image vs. a true image $\\min _{\\theta _g}$. In other words, the discriminator sets the characteristics and the generator produces elements, often images, iteratively until it meets the attributes set forth by the discriminator. GANs are often used with images and other visual elements and are notoriously efficient in generating compelling and convincing photorealistic images. Most recently, GANs were used to generate an original painting in an unsupervised fashion BIBREF24. The following sections go into further detail regarding how the generator and discriminator are trained in GANs.", + "Generator - In image synthesis, the generator network can be thought of as a mapping from one representation space (latent space) to another (actual data) BIBREF21. When it comes to image synthesis, all of the images in the data space fall into some distribution in a very complex and high-dimensional feature space. Sampling from such a complex space is very difficult, so GANs instead train a generator to create synthetic images from a much more simple feature space (usually random noise) called the latent space. The generator network performs up-sampling of the latent space and is usually a deep neural network consisting of several convolutional and/or fully connected layers BIBREF21. The generator is trained using gradient descent to update the weights of the generator network with the aim of producing data (in our case, images) that the discriminator classifies as real.", + "Discriminator - The discriminator network can be thought of as a mapping from image data to the probability of the image coming from the real data space, and is also generally a deep neural network consisting of several convolution and/or fully connected layers. However, the discriminator performs down-sampling as opposed to up-sampling. Like the generator, it is trained using gradient descent but its goal is to update the weights so that it is more likely to correctly classify images as real or fake.", + "In GANs, the ideal outcome is for both the generator's and discriminator's cost functions to converge so that the generator produces photo-realistic images that are indistinguishable from real data, and the discriminator at the same time becomes an expert at differentiating between real and synthetic data. This, however, is not possible since a reduction in cost of one model generally leads to an increase in cost of the other. This phenomenon makes training GANs very difficult, and training them simultaneously (both models performing gradient descent in parallel) often leads to a stable orbit where neither model is able to converge. To combat this, the generator and discriminator are often trained independently. In this case, the GAN remains the same, but there are different training stages. In one stage, the weights of the generator are kept constant and gradient descent updates the weights of the discriminator, and in the other stage the weights of the discriminator are kept constant while gradient descent updates the weights of the generator. This is repeated for some number of epochs until a desired low cost for each model is reached BIBREF25." + ], + [ + "Conditional Generative Adversarial Networks (cGAN) are an enhancement of GANs proposed by BIBREF26 shortly after the introduction of GANs by BIBREF9. The objective function of the cGAN is defined in Eq. (DISPLAY_FORM13) which is very similar to the GAN objective function in Eq. (DISPLAY_FORM10) except that the inputs to both discriminator and generator are conditioned by a class label $y$.", + "The main technical innovation of cGAN is that it introduces an additional input or inputs to the original GAN model, allowing the model to be trained on information such as class labels or other conditioning variables as well as the samples themselves, concurrently. Whereas the original GAN was trained only with samples from the data distribution, resulting in the generated sample reflecting the general data distribution, cGAN enables directing the model to generate more tailored outputs.", + "In Figure FIGREF14, the condition vector is the class label (text string) \"Red bird\", which is fed to both the generator and discriminator. It is important, however, that the condition vector is related to the real data. If the model in Figure FIGREF14 was trained with the same set of real data (red birds) but the condition text was \"Yellow fish\", the generator would learn to create images of red birds when conditioned with the text \"Yellow fish\".", + "Note that the condition vector in cGAN can come in many forms, such as texts, not just limited to the class label. Such a unique design provides a direct solution to generate images conditioned by predefined specifications. As a result, cGAN has been used in text-to-image synthesis since the very first day of its invention although modern approaches can deliver much better text-to-image synthesis results.", + "black" + ], + [ + "In order to generate images from text, one simple solution is to employ the conditional GAN (cGAN) designs and add conditions to the training samples, such that the GAN is trained with respect to the underlying conditions. Several pioneer works have followed similar designs for text-to-image synthesis.", + "black An essential disadvantage of using cGAN for text-to-image synthesis is that that it cannot handle complicated textual descriptions for image generation, because cGAN uses labels as conditions to restrict the GAN inputs. If the text inputs have multiple keywords (or long text descriptions) they cannot be used simultaneously to restrict the input. Instead of using text as conditions, another two approaches BIBREF8, BIBREF16 use text as input features, and concatenate such features with other features to train discriminator and generator, as shown in Figure FIGREF15(b) and (c). To ensure text being used as GAN input, a feature embedding or feature representation learning BIBREF29, BIBREF30 function $\\varphi ()$ is often introduced to convert input text as numeric features, which are further concatenated with other features to train GANs.", + "black" + ], + [ + "Motivated by the GAN and conditional GAN (cGAN) design, many GAN based frameworks have been proposed to generate images, with different designs and architectures, such as using multiple discriminators, using progressively trained discriminators, or using hierarchical discriminators. Figure FIGREF17 outlines several advanced GAN frameworks in the literature. In addition to these frameworks, many news designs are being proposed to advance the field with rather sophisticated designs. For example, a recent work BIBREF37 proposes to use a pyramid generator and three independent discriminators, blackeach focusing on a different aspect of the images, to lead the generator towards creating images that are photo-realistic on multiple levels. Another recent publication BIBREF38 proposes to use discriminator to measure semantic relevance between image and text instead of class prediction (like most discriminator in GANs does), resulting a new GAN structure outperforming text conditioned auxiliary classifier (TAC-GAN) BIBREF16 and generating diverse, realistic, and relevant to the input text regardless of class.", + "black In the following section, we will first propose a taxonomy that summarizes advanced GAN frameworks for text-to-image synthesis, and review most recent proposed solutions to the challenge of generating photo-realistic images conditioned on natural language text descriptions using GANs. The solutions we discuss are selected based on relevance and quality of contributions. Many publications exist on the subject of image-generation using GANs, but in this paper we focus specifically on models for text-to-image synthesis, with the review emphasizing on the \u201cmodel\u201d and \u201ccontributions\u201d for text-to-image synthesis. At the end of this section, we also briefly review methods using GANs for other image-synthesis applications.", + "black" + ], + [ + "In this section, we propose a taxonomy to summarize advanced GAN based text-to-image synthesis frameworks, as shown in Figure FIGREF24. The taxonomy organizes GAN frameworks into four categories, including Semantic Enhancement GANs, Resolution Enhancement GANs, Diversity Enhancement GANs, and Motion Enhancement GAGs. Following the proposed taxonomy, each subsection will introduce several typical frameworks and address their techniques of using GANS to solve certain aspects of the text-to-mage synthesis challenges.", + "black" + ], + [ + "Although the ultimate goal of Text-to-Image synthesis is to generate images closely related to the textual descriptions, the relevance of the images to the texts are often validated from different perspectives, due to the inherent diversity of human perceptions. For example, when generating images matching to the description \u201crose flowers\u201d, some users many know the exact type of flowers they like and intend to generate rose flowers with similar colors. Other users, may seek to generate high quality rose flowers with a nice background (e.g. garden). The third group of users may be more interested in generating flowers similar to rose but with different colors and visual appearance, e.g. roses, begonia, and peony. The fourth group of users may want to not only generate flower images, but also use them to form a meaningful action, e.g. a video clip showing flower growth, performing a magic show using those flowers, or telling a love story using the flowers.", + "blackFrom the text-to-Image synthesis point of view, the first group of users intend to precisely control the semantic of the generated images, and their goal is to match the texts and images at the semantic level. The second group of users are more focused on the resolutions and the qualify of the images, in addition to the requirement that the images and texts are semantically related. For the third group of users, their goal is to diversify the output images, such that their images carry diversified visual appearances and are also semantically related. The fourth user group adds a new dimension in image synthesis, and aims to generate sequences of images which are coherent in temporal order, i.e. capture the motion information.", + "black Based on the above descriptions, we categorize GAN based Text-to-Image Synthesis into a taxonomy with four major categories, as shown in Fig. FIGREF24.", + "Semantic Enhancement GANs: Semantic enhancement GANs represent pioneer works of GAN frameworks for text-to-image synthesis. The main focus of the GAN frameworks is to ensure that the generated images are semantically related to the input texts. This objective is mainly achieved by using a neural network to encode texts as dense features, which are further fed to a second network to generate images matching to the texts.", + "Resolution Enhancement GANs: Resolution enhancement GANs mainly focus on generating high qualify images which are semantically matched to the texts. This is mainly achieved through a multi-stage GAN framework, where the outputs from earlier stage GANs are fed to the second (or later) stage GAN to generate better qualify images.", + "Diversity Enhancement GANs: Diversity enhancement GANs intend to diversify the output images, such that the generated images are not only semantically related but also have different types and visual appearance. This objective is mainly achieved through an additional component to estimate semantic relevance between generated images and texts, in order to maximize the output diversity.", + "Motion Enhancement GANs: Motion enhancement GANs intend to add a temporal dimension to the output images, such that they can form meaningful actions with respect to the text descriptions. This goal mainly achieved though a two-step process which first generates images matching to the \u201cactions\u201d of the texts, followed by a mapping or alignment procedure to ensure that images are coherent in the temporal order.", + "black In the following, we will introduce how these GAN frameworks evolve for text-to-image synthesis, and will also review some typical methods of each category.", + "black" + ], + [ + "Semantic relevance is one the of most important criteria of the text-to-image synthesis. For most GNAs discussed in this survey, they are required to generate images semantically related to the text descriptions. However, the semantic relevance is a rather subjective measure, and images are inherently rich in terms of its semantics and interpretations. Therefore, many GANs are further proposed to enhance the text-to-image synthesis from different perspectives. In this subsection, we will review several classical approaches which are commonly served as text-to-image synthesis baseline.", + "black" + ], + [ + "Deep convolution generative adversarial network (DC-GAN) BIBREF8 represents the pioneer work for text-to-image synthesis using GANs. Its main goal is to train a deep convolutional generative adversarial network (DC-GAN) on text features. During this process these text features are encoded by another neural network. This neural network is a hybrid convolutional recurrent network at the character level. Concurrently, both neural networks have also feed-forward inference in the way they condition text features. Generating realistic images automatically from natural language text is the motivation of several of the works proposed in this computer vision field. However, actual artificial intelligence (AI) systems are far from achieving this task BIBREF8, BIBREF39, BIBREF40, BIBREF41, BIBREF42, BIBREF22, BIBREF26. Lately, recurrent neural networks led the way to develop frameworks that learn discriminatively on text features. At the same time, generative adversarial networks (GANs) began recently to show some promise on generating compelling images of a whole host of elements including but not limited to faces, birds, flowers, and non-common images such as room interiorsBIBREF8. DC-GAN is a multimodal learning model that attempts to bridge together both of the above mentioned unsupervised machine learning algorithms, the recurrent neural networks (RNN) and generative adversarial networks (GANs), with the sole purpose of speeding the generation of text-to-image synthesis.", + "black Deep learning shed some light to some of the most sophisticated advances in natural language representation, image synthesis BIBREF7, BIBREF8, BIBREF43, BIBREF35, and classification of generic data BIBREF44. However, a bulk of the latest breakthroughs in deep learning and computer vision were related to supervised learning BIBREF8. Even though natural language and image synthesis were part of several contributions on the supervised side of deep learning, unsupervised learning saw recently a tremendous rise in input from the research community specially on two subproblems: text-based natural language and image synthesis BIBREF45, BIBREF14, BIBREF8, BIBREF46, BIBREF47. These subproblems are typically subdivided as focused research areas. DC-GAN's contributions are mainly driven by these two research areas. In order to generate plausible images from natural language, DC-GAN contributions revolve around developing a straightforward yet effective GAN architecture and training strategy that allows natural text to image synthesis. These contributions are primarily tested on the Caltech-UCSD Birds and Oxford-102 Flowers datasets. Each image in these datasets carry five text descriptions. These text descriptions were created by the research team when setting up the evaluation environment. The DC-GANs model is subsequently trained on several subcategories. Subcategories in this research represent the training and testing sub datasets. The performance shown by these experiments display a promising yet effective way to generate images from textual natural language descriptions BIBREF8.", + "black" + ], + [ + "Following the pioneer DC-GAN framework BIBREF8, many researches propose revised network structures (e.g. different discriminaotrs) in order to improve images with better semantic relevance to the texts. Based on the deep convolutional adversarial network (DC-GAN) network architecture, GAN-CLS with image-text matching discriminator, GAN-INT learned with text manifold interpolation and GAN-INT-CLS which combines both are proposed to find semantic match between text and image. Similar to the DC-GAN architecture, an adaptive loss function (i.e. Perceptual Loss BIBREF48) is proposed for semantic image synthesis which can synthesize a realistic image that not only matches the target text description but also keep the irrelavant features(e.g. background) from source images BIBREF49. Regarding to the Perceptual Losses, three loss functions (i.e. Pixel reconstruction loss, Activation reconstruction loss and Texture reconstruction loss) are proposed in BIBREF50 in which they construct the network architectures based on the DC-GAN, i.e. GAN-INT-CLS-Pixel, GAN-INT-CLS-VGG and GAN-INT-CLS-Gram with respect to three losses. In BIBREF49, a residual transformation unit is added in the network to retain similar structure of the source image.", + "black Following the BIBREF49 and considering the features in early layers address background while foreground is obtained in latter layers in CNN, a pair of discriminators with different architectures (i.e. Paired-D GAN) is proposed to synthesize background and foreground from a source image seperately BIBREF51. Meanwhile, the skip-connection in the generator is employed to more precisely retain background information in the source image.", + "black" + ], + [ + "When synthesising images, most text-to-image synthesis methods consider each output image as one single unit to characterize its semantic relevance to the texts. This is likely problematic because most images naturally consist of two crucial components: foreground and background. Without properly separating these two components, it's hard to characterize the semantics of an image if the whole image is treated as a single unit without proper separation.", + "black In order to enhance the semantic relevance of the images, a multi-conditional GAN (MC-GAN) BIBREF52 is proposed to synthesize a target image by combining the background of a source image and a text-described foreground object which does not exist in the source image. A unique feature of MC-GAN is that it proposes a synthesis block in which the background feature is extracted from the given image without non-linear function (i.e. only using convolution and batch normalization) and the foreground feature is the feature map from the previous layer.", + "black Because MC-GAN is able to properly model the background and foreground of the generated images, a unique strength of MC-GAN is that users are able to provide a base image and MC-GAN is able to preserve the background information of the base image to generate new images. black" + ], + [ + "Due to the fact that training GANs will be much difficult when generating high-resolution images, a two stage GAN (i.e. stackGAN) is proposed in which rough images(i.e. low-resolution images) are generated in stage-I and refined in stage-II. To further improve the quality of generated images, the second version of StackGAN (i.e. Stack++) is proposed to use multi-stage GANs to generate multi-scale images. A color-consistency regularization term is also added into the loss to keep the consistency of images in different scales.", + "black While stackGAN and StackGAN++ are both built on the global sentence vector, AttnGAN is proposed to use attention mechanism (i.e. Deep Attentional Multimodal Similarity Model (DAMSM)) to model the multi-level information (i.e. word level and sentence level) into GANs. In the following, StackGAN, StackGAN++ and AttnGAN will be explained in detail.", + "black Recently, Dynamic Memory Generative Adversarial Network (i.e. DM-GAN)BIBREF53 which uses a dynamic memory component is proposed to focus on refiningthe initial generated image which is the key to the success of generating high quality images." + ], + [ + "In 2017, Zhang et al. proposed a model for generating photo-realistic images from text descriptions called StackGAN (Stacked Generative Adversarial Network) BIBREF33. In their work, they define a two-stage model that uses two cascaded GANs, each corresponding to one of the stages. The stage I GAN takes a text description as input, converts the text description to a text embedding containing several conditioning variables, and generates a low-quality 64x64 image with rough shapes and colors based on the computed conditioning variables. The stage II GAN then takes this low-quality stage I image as well as the same text embedding and uses the conditioning variables to correct and add more detail to the stage I result. The output of stage II is a photorealistic 256$times$256 image that resembles the text description with compelling accuracy.", + "One major contribution of StackGAN is the use of cascaded GANs for text-to-image synthesis through a sketch-refinement process. By conditioning the stage II GAN on the image produced by the stage I GAN and text description, the stage II GAN is able to correct defects in the stage I output, resulting in high-quality 256x256 images. Prior works have utilized \u201cstacked\u201d GANs to separate the image generation process into structure and style BIBREF42, multiple stages each generating lower-level representations from higher-level representations of the previous stage BIBREF35, and multiple stages combined with a laplacian pyramid approach BIBREF54, which was introduced for image compression by P. Burt and E. Adelson in 1983 and uses the differences between consecutive down-samples of an original image to reconstruct the original image from its down-sampled version BIBREF55. However, these works did not use text descriptions to condition their generator models.", + "Conditioning Augmentation is the other major contribution of StackGAN. Prior works transformed the natural language text description into a fixed text embedding containing static conditioning variables which were fed to the generator BIBREF8. StackGAN does this and then creates a Gaussian distribution from the text embedding and randomly selects variables from the Gaussian distribution to add to the set of conditioning variables during training. This encourages robustness by introducing small variations to the original text embedding for a particular training image while keeping the training image that the generated output is compared to the same. The result is that the trained model produces more diverse images in the same distribution when using Conditioning Augmentation than the same model using a fixed text embedding BIBREF33." + ], + [ + "Proposed by the same users as StackGAN, StackGAN++ is also a stacked GAN model, but organizes the generators and discriminators in a \u201ctree-like\u201d structure BIBREF47 with multiple stages. The first stage combines a noise vector and conditioning variables (with Conditional Augmentation introduced in BIBREF33) for input to the first generator, which generates a low-resolution image, 64$\\times $64 by default (this can be changed depending on the desired number of stages). Each following stage uses the result from the previous stage and the conditioning variables to produce gradually higher-resolution images. These stages do not use the noise vector again, as the creators assume that the randomness it introduces is already preserved in the output of the first stage. The final stage produces a 256$\\times $256 high-quality image.", + "StackGAN++ introduces the joint conditional and unconditional approximation in their designs BIBREF47. The discriminators are trained to calculate the loss between the image produced by the generator and the conditioning variables (measuring how accurately the image represents the description) as well as the loss between the image and real images (probability of the image being real or fake). The generators then aim to minimize the sum of these losses, improving the final result." + ], + [ + "Attentional Generative Adversarial Network (AttnGAN) BIBREF10 is very similar, in terms of its structure, to StackGAN++ BIBREF47, discussed in the previous section, but some novel components are added. Like previous works BIBREF56, BIBREF8, BIBREF33, BIBREF47, a text encoder generates a text embedding with conditioning variables based on the overall sentence. Additionally, the text encoder generates a separate text embedding with conditioning variables based on individual words. This process is optimized to produce meaningful variables using a bidirectional recurrent neural network (BRNN), more specifically bidirectional Long Short Term Memory (LSTM) BIBREF57, which, for each word in the description, generates conditions based on the previous word as well as the next word (bidirectional). The first stage of AttnGAN generates a low-resolution image based on the sentence-level text embedding and random noise vector. The output is fed along with the word-level text embedding to an \u201cattention model\u201d, which matches the word-level conditioning variables to regions of the stage I image, producing a word-context matrix. This is then fed to the next stage of the model along with the raw previous stage output. Each consecutive stage works in the same manner, but produces gradually higher-resolution images conditioned on the previous stage.", + "Two major contributions were introduced in AttnGAN: the attentional generative network and the Deep Attentional Multimodal Similarity Model (DAMSM) BIBREF47. The attentional generative network matches specific regions of each stage's output image to conditioning variables from the word-level text embedding. This is a very worthy contribution, allowing each consecutive stage to focus on specific regions of the image independently, adding \u201cattentional\u201d details region by region as opposed to the whole image. The DAMSM is also a key feature introduced by AttnGAN, which is used after the result of the final stage to calculate the similarity between the generated image and the text embedding at both the sentence level and the more fine-grained word level. Table TABREF48 shows scores from different metrics for StackGAN, StackGAN++, AttnGAN, and HDGAN on the CUB, Oxford, and COCO datasets. The table shows that AttnGAN outperforms the other models in terms of IS on the CUB dataset by a small amount and greatly outperforms them on the COCO dataset." + ], + [ + "Hierarchically-nested adversarial network (HDGAN) is a method proposed by BIBREF36, and its main objective is to tackle the difficult problem of dealing with photographic images from semantic text descriptions. These semantic text descriptions are applied on images from diverse datasets. This method introduces adversarial objectives nested inside hierarchically oriented networks BIBREF36. Hierarchical networks helps regularize mid-level manifestations. In addition to regularize mid-level manifestations, it assists the training of the generator in order to capture highly complex still media elements. These elements are captured in statistical order to train the generator based on settings extracted directly from the image. The latter is an ideal scenario. However, this paper aims to incorporate a single-stream architecture. This single-stream architecture functions as the generator that will form an optimum adaptability towards the jointed discriminators. Once jointed discriminators are setup in an optimum manner, the single-stream architecture will then advance generated images to achieve a much higher resolution BIBREF36.", + "The main contributions of the HDGANs include the introduction of a visual-semantic similarity measure BIBREF36. This feature will aid in the evaluation of the consistency of generated images. In addition to checking the consistency of generated images, one of the key objectives of this step is to test the logical consistency of the end product BIBREF36. The end product in this case would be images that are semantically mapped from text-based natural language descriptions to each area on the picture e.g. a wing on a bird or petal on a flower. Deep learning has created a multitude of opportunities and challenges for researchers in the computer vision AI field. Coupled with GAN and multimodal learning architectures, this field has seen tremendous growth BIBREF8, BIBREF39, BIBREF40, BIBREF41, BIBREF42, BIBREF22, BIBREF26. Based on these advancements, HDGANs attempt to further extend some desirable and less common features when generating images from textual natural language BIBREF36. In other words, it takes sentences and treats them as a hierarchical structure. This has some positive and negative implications in most cases. For starters, it makes it more complex to generate compelling images. However, one of the key benefits of this elaborate process is the realism obtained once all processes are completed. In addition, one common feature added to this process is the ability to identify parts of sentences with bounding boxes. If a sentence includes common characteristics of a bird, it will surround the attributes of such bird with bounding boxes. In practice, this should happen if the desired image have other elements such as human faces (e.g. eyes, hair, etc), flowers (e.g. petal size, color, etc), or any other inanimate object (e.g. a table, a mug, etc). Finally, HDGANs evaluated some of its claims on common ideal text-to-image datasets such as CUB, COCO, and Oxford-102 BIBREF8, BIBREF36, BIBREF39, BIBREF40, BIBREF41, BIBREF42, BIBREF22, BIBREF26. These datasets were first utilized on earlier works BIBREF8, and most of them sport modified features such image annotations, labels, or descriptions. The qualitative and quantitative results reported by researchers in this study were far superior of earlier works in this same field of computer vision AI.", + "black" + ], + [ + "In this subsection, we introduce text-to-image synthesis methods which try to maximize the diversity of the output images, based on the text descriptions.", + "black" + ], + [ + "Two issues arise in the traditional GANs BIBREF58 for image synthesis: (1) scalabilirty problem: traditional GANs cannot predict a large number of image categories; and (2) diversity problem: images are often subject to one-to-many mapping, so one image could be labeled as different tags or being described using different texts. To address these problems, GAN conditioned on additional information, e.g. cGAN, is an alternative solution. However, although cGAN and many previously introduced approaches are able to generate images with respect to the text descriptions, they often output images with similar types and visual appearance.", + "black Slightly different from the cGAN, auxiliary classifier GANs (AC-GAN) BIBREF27 proposes to improve the diversity of output images by using an auxiliary classifier to control output images. The overall structure of AC-GAN is shown in Fig. FIGREF15(c). In AC-GAN, every generated image is associated with a class label, in addition to the true/fake label which are commonly used in GAN or cGAN. The discriminator of AC-GAN not only outputs a probability distribution over sources (i.e. whether the image is true or fake), it also output a probability distribution over the class label (i.e. predict which class the image belong to).", + "black By using an auxiliary classifier layer to predict the class of the image, AC-GAN is able to use the predicted class labels of the images to ensure that the output consists of images from different classes, resulting in diversified synthesis images. The results show that AC-GAN can generate images with high diversity.", + "black" + ], + [ + "Building on the AC-GAN, TAC-GAN BIBREF59 is proposed to replace the class information with textual descriptions as the input to perform the task of text to image synthesis. The architecture of TAC-GAN is shown in Fig. FIGREF15(d), which is similar to AC-GAN. Overall, the major difference between TAC-GAN and AC-GAN is that TAC-GAN conditions the generated images on text descriptions instead of on a class label. This design makes TAC-GAN more generic for image synthesis.", + "black For TAC-GAN, it imposes restrictions on generated images in both texts and class labels. The input vector of TAC-GAN's generative network is built based on a noise vector and embedded vector representation of textual descriptions. The discriminator of TAC-GAN is similar to that of the AC-GAN, which not only predicts whether the image is fake or not, but also predicts the label of the images. A minor difference of TAC-GAN's discriminator, compared to that of the AC-GAN, is that it also receives text information as input before performing its classification.", + "black The experiments and validations, on the Oxford-102 flowers dataset, show that the results produced by TAC-GAN are \u201cslightly better\u201d that other approaches, including GAN-INT-CLS and StackGAN.", + "black" + ], + [ + "In order to improve the diversity of the output images, both AC-GAN and TAC-GAN's discriminators predict class labels of the synthesised images. This process likely enforces the semantic diversity of the images, but class labels are inherently restrictive in describing image semantics, and images described by text can be matched to multiple labels. Therefore, instead of predicting images' class labels, an alternative solution is to directly quantify their semantic relevance.", + "black The architecture of Text-SeGAN is shown in Fig. FIGREF15(e). In order to directly quantify semantic relevance, Text-SeGAN BIBREF28 adds a regression layer to estimate the semantic relevance between the image and text instead of a classifier layer of predicting labels. The estimated semantic reference is a fractional value ranging between 0 and 1, with a higher value reflecting better semantic relevance between the image and text. Due to this unique design, an inherent advantage of Text-SeGAN is that the generated images are not limited to certain classes and are semantically matching to the text input.", + "black Experiments and validations, on Oxford-102 flower dataset, show that Text-SeGAN can generate diverse images that are semantically relevant to the input text. In addition, the results of Text-SeGAN show improved inception score compared to other approaches, including GAN-INT-CLS, StackGAN, TAC-GAN, and HDGAN.", + "black" + ], + [ + "Due to the inherent complexity of the visual images, and the diversity of text descriptions (i.e. same words could imply different meanings), it is difficulty to precisely match the texts to the visual images at the semantic levels. For most methods we have discussed so far, they employ a direct text to image generation process, but there is no validation about how generated images comply with the text in a reverse fashion.", + "black To ensure the semantic consistency and diversity, MirrorGAN BIBREF60 employs a mirror structure, which reversely learns from generated images to output texts (an image-to-text process) to further validate whether generated are indeed consistent to the input texts. MirrowGAN includes three modules: a semantic text embedding module (STEM), a global-local collaborative attentive module for cascaded image generation (GLAM), and a semantic text regeneration and alignment module (STREAM). The back to back Text-to-Image (T2I) and Image-to-Text (I2T) are combined to progressively enhance the diversity and semantic consistency of the generated images.", + "black In order to enhance the diversity of the output image, Scene Graph GAN BIBREF61 proposes to use visual scene graphs to describe the layout of the objects, allowing users to precisely specific the relationships between objects in the images. In order to convert the visual scene graph as input for GAN to generate images, this method uses graph convolution to process input graphs. It computes a scene layout by predicting bounding boxes and segmentation masks for objects. After that, it converts the computed layout to an image with a cascaded re\ufb01nement network.", + "black" + ], + [ + "Instead of focusing on generating static images, another line of text-to-image synthesis research focuses on generating videos (i.e. sequences of images) from texts. In this context, the synthesised videos are often useful resources for automated assistance or story telling.", + "black" + ], + [ + "One early/interesting work of motion enhancement GANs is to generate spoofed speech and lip-sync videos (or talking face) of Barack Obama (i.e. ObamaNet) based on text input BIBREF62. This framework is consisted of three parts, i.e. text to speech using \u201cChar2Wav\u201d, mouth shape representation synced to the audio using a time-delayed LSTM and \u201cvideo generation\u201d conditioned on the mouth shape using \u201cU-Net\u201d architecture. Although the results seem promising, ObamaNet only models the mouth region and the videos are not generated from noise which can be regarded as video prediction other than video generation.", + "black Another meaningful trial of using synthesised videos for automated assistance is to translate spoken language (e.g. text) into sign language video sequences (i.e. T2S) BIBREF63. This is often achieved through a two step process: converting texts as meaningful units to generate images, followed by a learning component to arrange images into sequential order for best representation. More specifically, using RNN based machine translation methods, texts are translated into sign language gloss sequences. Then, glosses are mapped to skeletal pose sequences using a lookup-table. To generate videos, a conditional DCGAN with the input of concatenation of latent representation of the image for a base pose and skeletal pose information is built.", + "black" + ], + [ + "In BIBREF64, a text-to-video model (T2V) is proposed based on the cGAN in which the input is the isometric Gaussian noise with the text-gist vector served as the generator. A key component of generating videos from text is to train a conditional generative model to extract both static and dynamic information from text, followed by a hybrid framework combining a Variational Autoencoder (VAE) and a Generative Adversarial Network (GAN).", + "black More specifically, T2V relies on two types of features, static features and dynamic features, to generate videos. Static features, called \u201cgist\u201d are used to sketch text-conditioned background color and object layout structure. Dynamic features, on the other hand, are considered by transforming input text into an image filter which eventually forms the video generator which consists of three entangled neural networks. The text-gist vector is generated by a gist generator which maintains static information (e.g. background) and a text2filter which captures the dynamic information (i.e. actions) in the text to generate videos.", + "black As demonstrated in the paper BIBREF64, the generated videos are semantically related to the texts, but have a rather low quality (e.g. only $64 \\times 64$ resolution).", + "black" + ], + [ + "Different from T2V which generates videos from a single text, StoryGAN aims to produce dynamic scenes consistent of specified texts (i.e. story written in a multi-sentence paragraph) using a sequential GAN model BIBREF65. Story encoder, context encoder, and discriminators are the main components of this model. By using stochastic sampling, the story encoder intends to learn an low-dimensional embedding vector for the whole story to keep the continuity of the story. The context encoder is proposed to capture contextual information during sequential image generation based on a deep RNN. Two discriminators of StoryGAN are image discriminator which evaluates the generated images and story discriminator which ensures the global consistency.", + "black The experiments and comparisons, on CLEVR dataset and Pororo cartoon dataset which are originally used for visual question answering, show that StoryGAN improves the generated video qualify in terms of Structural Similarity Index (SSIM), visual qualify, consistence, and relevance (the last three measure are based on human evaluation)." + ], + [ + "Computer vision applications have strong potential for industries including but not limited to the medical, government, military, entertainment, and online social media fields BIBREF7, BIBREF66, BIBREF67, BIBREF68, BIBREF69, BIBREF70. Text-to-image synthesis is one such application in computer vision AI that has become the main focus in recent years due to its potential for providing beneficial properties and opportunities for a wide range of applicable areas.", + "Text-to-image synthesis is an application byproduct of deep convolutional decoder networks in combination with GANs BIBREF7, BIBREF8, BIBREF10. Deep convolutional networks have contributed to several breakthroughs in image, video, speech, and audio processing. This learning method intends, among other possibilities, to help translate sequential text descriptions to images supplemented by one or many additional methods. Algorithms and methods developed in the computer vision field have allowed researchers in recent years to create realistic images from plain sentences. Advances in the computer vision, deep convolutional nets, and semantic units have shined light and redirected focus to this research area of text-to-image synthesis, having as its prime directive: to aid in the generation of compelling images with as much fidelity to text descriptions as possible.", + "To date, models for generating synthetic images from textual natural language in research laboratories at universities and private companies have yielded compelling images of flowers and birds BIBREF8. Though flowers and birds are the most common objects studied thus far, research has been applied to other classes as well. For example, there have been studies focused solely on human faces BIBREF7, BIBREF8, BIBREF71, BIBREF72.", + "It\u2019s a fascinating time for computer vision AI and deep learning researchers and enthusiasts. The consistent advancement in hardware, software, and contemporaneous development of computer vision AI research disrupts multiple industries. These advances in technology allow for the extraction of several data types from a variety of sources. For example, image data captured from a variety of photo-ready devices, such as smart-phones, and online social media services opened the door to the analysis of large amounts of media datasets BIBREF70. The availability of large media datasets allow new frameworks and algorithms to be proposed and tested on real-world data." + ], + [ + "A summary of some reviewed methods and benchmark datasets used for validation is reported in Table TABREF43. In addition, the performance of different GANs with respect to the benchmark datasets and performance metrics is reported in Table TABREF48.", + "In order to synthesize images from text descriptions, many frameworks have taken a minimalistic approach by creating small and background-less images BIBREF73. In most cases, the experiments were conducted on simple datasets, initially containing images of birds and flowers. BIBREF8 contributed to these data sets by adding corresponding natural language text descriptions to subsets of the CUB, MSCOCO, and Oxford-102 datasets, which facilitated the work on text-to-image synthesis for several papers released more recently.", + "While most deep learning algorithms use MNIST BIBREF74 dataset as the benchmark, there are three main datasets that are commonly used for evaluation of proposed GAN models for text-to-image synthesis: CUB BIBREF75, Oxford BIBREF76, COCO BIBREF77, and CIFAR-10 BIBREF78. CUB BIBREF75 contains 200 birds with matching text descriptions and Oxford BIBREF76 contains 102 categories of flowers with 40-258 images each and matching text descriptions. These datasets contain individual objects, with the text description corresponding to that object, making them relatively simple. COCO BIBREF77 is much more complex, containing 328k images with 91 different object types. CIFAI-10 BIBREF78 dataset consists of 60000 32$times$32 colour images in 10 classes, with 6000 images per class. In contrast to CUB and Oxford, whose images each contain an individual object, COCO\u2019s images may contain multiple objects, each with a label, so there are many labels per image. The total number of labels over the 328k images is 2.5 million BIBREF77." + ], + [ + "Several evaluation metrics are used for judging the images produced by text-to-image GANs. Proposed by BIBREF25, Inception Scores (IS) calculates the entropy (randomness) of the conditional distribution, obtained by applying the Inception Model introduced in BIBREF79, and marginal distribution of a large set of generated images, which should be low and high, respectively, for meaningful images. Low entropy of conditional distribution means that the evaluator is confident that the images came from the data distribution, and high entropy of the marginal distribution means that the set of generated images is diverse, which are both desired features. The IS score is then computed as the KL-divergence between the two entropies. FCN-scores BIBREF2 are computed in a similar manner, relying on the intuition that realistic images generated by a GAN should be able to be classified correctly by a classifier trained on real images of the same distribution. Therefore, if the FCN classifier classifies a set of synthetic images accurately, the image is probably realistic, and the corresponding GAN gets a high FCN score. Frechet Inception Distance (FID) BIBREF80 is the other commonly used evaluation metric, and takes a different approach, actually comparing the generated images to real images in the distribution. A high FID means there is little relationship between statistics of the synthetic and real images and vice versa, so lower FIDs are better.", + "black The performance of different GANs with respect to the benchmark datasets and performance metrics is reported in Table TABREF48. In addition, Figure FIGREF49 further lists the performance of 14 GANs with respect to their Inception Scores (IS)." + ], + [ + "While we gathered all the data we could find on scores for each model on the CUB, Oxford, and COCO datasets using IS, FID, FCN, and human classifiers, we unfortunately were unable to find certain data for AttnGAN and HDGAN (missing in Table TABREF48). The best evaluation we can give for those with missing data is our own opinions by looking at examples of generated images provided in their papers. In this regard, we observed that HDGAN produced relatively better visual results on the CUB and Oxford datasets while AttnGAN produced far more impressive results than the rest on the more complex COCO dataset. This is evidence that the attentional model and DAMSM introduced by AttnGAN are very effective in producing high-quality images. Examples of the best results of birds and plates of vegetables generated by each model are presented in Figures FIGREF50 and FIGREF51, respectively.", + "blackIn terms of inception score (IS), which is the metric that was applied to majority models except DC-GAN, the results in Table TABREF48 show that StackGAN++ only showed slight improvement over its predecessor, StackGAN, for text-to-image synthesis. However, StackGAN++ did introduce a very worthy enhancement for unconditional image generation by organizing the generators and discriminators in a \u201ctree-like\u201d structure. This indicates that revising the structures of the discriminators and/or generators can bring a moderate level of improvement in text-to-image synthesis.", + "blackIn addition, the results in Table TABREF48 also show that DM-GAN BIBREF53 has the best performance, followed by Obj-GAN BIBREF81. Notice that both DM-GAN and Obj-GAN are most recently developed methods in the field (both published in 2019), indicating that research in text to image synthesis is continuously improving the results for better visual perception and interception. Technical wise, DM-GAN BIBREF53 is a model using dynamic memory to refine fuzzy image contents initially generated from the GAN networks. A memory writing gate is used for DM-GAN to select important text information and generate images based on he selected text accordingly. On the other hand, Obj-GAN BIBREF81 focuses on object centered text-to-image synthesis. The proposed framework of Obj-GAN consists of a layout generation, including a bounding box generator and a shape generator, and an object-driven attentive image generator. The designs and advancement of DM-GAN and Obj-GAN indicate that research in text-to-image synthesis is advancing to put more emphasis on the image details and text semantics for better understanding and perception." + ], + [ + "It is worth noting that although this survey mainly focuses on text-to-image synthesis, there have been other applications of GANs in broader image synthesis field that we found fascinating and worth dedicating a small section to. For example, BIBREF72 used Sem-Latent GANs to generate images of faces based on facial attributes, producing impressive results that, at a glance, could be mistaken for real faces. BIBREF82, BIBREF70, and BIBREF83 demonstrated great success in generating text descriptions from images (image captioning) with great accuracy, with BIBREF82 using an attention-based model that automatically learns to focus on salient objects and BIBREF83 using deep visual-semantic alignments. Finally, there is a contribution made by StackGAN++ that was not mentioned in the dedicated section due to its relation to unconditional image generation as opposed to conditional, namely a color-regularization term BIBREF47. This additional term aims to keep the samples generated from the same input at different stages more consistent in color, which resulted in significantly better results for the unconditional model." + ], + [ + "The recent advancement in text-to-image synthesis research opens the door to several compelling methods and architectures. The main objective of text-to-image synthesis initially was to create images from simple labels, and this objective later scaled to natural languages. In this paper, we reviewed novel methods that generate, in our opinion, the most visually-rich and photo-realistic images, from text-based natural language. These generated images often rely on generative adversarial networks (GANs), deep convolutional decoder networks, and multimodal learning methods.", + "blackIn the paper, we first proposed a taxonomy to organize GAN based text-to-image synthesis frameworks into four major groups: semantic enhancement GANs, resolution enhancement GANs, diversity enhancement GANs, and motion enhancement GANs. The taxonomy provides a clear roadmap to show the motivations, architectures, and difference of different methods, and also outlines their evolution timeline and relationships. Following the proposed taxonomy, we reviewed important features of each method and their architectures. We indicated the model definition and key contributions from some advanced GAN framworks, including StackGAN, StackGAN++, AttnGAN, DC-GAN, AC-GAN, TAC-GAN, HDGAN, Text-SeGAn, StoryGAN etc. Many of the solutions surveyed in this paper tackled the highly complex challenge of generating photo-realistic images beyond swatch size samples. In other words, beyond the work of BIBREF8 in which images were generated from text in 64$\\times $64 tiny swatches. Lastly, all methods were evaluated on datasets that included birds, flowers, humans, and other miscellaneous elements. We were also able to allocate some important papers that were as impressive as the papers we finally surveyed. Though, these notable papers have yet to contribute directly or indirectly to the expansion of the vast computer vision AI field. Looking into the future, an excellent extension from the works surveyed in this paper would be to give more independence to the several learning methods (e.g. less human intervention) involved in the studies as well as increasing the size of the output images." + ], + [ + "The authors declare that there is no conflict of interest regarding the publication of this article." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0236/instruction.md b/qasper-0236/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..f247508f03b988361fafbf31bfdc521e21843151 --- /dev/null +++ b/qasper-0236/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction + +Question: Are there datasets with relation tuples annotated, how big are datasets available? \ No newline at end of file diff --git a/qasper-0237/instruction.md b/qasper-0237/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..7c02971cfddb40b056e21c64c1ec07a0434ab837 --- /dev/null +++ b/qasper-0237/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction + +Question: Which one of two proposed approaches performed better in experiments? \ No newline at end of file diff --git a/qasper-0238/instruction.md b/qasper-0238/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..431a658e9422d686649a0829772b680253b9f1f8 --- /dev/null +++ b/qasper-0238/instruction.md @@ -0,0 +1,124 @@ +Name of Paper: Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction + +Question: What is previous work authors reffer to? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Task Description", + "Encoder-Decoder Architecture", + "Encoder-Decoder Architecture ::: Embedding Layer & Encoder", + "Encoder-Decoder Architecture ::: Word-level Decoder & Copy Mechanism", + "Encoder-Decoder Architecture ::: Pointer Network-Based Decoder", + "Encoder-Decoder Architecture ::: Relation Tuple Extraction", + "Encoder-Decoder Architecture ::: Attention Modeling", + "Encoder-Decoder Architecture ::: Loss Function", + "Experiments ::: Datasets", + "Experiments ::: Parameter Settings", + "Experiments ::: Baselines and Evaluation Metrics", + "Experiments ::: Experimental Results", + "Analysis and Discussion ::: Ablation Studies", + "Analysis and Discussion ::: Performance Analysis", + "Analysis and Discussion ::: Error Analysis", + "Related Work", + "Conclusion", + "Acknowledgments" + ], + "paragraphs": [ + [ + "Distantly-supervised information extraction systems extract relation tuples with a set of pre-defined relations from text. Traditionally, researchers BIBREF0, BIBREF1, BIBREF2 use pipeline approaches where a named entity recognition (NER) system is used to identify the entities in a sentence and then a classifier is used to find the relation (or no relation) between them. However, due to the complete separation of entity detection and relation classification, these models miss the interaction between multiple relation tuples present in a sentence.", + "Recently, several neural network-based models BIBREF3, BIBREF4 were proposed to jointly extract entities and relations from a sentence. These models used a parameter-sharing mechanism to extract the entities and relations in the same network. But they still find the relations after identifying all the entities and do not fully capture the interaction among multiple tuples. BIBREF5 (BIBREF5) proposed a joint extraction model based on neural sequence tagging scheme. But their model could not extract tuples with overlapping entities in a sentence as it could not assign more than one tag to a word. BIBREF6 (BIBREF6) proposed a neural encoder-decoder model for extracting relation tuples with overlapping entities. However, they used a copy mechanism to copy only the last token of the entities, thus this model could not extract the full entity names. Also, their best performing model used a separate decoder to extract each tuple which limited the power of their model. This model was trained with a fixed number of decoders and could not extract tuples beyond that number during inference. Encoder-decoder models are powerful models and they are successful in many NLP tasks such as machine translation, sentence generation from structured data, and open information extraction.", + "In this paper, we explore how encoder-decoder models can be used effectively for extracting relation tuples from sentences. There are three major challenges in this task: (i) The model should be able to extract entities and relations together. (ii) It should be able to extract multiple tuples with overlapping entities. (iii) It should be able to extract exactly two entities of a tuple with their full names. To address these challenges, we propose two novel approaches using encoder-decoder architecture. We first propose a new representation scheme for relation tuples (Table TABREF1) such that it can represent multiple tuples with overlapping entities and different lengths of entities in a simple way. We employ an encoder-decoder model where the decoder extracts one word at a time like machine translation models. At the end of sequence generation, due to the unique representation of the tuples, we can extract the tuples from the sequence of words. Although this model performs quite well, generating one word at a time is somewhat unnatural for this task. Each tuple has exactly two entities and one relation, and each entity appears as a continuous text span in a sentence. The most effective way to identify them is to find their start and end location in the sentence. Each relation tuple can then be represented using five items: start and end location of the two entities and the relation between them (see Table TABREF1). Keeping this in mind, we propose a pointer network-based decoding framework. This decoder consists of two pointer networks which find the start and end location of the two entities in a sentence, and a classification network which identifies the relation between them. At every time step of the decoding, this decoder extracts an entire relation tuple, not just a word. Experiments on the New York Times (NYT) datasets show that our approaches work effectively for this task and achieve state-of-the-art performance. To summarize, the contributions of this paper are as follows:", + "(1) We propose a new representation scheme for relation tuples such that an encoder-decoder model, which extracts one word at each time step, can still find multiple tuples with overlapping entities and tuples with multi-token entities from sentences. We also propose a masking-based copy mechanism to extract the entities from the source sentence only.", + "(2) We propose a modification in the decoding framework with pointer networks to make the encoder-decoder model more suitable for this task. At every time step, this decoder extracts an entire relation tuple, not just a word. This new decoding framework helps in speeding up the training process and uses less resources (GPU memory). This will be an important factor when we move from sentence-level tuple extraction to document-level extraction.", + "(3) Experiments on the NYT datasets show that our approaches outperform all the previous state-of-the-art models significantly and set a new benchmark on these datasets." + ], + [ + "A relation tuple consists of two entities and a relation. Such tuples can be found in sentences where an entity is a text span in a sentence and a relation comes from a pre-defined set $R$. These tuples may share one or both entities among them. Based on this, we divide the sentences into three classes: (i) No Entity Overlap (NEO): A sentence in this class has one or more tuples, but they do not share any entities. (ii) Entity Pair Overlap (EPO): A sentence in this class has more than one tuple, and at least two tuples share both the entities in the same or reverse order. (iii) Single Entity Overlap (SEO): A sentence in this class has more than one tuple and at least two tuples share exactly one entity. It should be noted that a sentence can belong to both EPO and SEO classes. Our task is to extract all relation tuples present in a sentence." + ], + [ + "In this task, input to the system is a sequence of words, and output is a set of relation tuples. In our first approach, we represent each tuple as entity1 ; entity2 ; relation. We use `;' as a separator token to separate the tuple components. Multiple tuples are separated using the `$\\vert $' token. We have included one example of such representation in Table TABREF1. Multiple relation tuples with overlapping entities and different lengths of entities can be represented in a simple way using these special tokens (; and $\\vert $). During inference, after the end of sequence generation, relation tuples can be extracted easily using these special tokens. Due to this uniform representation scheme, where entity tokens, relation tokens, and special tokens are treated similarly, we use a shared vocabulary between the encoder and decoder which includes all of these tokens. The input sentence contains clue words for every relation which can help generate the relation tokens. We use two special tokens so that the model can distinguish between the beginning of a relation tuple and the beginning of a tuple component. To extract the relation tuples from a sentence using the encoder-decoder model, the model has to generate the entity tokens, find relation clue words and map them to the relation tokens, and generate the special tokens at appropriate time. Our experiments show that the encoder-decoder models can achieve this quite effectively." + ], + [ + "We create a single vocabulary $V$ consisting of the source sentence tokens, relation names from relation set $R$, special separator tokens (`;', `$\\vert $'), start-of-target-sequence token (SOS), end-of-target-sequence token (EOS), and unknown word token (UNK). Word-level embeddings are formed by two components: (1) pre-trained word vectors (2) character embedding-based feature vectors. We use a word embedding layer $\\mathbf {E}_w \\in \\mathbb {R}^{\\vert V \\vert \\times d_w}$ and a character embedding layer $\\mathbf {E}_c \\in \\mathbb {R}^{\\vert A \\vert \\times d_c}$, where $d_w$ is the dimension of word vectors, $A$ is the character alphabet of input sentence tokens, and $d_c$ is the dimension of character embedding vectors. Following BIBREF7 (BIBREF7), we use a convolutional neural network with max-pooling to extract a feature vector of size $d_f$ for every word. Word embeddings and character embedding-based feature vectors are concatenated ($\\Vert $) to obtain the representation of the input tokens.", + "A source sentence $\\mathbf {S}$ is represented by vectors of its tokens $\\mathbf {x}_1, \\mathbf {x}_2,....,\\mathbf {x}_n$, where $\\mathbf {x}_i \\in \\mathbb {R}^{(d_w+d_f)}$ is the vector representation of the $i$th word and $n$ is the length of $\\mathbf {S}$. These vectors $\\mathbf {x}_i$ are passed to a bi-directional LSTM BIBREF8 (Bi-LSTM) to obtain the hidden representation $\\mathbf {h}_i^E$. We set the hidden dimension of the forward and backward LSTM of the Bi-LSTM to be $d_h/2$ to obtain $\\mathbf {h}_i^E \\in \\mathbb {R}^{d_h}$, where $d_h$ is the hidden dimension of the sequence generator LSTM of the decoder described below." + ], + [ + "A target sequence $\\mathbf {T}$ is represented by only word embedding vectors of its tokens $\\mathbf {y}_0, \\mathbf {y}_1,....,\\mathbf {y}_m$ where $\\mathbf {y}_i \\in \\mathbb {R}^{d_w}$ is the embedding vector of the $i$th token and $m$ is the length of the target sequence. $\\mathbf {y}_0$ and $\\mathbf {y}_m$ represent the embedding vector of the SOS and EOS token respectively. The decoder generates one token at a time and stops when EOS is generated. We use an LSTM as the decoder and at time step $t$, the decoder takes the source sentence encoding ($\\mathbf {e}_t \\in \\mathbb {R}^{d_h}$) and the previous target word embedding ($\\mathbf {y}_{t-1}$) as the input and generates the hidden representation of the current token ($\\mathbf {h}_t^D \\in \\mathbb {R}^{d_h}$). The sentence encoding vector $\\mathbf {e}_t$ can be obtained using attention mechanism. $\\mathbf {h}_t^D$ is projected to the vocabulary $V$ using a linear layer with weight matrix $\\mathbf {W}_v \\in \\mathbb {R}^{\\vert V \\vert \\times d_h}$ and bias vector $\\mathbf {b}_v \\in \\mathbb {R}^{\\vert V \\vert }$ (projection layer).", + "$\\mathbf {o}_t$ represents the normalized scores of all the words in the embedding vocabulary at time step $t$. $\\mathbf {h}_{t-1}^D$ is the previous hidden state of the LSTM.", + "The projection layer of the decoder maps the decoder output to the entire vocabulary. During training, we use the gold label target tokens directly. However, during inference, the decoder may predict a token from the vocabulary which is not present in the current sentence or the set of relations or the special tokens. To prevent this, we use a masking technique while applying the softmax operation at the projection layer. We mask (exclude) all words of the vocabulary except the current source sentence tokens, relation tokens, separator tokens (`;', `$\\vert $'), UNK, and EOS tokens in the softmax operation. To mask (exclude) some word from softmax, we set the corresponding value in $\\hat{\\mathbf {o}}_t$ at $-\\infty $ and the corresponding softmax score will be zero. This ensures the copying of entities from the source sentence only. We include the UNK token in the softmax operation to make sure that the model generates new entities during inference. If the decoder predicts an UNK token, we replace it with the corresponding source word which has the highest attention score. During inference, after decoding is finished, we extract all tuples based on the special tokens, remove duplicate tuples and tuples in which both entities are the same or tuples where the relation token is not from the relation set. This model is referred to as WordDecoding (WDec) henceforth." + ], + [ + "In the second approach, we identify the entities in the sentence using their start and end locations. We remove the special tokens and relation names from the word vocabulary and word embeddings are used only at the encoder side along with character embeddings. We use an additional relation embedding matrix $\\mathbf {E}_r \\in \\mathbb {R}^{\\vert R \\vert \\times d_r}$ at the decoder side of our model, where $R$ is the set of relations and $d_r$ is the dimension of relation vectors. The relation set $R$ includes a special relation token EOS which indicates the end of the sequence. Relation tuples are represented as a sequence $T=y_0, y_1,....,y_m$, where $y_t$ is a tuple consisting of four indexes in the source sentence indicating the start and end location of the two entities and a relation between them (see Table TABREF1). $y_0$ is a dummy tuple that represents the start tuple of the sequence and $y_m$ functions as the end tuple of the sequence which has EOS as the relation (entities are ignored for this tuple). The decoder consists of an LSTM with hidden dimension $d_h$ to generate the sequence of tuples, two pointer networks to find the two entities, and a classification network to find the relation of a tuple. At time step $t$, the decoder takes the source sentence encoding ($\\mathbf {e}_t \\in \\mathbb {R}^{d_h}$) and the representation of all previously generated tuples ($\\mathbf {y}_{prev}=\\sum _{j=0}^{t-1}\\mathbf {y}_{j}$) as the input and generates the hidden representation of the current tuple, $\\mathbf {h}_t^D \\in \\mathbb {R}^{d_h}$. The sentence encoding vector $\\mathbf {e}_t$ is obtained using an attention mechanism as explained later. Relation tuples are a set and to prevent the decoder from generating the same tuple again, we pass the information about all previously generated tuples at each time step of decoding. $\\mathbf {y}_j$ is the vector representation of the tuple predicted at time step $j < t$ and we use the zero vector ($\\mathbf {y}_0=\\overrightarrow{0}$) to represent the dummy tuple $y_0$. $\\mathbf {h}_{t-1}^D$ is the hidden state of the LSTM at time step $t-1$." + ], + [ + "After obtaining the hidden representation of the current tuple $\\mathbf {h}_t^D$, we first find the start and end pointers of the two entities in the source sentence. We concatenate the vector $\\mathbf {h}_t^D$ with the hidden vectors $\\mathbf {h}_i^E$ of the encoder and pass them to a Bi-LSTM layer with hidden dimension $d_p$ for forward and backward LSTM. The hidden vectors of this Bi-LSTM layer $\\mathbf {h}_i^k \\in \\mathbb {R}^{2d_p}$ are passed to two feed-forward networks (FFN) with softmax to convert each hidden vector into two scalar values between 0 and 1. Softmax operation is applied across all the words in the input sentence. These two scalar values represent the probability of the corresponding source sentence token to be the start and end location of the first entity. This Bi-LSTM layer with the two feed-forward layers is the first pointer network which identifies the first entity of the current relation tuple.", + "where $\\mathbf {W}_s^1 \\in \\mathbb {R}^{1 \\times 2d_p}$, $\\mathbf {W}_e^1 \\in \\mathbb {R}^{1 \\times 2d_p}$, ${b}_s^1$, and ${b}_e^1$ are the weights and bias parameters of the feed-forward layers. ${s}_i^1$, ${e}_i^1$ represent the normalized probabilities of the $i$th source word being the start and end token of the first entity of the predicted tuple. We use another pointer network to extract the second entity of the tuple. We concatenate the hidden vectors $\\mathbf {h}_i^k$ with $\\mathbf {h}_t^D$ and $\\mathbf {h}_i^E$ and pass them to the second pointer network to obtain ${s}_i^2$ and ${e}_i^2$, which represent the normalized probabilities of the $i$th source word being the start and end of the second entity. These normalized probabilities are used to find the vector representation of the two entities, $\\mathbf {a}_t^1$ and $\\mathbf {a}_t^2$.", + "We concatenate the entity vector representations $\\mathbf {a}_t^1$ and $\\mathbf {a}_t^2$ with $\\mathbf {h}_t^D$ and pass it to a feed-forward network (FFN) with softmax to find the relation. This feed-forward layer has a weight matrix $\\mathbf {W}_r \\in \\mathbb {R}^{\\vert R \\vert \\times (8d_p + d_h)}$ and a bias vector $\\mathbf {b}_r \\in \\mathbb {R}^{\\vert R \\vert }$.", + "$\\mathbf {r}_t$ represents the normalized probabilities of the relation at time step $t$. The relation embedding vector $\\mathbf {z}_t$ is obtained using $\\mathrm {argmax}$ of $\\mathbf {r}_t$ and $\\mathbf {E}_r$. $\\mathbf {y}_t \\in \\mathbb {R}^{(8d_p + d_r)}$ is the vector representation of the tuple predicted at time step $t$. During training, we pass the embedding vector of the gold label relation in place of the predicted relation. So the $\\mathrm {argmax}$ function does not affect the back-propagation during training. The decoder stops the sequence generation process when the predicted relation is EOS. This is the classification network of the decoder.", + "During inference, we select the start and end location of the two entities such that the product of the four pointer probabilities is maximized keeping the constraints that the two entities do not overlap with each other and $1 \\le b \\le e \\le n$ where $b$ and $e$ are the start and end location of the corresponding entities. We first choose the start and end location of entity 1 based on the maximum product of the corresponding start and end pointer probabilities. Then we find entity 2 in a similar way excluding the span of entity 1 to avoid overlap. The same procedure is repeated but this time we first find entity 2 followed by entity 1. We choose that pair of entities which gives the higher product of four pointer probabilities between these two choices. This model is referred to as PtrNetDecoding (PNDec) henceforth." + ], + [ + "We experimented with three different attention mechanisms for our word-level decoding model to obtain the source context vector $\\mathbf {e}_t$:", + "(1) Avg.: The context vector is obtained by averaging the hidden vectors of the encoder: $\\mathbf {e}_t=\\frac{1}{n}\\sum _{i=1}^n \\mathbf {h}_i^E$", + "(2) N-gram: The context vector is obtained by the N-gram attention mechanism of BIBREF9 (BIBREF9) with N=3.", + "$\\textnormal {a}_i^g=(\\mathbf {h}_n^{E})^T \\mathbf {V}^g \\mathbf {w}_i^g$, $\\alpha ^g = \\mathrm {softmax}(\\mathbf {a}^g)$", + "$\\mathbf {e}_t=[\\mathbf {h}_n^E \\Vert \\sum _{g=1}^N \\mathbf {W}^g (\\sum _{i=1}^{\\vert G^g \\vert } \\alpha _i^g \\mathbf {w}_i^g)$]", + "Here, $\\mathbf {h}_n^E$ is the last hidden state of the encoder, $g \\in \\lbrace 1, 2, 3\\rbrace $ refers to the word gram combination, $G^g$ is the sequence of g-gram word representations for the input sentence, $\\mathbf {w}_i^g$ is the $i$th g-gram vector (2-gram and 3-gram representations are obtained by average pooling), $\\alpha _i^g$ is the normalized attention score for the $i$th g-gram vector, $\\mathbf {W} \\in \\mathbb {R}^{d_h \\times d_h}$ and $\\mathbf {V} \\in \\mathbb {R}^{d_h \\times d_h}$ are trainable parameters.", + "(3) Single: The context vector is obtained by the attention mechanism proposed by BIBREF10 (BIBREF10). This attention mechanism gives the best performance with the word-level decoding model.", + "$\\mathbf {u}_t^i = \\mathbf {W}_{u} \\mathbf {h}_i^E, \\quad \\mathbf {q}_t^i = \\mathbf {W}_{q} \\mathbf {h}_{t-1}^D + \\mathbf {b}_{q}$,", + "$\\textnormal {a}_t^i = \\mathbf {v}_a \\tanh (\\mathbf {q}_t^i + \\mathbf {u}_t^i), \\quad \\alpha _t = \\mathrm {softmax}(\\mathbf {a}_t)$,", + "$\\mathbf {e}_t = \\sum _{i=1}^n \\alpha _t^i \\mathbf {h}_i^E$", + "where $\\mathbf {W}_u \\in \\mathbb {R}^{d_h \\times d_h}$, $\\mathbf {W}_q \\in \\mathbb {R}^{d_h \\times d_h}$, and $\\mathbf {v}_a \\in \\mathbb {R}^{d_h}$ are all trainable attention parameters and $\\mathbf {b}_q \\in \\mathbb {R}^{d_h}$ is a bias vector. $\\alpha _t^i$ is the normalized attention score of the $i$th source word at the decoding time step $t$.", + "For our pointer network-based decoding model, we use three variants of the single attention model. First, we use $\\mathbf {h}_{t-1}^D$ to calculate $\\mathbf {q}_t^i$ in the attention mechanism. Next, we use $\\mathbf {y}_{prev}$ to calculate $\\mathbf {q}_t^i$, where $\\mathbf {W}_q \\in \\mathbb {R}^{(8d_p + d_r) \\times d_h}$. In the final variant, we obtain the attentive context vector by concatenating the two attentive vectors obtained using $\\mathbf {h}_{t-1}^D$ and $\\mathbf {y}_{prev}$. This gives the best performance with the pointer network-based decoding model. These variants are referred to as $\\mathrm {dec_{hid}}$, $\\mathrm {tup_{prev}}$, and $\\mathrm {combo}$ in Table TABREF17." + ], + [ + "We minimize the negative log-likelihood loss of the generated words for word-level decoding ($\\mathcal {L}_{word}$) and minimize the sum of negative log-likelihood loss of relation classification and the four pointer locations for pointer network-based decoding ($\\mathcal {L}_{ptr}$).", + "$v_t^b$ is the softmax score of the target word at time step $t$ for the word-level decoding model. $r$, $s$, and $e$ are the softmax score of the corresponding true relation label, true start and end pointer location of an entity. $b$, $t$, and $c$ refer to the $b$th training instance, $t$th time step of decoding, and the two entities of a tuple respectively. $B$ and $T$ are the batch size and maximum time step of the decoder respectively." + ], + [ + "We focus on the task of extracting multiple tuples with overlapping entities from sentences. We choose the New York Times (NYT) corpus for our experiments. This corpus has multiple versions, and we choose the following two versions as their test dataset has significantly larger number of instances of multiple relation tuples with overlapping entities. (i) The first version is used by BIBREF6 (BIBREF6) (mentioned as NYT in their paper) and has 24 relations. We name this version as NYT24. (ii) The second version is used by BIBREF11 (BIBREF11) (mentioned as NYT10 in their paper) and has 29 relations. We name this version as NYT29. We select 10% of the original training data and use it as the validation dataset. The remaining 90% is used for training. We include statistics of the training and test datasets in Table TABREF11." + ], + [ + "We run the Word2Vec BIBREF12 tool on the NYT corpus to initialize the word embeddings. The character embeddings and relation embeddings are initialized randomly. All embeddings are updated during training. We set the word embedding dimension $d_w=300$, relation embedding dimension $d_r=300$, character embedding dimension $d_c=50$, and character-based word feature dimension $d_f=50$. To extract the character-based word feature vector, we set the CNN filter width at 3 and the maximum length of a word at 10. The hidden dimension $d_h$ of the decoder LSTM cell is set at 300 and the hidden dimension of the forward and the backward LSTM of the encoder is set at 150. The hidden dimension of the forward and backward LSTM of the pointer networks is set at $d_p=300$. The model is trained with mini-batch size of 32 and the network parameters are optimized using Adam BIBREF13. Dropout layers with a dropout rate fixed at $0.3$ are used in our network to avoid overfitting." + ], + [ + "We compare our model with the following state-of-the-art joint entity and relation extraction models:", + "(1) SPTree BIBREF4: This is an end-to-end neural entity and relation extraction model using sequence LSTM and Tree LSTM. Sequence LSTM is used to identify all the entities first and then Tree LSTM is used to find the relation between all pairs of entities.", + "(2) Tagging BIBREF5: This is a neural sequence tagging model which jointly extracts the entities and relations using an LSTM encoder and an LSTM decoder. They used a Cartesian product of entity tags and relation tags to encode the entity and relation information together. This model does not work when tuples have overlapping entities.", + "(3) CopyR BIBREF6: This model uses an encoder-decoder approach for joint extraction of entities and relations. It copies only the last token of an entity from the source sentence. Their best performing multi-decoder model is trained with a fixed number of decoders where each decoder extracts one tuple.", + "(4) HRL BIBREF11: This model uses a reinforcement learning (RL) algorithm with two levels of hierarchy for tuple extraction. A high-level RL finds the relation and a low-level RL identifies the two entities using a sequence tagging approach. This sequence tagging approach cannot always ensure extraction of exactly two entities.", + "(5) GraphR BIBREF14: This model considers each token in a sentence as a node in a graph, and edges connecting the nodes as relations between them. They use graph convolution network (GCN) to predict the relations of every edge and then filter out some of the relations.", + "(6) N-gram Attention BIBREF9: This model uses an encoder-decoder approach with N-gram attention mechanism for knowledge-base completion using distantly supervised data. The encoder uses the source tokens as its vocabulary and the decoder uses the entire Wikidata BIBREF15 entity IDs and relation IDs as its vocabulary. The encoder takes the source sentence as input and the decoder outputs the two entity IDs and relation ID for every tuple. During training, it uses the mapping of entity names and their Wikidata IDs of the entire Wikidata for proper alignment. Our task of extracting relation tuples with the raw entity names from a sentence is more challenging since entity names are not of fixed length. Our more generic approach is also helpful for extracting new entities which are not present in the existing knowledge bases such as Wikidata. We use their N-gram attention mechanism in our model to compare its performance with other attention models (Table TABREF17).", + "We use the same evaluation method used by BIBREF11 (BIBREF11) in their experiments. We consider the extracted tuples as a set and remove the duplicate tuples. An extracted tuple is considered as correct if the corresponding full entity names are correct and the relation is also correct. We report precision, recall, and F1 score for comparison." + ], + [ + "Among the baselines, HRL achieves significantly higher F1 scores on the two datasets. We run their model and our models five times and report the median results in Table TABREF15. Scores of other baselines in Table TABREF15 are taken from previous published papers BIBREF6, BIBREF11, BIBREF14. Our WordDecoding (WDec) model achieves F1 scores that are $3.9\\%$ and $4.1\\%$ higher than HRL on the NYT29 and NYT24 datasets respectively. Similarly, our PtrNetDecoding (PNDec) model achieves F1 scores that are $3.0\\%$ and $1.3\\%$ higher than HRL on the NYT29 and NYT24 datasets respectively. We perform a statistical significance test (t-test) under a bootstrap pairing between HRL and our models and see that the higher F1 scores achieved by our models are statistically significant ($p < 0.001$). Next, we combine the outputs of five runs of our models and five runs of HRL to build ensemble models. For a test instance, we include those tuples which are extracted in the majority ($\\ge 3$) of the five runs. This ensemble mechanism increases the precision significantly on both datasets with a small improvement in recall as well. In the ensemble scenario, compared to HRL, WDec achieves $4.2\\%$ and $3.5\\%$ higher F1 scores and PNDec achieves $4.2\\%$ and $2.9\\%$ higher F1 scores on the NYT29 and NYT24 datasets respectively." + ], + [ + "We include the performance of different attention mechanisms with our WordDecoding model, effects of our masking-based copy mechanism, and ablation results of three variants of the single attention mechanism with our PtrNetDecoding model in Table TABREF17. WordDecoding with single attention achieves the highest F1 score on both datasets. We also see that our copy mechanism improves F1 scores by around 4\u20137% in each attention mechanism with both datasets. PtrNetDecoding achieves the highest F1 scores when we combine the two attention mechanisms with respect to the previous hidden vector of the decoder LSTM ($\\mathbf {h}_{t-1}^D$) and representation of all previously extracted tuples ($\\mathbf {y}_{prev}$)." + ], + [ + "From Table TABREF15, we see that CopyR, HRL, and our models achieve significantly higher F1 scores on the NYT24 dataset than the NYT29 dataset. Both datasets have a similar set of relations and similar texts (NYT). So task-wise both datasets should pose a similar challenge. However, the F1 scores suggest that the NYT24 dataset is easier than NYT29. The reason is that NYT24 has around 72.0% of overlapping tuples between the training and test data (% of test tuples that appear in the training data with different source sentences). In contrast, NYT29 has only 41.7% of overlapping tuples. Due to the memorization power of deep neural networks, it can achieve much higher F1 score on NYT24. The difference between the F1 scores of WordDecoding and PtrNetDecoding on NYT24 is marginally higher than NYT29, since WordDecoding has more trainable parameters (about 27 million) than PtrNetDecoding (about 24.5 million) and NYT24 has very high tuple overlap. However, their ensemble versions achieve closer F1 scores on both datasets.", + "Despite achieving marginally lower F1 scores, the pointer network-based model can be considered more intuitive and suitable for this task. WordDecoding may not extract the special tokens and relation tokens at the right time steps, which is critical for finding the tuples from the generated sequence of words. PtrNetDecoding always extracts two entities of varying length and a relation for every tuple. We also observe that PtrNetDecoding is more than two times faster and takes one-third of the GPU memory of WordDecoding during training and inference. This speedup and smaller memory consumption are achieved due to the fewer number of decoding steps of PtrNetDecoding compared to WordDecoding. PtrNetDecoding extracts an entire tuple at each time step, whereas WordDecoding extracts just one word at each time step and so requires eight time steps on average to extract a tuple (assuming that the average length of an entity is two). The softmax operation at the projection layer of WordDecoding is applied across the entire vocabulary and the vocabulary size can be large (more than 40,000 for our datasets). In case of PtrNetDecoding, the softmax operation is applied across the sentence length (maximum of 100 in our experiments) and across the relation set (24 and 29 for our datasets). The costly softmax operation and the higher number of decoding time steps significantly increase the training and inference time for WordDecoding. The encoder-decoder model proposed by BIBREF9 (BIBREF9) faces a similar softmax-related problem as their target vocabulary contains the entire Wikidata entity IDs and relation IDs which is in the millions. HRL, which uses a deep reinforcement learning algorithm, takes around 8x more time to train than PtrNetDecoding with a similar GPU configuration. The speedup and smaller memory consumption will be useful when we move from sentence-level extraction to document-level extraction, since document length is much higher than sentence length and a document contains a higher number of tuples." + ], + [ + "The relation tuples extracted by a joint model can be erroneous for multiple reasons such as: (i) extracted entities are wrong; (ii) extracted relations are wrong; (iii) pairings of entities with relations are wrong. To see the effects of the first two reasons, we analyze the performance of HRL and our models on entity generation and relation generation separately. For entity generation, we only consider those entities which are part of some tuple. For relation generation, we only consider the relations of the tuples. We include the performance of our two models and HRL on entity generation and relation generation in Table TABREF20. Our proposed models perform better than HRL on both tasks. Comparing our two models, PtrNetDecoding performs better than WordDecoding on both tasks, although WordDecoding achieves higher F1 scores in tuple extraction. This suggests that PtrNetDecoding makes more errors while pairing the entities with relations. We further analyze the outputs of our models and HRL to determine the errors due to ordering of entities (Order), mismatch of the first entity (Ent1), and mismatch of the second entity (Ent2) in Table TABREF21. WordDecoding generates fewer errors than the other two models in all the categories and thus achieves the highest F1 scores on both datasets." + ], + [ + "Traditionally, researchers BIBREF0, BIBREF1, BIBREF2, BIBREF16, BIBREF17, BIBREF18, BIBREF19, BIBREF20, BIBREF21, BIBREF22, BIBREF23, BIBREF24, BIBREF25 used a pipeline approach for relation tuple extraction where relations were identified using a classification network after all entities were detected. BIBREF26 (BIBREF26) used an encoder-decoder model to extract multiple relations present between two given entities.", + "Recently, some researchers BIBREF3, BIBREF4, BIBREF27, BIBREF28 tried to bring these two tasks closer together by sharing their parameters and optimizing them together. BIBREF5 (BIBREF5) used a sequence tagging scheme to jointly extract the entities and relations. BIBREF6 (BIBREF6) proposed an encoder-decoder model with copy mechanism to extract relation tuples with overlapping entities. BIBREF11 (BIBREF11) proposed a joint extraction model based on reinforcement learning (RL). BIBREF14 (BIBREF14) used a graph convolution network (GCN) where they treated each token in a sentence as a node in a graph and edges were considered as relations. BIBREF9 (BIBREF9) used an N-gram attention mechanism with an encoder-decoder model for completion of knowledge bases using distant supervised data.", + "Encoder-decoder models have been used for many NLP applications such as neural machine translation BIBREF29, BIBREF10, BIBREF30, sentence generation from structured data BIBREF31, BIBREF32, and open information extraction BIBREF33, BIBREF34. Pointer networks BIBREF35 have been used to extract a text span from text for tasks such as question answering BIBREF36, BIBREF37. For the first time, we use pointer networks with an encoder-decoder model to extract relation tuples from sentences." + ], + [ + "Extracting relation tuples from sentences is a challenging task due to different length of entities, the presence of multiple tuples, and overlapping of entities among tuples. In this paper, we propose two novel approaches using encoder-decoder architecture to address this task. Experiments on the New York Times (NYT) corpus show that our proposed models achieve significantly improved new state-of-the-art F1 scores. As future work, we would like to explore our proposed models for a document-level tuple extraction task." + ], + [ + "We would like to thank the anonymous reviewers for their valuable and constructive comments on this paper." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0240/instruction.md b/qasper-0240/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..71193ed06f28778df9f93ad688cee8d0dd0b0db6 --- /dev/null +++ b/qasper-0240/instruction.md @@ -0,0 +1,104 @@ +Name of Paper: Learning to Rank Scientific Documents from the Crowd + +Question: what were the baselines? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + null, + "Introduction", + "Benchmark Datasets", + "Learning to Rank", + "Features", + "Baseline Systems", + "Evaluation Measures", + "Forward Feature Selection", + "Results", + "Discussion", + "Acknowledgments" + ], + "paragraphs": [ + [ + "[block]I.1em", + "[block]i.1em", + " Learning to Rank Scientific Documents from the CrowdLearning to Rank Scientific Documents from the Crowd ", + "-4", + "[1]1" + ], + [ + "The number of biomedical research papers published has increased dramatically in recent years. As of October, 2016, PubMed houses over 26 million citations, with almost 1 million from the first 3 quarters of 2016 alone . It has become impossible for any one person to actually read all of the work being published. We require tools to help us determine which research articles would be most informative and related to a particular question or document. For example, a common task when reading articles is to find articles that are most related to another. Major research search engines offer such a \u201crelated articles\u201d feature. However, we propose that instead of measuring relatedness by text-similarity measures, we build a model that is able to infer relatedness from the authors' judgments.", + " BIBREF0 consider two kinds of queries important to bibliographic information retrieval: the first is a search query written by the user and the second is a request for documents most similar to a document already judged relevant by the user. Such a query-by-document (or query-by-example) system has been implemented in the de facto scientific search engine PubMed\u2014called Related Citation Search. BIBREF1 show that 19% of all PubMed searches performed by users have at least one click on a related article. Google Scholar provides a similar Related Articles system. Outside of bibliographic retrieval, query-by-document systems are commonly used for patent retrieval, Internet search, and plagiarism detection, amongst others. Most work in the area of query-by-document uses text-based similarity measures ( BIBREF2 , BIBREF3 , BIBREF4 ). However, scientific research is hypothesis driven and therefore we question whether text-based similarity alone is the best model for bibliographic retrieval. In this study we asked authors to rank documents by \u201ccloseness\u201d to their work. The definition of \u201ccloseness\u201d was left for the authors to interpret, as the goal is to model which documents the authors subjectively feel are closest to their own. Throughout the paper we will use \u201ccloseness\u201d and \u201crelatedness\u201d interchangeably.", + "We found that researchers' ranking by closeness differs significantly from the ranking provided by a traditional IR system. Our contributions are three fold:", + "The principal ranking algorithms of query-by-document in bibliographic information retrieval rely mainly on text similarity measures ( BIBREF1 , BIBREF0 ). For example, the foundational work of BIBREF0 introduced the concept of a \u201cdocument neighborhood\u201d in which they pre-compute a text-similarity based distance between each pair of documents. When a user issues a query, first an initial set of related documents is retrieved. Then, the neighbors of each of those documents is retrieved, i.e., documents with the highest text similarity to those in the initial set. In a later work, BIBREF1 develop the PMRA algorithm for PubMed related article search. PMRA is an unsupervised probabilistic topic model that is trained to model \u201crelatedness\u201d between documents. BIBREF5 introduce the competing algorithm Find-Similar for this task, treating the full text of documents as a query and selecting related documents from the results.", + "Outside bibliographic IR, prior work in query-by-document includes patent retrieval ( BIBREF6 , BIBREF3 ), finding related documents given a manuscript ( BIBREF1 , BIBREF7 ), and web page search ( BIBREF8 , BIBREF9 ). Much of the work focuses on generating shorter queries from the lengthy document. For example, noun-phrase extraction has been used for extracting short, descriptive phrases from the original lengthy text ( BIBREF10 ). Topic models have been used to distill a document into a set of topics used to form query ( BIBREF11 ). BIBREF6 generated queries using the top TF*IDF weighted terms in each document. BIBREF4 suggested extracting phrasal concepts from a document, which are then used to generate queries. BIBREF2 combined query extraction and pseudo-relevance feedback for patent retrieval. BIBREF9 employ supervised machine learning model (i.e., Conditional Random Fields) ( BIBREF12 ) for query generation. BIBREF13 explored ontology to identify chemical concepts for queries.", + "There are also many biomedical-document specific search engines available. Many information retrieval systems focus on question answering systems such as those developed for the TREC Genomics Track ( BIBREF14 ) or BioASQ Question-Answer ( BIBREF15 ) competitions. Systems designed for question-answering use a combination of natural language processing techniques to identify biomedical entities, and then information retrieval systems to extract relevant answers to questions. Systems like those detailed in BIBREF16 can provide answers to yes/no biomedical questions with high precision. However what we propose differs from these systems in a fundamental way: given a specific document, suggest the most important documents that are related to it.", + "The body of work most related to ours is that of citation recommendation. The goal of citation recommendation is to suggest a small number of publications that can be used as high quality references for a particular article ( BIBREF17 , BIBREF1 ). Topic models have been used to rank articles based on the similarity of latent topic distribution ( BIBREF11 , BIBREF18 , BIBREF1 ). These models attempt to decompose a document into a few important keywords. Specifically, these models attempt to find a latent vector representation of a document that has a much smaller dimensionality than the document itself and compare the reduced dimension vectors.", + "Citation networks have also been explored for ranking articles by importance, i.e., authority ( BIBREF19 , BIBREF20 ). BIBREF17 introduced heterogeneous network models, called meta-path based models, to incorporate venues (the conference where a paper is published) and content (the term which links two articles, for citation recommendation). Another highly relevant work is BIBREF8 who decomposed a document to represent it with a compact vector, which is then used to measure the similarity with other documents. Note that we exclude the work of context-aware recommendation, which analyze each citation's local context, which is typically short and does not represent a full document.", + "One of the key contributions of our study is an innovative approach for automatically generating a query-by-document gold standard. Crowd-sourcing has generated large databases, including Wikipedia and Freebase. Recently, BIBREF21 concluded that unpaid participants performed better than paid participants for question answering. They attribute this to unpaid participants being more intrinsically motivated than the paid test takers: they performed the task for fun and already had knowledge about the subject being tested. In contrast, another study, BIBREF22 , compared unpaid workers found through Google Adwords (GA) to paid workers found through Amazon Mechanical Turk (AMT). They found that the paid participants from AMT outperform the unpaid ones. This is attributed to the paid workers being more willing to look up information they didn't know. In the bibliographic domain, authors of scientific publications have contributed annotations ( BIBREF23 ). They found that authors are more willing to annotate their own publications ( BIBREF23 ) than to annotate other publications ( BIBREF24 ) even though they are paid. In this work, our annotated dataset was created by the unpaid authors of the articles." + ], + [ + "In order to develop and evaluate ranking algorithms we need a benchmark dataset. However, to the best of our knowledge, we know of no openly available benchmark dataset for bibliographic query-by-document systems. We therefore created such a benchmark dataset.", + "The creation of any benchmark dataset is a daunting labor-intensive task, and in particular, challenging in the scientific domain because one must master the technical jargon of a scientific article, and such experts are not easy to find when using traditional crowd-sourcing technologies (e.g., AMT). For our task, the ideal annotator for each of our articles are the authors themselves. The authors of a publication typically have a clear knowledge of the references they cite and their scientific importance to their publication, and therefore may be excellent judges for ranking the reference articles.", + "Given the full text of a scientific publication, we want to rank its citations according to the author's judgments. We collected recent publications from the open-access PLoS journals and asked the authors to rank by closeness five citations we selected from their paper. PLoS articles were selected because its journals cover a wide array of topics and the full text articles are available in XML format. We selected the most recent publications as previous work in crowd-sourcing annotation shows that authors' willingness to participate in an unpaid annotation task declines with the age of publication ( BIBREF23 ). We then extracted the abstract, citations, full text, authors, and corresponding author email address from each document. The titles and abstracts of the citations were retrieved from PubMed, and the cosine similarity between the PLoS abstract and the citation's abstract was calculated. We selected the top five most similar abstracts using TF*IDF weighted cosine similarity, shuffled their order, and emailed them to the corresponding author for annotation. We believe that ranking five articles (rather than the entire collection of the references) is a more manageable task for an author compared to asking them to rank all references. Because the documents to be annotated were selected based on text similarity, they also represent a challenging baseline for models based on text-similarity features. In total 416 authors were contacted, and 92 responded (22% response rate). Two responses were removed from the dataset for incomplete annotation.", + "We asked authors to rank documents by how \u201cclose to your work\u201d they were. The definition of closeness was left to the discretion of the author. The dataset is composed of 90 annotated documents with 5 citations each ranked 1 to 5, where 1 is least relevant and 5 is most relevant for a total of 450 annotated citations." + ], + [ + "Learning-to-rank is a technique for reordering the results returned from a search engine query. Generally, the initial query to a search engine is concerned more with recall than precision: the goal is to obtain a subset of potentially related documents from the corpus. Then, given this set of potentially related documents, learning-to-rank algorithms reorder the documents such that the most relevant documents appear at the top of the list. This process is illustrated in Figure FIGREF6 .", + "There are three basic types of learning-to-rank algorithms: point-wise, pair-wise, and list-wise. Point-wise algorithms assign a score to each retrieved document and rank them by their scores. Pair-wise algorithms turn learning-to-rank into a binary classification problem, obtaining a ranking by comparing each individual pair of documents. List-wise algorithms try to optimize an evaluation parameter over all queries in the dataset.", + "Support Vector Machine (SVM) ( BIBREF25 ) is a commonly used supervised classification algorithm that has shown good performance over a range of tasks. SVM can be thought of as a binary linear classifier where the goal is to maximize the size of the gap between the class-separating line and the points on either side of the line. This helps avoid over-fitting on the training data. SVMRank is a modification to SVM that assigns scores to each data point and allows the results to be ranked ( BIBREF26 ). We use SVMRank in the experiments below. SVMRank has previously been used in the task of document retrieval in ( BIBREF27 ) for a more traditional short query task and has been shown to be a top-performing system for ranking.", + "SVMRank is a point-wise learning-to-rank algorithm that returns scores for each document. We rank the documents by these scores. It is possible that sometimes two documents will have the same score, resulting in a tie. In this case, we give both documents the same rank, and then leave a gap in the ranking. For example, if documents 2 and 3 are tied, their ranked list will be [5, 3, 3, 2, 1].", + "Models are trained by randomly splitting the dataset into 70% training data and 30% test data. We apply a random sub-sampling approach where the dataset is randomly split, trained, and tested 100 times due to the relatively small size of the data. A model is learned for each split and a ranking is produced for each annotated document.", + "We test three different supervised models. The first supervised model uses only text similarity features, the second model uses all of the features, and the third model runs forward feature selection to select the best performing combination of features. We also test using two different models trained on two different datasets: one trained using the gold standard annotations, and another trained using the judgments based on text similarity that were used to select the citations to give to the authors.", + "We tested several different learning to rank algorithms for this work. We found in preliminary testing that SVMRank had the best performance, so it will be used in the following experiments." + ], + [ + "Each citation is turned into a feature vector representing the relationship between the published article and the citation. Four types of features are used: text similarity, citation count and location, age of the citation, and the number of times the citation has appeared in the literature (citation impact). Text similarity features measure the similarity of the words used in different parts of the document. In this work, we calculate the similarity between a document INLINEFORM0 and a document it cites INLINEFORM1 by transforming the their text into term vectors. For example, to calculate the similarity of the abstracts between INLINEFORM2 and INLINEFORM3 we transform the abstracts into two term vectors, INLINEFORM4 and INLINEFORM5 . The length of each of the term vectors is INLINEFORM6 . We then weight each word by its Term-frequency * Inverse-document frequency (TF*IDF) weight. TF*IDF is a technique to give higher weight to words that appear frequently in a document but infrequently in the corpus. Term frequency is simply the number of times that a word INLINEFORM7 appears in a document. Inverse-document frequency is the logarithmically-scaled fraction of documents in the corpus in which the word INLINEFORM8 appears. Or, more specifically: INLINEFORM9 ", + "where INLINEFORM0 is the total number of documents in the corpus, and the denominator is the number of documents in which a term INLINEFORM1 appears in the corpus INLINEFORM2 . Then, TF*IDF is defined as: INLINEFORM3 ", + "where INLINEFORM0 is a term, INLINEFORM1 is the document, and INLINEFORM2 is the corpus. For example, the word \u201cthe\u201d may appear often in a document, but because it also appears in almost every document in the corpus it is not useful for calculating similarity, thus it receives a very low weight. However, a word such as \u201cneurogenesis\u201d may appear often in a document, but does not appear frequently in the corpus, and so it receives a high weight. The similarity between term vectors is then calculated using cosine similarity: INLINEFORM3 ", + "where INLINEFORM0 and INLINEFORM1 are two term vectors. The cosine similarity is a measure of the angle between the two vectors. The smaller the angle between the two vectors, i.e., the more similar they are, then the closer the value is to 1. Conversely, the more dissimilar the vectors, the closer the cosine similarity is to 0.", + "We calculate the text similarity between several different sections of the document INLINEFORM0 and the document it cites INLINEFORM1 . From the citing article INLINEFORM2 , we use the title, full text, abstract, the combined discussion/conclusion sections, and the 10 words on either side of the place in the document where the actual citation occurs. From the document it cites INLINEFORM3 we only use the title and the abstract due to limited availability of the full text. In this work we combine the discussion and conclusion sections of each document because some documents have only a conclusion section, others have only a discussion, and some have both. The similarity between each of these sections from the two documents is calculated and used as features in the model.", + "The age of the citation may be relevant to its importance. As a citation ages, we hypothesize that it is more likely to become a \u201cfoundational\u201d citation rather than one that directly influenced the development of the article. Therefore more recent citations may be more likely relevant to the article. Similarly, \u201ccitation impact\u201d, that is, the number of times a citation has appeared in the literature (as measured by Google Scholar) may be an indicator of whether or not an article is foundational rather than directly related. We hypothesize that the fewer times an article is cited in the literature, the more impact it had on the article at hand.", + "We also keep track of the number of times a citation is mentioned in both the full text and discussion/conclusion sections. We hypothesize that if a citation is mentioned multiple times, it is more important than citations that are mentioned only once. Further, citations that appear in the discussion/conclusion sections are more likely to be crucial to understanding the results. We normalize the counts of the citations by the total number of citations in that section. In total we select 15 features, shown in Table TABREF15 . The features are normalized within each document so that each of citation features is on a scale from 0 to 1, and are evenly distributed within that range. This is done because some of the features (such as years since citation) are unbounded." + ], + [ + "We compare our system to a variety of baselines. (1) Rank by the number of times a citation is mentioned in the document. (2) Rank by the number of times the citation is cited in the literature (citation impact). (3) Rank using Google Scholar Related Articles. (4) Rank by the TF*IDF weighted cosine similarity. (5) Rank using a learning-to-rank model trained on text similarity rankings. The first two baseline systems are models where the values are ordered from highest to lowest to generate the ranking. The idea behind them is that the number of times a citation is mentioned in an article, or the citation impact may already be good indicators of their closeness. The text similarity model is trained using the same features and methods used by the annotation model, but trained using text similarity rankings instead of the author's judgments.", + "We also compare our rankings to those found on the popular scientific article search engine Google Scholar. Google Scholar is a \u201cblack box\u201d IR system: they do not release details about which features they are using and how they judge relevance of documents. Google Scholar provides a \u201cRelated Articles\u201d feature for each document in its index that shows the top 100 related documents for each article. To compare our rankings, we search through these related documents and record the ranking at which each of the citations we selected appeared. We scale these rankings such that the lowest ranked article from Google Scholar has the highest relevance ranking in our set. If the cited document does not appear in the set, we set its relevance-ranking equal to one below the lowest relevance ranking found.", + "Four comparisons are performed with the Google Scholar data. (1) We first train a model using our gold standard and see if we can predict Google Scholar's ranking. (2) We compare to a baseline of using Google Scholar's rankings to train and compare with their own rankings using our feature set. (3) Then we train a model using Google Scholar's rankings and try to predict our gold standard. (4) We compare it to the model trained on our gold standard to predict our gold standard." + ], + [ + "Normalized Discounted Cumulative Gain (NDCG) is a common measure for comparing a list of estimated document relevance judgments with a list of known judgments ( BIBREF28 ). To calculate NDCG we first calculate a ranking's Discounted Cumulative Gain (DCG) as: DISPLAYFORM0 ", + "where rel INLINEFORM0 is the relevance judgment at position INLINEFORM1 . Intuitively, DCG penalizes retrieval of documents that are not relevant (rel INLINEFORM2 ). However, DCG is an unbounded value. In order to compare the DCG between two models, we must normalize it. To do this, we use the ideal DCG (IDCG), i.e., the maximum possible DCG given the relevance judgments. The maximum possible DCG occurs when the relevance judgments are in the correct order. DISPLAYFORM0 ", + "The NDCG value is in the range of 0 to 1, where 0 means that no relevant documents were retrieved, and 1 means that the relevant documents were retrieved and in the correct order of their relevance judgments.", + "Kendall's INLINEFORM0 is a measure of the correlation between two ranked lists. It compares the number of concordant pairs with the number of discordant pairs between each list. A concordant pair is defined over two observations INLINEFORM1 and INLINEFORM2 . If INLINEFORM3 and INLINEFORM4 , then the pair at indices INLINEFORM5 is concordant, that is, the ranking at INLINEFORM6 in both ranking sets INLINEFORM7 and INLINEFORM8 agree with each other. Similarly, a pair INLINEFORM9 is discordant if INLINEFORM10 and INLINEFORM11 or INLINEFORM12 and INLINEFORM13 . Kendall's INLINEFORM14 is then defined as: DISPLAYFORM0 ", + "where C is the number of concordant pairs, D is the number of discordant pairs, and the denominator represents the total number of possible pairs. Thus, Kendall's INLINEFORM0 falls in the range of INLINEFORM1 , where -1 means that the ranked lists are perfectly negatively correlated, 0 means that they are not significantly correlated, and 1 means that the ranked lists are perfectly correlated. One downside of this measure is that it does not take into account where in the ranked list an error occurs. Information retrieval, in general, cares more about errors near the top of the list rather than errors near the bottom of the list.", + "Average-Precision INLINEFORM0 ( BIBREF29 ) (or INLINEFORM1 ) extends on Kendall's INLINEFORM2 by incorporating the position of errors. If an error occurs near the top of the list, then that is penalized heavier than an error occurring at the bottom of the list. To achieve this, INLINEFORM3 incorporates ideas from the popular Average Precision measure, were we calculate the precision at each index of the list and then average them together. INLINEFORM4 is defined as: DISPLAYFORM0 ", + "Intuitively, if an error occurs at the top of the list, then that error is propagated into each iteration of the summation, meaning that it's penalty is added multiple times. INLINEFORM0 's range is between -1 and 1, where -1 means the lists are perfectly negatively correlated, 0 means that they are not significantly correlated, and 1 means that they are perfectly correlated." + ], + [ + "Forward feature selection was performed by iteratively testing each feature one at a time. The highest performing feature is kept in the model, and another sweep is done over the remaining features. This continues until all features have been selected. This approach allows us to explore the effect of combinations of features and the effect of having too many or too few features. It also allows us to evaluate which features and combinations of features are the most powerful." + ], + [ + "We first compare our gold standard to the baselines. A random baseline is provided for reference. Because all of the documents that we rank are relevant, NDCG will be fairly high simply by chance. We find that the number of times a document is mentioned in the annotated document is significantly better than the random baseline or the citation impact. The more times a document is mentioned in a paper, the more likely the author was to annotate it as important. Interestingly, we see a negative correlation with the citation impact. The more times a document is mentioned in the literature, the less likely it is to be important. These results are shown in Table TABREF14 .", + "Next we rank the raw values of the features and compare them to our gold standard to obtain a baseline (Table TABREF15 ). The best performing text similarity feature is the similarity between the abstract of the annotated document and the abstract of the cited document. However, the number of times that a cited document is mentioned in the text of the annotated document are also high-scoring features, especially in the INLINEFORM0 correlation coefficient. These results indicate that text similarity alone may not be a good measure for judging the rank of a document.", + "Next we test three different feature sets for our supervised learning-to-rank models. The model using only the text similarity features performs poorly: NDCG stays at baseline and the correlation measures are low. Models that incorporate information about the age, number of times a cited document was referenced, and the citation impact of that document in addition to the text similarity features significantly outperformed models that used only text similarity features INLINEFORM0 . Because INLINEFORM1 takes into account the position in the ranking of the errors, this indicates that the All Features model was able to better correctly place highly ranked documents above lower ranked ones. Similarly, because Kendall's INLINEFORM2 is an overall measure of correlation that does not take into account the position of errors, the higher value here means that more rankings were correctly placed. Interestingly, feature selection (which is optimized for NDCG) does not outperform the model using all of the features in terms of our correlation measures. The features chosen during forward feature selection are (1) the citation impact, (2) number of mentions in the full text, (3) text similarity between the annotated document's title and the referenced document's abstract, (4) the text similarity between the annotated document's discussion/conclusion section and the referenced document's title. These results are shown in Table TABREF16 . The models trained on the text similarity judgments perform worse than the models trained on the annotated data. However, in terms of both NDCG and the correlation measures, they perform significantly better than the random baseline.", + "Next we compare our model to Google Scholar's rankings. Using the ranking collected from Google Scholar, we build a training set to try to predict our authors' rankings. We find that Google Scholar performs similarly to the text-only features model. This indicates that the rankings we obtained from the authors are substantially different than the rankings that Google Scholar provides. Results appear in Table TABREF17 ." + ], + [ + "We found that authors rank the references they cite substantially differently from rankings based on text-similarity. Our results show that decomposing a document into a set of features that is able to capture that difference is key. While text similarity is indeed important (as evidenced by the Similarity(a,a) feature in Table TABREF15 ), we also found that the number of times a document is referenced in the text and the number of times a document is referenced in the literature are also both important features (via feature selection). The more often a citation is mentioned in the text, the more likely it is to be important. This feature is often overlooked in article citation recommendation. We also found that recency is important: the age of the citation is negatively correlated with the rank. Newer citations are more likely to be directly important than older, more foundational citations. Additionally, the number of times a document is cited in the literature is negatively correlated with rank. This is likely due to highly cited documents being more foundational works; they may be older papers that are important to the field but not directly influential to the new work.", + "The model trained using the author's judgments does significantly better than the model trained using the text-similarity-based judgments. An error analysis was performed to find out why some of the rankings disagreed with the author's annotations. We found that in some cases our features were unable to capture the relationship: for example a biomedical document applying a model developed in another field to the dataset may use very different language to describe the model than the citation. Previous work adopting topic models to query document search may prove useful for such cases.", + "A small subset of features ended up performing as well as the full list of features. The number of times a citation was mentioned and the citation impact score in the literature ended up being two of the most important features. Indeed, without the citation-based features, the model performs as though it were trained with the text-similarity rankings. Feature engineering is a part of any learning-to-rank system, especially in domain-specific contexts. Citations are an integral feature of our dataset. For learning-to-rank to be applied to other datasets feature engineering must also occur to exploit the unique properties of those datasets. However, we show that combining the domain-specific features with more traditional text-based features does improve the model's scores over simply using the domain-specific features themselves.", + "Interestingly, citation impact and age of the citation are both negatively correlated with rank. We hypothesize that this is because both measures can be indicators of recency: a new publication is more likely to be directly influenced by more recent work. Many other related search tools, however, treat the citation impact as a positive feature of relatedness: documents with a higher citation impact appear higher on the list of related articles than those with lower citation impacts. This may be the opposite of what the user actually desires.", + "We also found that rankings from our text-similarity based IR system or Google Scholar's IR system were unable to rank documents by the authors' annotations as well as our system. In one sense, this is reasonable: the rankings coming from these systems were from a different system than the author annotations. However, in domain-specific IR, domain experts are the best judges. We built a system that exploits these expert judgments. The text similarity and Google Scholar models were able to do this to some extent, performing above the random baseline, but not on the level of our model.", + "Additionally, we observe that NDCG may not be the most appropriate measure for comparing short ranked lists where all of the documents are relevant to some degree. NDCG gives a lot of credit to relevant documents that occur in the highest ranks. However, all of the documents here are relevant, just to varying degrees. Thus, NDCG does not seem to be the most appropriate measure, as is evident in our scores. The correlation coefficients from Kendall's INLINEFORM0 and INLINEFORM1 seem to be far more appropriate for this case, as they are not concerned with relevance, only ranking.", + "One limitation of our work is that we selected a small set of references based on their similarities to the article that cites them. Ideally, we would have had authors rank all of their citations for us, but this would have been a daunting task for authors to perform. We chose to use the Google Scholar dataset in order to attempt to mitigate this: we obtain a ranking for the set of references from a system that is also ranking many other documents. The five citations selected by TF*IDF weighted cosine similarity represent a \u201chard\u201d gold standard: we are attempting to rank documents that are known to all be relevant by their nature, and have high similarity with the text. Additionally, there are plethora of other, more expensive features we could explore to improve the model. Citation network features, phrasal concepts, and topic models could all be used to help improve our results, at the cost of computational complexity.", + "We have developed a model for fast related-document ranking based on crowd-sourced data. The model, data, and data collection software are all publicly available and can easily be used in future applications as an automatic search to help users find the most important citations given a particular document. The experimental setup is portable to other datasets with some feature engineering. We were able to identify that several domain-specific features were crucial to our model, and that we were able to improve on the results of simply using those features alone by adding more traditional features.", + "Query-by-document is a complicated and challenging task. We provide an approach with an easily obtained dataset and a computationally inexpensive model. By working with biomedical researchers we were able to build a system that ranks documents in a quantitatively different way than previous systems, and to provide a tool that helps researchers find related documents." + ], + [ + "We would like to thank all of the authors who took the time to answer our citation ranking survey. This work is supported by National Institutes of Health with the grant number 1R01GM095476. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0241/instruction.md b/qasper-0241/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..4b91f60167db1bd33be071b737f4c780b5a6b514 --- /dev/null +++ b/qasper-0241/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Learning to Rank Scientific Documents from the Crowd + +Question: what is the supervised model they developed? \ No newline at end of file diff --git a/qasper-0246/instruction.md b/qasper-0246/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..51094cb6a8adedce720b418787f76fa52d3bbddb --- /dev/null +++ b/qasper-0246/instruction.md @@ -0,0 +1,46 @@ +Name of Paper: Exploiting Deep Learning for Persian Sentiment Analysis + +Question: What was their performance on the dataset? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Works", + "Methodology and Experimental Results", + "Conclusion", + "Acknowledgment" + ], + "paragraphs": [ + [ + "In recent years, social media, forums, blogs and other forms of online communication tools have radically affected everyday life, especially how people express their opinions and comments. The extraction of useful information (such as people's opinion about companies brand) from the huge amount of unstructured data is vital for most companies and organizations BIBREF0 . The product reviews are important for business owners as they can take business decision accordingly to automatically classify user\u2019s opinions towards products and services. The application of sentiment analysis is not limited to product or movie reviews but can be applied to different fields such as news, politics, sport etc. For example, in online political debates, the sentiment analysis can be used to identify people's opinions on a certain election candidate or political parties BIBREF1 BIBREF2 BIBREF3 . In this context, sentiment analysis has been widely used in different languages by using traditional and advanced machine learning techniques. However, limited research has been conducted to develop models for the Persian language.", + "The sentiment analysis is a method to automatically process large amounts of data and classify text into positive or negative sentiments) BIBREF4 BIBREF5 . Sentiment analysis can be performed at two levels: at the document level or at the sentence level. At document level it is used to classify the sentiment expressed in the document (positive or negative), whereas, at sentence level is used to identify the sentiments expressed only in the sentence under analysis BIBREF6 BIBREF7 .", + "In the literature, deep learning based automated feature extraction has been shown to outperform state-of-the-art manual feature engineering based classifiers such as Support Vector Machine (SVM), Naive Bayes (NB) or Multilayer Perceptron (MLP) etc. One of the important techniques in deep learning is the autoencoder that generally involves reducing the number of feature dimensions under consideration. The aim of dimensionality reduction is to obtain a set of principal variables to improve the performance of the approach. Similarly, CNNs have been proven to be very effective in sentiment analysis. However, little work has been carried out to exploit deep learning based feature representation for Persian sentiment analysis BIBREF8 BIBREF9 . In this paper, we present two deep learning models (deep autoencoders and CNNs) for Persian sentiment analysis. The obtained deep learning results are compared with MLP.", + "The rest of the paper is organized as follows: Section 2 presents related work. Section 3 presents methodology and experimental results. Finally, section 4 concludes this paper." + ], + [ + "In the literature, extensive research has been carried out to model novel sentiment analysis models using both shallow and deep learning algorithms. For example, the authors in BIBREF10 proposed a novel deep learning approach for polarity detection in product reviews. The authors addressed two major limitations of stacked denoising of autoencoders, high computational cost and the lack of scalability of high dimensional features. Their experimental results showed the effectiveness of proposed autoencoders in achieving accuracy upto 87%. Zhai et al., BIBREF11 proposed a five layers autoencoder for learning the specific representation of textual data. The autoencoders are generalised using loss function and derived discriminative loss function from label information. The experimental results showed that the model outperformed bag of words, denoising autoencoders and other traditional methods, achieving accuracy rate up to 85% . Sun et al., BIBREF12 proposed a novel method to extract contextual information from text using a convolutional autoencoder architecture. The experimental results showed that the proposed model outperformed traditional SVM and Nave Bayes models, reporting accuracy of 83.1 %, 63.9% and 67.8% respectively.", + "Su et al., BIBREF13 proposed an approach for a neural generative autoencoder for learning bilingual word embedding. The experimental results showed the effectiveness of their approach on English-Chinese, English-German, English-French and English-Spanish (75.36% accuracy). Kim et al., BIBREF14 proposed a method to capture the non-linear structure of data using CNN classifier. The experimental results showed the effectiveness of the method on the multi-domain dataset (movie reviews and product reviews). However, the disadvantage is only SVM and Naive Bayes classifiers are used to evaluate the performance of the method and deep learning classifiers are not exploited. Zhang et al., BIBREF15 proposed an approach using deep learning classifiers to detect polarity in Japanese movie reviews. The approach used denoising autoencoder and adapted to other domains such as product reviews. The advantage of the approach is not depended on any language and could be used for various languages by applying different datasets. AP et al., BIBREF16 proposed a CNN based model for cross-language learning of vectorial word representations that is coherent between two languages. The method is evaluated using English and German movie reviews dataset. The experimental results showed CNN (83.45% accuracy) outperformed as compared to SVM (65.25% accuracy).", + "Zhou et al., BIBREF17 proposed an autoencoder architecture constituting an LSTM-encoder and decoder in order to capture features in the text and reduce dimensionality of data. The LSTM encoder used the interactive scheme to go through the sequence of sentences and LSTM decoder reconstructed the vector of sentences. The model is evaluated using different datasets such as book reviews, DVD reviews, and music reviews, acquiring accuracy up to 81.05%, 81.06%, and 79.40% respectively. Mesnil et al., BIBREF18 proposed an approach using ensemble classification to detect polarity in the movie reviews. The authors combined several machine learning algorithms such as SVM, Naive Bayes and RNN to achieve better results, where autoencoders were used to reduce the dimensionality of features. The experimental results showed the combination of unigram, bigram and trigram features (91.87% accuracy) outperformed unigram (91.56% accuracy) and bigram (88.61% accuracy).", + "Scheible et al., BIBREF19 trained an approach using semi-supervised recursive autoencoder to detect polarity in movie reviews dataset, consisted of 5000 positive and 5000 negative sentiments. The experimental results demonstrated that the proposed approach successfully detected polarity in movie reviews dataset (83.13% accuracy) and outperformed standard SVM (68.36% accuracy) model. Dai et al., BIBREF20 developed an autoencoder to detect polarity in the text using deep learning classifier. The LSTM was trained on IMDB movie reviews dataset. The experimental results showed the outperformance of their proposed approach over SVM. In table 1 some of the autoencoder approaches are depicted." + ], + [ + "The novel dataset used in this work was collected manually and includes Persian movie reviews from 2014 to 2016. A subset of dataset was used to train the neural network (60% training dataset) and rest of the data (40%) was used to test and validate the performance of the trained neural network (testing set (30%), validation set (10%)). There are two types of labels in the dataset: positive or negative. The reviews were manually annotated by three native Persian speakers aged between 30 and 50 years old.", + "After data collection, the corpus was pre-processed using tokenisation, normalisation and stemming techniques. The process of converting sentences into single word or token is called tokenisation. For example, \"The movie is great\" is changed to \"The\", \"movie\", \"is\", \"great\" BIBREF21 . There are some words which contain numbers. For example, \"great\" is written as \"gr8\" or \"gooood\" as written as \"good\" . The normalisation is used to convert these words into normal forms BIBREF22 . The process of converting words into their root is called stemming. For example, going was changed to go BIBREF23 . Words were converted into vectors. The fasttext was used to convert each word into 300-dimensions vectors. Fasttext is a library for text classification and representation BIBREF24 BIBREF25 BIBREF9 .", + "For classification, MLP, autoencoders and CNNs have been used. Fig. 1. depicts the modelled MLP architectures. MLP classifer was trained for 100 iterations BIBREF26 . Fig. 2. depicts the modelled autoencoder architecture. Autoencoder is a feed-forward deep neural network with unsupervised learning and it is used for dimensionality reduction. The autoencoder consists of input, output and hidden layers. Autoencoder is used to compress the input into a latent-space and then the output is reconstructed BIBREF27 BIBREF28 BIBREF29 . The exploited autoencoder model is depcited in Fig. 1. The autoencoder consists of one input layer three hidden layers (1500, 512, 1500) and an output layer. Convolutional Neural Networks contains three layers (input, hidden and output layer). The hidden layer consists of convolutional layers, pooling layers, fully connected layers and normalisation layer. The INLINEFORM0 is denotes the hidden neurons of j, with bias of INLINEFORM1 , is a weight sum over continuous visible nodes v which is given by: DISPLAYFORM0 ", + "The modelled CNN architecture is depicted in Fig. 3 BIBREF29 BIBREF28 . For CNN modelling, each utterance was represented as a concatenation vector of constituent words. The network has total 11 layers: 4 convolution layers, 4 max pooling and 3 fully connected layers. Convolution layers have filters of size 2 and with 15 feature maps. Each convolution layer is followed by a max polling layer with window size 2. The last max pooling layer is followed by fully connected layers of size 5000, 500 and 4. For final layer, softmax activation is used.", + "To evaluate the performance of the proposed approach, precision (1), recall (2), f-Measure (3), and prediction accuracy (4) have been used as a performance matrices. The experimental results are shown in Table 1, where it can be seen that autoencoders outperformed MLP and CNN outperformed autoencoders with the highest achieved accuracy of 82.6%. DISPLAYFORM0 DISPLAYFORM1 ", + "where TP is denotes true positive, TN is true negative, FP is false positive, and FN is false negative." + ], + [ + "Sentiment analysis has been used extensively for a wide of range of real-world applications, ranging from product reviews, surveys feedback, to business intelligence, and operational improvements. However, the majority of research efforts are devoted to English-language only, where information of great importance is also available in other languages. In this work, we focus on developing sentiment analysis models for Persian language, specifically for Persian movie reviews. Two deep learning models (deep autoencoders and deep CNNs) are developed and compared with the the state-of-the-art shallow MLP based machine learning model. Simulations results revealed the outperformance of our proposed CNN model over autoencoders and MLP. In future, we intend to exploit more advanced deep learning models such as Long Short-Term Memory (LSTM) and LSTM-CNNs to further evaluate the performance of our developed novel Persian dataset." + ], + [ + "Amir Hussain and Ahsan Adeel were supported by the UK Engineering and Physical Sciences Research Council (EPSRC) grant No.EP/M026981/1.", + "" + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0247/instruction.md b/qasper-0247/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..d80a1e71f37f40d9c2dd632678929f68010f9488 --- /dev/null +++ b/qasper-0247/instruction.md @@ -0,0 +1,46 @@ +Name of Paper: Exploiting Deep Learning for Persian Sentiment Analysis + +Question: How large is the dataset? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Works", + "Methodology and Experimental Results", + "Conclusion", + "Acknowledgment" + ], + "paragraphs": [ + [ + "In recent years, social media, forums, blogs and other forms of online communication tools have radically affected everyday life, especially how people express their opinions and comments. The extraction of useful information (such as people's opinion about companies brand) from the huge amount of unstructured data is vital for most companies and organizations BIBREF0 . The product reviews are important for business owners as they can take business decision accordingly to automatically classify user\u2019s opinions towards products and services. The application of sentiment analysis is not limited to product or movie reviews but can be applied to different fields such as news, politics, sport etc. For example, in online political debates, the sentiment analysis can be used to identify people's opinions on a certain election candidate or political parties BIBREF1 BIBREF2 BIBREF3 . In this context, sentiment analysis has been widely used in different languages by using traditional and advanced machine learning techniques. However, limited research has been conducted to develop models for the Persian language.", + "The sentiment analysis is a method to automatically process large amounts of data and classify text into positive or negative sentiments) BIBREF4 BIBREF5 . Sentiment analysis can be performed at two levels: at the document level or at the sentence level. At document level it is used to classify the sentiment expressed in the document (positive or negative), whereas, at sentence level is used to identify the sentiments expressed only in the sentence under analysis BIBREF6 BIBREF7 .", + "In the literature, deep learning based automated feature extraction has been shown to outperform state-of-the-art manual feature engineering based classifiers such as Support Vector Machine (SVM), Naive Bayes (NB) or Multilayer Perceptron (MLP) etc. One of the important techniques in deep learning is the autoencoder that generally involves reducing the number of feature dimensions under consideration. The aim of dimensionality reduction is to obtain a set of principal variables to improve the performance of the approach. Similarly, CNNs have been proven to be very effective in sentiment analysis. However, little work has been carried out to exploit deep learning based feature representation for Persian sentiment analysis BIBREF8 BIBREF9 . In this paper, we present two deep learning models (deep autoencoders and CNNs) for Persian sentiment analysis. The obtained deep learning results are compared with MLP.", + "The rest of the paper is organized as follows: Section 2 presents related work. Section 3 presents methodology and experimental results. Finally, section 4 concludes this paper." + ], + [ + "In the literature, extensive research has been carried out to model novel sentiment analysis models using both shallow and deep learning algorithms. For example, the authors in BIBREF10 proposed a novel deep learning approach for polarity detection in product reviews. The authors addressed two major limitations of stacked denoising of autoencoders, high computational cost and the lack of scalability of high dimensional features. Their experimental results showed the effectiveness of proposed autoencoders in achieving accuracy upto 87%. Zhai et al., BIBREF11 proposed a five layers autoencoder for learning the specific representation of textual data. The autoencoders are generalised using loss function and derived discriminative loss function from label information. The experimental results showed that the model outperformed bag of words, denoising autoencoders and other traditional methods, achieving accuracy rate up to 85% . Sun et al., BIBREF12 proposed a novel method to extract contextual information from text using a convolutional autoencoder architecture. The experimental results showed that the proposed model outperformed traditional SVM and Nave Bayes models, reporting accuracy of 83.1 %, 63.9% and 67.8% respectively.", + "Su et al., BIBREF13 proposed an approach for a neural generative autoencoder for learning bilingual word embedding. The experimental results showed the effectiveness of their approach on English-Chinese, English-German, English-French and English-Spanish (75.36% accuracy). Kim et al., BIBREF14 proposed a method to capture the non-linear structure of data using CNN classifier. The experimental results showed the effectiveness of the method on the multi-domain dataset (movie reviews and product reviews). However, the disadvantage is only SVM and Naive Bayes classifiers are used to evaluate the performance of the method and deep learning classifiers are not exploited. Zhang et al., BIBREF15 proposed an approach using deep learning classifiers to detect polarity in Japanese movie reviews. The approach used denoising autoencoder and adapted to other domains such as product reviews. The advantage of the approach is not depended on any language and could be used for various languages by applying different datasets. AP et al., BIBREF16 proposed a CNN based model for cross-language learning of vectorial word representations that is coherent between two languages. The method is evaluated using English and German movie reviews dataset. The experimental results showed CNN (83.45% accuracy) outperformed as compared to SVM (65.25% accuracy).", + "Zhou et al., BIBREF17 proposed an autoencoder architecture constituting an LSTM-encoder and decoder in order to capture features in the text and reduce dimensionality of data. The LSTM encoder used the interactive scheme to go through the sequence of sentences and LSTM decoder reconstructed the vector of sentences. The model is evaluated using different datasets such as book reviews, DVD reviews, and music reviews, acquiring accuracy up to 81.05%, 81.06%, and 79.40% respectively. Mesnil et al., BIBREF18 proposed an approach using ensemble classification to detect polarity in the movie reviews. The authors combined several machine learning algorithms such as SVM, Naive Bayes and RNN to achieve better results, where autoencoders were used to reduce the dimensionality of features. The experimental results showed the combination of unigram, bigram and trigram features (91.87% accuracy) outperformed unigram (91.56% accuracy) and bigram (88.61% accuracy).", + "Scheible et al., BIBREF19 trained an approach using semi-supervised recursive autoencoder to detect polarity in movie reviews dataset, consisted of 5000 positive and 5000 negative sentiments. The experimental results demonstrated that the proposed approach successfully detected polarity in movie reviews dataset (83.13% accuracy) and outperformed standard SVM (68.36% accuracy) model. Dai et al., BIBREF20 developed an autoencoder to detect polarity in the text using deep learning classifier. The LSTM was trained on IMDB movie reviews dataset. The experimental results showed the outperformance of their proposed approach over SVM. In table 1 some of the autoencoder approaches are depicted." + ], + [ + "The novel dataset used in this work was collected manually and includes Persian movie reviews from 2014 to 2016. A subset of dataset was used to train the neural network (60% training dataset) and rest of the data (40%) was used to test and validate the performance of the trained neural network (testing set (30%), validation set (10%)). There are two types of labels in the dataset: positive or negative. The reviews were manually annotated by three native Persian speakers aged between 30 and 50 years old.", + "After data collection, the corpus was pre-processed using tokenisation, normalisation and stemming techniques. The process of converting sentences into single word or token is called tokenisation. For example, \"The movie is great\" is changed to \"The\", \"movie\", \"is\", \"great\" BIBREF21 . There are some words which contain numbers. For example, \"great\" is written as \"gr8\" or \"gooood\" as written as \"good\" . The normalisation is used to convert these words into normal forms BIBREF22 . The process of converting words into their root is called stemming. For example, going was changed to go BIBREF23 . Words were converted into vectors. The fasttext was used to convert each word into 300-dimensions vectors. Fasttext is a library for text classification and representation BIBREF24 BIBREF25 BIBREF9 .", + "For classification, MLP, autoencoders and CNNs have been used. Fig. 1. depicts the modelled MLP architectures. MLP classifer was trained for 100 iterations BIBREF26 . Fig. 2. depicts the modelled autoencoder architecture. Autoencoder is a feed-forward deep neural network with unsupervised learning and it is used for dimensionality reduction. The autoencoder consists of input, output and hidden layers. Autoencoder is used to compress the input into a latent-space and then the output is reconstructed BIBREF27 BIBREF28 BIBREF29 . The exploited autoencoder model is depcited in Fig. 1. The autoencoder consists of one input layer three hidden layers (1500, 512, 1500) and an output layer. Convolutional Neural Networks contains three layers (input, hidden and output layer). The hidden layer consists of convolutional layers, pooling layers, fully connected layers and normalisation layer. The INLINEFORM0 is denotes the hidden neurons of j, with bias of INLINEFORM1 , is a weight sum over continuous visible nodes v which is given by: DISPLAYFORM0 ", + "The modelled CNN architecture is depicted in Fig. 3 BIBREF29 BIBREF28 . For CNN modelling, each utterance was represented as a concatenation vector of constituent words. The network has total 11 layers: 4 convolution layers, 4 max pooling and 3 fully connected layers. Convolution layers have filters of size 2 and with 15 feature maps. Each convolution layer is followed by a max polling layer with window size 2. The last max pooling layer is followed by fully connected layers of size 5000, 500 and 4. For final layer, softmax activation is used.", + "To evaluate the performance of the proposed approach, precision (1), recall (2), f-Measure (3), and prediction accuracy (4) have been used as a performance matrices. The experimental results are shown in Table 1, where it can be seen that autoencoders outperformed MLP and CNN outperformed autoencoders with the highest achieved accuracy of 82.6%. DISPLAYFORM0 DISPLAYFORM1 ", + "where TP is denotes true positive, TN is true negative, FP is false positive, and FN is false negative." + ], + [ + "Sentiment analysis has been used extensively for a wide of range of real-world applications, ranging from product reviews, surveys feedback, to business intelligence, and operational improvements. However, the majority of research efforts are devoted to English-language only, where information of great importance is also available in other languages. In this work, we focus on developing sentiment analysis models for Persian language, specifically for Persian movie reviews. Two deep learning models (deep autoencoders and deep CNNs) are developed and compared with the the state-of-the-art shallow MLP based machine learning model. Simulations results revealed the outperformance of our proposed CNN model over autoencoders and MLP. In future, we intend to exploit more advanced deep learning models such as Long Short-Term Memory (LSTM) and LSTM-CNNs to further evaluate the performance of our developed novel Persian dataset." + ], + [ + "Amir Hussain and Ahsan Adeel were supported by the UK Engineering and Physical Sciences Research Council (EPSRC) grant No.EP/M026981/1.", + "" + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0248/instruction.md b/qasper-0248/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..6feb72e542c88be46a3ba2e1d754c9a64d878041 --- /dev/null +++ b/qasper-0248/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Talk the Walk: Navigating New York City through Grounded Dialogue + +Question: Did the authors use crowdsourcing platforms? \ No newline at end of file diff --git a/qasper-0249/instruction.md b/qasper-0249/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..6c38afc4dc0a4ce9a4c56dcade69eef03430e969 --- /dev/null +++ b/qasper-0249/instruction.md @@ -0,0 +1,254 @@ +Name of Paper: Talk the Walk: Navigating New York City through Grounded Dialogue + +Question: How was the dataset collected? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + null, + "Introduction", + "Talk The Walk", + "Task", + "Data Collection", + "Dataset Statistics", + "Experiments", + "Tourist Localization", + "Model", + "The Tourist", + "The Guide", + "Comparisons", + "Results and Discussion", + "Analysis of Localization Task", + "Emergent Language Localization", + "Natural Language Localization", + "Localization-based Baseline", + "Conclusion", + "Related Work", + "Implementation Details", + "Additional Natural Language Experiments", + "Tourist Generation Models", + "Localization from Human Utterances", + "Visualizing MASC predictions", + "Evaluation on Full Setup", + "Landmark Classification", + "Dataset Details" + ], + "paragraphs": [ + [ + "0pt0.03.03 *", + "0pt0.030.03 *", + "0pt0.030.03", + "We introduce \u201cTalk The Walk\u201d, the first large-scale dialogue dataset grounded in action and perception. The task involves two agents (a \u201cguide\u201d and a \u201ctourist\u201d) that communicate via natural language in order to achieve a common goal: having the tourist navigate to a given target location. The task and dataset, which are described in detail, are challenging and their full solution is an open problem that we pose to the community. We (i) focus on the task of tourist localization and develop the novel Masked Attention for Spatial Convolutions (MASC) mechanism that allows for grounding tourist utterances into the guide's map, (ii) show it yields significant improvements for both emergent and natural language communication, and (iii) using this method, we establish non-trivial baselines on the full task." + ], + [ + "As artificial intelligence plays an ever more prominent role in everyday human lives, it becomes increasingly important to enable machines to communicate via natural language\u2014not only with humans, but also with each other. Learning algorithms for natural language understanding, such as in machine translation and reading comprehension, have progressed at an unprecedented rate in recent years, but still rely on static, large-scale, text-only datasets that lack crucial aspects of how humans understand and produce natural language. Namely, humans develop language capabilities by being embodied in an environment which they can perceive, manipulate and move around in; and by interacting with other humans. Hence, we argue that we should incorporate all three fundamental aspects of human language acquisition\u2014perception, action and interactive communication\u2014and develop a task and dataset to that effect.", + "We introduce the Talk the Walk dataset, where the aim is for two agents, a \u201cguide\u201d and a \u201ctourist\u201d, to interact with each other via natural language in order to achieve a common goal: having the tourist navigate towards the correct location. The guide has access to a map and knows the target location, but does not know where the tourist is; the tourist has a 360-degree view of the world, but knows neither the target location on the map nor the way to it. The agents need to work together through communication in order to successfully solve the task. An example of the task is given in Figure FIGREF3 .", + "Grounded language learning has (re-)gained traction in the AI community, and much attention is currently devoted to virtual embodiment\u2014the development of multi-agent communication tasks in virtual environments\u2014which has been argued to be a viable strategy for acquiring natural language semantics BIBREF0 . Various related tasks have recently been introduced, but in each case with some limitations. Although visually grounded dialogue tasks BIBREF1 , BIBREF2 comprise perceptual grounding and multi-agent interaction, their agents are passive observers and do not act in the environment. By contrast, instruction-following tasks, such as VNL BIBREF3 , involve action and perception but lack natural language interaction with other agents. Furthermore, some of these works use simulated environments BIBREF4 and/or templated language BIBREF5 , which arguably oversimplifies real perception or natural language, respectively. See Table TABREF15 for a comparison.", + "Talk The Walk is the first task to bring all three aspects together: perception for the tourist observing the world, action for the tourist to navigate through the environment, and interactive dialogue for the tourist and guide to work towards their common goal. To collect grounded dialogues, we constructed a virtual 2D grid environment by manually capturing 360-views of several neighborhoods in New York City (NYC). As the main focus of our task is on interactive dialogue, we limit the difficulty of the control problem by having the tourist navigating a 2D grid via discrete actions (turning left, turning right and moving forward). Our street view environment was integrated into ParlAI BIBREF6 and used to collect a large-scale dataset on Mechanical Turk involving human perception, action and communication.", + "We argue that for artificial agents to solve this challenging problem, some fundamental architecture designs are missing, and our hope is that this task motivates their innovation. To that end, we focus on the task of localization and develop the novel Masked Attention for Spatial Convolutions (MASC) mechanism. To model the interaction between language and action, this architecture repeatedly conditions the spatial dimensions of a convolution on the communicated message sequence.", + "This work makes the following contributions: 1) We present the first large scale dialogue dataset grounded in action and perception; 2) We introduce the MASC architecture for localization and show it yields improvements for both emergent and natural language; 4) Using localization models, we establish initial baselines on the full task; 5) We show that our best model exceeds human performance under the assumption of \u201cperfect perception\u201d and with a learned emergent communication protocol, and sets a non-trivial baseline with natural language." + ], + [ + "We create a perceptual environment by manually capturing several neighborhoods of New York City (NYC) with a 360 camera. Most parts of the city are grid-like and uniform, which makes it well-suited for obtaining a 2D grid. For Talk The Walk, we capture parts of Hell's Kitchen, East Village, the Financial District, Williamsburg and the Upper East Side\u2014see Figure FIGREF66 in Appendix SECREF14 for their respective locations within NYC. For each neighborhood, we choose an approximately 5x5 grid and capture a 360 view on all four corners of each intersection, leading to a grid-size of roughly 10x10 per neighborhood.", + "The tourist's location is given as a tuple INLINEFORM0 , where INLINEFORM1 are the coordinates and INLINEFORM2 signifies the orientation (north, east, south or west). The tourist can take three actions: turn left, turn right and go forward. For moving forward, we add INLINEFORM3 , INLINEFORM4 , INLINEFORM5 , INLINEFORM6 to the INLINEFORM7 coordinates for the respective orientations. Upon a turning action, the orientation is updated by INLINEFORM8 where INLINEFORM9 for left and INLINEFORM10 for right. If the tourist moves outside the grid, we issue a warning that they cannot go in that direction and do not update the location. Moreover, tourists are shown different types of transitions: a short transition for actions that bring the tourist to a different corner of the same intersection; and a longer transition for actions that bring them to a new intersection.", + "The guide observes a map that corresponds to the tourist's environment. We exploit the fact that urban areas like NYC are full of local businesses, and overlay the map with these landmarks as localization points for our task. Specifically, we manually annotate each corner of the intersection with a set of landmarks INLINEFORM0 , each coming from one of the following categories:", + " Bar Playfield Bank Hotel Shop Subway Coffee Shop Restaurant Theater ", + "The right-side of Figure FIGREF3 illustrates how the map is presented. Note that within-intersection transitions have a smaller grid distance than transitions to new intersections. To ensure that the localization task is not too easy, we do not include street names in the overhead map and keep the landmark categories coarse. That is, the dialogue is driven by uncertainty in the tourist's current location and the properties of the target location: if the exact location and orientation of the tourist were known, it would suffice to communicate a sequence of actions." + ], + [ + "For the Talk The Walk task, we randomly choose one of the five neighborhoods, and subsample a 4x4 grid (one block with four complete intersections) from the entire grid. We specify the boundaries of the grid by the top-left and bottom-right corners INLINEFORM0 . Next, we construct the overhead map of the environment, i.e. INLINEFORM1 with INLINEFORM2 and INLINEFORM3 . We subsequently sample a start location and orientation INLINEFORM4 and a target location INLINEFORM5 at random.", + "The shared goal of the two agents is to navigate the tourist to the target location INLINEFORM0 , which is only known to the guide. The tourist perceives a \u201cstreet view\u201d planar projection INLINEFORM1 of the 360 image at location INLINEFORM2 and can simultaneously chat with the guide and navigate through the environment. The guide's role consists of reading the tourist description of the environment, building a \u201cmental map\u201d of their current position and providing instructions for navigating towards the target location. Whenever the guide believes that the tourist has reached the target location, they instruct the system to evaluate the tourist's location. The task ends when the evaluation is successful\u2014i.e., when INLINEFORM3 \u2014or otherwise continues until a total of three failed attempts. The additional attempts are meant to ease the task for humans, as we found that they otherwise often fail at the task but still end up close to the target location, e.g., at the wrong corner of the correct intersection." + ], + [ + "We crowd-sourced the collection of the dataset on Amazon Mechanical Turk (MTurk). We use the MTurk interface of ParlAI BIBREF6 to render 360 images via WebGL and dynamically display neighborhood maps with an HTML5 canvas. Detailed task instructions, which were also given to our workers before they started their task, are shown in Appendix SECREF15 . We paired Turkers at random and let them alternate between the tourist and guide role across different HITs." + ], + [ + "The Talk The Walk dataset consists of over 10k successful dialogues\u2014see Table FIGREF66 in the appendix for the dataset statistics split by neighborhood. Turkers successfully completed INLINEFORM0 of all finished tasks (we use this statistic as the human success rate). More than six hundred participants successfully completed at least one Talk The Walk HIT. Although the Visual Dialog BIBREF2 and GuessWhat BIBREF1 datasets are larger, the collected Talk The Walk dialogs are significantly longer. On average, Turkers needed more than 62 acts (i.e utterances and actions) before they successfully completed the task, whereas Visual Dialog requires 20 acts. The majority of acts comprise the tourist's actions, with on average more than 44 actions per dialogue. The guide produces roughly 9 utterances per dialogue, slightly more than the tourist's 8 utterances. Turkers use diverse discourse, with a vocabulary size of more than 10K (calculated over all successful dialogues). An example from the dataset is shown in Appendix SECREF14 . The dataset is available at https://github.com/facebookresearch/talkthewalk." + ], + [ + "We investigate the difficulty of the proposed task by establishing initial baselines. The final Talk The Walk task is challenging and encompasses several important sub-tasks, ranging from landmark recognition to tourist localization and natural language instruction-giving. Arguably the most important sub-task is localization: without such capabilities the guide can not tell whether the tourist reached the target location. In this work, we establish a minimal baseline for Talk The Walk by utilizing agents trained for localization. Specifically, we let trained tourist models undertake random walks, using the following protocol: at each step, the tourist communicates its observations and actions to the guide, who predicts the tourist's location. If the guide predicts that the tourist is at target, we evaluate its location. If successful, the task ends, otherwise we continue until there have been three wrong evaluations. The protocol is given as pseudo-code in Appendix SECREF12 ." + ], + [ + "The designed navigation protocol relies on a trained localization model that predicts the tourist's location from a communicated message. Before we formalize this localization sub-task in Section UID21 , we further introduce two simplifying assumptions\u2014perfect perception and orientation-agnosticism\u2014so as to overcome some of the difficulties we encountered in preliminary experiments.", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Perfect Perception Early experiments revealed that perceptual grounding of landmarks is difficult: we set up a landmark classification problem, on which models with extracted CNN BIBREF7 or text recognition features BIBREF8 barely outperform a random baseline\u2014see Appendix SECREF13 for full details. This finding implies that localization models from image input are limited by their ability to recognize landmarks, and, as a result, would not generalize to unseen environments. To ensure that perception is not the limiting factor when investigating the landmark-grounding and action-grounding capabilities of localization models, we assume \u201cperfect perception\u201d: in lieu of the 360 image view, the tourist is given the landmarks at its current location. More formally, each state observation INLINEFORM0 now equals the set of landmarks at the INLINEFORM1 -location, i.e. INLINEFORM2 . If the INLINEFORM3 -location does not have any visible landmarks, we return a single \u201cempty corner\u201d symbol. We stress that our findings\u2014including a novel architecture for grounding actions into an overhead map, see Section UID28 \u2014should carry over to settings without the perfect perception assumption.", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Orientation-agnosticism We opt to ignore the tourist's orientation, which simplifies the set of actions to [Left, Right, Up, Down], corresponding to adding [(-1, 0), (1, 0), (0, 1), (0, -1)] to the current INLINEFORM0 coordinates, respectively. Note that actions are now coupled to an orientation on the map\u2014e.g. up is equal to going north\u2014and this implicitly assumes that the tourist has access to a compass. This also affects perception, since the tourist now has access to views from all orientations: in conjunction with \u201cperfect perception\u201d, implying that only landmarks at the current corner are given, whereas landmarks from different corners (e.g. across the street) are not visible.", + "Even with these simplifications, the localization-based baseline comes with its own set of challenges. As we show in Section SECREF34 , the task requires communication about a short (random) path\u2014i.e., not only a sequence of observations but also actions\u2014in order to achieve high localization accuracy. This means that the guide needs to decode observations from multiple time steps, as well as understand their 2D spatial arrangement as communicated via the sequence of actions. Thus, in order to get to a good understanding of the task, we thoroughly examine whether the agents can learn a communication protocol that simultaneously grounds observations and actions into the guide's map. In doing so, we thoroughly study the role of the communication channel in the localization task, by investigating increasingly constrained forms of communication: from differentiable continuous vectors to emergent discrete symbols to the full complexity of natural language.", + "The full navigation baseline hinges on a localization model from random trajectories. While we can sample random actions in the emergent communication setup, this is not possible for the natural language setup because the messages are coupled to the trajectories of the human annotators. This leads to slightly different problem setups, as described below.", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Emergent language A tourist, starting from a random location, takes INLINEFORM0 random actions INLINEFORM1 to reach target location INLINEFORM2 . Every location in the environment has a corresponding set of landmarks INLINEFORM3 for each of the INLINEFORM4 coordinates. As the tourist navigates, the agent perceives INLINEFORM5 state-observations INLINEFORM6 where each observation INLINEFORM7 consists of a set of INLINEFORM8 landmark symbols INLINEFORM9 . Given the observations INLINEFORM10 and actions INLINEFORM11 , the tourist generates a message INLINEFORM12 which is communicated to the other agent. The objective of the guide is to predict the location INLINEFORM13 from the tourist's message INLINEFORM14 .", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Natural language In contrast to our emergent communication experiments, we do not take random actions but instead extract actions, observations, and messages from the dataset. Specifically, we consider each tourist utterance (i.e. at any point in the dialogue), obtain the current tourist location as target location INLINEFORM0 , the utterance itself as message INLINEFORM1 , and the sequence of observations and actions that took place between the current and previous tourist utterance as INLINEFORM2 and INLINEFORM3 , respectively. Similar to the emergent language setting, the guide's objective is to predict the target location INLINEFORM4 models from the tourist message INLINEFORM5 . We conduct experiments with INLINEFORM6 taken from the dataset and with INLINEFORM7 generated from the extracted observations INLINEFORM8 and actions INLINEFORM9 ." + ], + [ + "This section outlines the tourist and guide architectures. We first describe how the tourist produces messages for the various communication channels across which the messages are sent. We subsequently describe how these messages are processed by the guide, and introduce the novel Masked Attention for Spatial Convolutions (MASC) mechanism that allows for grounding into the 2D overhead map in order to predict the tourist's location." + ], + [ + "For each of the communication channels, we outline the procedure for generating a message INLINEFORM0 . Given a set of state observations INLINEFORM1 , we represent each observation by summing the INLINEFORM2 -dimensional embeddings of the observed landmarks, i.e. for INLINEFORM3 , INLINEFORM4 , where INLINEFORM5 is the landmark embedding lookup table. In addition, we embed action INLINEFORM6 into a INLINEFORM7 -dimensional embedding INLINEFORM8 via a look-up table INLINEFORM9 . We experiment with three types of communication channel.", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Continuous vectors The tourist has access to observations of several time steps, whose order is important for accurate localization. Because summing embeddings is order-invariant, we introduce a sum over positionally-gated embeddings, which, conditioned on time step INLINEFORM0 , pushes embedding information into the appropriate dimensions. More specifically, we generate an observation message INLINEFORM1 , where INLINEFORM2 is a learned gating vector for time step INLINEFORM3 . In a similar fashion, we produce action message INLINEFORM4 and send the concatenated vectors INLINEFORM5 as message to the guide. We can interpret continuous vector communication as a single, monolithic model because its architecture is end-to-end differentiable, enabling gradient-based optimization for training.", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Discrete symbols Like the continuous vector communication model, with discrete communication the tourist also uses separate channels for observations and actions, as well as a sum over positionally gated embeddings to generate observation embedding INLINEFORM0 . We pass this embedding through a sigmoid and generate a message INLINEFORM1 by sampling from the resulting Bernoulli distributions:", + " INLINEFORM0 ", + "The action message INLINEFORM0 is produced in the same way, and we obtain the final tourist message INLINEFORM1 through concatenating the messages.", + "The communication channel's sampling operation yields the model non-differentiable, so we use policy gradients BIBREF9 , BIBREF10 to train the parameters INLINEFORM0 of the tourist model. That is, we estimate the gradient by INLINEFORM1 ", + " where the reward function INLINEFORM0 is the negative guide's loss (see Section SECREF25 ) and INLINEFORM1 a state-value baseline to reduce variance. We use a linear transformation over the concatenated embeddings as baseline prediction, i.e. INLINEFORM2 , and train it with a mean squared error loss.", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Natural Language Because observations and actions are of variable-length, we use an LSTM encoder over the sequence of observations embeddings INLINEFORM0 , and extract its last hidden state INLINEFORM1 . We use a separate LSTM encoder for action embeddings INLINEFORM2 , and concatenate both INLINEFORM3 and INLINEFORM4 to the input of the LSTM decoder at each time step: DISPLAYFORM0 ", + " where INLINEFORM0 a look-up table, taking input tokens INLINEFORM1 . We train with teacher-forcing, i.e. we optimize the cross-entropy loss: INLINEFORM2 . At test time, we explore the following decoding strategies: greedy, sampling and a beam-search. We also fine-tune a trained tourist model (starting from a pre-trained model) with policy gradients in order to minimize the guide's prediction loss." + ], + [ + "Given a tourist message INLINEFORM0 describing their observations and actions, the objective of the guide is to predict the tourist's location on the map. First, we outline the procedure for extracting observation embedding INLINEFORM1 and action embeddings INLINEFORM2 from the message INLINEFORM3 for each of the types of communication. Next, we discuss the MASC mechanism that takes the observations and actions in order to ground them on the guide's map in order to predict the tourist's location.", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Continuous For the continuous communication model, we assign the observation message to the observation embedding, i.e. INLINEFORM0 . To extract the action embedding for time step INLINEFORM1 , we apply a linear layer to the action message, i.e. INLINEFORM2 .", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Discrete For discrete communication, we obtain observation INLINEFORM0 by applying a linear layer to the observation message, i.e. INLINEFORM1 . Similar to the continuous communication model, we use a linear layer over action message INLINEFORM2 to obtain action embedding INLINEFORM3 for time step INLINEFORM4 .", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Natural Language The message INLINEFORM0 contains information about observations and actions, so we use a recurrent neural network with attention mechanism to extract the relevant observation and action embeddings. Specifically, we encode the message INLINEFORM1 , consisting of INLINEFORM2 tokens INLINEFORM3 taken from vocabulary INLINEFORM4 , with a bidirectional LSTM: DISPLAYFORM0 ", + " where INLINEFORM0 is the word embedding look-up table. We obtain observation embedding INLINEFORM1 through an attention mechanism over the hidden states INLINEFORM2 : DISPLAYFORM0 ", + "where INLINEFORM0 is a learned control embedding who is updated through a linear transformation of the previous control and observation embedding: INLINEFORM1 . We use the same mechanism to extract the action embedding INLINEFORM2 from the hidden states. For the observation embedding, we obtain the final representation by summing positionally gated embeddings, i.e., INLINEFORM3 .", + "We represent the guide's map as INLINEFORM0 , where in this case INLINEFORM1 , where each INLINEFORM2 -dimensional INLINEFORM3 location embedding INLINEFORM4 is computed as the sum of the guide's landmark embeddings for that location.", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Motivation While the guide's map representation contains only local landmark information, the tourist communicates a trajectory of the map (i.e. actions and observations from multiple locations), implying that directly comparing the tourist's message with the individual landmark embeddings is probably suboptimal. Instead, we want to aggregate landmark information from surrounding locations by imputing trajectories over the map to predict locations. We propose a mechanism for translating landmark embeddings according to state transitions (left, right, up, down), which can be expressed as a 2D convolution over the map embeddings. For simplicity, let us assume that the map embedding INLINEFORM0 is 1-dimensional, then a left action can be realized through application of the following INLINEFORM1 kernel: INLINEFORM2 which effectively shifts all values of INLINEFORM3 one position to the left. We propose to learn such state-transitions from the tourist message through a differentiable attention-mask over the spatial dimensions of a 3x3 convolution.", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em MASC We linearly project each predicted action embedding INLINEFORM0 to a 9-dimensional vector INLINEFORM1 , normalize it by a softmax and subsequently reshape the vector into a 3x3 mask INLINEFORM2 : DISPLAYFORM0 ", + " We learn a 3x3 convolutional kernel INLINEFORM0 , with INLINEFORM1 features, and apply the mask INLINEFORM2 to the spatial dimensions of the convolution by first broadcasting its values along the feature dimensions, i.e. INLINEFORM3 , and subsequently taking the Hadamard product: INLINEFORM4 . For each action step INLINEFORM5 , we then apply a 2D convolution with masked weight INLINEFORM6 to obtain a new map embedding INLINEFORM7 , where we zero-pad the input to maintain identical spatial dimensions.", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Prediction model We repeat the MASC operation INLINEFORM0 times (i.e. once for each action), and then aggregate the map embeddings by a sum over positionally-gated embeddings: INLINEFORM1 . We score locations by taking the dot-product of the observation embedding INLINEFORM2 , which contains information about the sequence of observed landmarks by the tourist, and the map. We compute a distribution over the locations of the map INLINEFORM3 by taking a softmax over the computed scores: DISPLAYFORM0 ", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Predicting T While emergent communication models use a fixed length trasjectory INLINEFORM0 , natural language messages may differ in the number of communicated observations and actions. Hence, we predict INLINEFORM1 from the communicated message. Specifically, we use a softmax regression layer over the last hidden state INLINEFORM2 of the RNN, and subsequently sample INLINEFORM3 from the resulting multinomial distribution: DISPLAYFORM0 ", + "We jointly train the INLINEFORM0 -prediction model via REINFORCE, with the guide's loss as reward function and a mean-reward baseline." + ], + [ + "To better analyze the performance of the models incorporating MASC, we compare against a no-MASC baseline in our experiments, as well as a prediction upper bound.", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em No MASC We compare the proposed MASC model with a model that does not include this mechanism. Whereas MASC predicts a convolution mask from the tourist message, the \u201cNo MASC\u201d model uses INLINEFORM0 , the ordinary convolutional kernel to convolve the map embedding INLINEFORM1 to obtain INLINEFORM2 . We also share the weights of this convolution at each time step.", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Prediction upper-bound Because we have access to the class-conditional likelihood INLINEFORM0 , we are able to compute the Bayes error rate (or irreducible error). No model (no matter how expressive) with any amount of data can ever obtain better localization accuracy as there are multiple locations consistent with the observations and actions." + ], + [ + "In this section, we describe the findings of various experiments. First, we analyze how much information needs to be communicated for accurate localization in the Talk The Walk environment, and find that a short random path (including actions) is necessary. Next, for emergent language, we show that the MASC architecture can achieve very high localization accuracy, significantly outperforming the baseline that does not include this mechanism. We then turn our attention to the natural language experiments, and find that localization from human utterances is much harder, reaching an accuracy level that is below communicating a single landmark observation. We show that generated utterances from a conditional language model leads to significantly better localization performance, by successfully grounding the utterance on a single landmark observation (but not yet on multiple observations and actions). Finally, we show performance of the localization baseline on the full task, which can be used for future comparisons to this work." + ], + [ + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Task is not too easy The upper-bound on localization performance in Table TABREF32 suggest that communicating a single landmark observation is not sufficient for accurate localization of the tourist ( INLINEFORM0 35% accuracy). This is an important result for the full navigation task because the need for two-way communication disappears if localization is too easy; if the guide knows the exact location of the tourist it suffices to communicate a list of instructions, which is then executed by the tourist. The uncertainty in the tourist's location is what drives the dialogue between the two agents.", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Importance of actions We observe that the upperbound for only communicating observations plateaus around 57% (even for INLINEFORM0 actions), whereas it exceeds 90% when we also take actions into account. This implies that, at least for random walks, it is essential to communicate a trajectory, including observations and actions, in order to achieve high localization accuracy." + ], + [ + "We first report the results for tourist localization with emergent language in Table TABREF32 .", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em MASC improves performance The MASC architecture significantly improves performance compared to models that do not include this mechanism. For instance, for INLINEFORM0 action, MASC already achieves 56.09 % on the test set and this further increases to 69.85% for INLINEFORM1 . On the other hand, no-MASC models hit a plateau at 43%. In Appendix SECREF11 , we analyze learned MASC values, and show that communicated actions are often mapped to corresponding state-transitions.", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Continuous vs discrete We observe similar performance for continuous and discrete emergent communication models, implying that a discrete communication channel is not a limiting factor for localization performance." + ], + [ + "We report the results of tourist localization with natural language in Table TABREF36 . We compare accuracy of the guide model (with MASC) trained on utterances from (i) humans, (ii) a supervised model with various decoding strategies, and (iii) a policy gradient model optimized with respect to the loss of a frozen, pre-trained guide model on human utterances.", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Human utterances Compared to emergent language, localization from human utterances is much harder, achieving only INLINEFORM0 on the test set. Here, we report localization from a single utterance, but in Appendix SECREF45 we show that including up to five dialogue utterances only improves performance to INLINEFORM1 . We also show that MASC outperform no-MASC models for natural language communication.", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Generated utterances We also investigate generated tourist utterances from conditional language models. Interestingly, we observe that the supervised model (with greedy and beam-search decoding) as well as the policy gradient model leads to an improvement of more than 10 accuracy points over the human utterances. However, their level of accuracy is slightly below the baseline of communicating a single observation, indicating that these models only learn to ground utterances in a single landmark observation.", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Better grounding of generated utterances We analyze natural language samples in Table TABREF38 , and confirm that, unlike human utterances, the generated utterances are talking about the observed landmarks. This observation explains why the generated utterances obtain higher localization accuracy. The current language models are most successful when conditioned on a single landmark observation; We show in Appendix UID43 that performance quickly deteriorates when the model is conditioned on more observations, suggesting that it can not produce natural language utterances about multiple time steps." + ], + [ + "Table TABREF36 shows results for the best localization models on the full task, evaluated via the random walk protocol defined in Algorithm SECREF12 .", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Comparison with human annotators Interestingly, our best localization model (continuous communication, with MASC, and INLINEFORM0 ) achieves 88.33% on the test set and thus exceed human performance of 76.74% on the full task. While emergent models appear to be stronger localizers, humans might cope with their localization uncertainty through other mechanisms (e.g. better guidance, bias towards taking particular paths, etc). The simplifying assumption of perfect perception also helps.", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Number of actions Unsurprisingly, humans take fewer steps (roughly 15) than our best random walk model (roughly 34). Our human annotators likely used some form of guidance to navigate faster to the target." + ], + [ + "We introduced the Talk The Walk task and dataset, which consists of crowd-sourced dialogues in which two human annotators collaborate to navigate to target locations in the virtual streets of NYC. For the important localization sub-task, we proposed MASC\u2014a novel grounding mechanism to learn state-transition from the tourist's message\u2014and showed that it improves localization performance for emergent and natural language. We use the localization model to provide baseline numbers on the Talk The Walk task, in order to facilitate future research." + ], + [ + "The Talk the Walk task and dataset facilitate future research on various important subfields of artificial intelligence, including grounded language learning, goal-oriented dialogue research and situated navigation. Here, we describe related previous work in these areas.", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Related tasks There has been a long line of work involving related tasks. Early work on task-oriented dialogue dates back to the early 90s with the introduction of the Map Task BIBREF11 and Maze Game BIBREF25 corpora. Recent efforts have led to larger-scale goal-oriented dialogue datasets, for instance to aid research on visually-grounded dialogue BIBREF2 , BIBREF1 , knowledge-base-grounded discourse BIBREF29 or negotiation tasks BIBREF36 . At the same time, there has been a big push to develop environments for embodied AI, many of which involve agents following natural language instructions with respect to an environment BIBREF13 , BIBREF50 , BIBREF5 , BIBREF39 , BIBREF19 , BIBREF18 , following-up on early work in this area BIBREF38 , BIBREF20 . An early example of navigation using neural networks is BIBREF28 , who propose an online learning approach for robot navigation. Recently, there has been increased interest in using end-to-end trainable neural networks for learning to navigate indoor scenes BIBREF27 , BIBREF26 or large cities BIBREF17 , BIBREF40 , but, unlike our work, without multi-agent communication. Also the task of localization (without multi-agent communication) has recently been studied BIBREF18 , BIBREF48 .", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Grounded language learning Grounded language learning is motivated by the observation that humans learn language embodied (grounded) in sensorimotor experience of the physical world BIBREF15 , BIBREF45 . On the one hand, work in multi-modal semantics has shown that grounding can lead to practical improvements on various natural language understanding tasks BIBREF14 , BIBREF31 . In robotics, researchers dissatisfied with purely symbolic accounts of meaning attempted to build robotic systems with the aim of grounding meaning in physical experience of the world BIBREF44 , BIBREF46 . Recently, grounding has also been applied to the learning of sentence representations BIBREF32 , image captioning BIBREF37 , BIBREF49 , visual question answering BIBREF12 , BIBREF22 , visual reasoning BIBREF30 , BIBREF42 , and grounded machine translation BIBREF43 , BIBREF23 . Grounding also plays a crucial role in the emergent research of multi-agent communication, where, agents communicate (in natural language or otherwise) in order to solve a task, with respect to their shared environment BIBREF35 , BIBREF21 , BIBREF41 , BIBREF24 , BIBREF36 , BIBREF47 , BIBREF34 ." + ], + [ + "For the emergent communication models, we use an embedding size INLINEFORM0 . The natural language experiments use 128-dimensional word embeddings and a bidirectional RNN with 256 units. In all experiments, we train the guide with a cross entropy loss using the ADAM optimizer with default hyper-parameters BIBREF33 . We perform early stopping on the validation accuracy, and report the corresponding train, valid and test accuracy. We optimize the localization models with continuous, discrete and natural language communication channels for 200, 200, and 25 epochs, respectively. To facilitate further research on Talk The Walk, we make our code base for reproducing experiments publicly available at https://github.com/facebookresearch/talkthewalk." + ], + [ + "First, we investigate the sensitivity of tourist generation models to the trajectory length, finding that the model conditioned on a single observation (i.e. INLINEFORM0 ) achieves best performance. In the next subsection, we further analyze localization models from human utterances by investigating MASC and no-MASC models with increasing dialogue context." + ], + [ + "After training the supervised tourist model (conditioned on observations and action from human expert trajectories), there are two ways to train an accompanying guide model. We can optimize a location prediction model on either (i) extracted human trajectories (as in the localization setup from human utterances) or (ii) on all random paths of length INLINEFORM0 (as in the full task evaluation). Here, we investigate the impact of (1) using either human or random trajectories for training the guide model, and (2) the effect of varying the path length INLINEFORM1 during the full-task evaluation. For random trajectories, guide training uses the same path length INLINEFORM2 as is used during evaluation. We use a pre-trained tourist model with greedy decoding for generating the tourist utterances. Table TABREF40 summarizes the results.", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Human vs random trajectories We only observe small improvements for training on random trajectories. Human trajectories are thus diverse enough to generalize to random trajectories.", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Effect of path length There is a strong negative correlation between task success and the conditioned trajectory length. We observe that the full task performance quickly deteriorates for both human and random trajectories. This suggests that the tourist generation model can not produce natural language utterances that describe multiple observations and actions. Although it is possible that the guide model can not process such utterances, this is not very likely because the MASC architectures handles such messages successfully for emergent communication.", + "We report localization performance of tourist utterances generated by beam search decoding of varying beam size in Table TABREF40 . We find that performance decreases from 29.05% to 20.87% accuracy on the test set when we increase the beam-size from one to eight." + ], + [ + "We conduct an ablation study for MASC on natural language with varying dialogue context. Specifically, we compare localization accuracy of MASC and no-MASC models trained on the last [1, 3, 5] utterances of the dialogue (including guide utterances). We report these results in Table TABREF41 . In all cases, MASC outperforms the no-MASC models by several accuracy points. We also observe that mean predicted INLINEFORM0 (over the test set) increases from 1 to 2 when more dialogue context is included." + ], + [ + "Figure FIGREF46 shows the MASC values for a learned model with emergent discrete communications and INLINEFORM0 actions. Specifically, we look at the predicted MASC values for different action sequences taken by the tourist. We observe that the first action is always mapped to the correct state-transition, but that the second and third MASC values do not always correspond to right state-transitions." + ], + [ + "We provide pseudo-code for evaluation of localization models on the full task in Algorithm SECREF12 , as well as results for all emergent communication models in Table TABREF55 .", + " INLINEFORM0 INLINEFORM1 ", + " INLINEFORM0 take new action INLINEFORM1 INLINEFORM2 ", + "Performance evaluation of location prediction model on full Talk The Walk setup" + ], + [ + "While the guide has access to the landmark labels, the tourist needs to recognize these landmarks from raw perceptual information. In this section, we study landmark classification as a supervised learning problem to investigate the difficulty of perceptual grounding in Talk The Walk.", + "The Talk The Walk dataset contains a total of 307 different landmarks divided among nine classes, see Figure FIGREF62 for how they are distributed. The class distribution is fairly imbalanced, with shops and restaurants as the most frequent landmarks and relatively few play fields and theaters. We treat landmark recognition as a multi-label classification problem as there can be multiple landmarks on a corner.", + "For the task of landmark classification, we extract the relevant views of the 360 image from which a landmark is visible. Because landmarks are labeled to be on a specific corner of an intersection, we assume that they are visible from one of the orientations facing away from the intersection. For example, for a landmark on the northwest corner of an intersection, we extract views from both the north and west direction. The orientation-specific views are obtained by a planar projection of the full 360-image with a small field of view (60 degrees) to limit distortions. To cover the full field of view, we extract two images per orientation, with their horizontal focus point 30 degrees apart. Hence, we obtain eight images per 360 image with corresponding orientation INLINEFORM0 .", + "We run the following pre-trained feature extractors over the extracted images:", + "For the text recognition model, we use a learned look-up table INLINEFORM0 to embed the extracted text features INLINEFORM1 , and fuse all embeddings of four images through a bag of embeddings, i.e., INLINEFORM2 . We use a linear layer followed by a sigmoid to predict the probability for each class, i.e. INLINEFORM3 . We also experiment with replacing the look-up embeddings with pre-trained FastText embeddings BIBREF16 . For the ResNet model, we use a bag of embeddings over the four ResNet features, i.e. INLINEFORM4 , before we pass it through a linear layer to predict the class probabilities: INLINEFORM5 . We also conduct experiments where we first apply PCA to the extracted ResNet and FastText features before we feed them to the model.", + "To account for class imbalance, we train all described models with a binary cross entropy loss weighted by the inverted class frequency. We create a 80-20 class-conditional split of the dataset into a training and validation set. We train for 100 epochs and perform early stopping on the validation loss.", + "The F1 scores for the described methods in Table TABREF65 . We compare to an \u201call positive\u201d baseline that always predicts that the landmark class is visible and observe that all presented models struggle to outperform this baseline. Although 256-dimensional ResNet features achieve slightly better precision on the validation set, it results in much worse recall and a lower F1 score. Our results indicate that perceptual grounding is a difficult task, which easily merits a paper of its own right, and so we leave further improvements (e.g. better text recognizers) for future work." + ], + [ + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Dataset split We split the full dataset by assigning entire 4x4 grids (independent of the target location) to the train, valid or test set. Specifically, we design the split such that the valid set contains at least one intersection (out of four) is not part of the train set. For the test set, all four intersections are novel. See our source code, available at URL ANONYMIZED, for more details on how this split is realized.", + "paragraph4 0.1ex plus0.1ex minus.1ex-1em Example", + "Tourist: ACTION:TURNRIGHT ACTION:TURNRIGHT", + "Guide: Hello, what are you near?", + "Tourist: ACTION:TURNLEFT ACTION:TURNLEFT ACTION:TURNLEFT", + "Tourist: Hello, in front of me is a Brooks Brothers", + "Tourist: ACTION:TURNLEFT ACTION:FORWARD ACTION:TURNLEFT ACTION:TURNLEFT", + "Guide: Is that a shop or restaurant?", + "Tourist: ACTION:TURNLEFT", + "Tourist: It is a clothing shop.", + "Tourist: ACTION:TURNLEFT", + "Guide: You need to go to the intersection in the northwest corner of the map", + "Tourist: ACTION:TURNLEFT", + "Tourist: There appears to be a bank behind me.", + "Tourist: ACTION:TURNLEFT ACTION:TURNLEFT ACTION:TURNRIGHT ACTION:TURNRIGHT", + "Guide: Ok, turn left then go straight up that road", + "Tourist: ACTION:TURNLEFT ACTION:TURNLEFT ACTION:TURNLEFT ACTION:FORWARD ACTION:TURNRIGHT", + " ACTION:FORWARD ACTION:FORWARD ACTION:TURNLEFT ACTION:TURNLEFT ACTION:TURNLEFT", + "Guide: There should be shops on two of the corners but you", + " need to go to the corner without a shop.", + "Tourist: ACTION:FORWARD ACTION:FORWARD ACTION:FORWARD ACTION:TURNLEFT ACTION:TURNLEFT", + "Guide: let me know when you get there.", + "Tourist: on my left is Radio city Music hall", + "Tourist: ACTION:TURNLEFT ACTION:FORWARD ACTION:TURNLEFT ACTION:TURNRIGHT ACTION:TURNRIGHT", + "Tourist: I can't go straight any further.", + "Guide: ok. turn so that the theater is on your right.", + "Guide: then go straight", + "Tourist: That would be going back the way I came", + "Guide: yeah. I was looking at the wrong bank", + "Tourist: I'll notify when I am back at the brooks brothers, and the bank.", + "Tourist: ACTION:TURNRIGHT", + "Guide: make a right when the bank is on your left", + "Tourist: ACTION:FORWARD ACTION:FORWARD ACTION:TURNRIGHT", + "Tourist: Making the right at the bank.", + "Tourist: ACTION:FORWARD ACTION:FORWARD", + "Tourist: I can't go that way.", + "Tourist: ACTION:TURNLEFT", + "Tourist: Bank is ahead of me on the right", + "Tourist: ACTION:FORWARD ACTION:FORWARD ACTION:TURNLEFT", + "Guide: turn around on that intersection", + "Tourist: I can only go to the left or back the way I just came.", + "Tourist: ACTION:TURNLEFT", + "Guide: you're in the right place. do you see shops on the corners?", + "Guide: If you're on the corner with the bank, cross the street", + "Tourist: I'm back where I started by the shop and the bank.", + "Tourist: ACTION:TURNRIGHT", + "Guide: on the same side of the street?", + "Tourist: crossing the street now", + "Tourist: ACTION:FORWARD ACTION:FORWARD ACTION:TURNLEFT", + "Tourist: there is an I love new york shop across the street on the left from me now", + "Tourist: ACTION:TURNRIGHT ACTION:FORWARD", + "Guide: ok. I'll see if it's right.", + "Guide: EVALUATE_LOCATION", + "Guide: It's not right.", + "Tourist: What should I be on the look for?", + "Tourist: ACTION:TURNRIGHT ACTION:TURNRIGHT ACTION:TURNRIGHT", + "Guide: There should be shops on two corners but you need to be on one of the corners", + " without the shop.", + "Guide: Try the other corner.", + "Tourist: this intersection has 2 shop corners and a bank corner", + "Guide: yes. that's what I see on the map.", + "Tourist: should I go to the bank corner? or one of the shop corners?", + " or the blank corner (perhaps a hotel)", + "Tourist: ACTION:TURNLEFT ACTION:TURNLEFT ACTION:TURNRIGHT ACTION:TURNRIGHT", + "Guide: Go to the one near the hotel. The map says the hotel is a little", + " further down but it might be a little off.", + "Tourist: It's a big hotel it's possible.", + "Tourist: ACTION:FORWARD ACTION:TURNLEFT ACTION:FORWARD ACTION:TURNRIGHT", + "Tourist: I'm on the hotel corner", + "Guide: EVALUATE_LOCATION" + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0263/instruction.md b/qasper-0263/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..d9dec25647ecdddbdd391ceb6a977776ae1d73da --- /dev/null +++ b/qasper-0263/instruction.md @@ -0,0 +1,139 @@ +Name of Paper: Event Outcome Prediction using Sentiment Analysis and Crowd Wisdom in Microblog Feeds + +Question: How many label options are there in the multi-label task? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Data Set and Preprocessing ::: Data Collection", + "Data Set and Preprocessing ::: Preprocessing", + "Methodology ::: Procedure", + "Methodology ::: Machine Learning Models", + "Methodology ::: Machine Learning Models ::: Single-label Classification", + "Methodology ::: Machine Learning Models ::: Multi-label Classification", + "Methodology ::: Feature Space", + "Data Analysis", + "Evaluation Metrics", + "Evaluation Metrics ::: Sentiment Analysis", + "Evaluation Metrics ::: Outcome Prediction", + "Results ::: Sentiment Analysis", + "Results ::: Results for Outcome Prediction", + "Results ::: Results for Outcome Prediction ::: Presidential Debates", + "Results ::: Results for Outcome Prediction ::: Grammy Awards", + "Results ::: Results for Outcome Prediction ::: Super Bowl", + "Conclusions" + ], + "paragraphs": [ + [ + "Over the past few years, microblogs have become one of the most popular online social networks. Microblogging websites have evolved to become a source of varied kinds of information. This is due to the nature of microblogs: people post real-time messages about their opinions and express sentiment on a variety of topics, discuss current issues, complain, etc. Twitter is one such popular microblogging service where users create status messages (called \u201ctweets\"). With over 400 million tweets per day on Twitter, microblog users generate large amount of data, which cover very rich topics ranging from politics, sports to celebrity gossip. Because the user generated content on microblogs covers rich topics and expresses sentiment/opinions of the mass, mining and analyzing this information can prove to be very beneficial both to the industrial and the academic community. Tweet classification has attracted considerable attention because it has become very important to analyze peoples' sentiments and opinions over social networks.", + "Most of the current work on analysis of tweets is focused on sentiment analysis BIBREF0, BIBREF1, BIBREF2. Not much has been done on predicting outcomes of events based on the tweet sentiments, for example, predicting winners of presidential debates based on the tweets by analyzing the users' sentiments. This is possible intuitively because the sentiment of the users in their tweets towards the candidates is proportional to the performance of the candidates in the debate.", + "In this paper, we analyze three such events: 1) US Presidential Debates 2015-16, 2) Grammy Awards 2013, and 3) Super Bowl 2013. The main focus is on the analysis of the presidential debates. For the Grammys and the Super Bowl, we just perform sentiment analysis and try to predict the outcomes in the process. For the debates, in addition to the analysis done for the Grammys and Super Bowl, we also perform a trend analysis. Our analysis of the tweets for the debates is 3-fold as shown below.", + "Sentiment: Perform a sentiment analysis on the debates. This involves: building a machine learning model which learns the sentiment-candidate pair (candidate is the one to whom the tweet is being directed) from the training data and then using this model to predict the sentiment-candidate pairs of new tweets.", + "Predicting Outcome: Here, after predicting the sentiment-candidate pairs on the new data, we predict the winner of the debates based on the sentiments of the users.", + "Trends: Here, we analyze certain trends of the debates like the change in sentiments of the users towards the candidates over time (hours, days, months) and how the opinion of experts such as Washington Post affect the sentiments of the users.", + "For the sentiment analysis, we look at our problem in a multi-label setting, our two labels being sentiment polarity and the candidate/category in consideration. We test both single-label classifiers and multi-label ones on the problem and as intuition suggests, the multi-label classifier RaKel performs better. A combination of document-embedding features BIBREF3 and topic features (essentially the document-topic probabilities) BIBREF4 is shown to give the best results. These features make sense intuitively because the document-embedding features take context of the text into account, which is important for sentiment polarity classification, and topic features take into account the topic of the tweet (who/what is it about).", + "The prediction of outcomes of debates is very interesting in our case. Most of the results seem to match with the views of some experts such as the political pundits of the Washington Post. This implies that certain rules that were used to score the candidates in the debates by said-experts were in fact reflected by reading peoples' sentiments expressed over social media. This opens up a wide variety of learning possibilities from users' sentiments on social media, which is sometimes referred to as the wisdom of crowd.", + "We do find out that the public sentiments are not always coincident with the views of the experts. In this case, it is interesting to check whether the views of the experts can affect the public, for example, by spreading through the social media microblogs such as Twitter. Hence, we also conduct experiments to compare the public sentiment before and after the experts' views become public and thus notice the impact of the experts' views on the public sentiment. In our analysis of the debates, we observe that in certain debates, such as the 5th Republican Debate, held on December 15, 2015, the opinions of the users vary from the experts. But we see the effect of the experts on the sentiment of the users by looking at their opinions of the same candidates the next day.", + "Our contributions are mainly: we want to see how predictive the sentiment/opinion of the users are in social media microblogs and how it compares to that of the experts. In essence, we find that the crowd wisdom in the microblog domain matches that of the experts in most cases. There are cases, however, where they don't match but we observe that the crowd's sentiments are actually affected by the experts. This can be seen in our analysis of the presidential debates.", + "The rest of the paper is organized as follows: in section SECREF2, we review some of the literature. In section SECREF3, we discuss the collection and preprocessing of the data. Section SECREF4 details the approach taken, along with the features and the machine learning methods used. Section SECREF7 discusses the results of the experiments conducted and lastly section SECREF8 ends with a conclusion on the results including certain limitations and scopes for improvement to work on in the future." + ], + [ + "Sentiment analysis as a Natural Language Processing task has been handled at many levels of granularity. Specifically on the microblog front, some of the early results on sentiment analysis are by BIBREF0, BIBREF1, BIBREF2, BIBREF5, BIBREF6. Go et al. BIBREF0 applied distant supervision to classify tweet sentiment by using emoticons as noisy labels. Kouloumpis et al. BIBREF7 exploited hashtags in tweets to build training data. Chenhao Tan et al. BIBREF8 determined user-level sentiments on particular topics with the help of the social network graph.", + "There has been some work in event detection and extraction in microblogs as well. In BIBREF9, the authors describe a way to extract major life events of a user based on tweets that either congratulate/offer condolences. BIBREF10 build a key-word graph from the data and then detect communities in this graph (cluster) to find events. This works because words that describe similar events will form clusters. In BIBREF11, the authors use distant supervision to extract events. There has also been some work on event retrieval in microblogs BIBREF12. In BIBREF13, the authors detect time points in the twitter stream when an important event happens and then classify such events based on the sentiments they evoke using only non-textual features to do so. In BIBREF14, the authors study how much of the opinion extracted from Online Social Networks (OSN) user data is reflective of the opinion of the larger population. Researchers have also mined Twitter dataset to analyze public reaction to various events: from election debate performance BIBREF15, where the authors demonstrate visuals and metrics that can be used to detect sentiment pulse, anomalies in that pulse, and indications of controversial topics that can be used to inform the design of visual analytic systems for social media events, to movie box-office predictions on the release day BIBREF16. Mishne and Glance BIBREF17 correlate sentiments in blog posts with movie box-office scores. The correlations they observed for positive sentiments are fairly low and not sufficient to use for predictive purposes. Recently, several approaches involving machine learning and deep learning have also been used in the visual and language domains BIBREF18, BIBREF19, BIBREF20, BIBREF21, BIBREF22, BIBREF23, BIBREF24." + ], + [ + "Twitter is a social networking and microblogging service that allows users to post real-time messages, called tweets. Tweets are very short messages, a maximum of 140 characters in length. Due to such a restriction in length, people tend to use a lot of acronyms, shorten words etc. In essence, the tweets are usually very noisy. There are several aspects to tweets such as: 1) Target: Users use the symbol \u201c@\" in their tweets to refer to other users on the microblog. 2) Hashtag: Hashtags are used by users to mark topics. This is done to increase the visibility of the tweets.", + "We conduct experiments on 3 different datasets, as mentioned earlier: 1) US Presidential Debates, 2) Grammy Awards 2013, 3) Superbowl 2013. To construct our presidential debates dataset, we have used the Twitter Search API to collect the tweets. Since there was no publicly available dataset for this, we had to collect the data manually. The data was collected on 10 different presidential debates: 7 republican and 3 democratic, from October 2015 to March 2016. Different hashtags like \u201c#GOP, #GOPDebate\u201d were used to filter out tweets specific to the debate. This is given in Table TABREF2. We extracted only english tweets for our dataset. We collected a total of 104961 tweets were collected across all the debates. But there were some limitations with the API. Firstly, the server imposes a rate limit and discards tweets when the limit is reached. The second problem is that the API returns many duplicates. Thus, after removing the duplicates and irrelevant tweets, we were left with a total of 17304 tweets. This includes the tweets only on the day of the debate. We also collected tweets on the days following the debate.", + "As for the other two datasets, we collected them from available-online repositories. There were a total of 2580062 tweets for the Grammy Awards 2013, and a total of 2428391 tweets for the Superbowl 2013. The statistics are given in Tables TABREF3 and TABREF3. The tweets for the grammy were before the ceremony and during. However, we only use the tweets before the ceremony (after the nominations were announced), to predict the winners. As for the superbowl, the tweets collected were during the game. But we can predict interesting things like Most Valuable Player etc. from the tweets. The tweets for both of these datasets were annotated and thus did not require any human intervention. However, the tweets for the debates had to be annotated.", + "Since we are using a supervised approach in this paper, we have all the tweets (for debates) in the training set human-annotated. The tweets were already annotated for the Grammys and Super Bowl. Some statistics about our datasets are presented in Tables TABREF3, TABREF3 and TABREF3. The annotations for the debate dataset comprised of 2 labels for each tweet: 1) Candidate: This is the candidate of the debate to whom the tweet refers to, 2) Sentiment: This represents the sentiment of the tweet towards that candidate. This is either positive or negative.", + "The task then becomes a case of multi-label classification. The candidate labels are not so trivial to obtain, because there are tweets that do not directly contain any candidates' name. For example, the tweets, \u201ca business man for president??\u201d and \u201ca doctor might sure bring about a change in America!\u201d are about Donal Trump and Ben Carson respectively. Thus, it is meaningful to have a multi-label task.", + "The annotations for the other two datasets are similar, in that one of the labels was the sentiment and the other was category-dependent in the outcome-prediction task, as discussed in the sections below. For example, if we are trying to predict the \"Album of the Year\" winners for the Grammy dataset, the second label would be the nominees for that category (album of the year)." + ], + [ + "As noted earlier, tweets are generally noisy and thus require some preprocessing done before using them. Several filters were applied to the tweets such as: (1) Usernames: Since users often include usernames in their tweets to direct their message, we simplify it by replacing the usernames with the token \u201cUSER\u201d. For example, @michael will be replaced by USER. (2) URLs: In most of the tweets, users include links that add on to their text message. We convert/replace the link address to the token \u201cURL\u201d. (3) Repeated Letters: Oftentimes, users use repeated letters in a word to emphasize their notion. For example, the word \u201clol\u201d (which stands for \u201claugh out loud\u201d) is sometimes written as \u201clooooool\u201d to emphasize the degree of funnyness. We replace such repeated occurrences of letters (more than 2), with just 3 occurrences. We replace with 3 occurrences and not 2, so that we can distinguish the exaggerated usage from the regular ones. (4) Multiple Sentiments: Tweets which contain multiple sentiments are removed, such as \"I hate Donald Trump, but I will vote for him\". This is done so that there is no ambiguity. (5) Retweets: In Twitter, many times tweets of a person are copied and posted by another user. This is known as retweeting and they are commonly abbreviated with \u201cRT\u201d. These are removed and only the original tweets are processed. (6) Repeated Tweets: The Twitter API sometimes returns a tweet multiple times. We remove such duplicates to avoid putting extra weight on any particular tweet." + ], + [ + "Our analysis of the debates is 3-fold including sentiment analysis, outcome prediction, and trend analysis.", + "Sentiment Analysis: To perform a sentiment analysis on the debates, we first extract all the features described below from all the tweets in the training data. We then build the different machine learning models described below on these set of features. After that, we evaluate the output produced by the models on unseen test data. The models essentially predict candidate-sentiment pairs for each tweet.", + "Outcome Prediction: Predict the outcome of the debates. After obtaining the sentiments on the test data for each tweet, we can compute the net normalized sentiment for each presidential candidate in the debate. This is done by looking at the number of positive and negative sentiments for each candidate. We then normalize the sentiment scores of each candidate to be in the same scale (0-1). After that, we rank the candidates based on the sentiment scores and predict the top $k$ as the winners.", + "Trend Analysis: We also analyze some certain trends of the debates. Firstly, we look at the change in sentiments of the users towards the candidates over time (hours, days, months). This is done by computing the sentiment scores for each candidate in each of the debates and seeing how it varies over time, across debates. Secondly, we examine the effect of Washington Post on the views of the users. This is done by looking at the sentiments of the candidates (to predict winners) of a debate before and after the winners are announced by the experts in Washington Post. This way, we can see if Washington Post has had any effect on the sentiments of the users. Besides that, to study the behavior of the users, we also look at the correlation of the tweet volume with the number of viewers as well as the variation of tweet volume over time (hours, days, months) for debates.", + "As for the Grammys and the Super Bowl, we only perform the sentiment analysis and predict the outcomes." + ], + [ + "We compare 4 different models for performing our task of sentiment classification. We then pick the best performing model for the task of outcome prediction. Here, we have two categories of algorithms: single-label and multi-label (We already discussed above why it is meaningful to have a multi-label task earlier), because one can represent $<$candidate, sentiment$>$ as a single class label or have candidate and sentiment as two separate labels. They are listed below:" + ], + [ + "Naive Bayes: We use a multinomial Naive Bayes model. A tweet $t$ is assigned a class $c^{*}$ such that", + "where there are $m$ features and $f_i$ represents the $i^{th}$ feature.", + "Support Vector Machines: Support Vector Machines (SVM) constructs a hyperplane or a set of hyperplanes in a high-dimensional space, which can then be used for classification. In our case, we use SVM with Sequential Minimal Optimization (SMO) BIBREF25, which is an algorithm for solving the quadratic programming (QP) problem that arises during the training of SVMs.", + "Elman Recurrent Neural Network: Recurrent Neural Networks (RNNs) are gaining popularity and are being applied to a wide variety of problems. They are a class of artificial neural networks, where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. The Elman RNN was proposed by Jeff Elman in the year 1990 BIBREF26. We use this in our task." + ], + [ + "RAkEL (RAndom k labELsets): RAkEL BIBREF27 is a multi-label classification algorithm that uses labeled powerset (LP) transformation: it basically creates a single binary classifier for every label combination and then uses multiple LP classifiers, each trained on a random subset of the actual labels, for classification." + ], + [ + "In order to classify the tweets, a set of features is extracted from each of the tweets, such as n-gram, part-of-speech etc. The details of these features are given below:", + "n-gram: This represents the frequency counts of n-grams, specifically that of unigrams and bigrams.", + "punctuation: The number of occurrences of punctuation symbols such as commas, exclamation marks etc.", + "POS (part-of-speech): The frequency of each POS tagger is used as the feature.", + "prior polarity scoring: Here, we obtain the prior polarity of the words BIBREF6 using the Dictionary of Affect in Language (DAL) BIBREF28. This dictionary (DAL) of about 8000 English words assigns a pleasantness score to each word on a scale of 1-3. After normalizing, we can assign the words with polarity higher than $0.8$ as positive and less than $0.5$ as negative. If a word is not present in the dictionary, we lookup its synonyms in WordNet: if this word is there in the dictionary, we assign the original word its synonym's score.", + "Twitter Specific features:", + "Number of hashtags ($\\#$ symbol)", + "Number of mentioning users ( symbol)", + "Number of hyperlinks", + "Document embedding features: Here, we use the approach proposed by Mikolov et al. BIBREF3 to embed an entire tweet into a vector of features", + "Topic features: Here, LDA (Latent Dirichlet Allocation) BIBREF4 is used to extract topic-specific features for a tweet (document). This is basically the topic-document probability that is outputted by the model.", + "In the following experiments, we use 1-$gram$, 2-$gram$ and $(1+2)$-$gram$ to denote unigram, bigram and a combination of unigram and bigram features respectively. We also combine punctuation and the other features as miscellaneous features and use $MISC$ to denote this. We represent the document-embedding features by $DOC$ and topic-specific features by $TOPIC$." + ], + [ + "In this section, we analyze the presidential debates data and show some trends.", + "First, we look at the trend of the tweet frequency. Figure FIGREF21 shows the trends of the tweet frequency and the number of TV viewers as the debates progress over time. We observe from Figures FIGREF21 and FIGREF21 that for the first 5 debates considered, the trend of the number of TV viewers matches the trend of the number of tweets. However, we can see that towards the final debates, the frequency of the tweets decreases consistently. This shows an interesting fact that although the people still watch the debates, the number of people who tweet about it are greatly reduced. But the tweeting community are mainly youngsters and this shows that most of the tweeting community, who actively tweet, didn't watch the later debates. Because if they did, then the trends should ideally match.", + "Next we look at how the tweeting activity is on days of the debate: specifically on the day of the debate, the next day and two days later. Figure FIGREF22 shows the trend of the tweet frequency around the day of the 5th republican debate, i.e December 15, 2015. As can be seen, the average number of people tweet more on the day of the debate than a day or two after it. This makes sense intuitively because the debate would be fresh in their heads.", + "Then, we look at how people tweet in the hours of the debate: specifically during the debate, one hour after and then two hours after. Figure FIGREF23 shows the trend of the tweet frequency around the hour of the 5th republican debate, i.e December 15, 2015. We notice that people don't tweet much during the debate but the activity drastically increases after two hours. This might be because people were busy watching the debate and then taking some time to process things, so that they can give their opinion.", + "We have seen the frequency of tweets over time in the previous trends. Now, we will look at how the sentiments of the candidates change over time.", + "First, Figure FIGREF24 shows how the sentiments of the candidates changed across the debates. We find that many of the candidates have had ups and downs towards in the debates. But these trends are interesting in that, it gives some useful information about what went down in the debate that caused the sentiments to change (sometimes drastically). For example, if we look at the graph for Donald Trump, we see that his sentiment was at its lowest point during the debate held on December 15. Looking into the debate, we can easily see why this was the case. At a certain point in the debate, Trump was asked about his ideas for the nuclear triad. It is very important that a presidential candidate knows about this, but Trump had no idea what the nuclear triad was and, in a transparent attempt to cover his tracks, resorted to a \u201cwe need to be strong\" speech. It can be due to this embarrassment that his sentiment went down during this debate.", + "Next, we investigate how the sentiments of the users towards the candidates change before and after the debate. In essence, we examine how the debate and the results of the debates given by the experts affects the sentiment of the candidates. Figure FIGREF25 shows the sentiments of the users towards the candidate during the 5th Republican Debate, 15th December 2015. It can be seen that the sentiments of the users towards the candidates does indeed change over the course of two days. One particular example is that of Jeb Bush. It seems that the populace are generally prejudiced towards the candidates, which is reflected in their sentiments of the candidates on the day of the debate. The results of the Washington Post are released in the morning after the debate. One can see the winners suggested by the Washington Post in Table TABREF35. One of the winners in that debate according to them is Jeb Bush. Coincidentally, Figure FIGREF25 suggests that the sentiment of Bush has gone up one day after the debate (essentially, one day after the results given by the experts are out).", + "There is some influence, for better or worse, of these experts on the minds of the users in the early debates, but towards the final debates the sentiments of the users are mostly unwavering, as can be seen in Figure FIGREF25. Figure FIGREF25 is on the last Republican debate, and suggests that the opinions of the users do not change much with time. Essentially the users have seen enough debates to make up their own minds and their sentiments are not easily wavered." + ], + [ + "In this section, we define the different evaluation metrics that we use for different tasks. We have two tasks at hand: 1) Sentiment Analysis, 2) Outcome Prediction. We use different metrics for these two tasks." + ], + [ + "In the study of sentiment analysis, we use \u201cHamming Loss\u201d to evaluate the performance of the different methods. Hamming Loss, based on Hamming distance, takes into account the prediction error and the missing error, normalized over the total number of classes and total number of examples BIBREF29. The Hamming Loss is given below:", + "where $|D|$ is the number of examples in the dataset and $|L|$ is the number of labels. $S_i$ and $Y_i$ denote the sets of true and predicted labels for instance $i$ respectively. $\\oplus $ stands for the XOR operation BIBREF30. Intuitively, the performance is better, when the Hamming Loss is smaller. 0 would be the ideal case." + ], + [ + "For the case of outcome prediction, we will have a predicted set and an actual set of results. Thus, we can use common information retrieval metrics to evaluate the prediction performance. Those metrics are listed below:", + "Mean F-measure: F-measure takes into account both the precision and recall of the results. In essence, it takes into account how many of the relevant results are returned and also how many of the returned results are relevant.", + "where $|D|$ is the number of queries (debates/categories for grammy winners etc.), $P_i$ and $R_i$ are the precision and recall for the $i^{th}$ query.", + "Mean Average Precision: As a standard metric used in information retrieval, Mean Average Precision for a set of queries is mean of the average precision scores for each query:", + "where $|D|$ is the number of queries (e.g., debates), $P_i(k)$ is the precision at $k$ ($P@k$) for the $i^{th}$ query, $rel_i(k)$ is an indicator function, equaling 1 if the document at position $k$ for the $i^th$ query is relevant, else 0, and $|RD_i|$ is the number of relevant documents for the $i^{th}$ query." + ], + [ + "We use 3 different datasets for the problem of sentiment analysis, as already mentioned. We test the four different algorithms mentioned in Section SECREF6, with a different combination of features that are described in Section SECREF10. To evaluate our models, we use the \u201cHamming Loss\u201d metric as discussed in Section SECREF6. We use this metric because our problem is in the multi-class classification domain. However, the single-label classifiers like SVM, Naive Bayes, Elman RNN cannot be evaluated against this metric directly. To do this, we split the predicted labels into a format that is consistent with that of multi-label classifiers like RaKel. The results of the experiments for each of the datasets are given in Tables TABREF34, TABREF34 and TABREF34. In the table, $f_1$, $f_2$, $f_3$, $f_4$, $f_5$ and $f_6$ denote the features 1-$gram$, 2-$gram$, $(1+2)$-$gram$, $(1+2)$-$gram + MISC$, $DOC$ and $DOC + TOPIC$ respectively. Note that lower values of hamming losses are more desirable. We find that RaKel performs the best out of all the algorithms. RaKel is more suited for the task because our task is a multi-class classification. Among all the single-label classifiers, SVM performs the best. We also observe that as we use more complex feature spaces, the performance increases. This is true for almost all of the algorithms listed.", + "Our best performing features is a combination of paragraph embedding features and topic features from LDA. This makes sense intuitively because paragraph-embedding takes into account the context in the text. Context is very important in determining the sentiment of a short text: having negative words in the text does not always mean that the text contains a negative sentiment. For example, the sentence \u201cnever say never is not a bad thing\u201d has many negative words; but the sentence as a whole does not have a negative sentiment. This is why we need some kind of context information to accurately determine the sentiment. Thus, with these embedded features, one would be able to better determine the polarity of the sentence. The other label is the entity (candidate/song etc.) in consideration. Topic features here make sense because this can be considered as the topic of the tweet in some sense. This can be done because that label captures what or whom the tweet is about." + ], + [ + "In this section, we show the results for the outcome-prediction of the events. RaKel, as the best performing method, is trained to predict the sentiment-labels for the unlabeled data. The sentiment labels are then used to determine the outcome of the events. In the Tables (TABREF35, TABREF36, TABREF37) of outputs given, we only show as many predictions as there are winners." + ], + [ + "The results obtained for the outcome prediction task for the US presidential debates is shown in Table TABREF35. Table TABREF35 shows the winners as given in the Washington Post (3rd column) and the winners that are predicted by our system (2nd column). By comparing the set of results obtained from both the sources, we find that the set of candidates predicted match to a large extent with the winners given out by the Washington Post. The result suggests that the opinions of the social media community match with that of the journalists in most cases." + ], + [ + "A Grammy Award is given to outstanding achievement in the music industry. There are two types of awards: \u201cGeneral Field\u201d awards, which are not restricted by genre, and genre-specific awards. Since, there can be upto 80 categories of awards, we only focus on the main 4: 1) Album of the Year, 2) Record of the Year, 3) Song of the Year, and 4) Best New Artist. These categories are the main in the awards ceremony and most looked forward to. That is also why we choose to predict the outcomes of these categories based on the tweets. We use the tweets before the ceremony (but after the nominations) to predict the outcomes.", + "Basically, we have a list of nominations for each category. We filter the tweets based on these nominations and then predict the winner as with the case of the debates. The outcomes are listed in Table TABREF36. We see that largely, the opinion of the users on the social network, agree with the deciding committee of the awards. The winners agree for all the categories except \u201cSong of the Year\u201d." + ], + [ + "The Super Bowl is the annual championship game of the National Football League. We have collected the data for the year 2013. Here, the match was between the Baltimore Ravens and the San Francisco 49ers. The tweets that we have collected are during the game. From these tweets, one could trivially determine the winner. But an interesting outcome would be to predict the Most Valuable Player (MVP) during the game. To determine this, all the tweets were looked at and we determined the candidate with the highest positive sentiment by the end of the game. The result in Table TABREF37 suggests that we are able to determine the outcomes accurately.", + "Table TABREF43 displays some evaluation metrics for this task. These were computed based on the predicted outcomes and the actual outcomes for each of the different datasets. Since the number of participants varies from debate-to-debate or category-to-category for Grammy etc., we cannot return a fixed number of winners for everything. So, the size of our returned ranked-list is set to half of the number of participants (except for the MVP for Super Bowl; there are so many players and returning half of them when only one of them is relevant is meaningless. So, we just return the top 10 players). As we can see from the metrics, the predicted outcomes match quite well with the actual ones (or the ones given by the experts)." + ], + [ + "This paper presents a study that compares the opinions of users on microblogs, which is essentially the crowd wisdom, to that of the experts in the field. Specifically, we explore three datasets: US Presidential Debates 2015-16, Grammy Awards 2013, Super Bowl 2013. We determined if the opinions of the crowd and the experts match by using the sentiments of the tweets to predict the outcomes of the debates/Grammys/Super Bowl. We observed that in most of the cases, the predictions were right indicating that crowd wisdom is indeed worth looking at and mining sentiments in microblogs is useful. In some cases where there were disagreements, however, we observed that the opinions of the experts did have some influence on the opinions of the users. We also find that the features that were most useful in our case of multi-label classification was a combination of the document-embedding and topic features." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0270/instruction.md b/qasper-0270/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..c26984bd3adf1adcc136a0afe20794f5c8949004 --- /dev/null +++ b/qasper-0270/instruction.md @@ -0,0 +1,119 @@ +Name of Paper: Learning High-order Structural and Attribute information by Knowledge Graph Attention Networks for Enhancing Knowledge Graph Embedding + +Question: What seven state-of-the-art methods are used for comparison? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Problem Formulation", + "Proposed Model", + "Proposed Model ::: Overall Architecture", + "Proposed Model ::: Attribute Embedding Layer", + "Proposed Model ::: Embedding Propagation Layer", + "Proposed Model ::: Output Layer and Training Details", + "Experiments ::: Date sets", + "Experiments ::: Experiments Setting", + "Experiments ::: Entity Classification ::: Evaluation Protocol.", + "Experiments ::: Entity Classification ::: Test Performance.", + "Experiments ::: Entity Classification ::: Efficiency Evaluation.", + "Experiments ::: Knowledge Graph Completion", + "Conclusion and Future Work" + ], + "paragraphs": [ + [ + "In the past decade, many large-scale Knowledge Graphs (KGs), such as Freebase BIBREF0, DBpedia BIBREF1 and YAGO BIBREF2 have been built to represent human complex knowledge about the real-world in the machine-readable format. The facts in KGs are usually encoded in the form of triples $(\\textit {head entity}, relation, \\textit {tail entity})$ (denoted $(h, r, t)$ in this study) through the Resource Description Framework, e.g.,$(\\textit {Donald Trump}, Born In, \\textit {New York City})$. Figure FIGREF2 shows the subgraph of knowledge graph about the family of Donald Trump. In many KGs, we can observe that some relations indicate attributes of entities, such as the $\\textit {Born}$ and $\\textit {Abstract}$ in Figure FIGREF2, and others indicates the relations between entities (the head entity and tail entity are real world entity). Hence, the relationship in KG can be divided into relations and attributes, and correspondingly two types of triples, namely relation triples and attribute triples BIBREF3. A relation triples in KGs represents relationship between entities, e.g.,$(\\textit {Donald Trump},Father of, \\textit {Ivanka Trump})$, while attribute triples denote a literal attribute value of an entity, e.g.,$(\\textit {Donald Trump},Born, \\textit {\"June 14, 1946\"})$.", + "Knowledge graphs have became important basis for many artificial intelligence applications, such as recommendation system BIBREF4, question answering BIBREF5 and information retrieval BIBREF6, which is attracting growing interests in both academia and industry communities. A common approach to apply KGs in these artificial intelligence applications is through embedding, which provide a simple method to encode both entities and relations into a continuous low-dimensional embedding spaces. Hence, learning distributional representation of knowledge graph has attracted many research attentions in recent years. TransE BIBREF7 is a seminal work in representation learning low-dimensional vectors for both entities and relations. The basic idea behind TransE is that the embedding $\\textbf {t}$ of tail entity should be close to the head entity's embedding $\\textbf {r}$ plus the relation vector $\\textbf {t}$ if $(h, r, t)$ holds, which indicates $\\textbf {h}+\\textbf {r}\\approx \\textbf {t}$. This model provide a flexible way to improve the ability in completing the KGs, such as predicating the missing items in knowledge graph. Since then, several methods like TransH BIBREF8 and TransR BIBREF9, which represent the relational translation in other effective forms, have been proposed. Recent attempts focused on either incorporating extra information beyond KG triples BIBREF10, BIBREF11, BIBREF12, BIBREF13, or designing more complicated strategies BIBREF14, BIBREF15, BIBREF16.", + "While these methods have achieved promising results in KG completion and link predication, existing knowledge graph embedding methods still have room for improvement. First, TransE and its most extensions only take direct relations between entities into consideration. We argue that the high-order structural relationship between entities also contain rich semantic relationships and incorporating these information can improve model performance. For example the fact $\\textit {Donald Trump}\\stackrel{Father of}{\\longrightarrow }\\textit {Ivanka Trump}\\stackrel{Spouse}{\\longrightarrow }\\textit {Jared Kushner} $ indicates the relationship between entity Donald Trump and entity Jared Kushner. Several path-based methods have attempted to take multiple-step relation paths into consideration for learning high-order structural information of KGs BIBREF17, BIBREF18. But note that huge number of paths posed a critical complexity challenge on these methods. In order to enable efficient path modeling, these methods have to make approximations by sampling or applying path selection algorithm. We argue that making approximations has a large impact on the final performance.", + "Second, to the best of our knowledge, most existing knowledge graph embedding methods just leverage relation triples of KGs while ignoring a large number of attribute triples. Therefore, these methods easily suffer from sparseness and incompleteness of knowledge graph. Even worse, structure information usually cannot distinguish the different meanings of relations and entities in different triples. We believe that these rich information encoded in attribute triples can help explore rich semantic information and further improve the performance of knowledge graph. For example, we can learn date of birth and abstraction from values of Born and Abstract about Donald Trump in Figure FIGREF2. There are a huge number of attribute triples in real KGs, for example the statistical results in BIBREF3 shows attribute triples are three times as many as relationship triples in English DBpedia (2016-04). Recent a few attempts try to incorporate attribute triples BIBREF11, BIBREF12. However, these are two limitations existing in these methods. One is that only a part of attribute triples are used in the existing methods, such as only entity description is used in BIBREF12. The other is some attempts try to jointly model the attribute triples and relation triples in one unified optimization problem. The loss of two kinds triples has to be carefully balanced during optimization. For example, BIBREF3 use hyper-parameters to weight the loss of two kinds triples in their models.", + "Considering limitations of existing knowledge graph embedding methods, we believe it is of critical importance to develop a model that can capture both high-order structural and attribute information of KGs in an efficient, explicit and unified manner. Towards this end, inspired by the recent developments of graph convolutional networks (GCN) BIBREF19, which have the potential of achieving the goal but have not been explored much for knowledge graph embedding, we propose Knowledge Graph Attention Networks for Enhancing Knowledge Graph Embedding (KANE). The key ideal of KANE is to aggregate all attribute triples with bias and perform embedding propagation based on relation triples when calculating the representations of given entity. Specifically, two carefully designs are equipped in KANE to correspondingly address the above two challenges: 1) recursive embedding propagation based on relation triples, which updates a entity embedding. Through performing such recursively embedding propagation, the high-order structural information of kGs can be successfully captured in a linear time complexity; and 2) multi-head attention-based aggregation. The weight of each attribute triples can be learned through applying the neural attention mechanism BIBREF20.", + "In experiments, we evaluate our model on two KGs tasks including knowledge graph completion and entity classification. Experimental results on three datasets shows that our method can significantly outperforms state-of-arts methods.", + "The main contributions of this study are as follows:", + "1) We highlight the importance of explicitly modeling the high-order structural and attribution information of KGs to provide better knowledge graph embedding.", + "2) We proposed a new method KANE, which achieves can capture both high-order structural and attribute information of KGs in an efficient, explicit and unified manner under the graph convolutional networks framework.", + "3) We conduct experiments on three datasets, demonstrating the effectiveness of KANE and its interpretability in understanding the importance of high-order relations." + ], + [ + "In recent years, there are many efforts in Knowledge Graph Embeddings for KGs aiming to encode entities and relations into a continuous low-dimensional embedding spaces. Knowledge Graph Embedding provides a very simply and effective methods to apply KGs in various artificial intelligence applications. Hence, Knowledge Graph Embeddings has attracted many research attentions in recent years. The general methodology is to define a score function for the triples and finally learn the representations of entities and relations by minimizing the loss function $f_r(h,t)$, which implies some types of transformations on $\\textbf {h}$ and $\\textbf {t}$. TransE BIBREF7 is a seminal work in knowledge graph embedding, which assumes the embedding $\\textbf {t}$ of tail entity should be close to the head entity's embedding $\\textbf {r}$ plus the relation vector $\\textbf {t}$ when $(h, r, t)$ holds as mentioned in section \u201cIntroduction\". Hence, TransE defines the following loss function:", + "TransE regarding the relation as a translation between head entity and tail entity is inspired by the word2vec BIBREF21, where relationships between words often correspond to translations in latent feature space. This model achieves a good trade-off between computational efficiency and accuracy in KGs with thousands of relations. but this model has flaws in dealing with one-to-many, many-to-one and many-to-many relations.", + "In order to address this issue, TransH BIBREF8 models a relation as a relation-specific hyperplane together with a translation on it, allowing entities to have distinct representation in different relations. TransR BIBREF9 models entities and relations in separate spaces, i.e., entity space and relation spaces, and performs translation from entity spaces to relation spaces. TransD BIBREF22 captures the diversity of relations and entities simultaneously by defining dynamic mapping matrix. Recent attempts can be divided into two categories: (i) those which tries to incorporate additional information to further improve the performance of knowledge graph embedding, e.g., entity types or concepts BIBREF13, relations paths BIBREF17, textual descriptions BIBREF11, BIBREF12 and logical rules BIBREF23; (ii) those which tries to design more complicated strategies, e.g., deep neural network models BIBREF24.", + "Except for TransE and its extensions, some efforts measure plausibility by matching latent semantics of entities and relations. The basic idea behind these models is that the plausible triples of a KG is assigned low energies. For examples, Distant Model BIBREF25 defines two different projections for head and tail entity in a specific relation, i.e., $\\textbf {M}_{r,1}$ and $\\textbf {M}_{r,2}$. It represents the vectors of head and tail entity can be transformed by these two projections. The loss function is $f_r(h,t)=||\\textbf {M}_{r,1}\\textbf {h}-\\textbf {M}_{r,2}\\textbf {t}||_{1}$.", + "Our KANE is conceptually advantageous to existing methods in that: 1) it directly factors high-order relations into the predictive model in linear time which avoids the labor intensive process of materializing paths, thus is more efficient and convenient to use; 2) it directly encodes all attribute triples in learning representation of entities which can capture rich semantic information and further improve the performance of knowledge graph embedding, and 3) KANE can directly factors high-order relations and attribute information into the predictive model in an efficient, explicit and unified manner, thus all related parameters are tailored for optimizing the embedding objective." + ], + [ + "In this study, wo consider two kinds of triples existing in KGs: relation triples and attribute triples. Relation triples denote the relation between entities, while attribute triples describe attributes of entities. Both relation and attribute triples denotes important information about entity, we will take both of them into consideration in the task of learning representation of entities. We let $I $ denote the set of IRIs (Internationalized Resource Identifier), $B $ are the set of blank nodes, and $L $ are the set of literals (denoted by quoted strings). The relation triples and attribute triples can be formalized as follows:", + "Definition 1. Relation and Attribute Triples: A set of Relation triples $ T_{R} $ can be represented by $ T_{R} \\subset E \\times R \\times E $, where $E \\subset I \\cup B $ is set of entities, $R \\subset I$ is set of relations between entities. Similarly, $ T_{A} \\subset E \\times R \\times A $ is the set of attribute triples, where $ A \\subset I \\cup B \\cup L $ is the set of attribute values.", + "Definition 2. Knowledge Graph: A KG consists of a combination of relation triples in the form of $ (h, r, t)\\in T_{R} $, and attribute triples in form of $ (h, r, a)\\in T_{A} $. Formally, we represent a KG as $G=(E,R,A,T_{R},T_{A})$, where $E=\\lbrace h,t|(h,r,t)\\in T_{R} \\cup (h,r,a)\\in T_{A}\\rbrace $ is set of entities, $R =\\lbrace r|(h,r,t)\\in T_{R} \\cup (h,r,a)\\in T_{A}\\rbrace $ is set of relations, $A=\\lbrace a|(h,r,a)\\in T_{A}\\rbrace $, respectively.", + "The purpose of this study is try to use embedding-based model which can capture both high-order structural and attribute information of KGs that assigns a continuous representations for each element of triples in the form $ (\\textbf {h}, \\textbf {r}, \\textbf {t})$ and $ (\\textbf {h}, \\textbf {r}, \\textbf {a})$, where Boldfaced $\\textbf {h}\\in \\mathbb {R}^{k}$, $\\textbf {r}\\in \\mathbb {R}^{k}$, $\\textbf {t}\\in \\mathbb {R}^{k}$ and $\\textbf {a}\\in \\mathbb {R}^{k}$ denote the embedding vector of head entity $h$, relation $r$, tail entity $t$ and attribute $a$ respectively.", + "Next, we detail our proposed model which models both high-order structural and attribute information of KGs in an efficient, explicit and unified manner under the graph convolutional networks framework." + ], + [ + "In this section, we present the proposed model in detail. We first introduce the overall framework of KANE, then discuss the input embedding of entities, relations and values in KGs, the design of embedding propagation layers based on graph attention network and the loss functions for link predication and entity classification task, respectively." + ], + [ + "The process of KANE is illustrated in Figure FIGREF2. We introduce the architecture of KANE from left to right. As shown in Figure FIGREF2, the whole triples of knowledge graph as input. The task of attribute embedding lays is embedding every value in attribute triples into a continuous vector space while preserving the semantic information. To capture both high-order structural information of KGs, we used an attention-based embedding propagation method. This method can recursively propagate the embeddings of entities from an entity's neighbors, and aggregate the neighbors with different weights. The final embedding of entities, relations and values are feed into two different deep neural network for two different tasks including link predication and entity classification." + ], + [ + "The value in attribute triples usually is sentence or a word. To encode the representation of value from its sentence or word, we need to encode the variable-length sentences to a fixed-length vector. In this study, we adopt two different encoders to model the attribute value.", + "Bag-of-Words Encoder. The representation of attribute value can be generated by a summation of all words embeddings of values. We denote the attribute value $a$ as a word sequence $a = w_{1},...,w_{n}$, where $w_{i}$ is the word at position $i$. The embedding of $\\textbf {a}$ can be defined as follows.", + "where $\\textbf {w}_{i}\\in \\mathbb {R}^{k}$ is the word embedding of $w_{i}$.", + "Bag-of-Words Encoder is a simple and intuitive method, which can capture the relative importance of words. But this method suffers in that two strings that contains the same words with different order will have the same representation.", + "LSTM Encoder. In order to overcome the limitation of Bag-of-Word encoder, we consider using LSTM networks to encoder a sequence of words in attribute value into a single vector. The final hidden state of the LSTM networks is selected as a representation of the attribute value.", + "where $f_{lstm}$ is the LSTM network." + ], + [ + "Next we describe the details of recursively embedding propagation method building upon the architecture of graph convolution network. Moreover, by exploiting the idea of graph attention network, out method learn to assign varying levels of importance to entity in every entity's neighborhood and can generate attentive weights of cascaded embedding propagation. In this study, embedding propagation layer consists of two mainly components: attentive embedding propagation and embedding aggregation. Here, we start by describing the attentive embedding propagation.", + "Attentive Embedding Propagation: Considering an KG $G$, the input to our layer is a set of entities, relations and attribute values embedding. We use $\\textbf {h}\\in \\mathbb {R}^{k}$ to denote the embedding of entity $h$. The neighborhood of entity $h$ can be described by $\\mathcal {N}_{h} = \\lbrace t,a|(h,r,t)\\in T_{R} \\cup (h,r,a)\\in T_{A}\\rbrace $. The purpose of attentive embedding propagation is encode $\\mathcal {N}_{h}$ and output a vector $\\vec{\\textbf {h}}$ as the new embedding of entity $h$.", + "In order to obtain sufficient expressive power, one learnable linear transformation $\\textbf {W}\\in \\mathbb {R}^{k^{^{\\prime }} \\times k}$ is adopted to transform the input embeddings into higher level feature space. In this study, we take a triple $(h,r,t)$ as example and the output a vector $\\vec{\\textbf {h}}$ can be formulated as follows:", + "where $\\pi (h,r,t)$ is attention coefficients which indicates the importance of entity's $t$ to entities $h$ .", + "In this study, the attention coefficients also control how many information being propagated from its neighborhood through the relation. To make attention coefficients easily comparable between different entities, the attention coefficient $\\pi (h,r,t)$ can be computed using a softmax function over all the triples connected with $h$. The softmax function can be formulated as follows:", + "Hereafter, we implement the attention coefficients $\\pi (h,r,t)$ through a single-layer feedforward neural network, which is formulated as follows:", + "where the leakyRelu is selected as activation function.", + "As shown in Equation DISPLAY_FORM13, the attention coefficient score is depend on the distance head entity $h$ and the tail entity $t$ plus the relation $r$, which follows the idea behind TransE that the embedding $\\textbf {t}$ of head entity should be close to the tail entity's embedding $\\textbf {r}$ plus the relation vector $\\textbf {t}$ if $(h, r, t)$ holds.", + "Embedding Aggregation. To stabilize the learning process of attention, we perform multi-head attention on final layer. Specifically, we use $m$ attention mechanism to execute the transformation of Equation DISPLAY_FORM11. A aggregators is needed to combine all embeddings of multi-head graph attention layer. In this study, we adapt two types of aggregators:", + "Concatenation Aggregator concatenates all embeddings of multi-head graph attention, followed by a nonlinear transformation:", + "where $\\mathop {\\Big |\\Big |}$ represents concatenation, $ \\pi (h,r,t)^{i}$ are normalized attention coefficient computed by the $i$-th attentive embedding propagation, and $\\textbf {W}^{i}$ denotes the linear transformation of input embedding.", + "Averaging Aggregator sums all embeddings of multi-head graph attention and the output embedding in the final is calculated applying averaging:", + "In order to encode the high-order connectivity information in KGs, we use multiple embedding propagation layers to gathering the deep information propagated from the neighbors. More formally, the embedding of entity $h$ in $l$-th layers can be defined as follows:", + "After performing $L$ embedding propagation layers, we can get the final embedding of entities, relations and attribute values, which include both high-order structural and attribute information of KGs. Next, we discuss the loss functions of KANE for two different tasks and introduce the learning and optimization detail." + ], + [ + "Here, we introduce the learning and optimization details for our method. Two different loss functions are carefully designed fro two different tasks of KG, which include knowledge graph completion and entity classification. Next details of these two loss functions are discussed.", + "knowledge graph completion. This task is a classical task in knowledge graph representation learning community. Specifically, two subtasks are included in knowledge graph completion: entity predication and link predication. Entity predication aims to infer the impossible head/tail entities in testing datasets when one of them is missing, while the link predication focus on complete a triple when relation is missing. In this study, we borrow the idea of translational scoring function from TransE, which the embedding $\\textbf {t}$ of tail entity should be close to the head entity's embedding $\\textbf {r}$ plus the relation vector $\\textbf {t}$ if $(h, r, t)$ holds, which indicates $d(h+r,t)= ||\\textbf {h}+\\textbf {r}- \\textbf {t}||$. Specifically, we train our model using hinge-loss function, given formally as", + "where $\\gamma >0$ is a margin hyper-parameter, $[x ]_{+}$ denotes the positive part of $x$, $T=T_{R} \\cup T_{A}$ is the set of valid triples, and $T^{\\prime }$ is set of corrupted triples which can be formulated as:", + "Entity Classification. For the task of entity classification, we simple uses a fully connected layers and binary cross-entropy loss (BCE) over sigmoid activation on the output of last layer. We minimize the binary cross-entropy on all labeled entities, given formally as:", + "where $E_{D}$ is the set of entities indicates have labels, $C$ is the dimension of the output features, which is equal to the number of classes, $y_{ej}$ is the label indicator of entity $e$ for $j$-th class, and $\\sigma (x)$ is sigmoid function $\\sigma (x) = \\frac{1}{1+e^{-x}}$.", + "We optimize these two loss functions using mini-batch stochastic gradient decent (SGD) over the possible $\\textbf {h}$, $\\textbf {r}$, $\\textbf {t}$, with the chin rule that applying to update all parameters. At each step, we update the parameter $\\textbf {h}^{\\tau +1}\\leftarrow \\textbf {h}^{\\tau }-\\lambda \\nabla _{\\textbf {h}}\\mathcal {L}$, where $\\tau $ labels the iteration step and $\\lambda $ is the learning rate." + ], + [ + "In this study, we evaluate our model on three real KG including two typical large-scale knowledge graph: Freebase BIBREF0, DBpedia BIBREF1 and a self-construction game knowledge graph. First, we adapt a dataset extracted from Freebase, i.e., FB24K, which used by BIBREF26. Then, we collect extra entities and relations that from DBpedia which that they should have at least 100 mentions BIBREF7 and they could link to the entities in the FB24K by the sameAs triples. Finally, we build a datasets named as DBP24K. In addition, we build a game datasets from our game knowledge graph, named as Game30K. The statistics of datasets are listed in Table TABREF24." + ], + [ + "In evaluation, we compare our method with three types of models:", + "1) Typical Methods. Three typical knowledge graph embedding methods includes TransE, TransR and TransH are selected as baselines. For TransE, the dissimilarity measure is implemented with L1-norm, and relation as well as entity are replaced during negative sampling. For TransR, we directly use the source codes released in BIBREF9. In order for better performance, the replacement of relation in negative sampling is utilized according to the suggestion of author.", + "2) Path-based Methods. We compare our method with two typical path-based model include PTransE, and ALL-PATHS BIBREF18. PTransE is the first method to model relation path in KG embedding task, and ALL-PATHS improve the PTransE through a dynamic programming algorithm which can incorporate all relation paths of bounded length.", + "3) Attribute-incorporated Methods. Several state-of-art attribute-incorporated methods including R-GCN BIBREF24 and KR-EAR BIBREF26 are used to compare with our methods on three real datasets.", + "In addition, four variants of KANE which each of which correspondingly defines its specific way of computing the attribute value embedding and embedding aggregation are used as baseline in evaluation. In this study, we name four three variants as KANE (BOW+Concatenation), KANE (BOW+Average), and KANE (LSTM+Concatenation), KANE (LSTM+Average). Our method is learned with mini-batch SGD. As for hyper-parameters, we select batch size among {16, 32, 64, 128}, learning rate $\\lambda $ for SGD among {0.1, 0.01, 0.001}. For a fair comparison, we also set the vector dimensions of all entity and relation to the same $k \\in ${128, 258, 512, 1024}, the same dissimilarity measure $l_{1}$ or $l_{2}$ distance in loss function, and the same number of negative examples $n$ among {1, 10, 20, 40}. The training time on both data sets is limited to at most 400 epochs. The best models are selected by a grid search and early stopping on validation sets." + ], + [ + "In entity classification, the aim is to predicate the type of entity. For all baseline models, we first get the entity embedding in different datasets through default parameter settings as in their original papers or implementations.Then, Logistic Regression is used as classifier, which regards the entity's embeddings as feature of classifier. In evaluation, we random selected 10% of training set as validation set and accuracy as evaluation metric." + ], + [ + "Experimental results of entity classification on the test sets of all the datasets is shown in Table TABREF25. The results is clearly demonstrate that our proposed method significantly outperforms state-of-art results on accuracy for three datasets. For more in-depth performance analysis, we note: (1) Among all baselines, Path-based methods and Attribute-incorporated methods outperform three typical methods. This indicates that incorporating extra information can improve the knowledge graph embedding performance; (2) Four variants of KANE always outperform baseline methods. The main reasons why KANE works well are two fold: 1) KANE can capture high-order structural information of KGs in an efficient, explicit manner and passe these information to their neighboring; 2) KANE leverages rich information encoded in attribute triples. These rich semantic information can further improve the performance of knowledge graph; (3) The variant of KANE that use LSTM Encoder and Concatenation aggregator outperform other variants. The main reasons is that LSTM encoder can distinguish the word order and concatenation aggregator combine all embedding of multi-head attention in a higher leaver feature space, which can obtain sufficient expressive power." + ], + [ + "Figure FIGREF30 shows the test accuracy with increasing epoch on DBP24K and Game30K. We can see that test accuracy first rapidly increased in the first ten iterations, but reaches a stable stages when epoch is larger than 40. Figure FIGREF31 shows test accuracy with different embedding size and training data proportions. We can note that too small embedding size or training data proportions can not generate sufficient global information. In order to further analysis the embeddings learned by our method, we use t-SNE tool BIBREF27 to visualize the learned embedding. Figure FIGREF32 shows the visualization of 256 dimensional entity's embedding on Game30K learned by KANE, R-GCN, PransE and TransE. We observe that our method can learn more discriminative entity's embedding than other other methods." + ], + [ + "The purpose of knowledge graph completion is to complete a triple $(h, r, t)$ when one of $h, r, t$ is missing, which is used many literature BIBREF7. Two measures are considered as our evaluation metrics: (1) the mean rank of correct entities or relations (Mean Rank); (2) the proportion of correct entities or relations ranked in top1 (Hits@1, for relations) or top 10 (Hits@10, for entities). Following the setting in BIBREF7, we also adopt the two evaluation settings named \"raw\" and \"filter\" in order to avoid misleading behavior.", + "The results of entity and relation predication on FB24K are shown in the Table TABREF33. This results indicates that KANE still outperforms other baselines significantly and consistently. This also verifies the necessity of modeling high-order structural and attribute information of KGs in Knowledge graph embedding models." + ], + [ + "Many recent works have demonstrated the benefits of knowledge graph embedding in knowledge graph completion, such as relation extraction. However, We argue that knowledge graph embedding method still have room for improvement. First, TransE and its most extensions only take direct relations between entities into consideration. Second, most existing knowledge graph embedding methods just leverage relation triples of KGs while ignoring a large number of attribute triples. In order to overcome these limitation, inspired by the recent developments of graph convolutional networks, we propose a new knowledge graph embedding methods, named KANE. The key ideal of KANE is to aggregate all attribute triples with bias and perform embedding propagation based on relation triples when calculating the representations of given entity. Empirical results on three datasets show that KANE significantly outperforms seven state-of-arts methods." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0271/instruction.md b/qasper-0271/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..b1a62b395935c5d44adf248465025b1c3a50e5ae --- /dev/null +++ b/qasper-0271/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Learning High-order Structural and Attribute information by Knowledge Graph Attention Networks for Enhancing Knowledge Graph Embedding + +Question: What three datasets are used to measure performance? \ No newline at end of file diff --git a/qasper-0276/instruction.md b/qasper-0276/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..71e08a14c4f2d3cd22be35e77d053536afa9b0e8 --- /dev/null +++ b/qasper-0276/instruction.md @@ -0,0 +1,64 @@ +Name of Paper: A Computational Approach to Automatic Prediction of Drunk Texting + +Question: What baseline model is used? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Motivation", + "Definition and Challenges", + "Dataset Creation", + "Feature Design", + "Evaluation", + "Performance for Datasets 1 and 2", + "Performance for Held-out Dataset H", + "Error Analysis", + "Conclusion & Future Work" + ], + "paragraphs": [ + [ + "The ubiquity of communication devices has made social media highly accessible. The content on these media reflects a user's day-to-day activities. This includes content created under the influence of alcohol. In popular culture, this has been referred to as `drunk-texting'. In this paper, we introduce automatic `drunk-texting prediction' as a computational task. Given a tweet, the goal is to automatically identify if it was written by a drunk user. We refer to tweets written under the influence of alcohol as `drunk tweets', and the opposite as `sober tweets'.", + "A key challenge is to obtain an annotated dataset. We use hashtag-based supervision so that the authors of the tweets mention if they were drunk at the time of posting a tweet. We create three datasets by using different strategies that are related to the use of hashtags. We then present SVM-based classifiers that use N-gram and stylistic features such as capitalisation, spelling errors, etc. Through our experiments, we make subtle points related to: (a) the performance of our features, (b) how our approach compares against human ability to detect drunk-texting, (c) most discriminative stylistic features, and (d) an error analysis that points to future work. To the best of our knowledge, this is a first study that shows the feasibility of text-based analysis for drunk-texting prediction." + ], + [ + "Past studies show the relation between alcohol abuse and unsociable behaviour such as aggression BIBREF0 , crime BIBREF1 , suicide attempts BIBREF2 , drunk driving BIBREF3 , and risky sexual behaviour BIBREF4 . suicide state that \u201cthose responsible for assessing cases of attempted suicide should be adept at detecting alcohol misuse\u201d. Thus, a drunk-texting prediction system can be used to identify individuals susceptible to these behaviours, or for investigative purposes after an incident.", + "Drunk-texting may also cause regret. Mail Goggles prompts a user to solve math questions before sending an email on weekend evenings. Some Android applications avoid drunk-texting by blocking outgoing texts at the click of a button. However, to the best of our knowledge, these tools require a user command to begin blocking. An ongoing text-based analysis will be more helpful, especially since it offers a more natural setting by monitoring stream of social media text and not explicitly seeking user input. Thus, automatic drunk-texting prediction will improve systems aimed to avoid regrettable drunk-texting. To the best of our knowledge, ours is the first study that does a quantitative analysis, in terms of prediction of the drunk state by using textual clues.", + "Several studies have studied linguistic traits associated with emotion expression and mental health issues, suicidal nature, criminal status, etc. BIBREF5 , BIBREF6 . NLP techniques have been used in the past to address social safety and mental health issues BIBREF7 ." + ], + [ + "Drunk-texting prediction is the task of classifying a text as drunk or sober. For example, a tweet `Feeling buzzed. Can't remember how the evening went' must be predicted as `drunk', whereas, `Returned from work late today, the traffic was bad' must be predicted as `sober'. The challenges are:" + ], + [ + "We use hashtag-based supervision to create our datasets, similar to tasks like emotion classification BIBREF8 . The tweets are downloaded using Twitter API (https://dev.twitter.com/). We remove non-Unicode characters, and eliminate tweets that contain hyperlinks and also tweets that are shorter than 6 words in length. Finally, hashtags used to indicate drunk or sober tweets are removed so that they provide labels, but do not act as features. The dataset is available on request. As a result, we create three datasets, each using a different strategy for sober tweets, as follows:", + "The drunk tweets for Datasets 1 and 2 are the same. Figure FIGREF9 shows a word-cloud for these drunk tweets (with stop words and forms of the word `drunk' removed), created using WordItOut. The size of a word indicates its frequency. In addition to topical words such as `bar', `bottle' and `wine', the word-cloud shows sentiment words such as `love' or `damn', along with profane words.", + "Heuristics other than these hashtags could have been used for dataset creation. For example, timestamps were a good option to account for time at which a tweet was posted. However, this could not be used because user's local times was not available, since very few users had geolocation enabled." + ], + [ + "The complete set of features is shown in Table TABREF7 . There are two sets of features: (a) N-gram features, and (b) Stylistic features. We use unigrams and bigrams as N-gram features- considering both presence and count.", + "Table TABREF7 shows the complete set of stylistic features of our prediction system. POS ratios are a set of features that record the proportion of each POS tag in the dataset (for example, the proportion of nouns/adjectives, etc.). The POS tags and named entity mentions are obtained from NLTK BIBREF9 . Discourse connectors are identified based on a manually created list. Spelling errors are identified using a spell checker by enchant. The repeated characters feature captures a situation in which a word contains a letter that is repeated three or more times, as in the case of happpy. Since drunk-texting is often associated with emotional expression, we also incorporate a set of sentiment-based features. These features include: count/presence of emoticons and sentiment ratio. Sentiment ratio is the proportion of positive and negative words in the tweet. To determine positive and negative words, we use the sentiment lexicon in mpqa. To identify a more refined set of words that correspond to the two classes, we also estimated 20 topics for the dataset by estimating an LDA model BIBREF10 . We then consider top 10 words per topic, for both classes. This results in 400 LDA-specific unigrams that are then used as features." + ], + [ + "Using the two sets of features, we train SVM classifiers BIBREF11 . We show the five-fold cross-validation performance of our features on Datasets 1 and 2, in Section SECREF17 , and on Dataset H in Section SECREF21 . Section SECREF22 presents an error analysis. Accuracy, positive/negative precision and positive/negative recall are shown as A, PP/NP and PR/NR respectively. `Drunk' forms the positive class, while `Sober' forms the negative class." + ], + [ + "Table TABREF14 shows the performance for five-fold cross-validation for Datasets 1 and 2. In case of Dataset 1, we observe that N-gram features achieve an accuracy of 85.5%. We see that our stylistic features alone exhibit degraded performance, with an accuracy of 75.6%, in the case of Dataset 1. Table TABREF16 shows top stylistic features, when trained on the two datasets. Spelling errors, POS ratios for nouns (POS_NOUN), length and sentiment ratios appear in both lists, in addition to LDA-based unigrams. However, negative recall reduces to a mere 3.2%. This degradation implies that our features capture a subset of drunk tweets and that there are properties of drunk tweets that may be more subtle. When both N-gram and stylistic features are used, there is negligible improvement. The accuracy for Dataset 2 increases from 77.9% to 78.1%. Precision/Recall metrics do not change significantly either. The best accuracy of our classifier is 78.1% for all features, and 75.6% for stylistic features. This shows that text-based clues can indeed be used for drunk-texting prediction." + ], + [ + "Using held-out dataset H, we evaluate how our system performs in comparison to humans. Three annotators, A1-A3, mark each tweet in the Dataset H as drunk or sober. Table TABREF19 shows a moderate agreement between our annotators (for example, it is 0.42 for A1 and A2). Table TABREF20 compares our classifier with humans. Our human annotators perform the task with an average accuracy of 68.8%, while our classifier (with all features) trained on Dataset 2 reaches 64%. The classifier trained on Dataset 2 is better than which is trained on Dataset 1." + ], + [ + "Some categories of errors that occur are:", + "Incorrect hashtag supervision: The tweet `Can't believe I lost my bag last night, literally had everything in! Thanks god the bar man found it' was marked with`#Drunk'. However, this tweet is not likely to be a drunk tweet, but describes a drunk episode in retrospective. Our classifier predicts it as sober.", + "Seemingly sober tweets: Human annotators as well as our classifier could not identify whether `Will you take her on a date? But really she does like you' was drunk, although the author of the tweet had marked it so. This example also highlights the difficulty of drunk-texting prediction.", + "Pragmatic difficulty: The tweet `National dress of Ireland is one's one vomit.. my family is lovely' was correctly identified by our human annotators as a drunk tweet. This tweet contains an element of humour and topic change, but our classifier could not capture it." + ], + [ + "In this paper, we introduce automatic drunk-texting prediction as the task of predicting a tweet as drunk or sober. First, we justify the need for drunk-texting prediction as means of identifying risky social behavior arising out of alcohol abuse, and the need to build tools that avoid privacy leaks due to drunk-texting. We then highlight the challenges of drunk-texting prediction: one of the challenges is selection of negative examples (sober tweets). Using hashtag-based supervision, we create three datasets annotated with drunk or sober labels. We then present SVM-based classifiers which use two sets of features: N-gram and stylistic features. Our drunk prediction system obtains a best accuracy of 78.1%. We observe that our stylistic features add negligible value to N-gram features. We use our heldout dataset to compare how our system performs against human annotators. While human annotators achieve an accuracy of 68.8%, our system reaches reasonably close and performs with a best accuracy of 64%.", + "Our analysis of the task and experimental findings make a case for drunk-texting prediction as a useful and feasible NLP application." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0277/instruction.md b/qasper-0277/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..0b0c782722d1d42544e864a4f06be92e3d8bbd0b --- /dev/null +++ b/qasper-0277/instruction.md @@ -0,0 +1,64 @@ +Name of Paper: A Computational Approach to Automatic Prediction of Drunk Texting + +Question: What stylistic features are used to detect drunk texts? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Motivation", + "Definition and Challenges", + "Dataset Creation", + "Feature Design", + "Evaluation", + "Performance for Datasets 1 and 2", + "Performance for Held-out Dataset H", + "Error Analysis", + "Conclusion & Future Work" + ], + "paragraphs": [ + [ + "The ubiquity of communication devices has made social media highly accessible. The content on these media reflects a user's day-to-day activities. This includes content created under the influence of alcohol. In popular culture, this has been referred to as `drunk-texting'. In this paper, we introduce automatic `drunk-texting prediction' as a computational task. Given a tweet, the goal is to automatically identify if it was written by a drunk user. We refer to tweets written under the influence of alcohol as `drunk tweets', and the opposite as `sober tweets'.", + "A key challenge is to obtain an annotated dataset. We use hashtag-based supervision so that the authors of the tweets mention if they were drunk at the time of posting a tweet. We create three datasets by using different strategies that are related to the use of hashtags. We then present SVM-based classifiers that use N-gram and stylistic features such as capitalisation, spelling errors, etc. Through our experiments, we make subtle points related to: (a) the performance of our features, (b) how our approach compares against human ability to detect drunk-texting, (c) most discriminative stylistic features, and (d) an error analysis that points to future work. To the best of our knowledge, this is a first study that shows the feasibility of text-based analysis for drunk-texting prediction." + ], + [ + "Past studies show the relation between alcohol abuse and unsociable behaviour such as aggression BIBREF0 , crime BIBREF1 , suicide attempts BIBREF2 , drunk driving BIBREF3 , and risky sexual behaviour BIBREF4 . suicide state that \u201cthose responsible for assessing cases of attempted suicide should be adept at detecting alcohol misuse\u201d. Thus, a drunk-texting prediction system can be used to identify individuals susceptible to these behaviours, or for investigative purposes after an incident.", + "Drunk-texting may also cause regret. Mail Goggles prompts a user to solve math questions before sending an email on weekend evenings. Some Android applications avoid drunk-texting by blocking outgoing texts at the click of a button. However, to the best of our knowledge, these tools require a user command to begin blocking. An ongoing text-based analysis will be more helpful, especially since it offers a more natural setting by monitoring stream of social media text and not explicitly seeking user input. Thus, automatic drunk-texting prediction will improve systems aimed to avoid regrettable drunk-texting. To the best of our knowledge, ours is the first study that does a quantitative analysis, in terms of prediction of the drunk state by using textual clues.", + "Several studies have studied linguistic traits associated with emotion expression and mental health issues, suicidal nature, criminal status, etc. BIBREF5 , BIBREF6 . NLP techniques have been used in the past to address social safety and mental health issues BIBREF7 ." + ], + [ + "Drunk-texting prediction is the task of classifying a text as drunk or sober. For example, a tweet `Feeling buzzed. Can't remember how the evening went' must be predicted as `drunk', whereas, `Returned from work late today, the traffic was bad' must be predicted as `sober'. The challenges are:" + ], + [ + "We use hashtag-based supervision to create our datasets, similar to tasks like emotion classification BIBREF8 . The tweets are downloaded using Twitter API (https://dev.twitter.com/). We remove non-Unicode characters, and eliminate tweets that contain hyperlinks and also tweets that are shorter than 6 words in length. Finally, hashtags used to indicate drunk or sober tweets are removed so that they provide labels, but do not act as features. The dataset is available on request. As a result, we create three datasets, each using a different strategy for sober tweets, as follows:", + "The drunk tweets for Datasets 1 and 2 are the same. Figure FIGREF9 shows a word-cloud for these drunk tweets (with stop words and forms of the word `drunk' removed), created using WordItOut. The size of a word indicates its frequency. In addition to topical words such as `bar', `bottle' and `wine', the word-cloud shows sentiment words such as `love' or `damn', along with profane words.", + "Heuristics other than these hashtags could have been used for dataset creation. For example, timestamps were a good option to account for time at which a tweet was posted. However, this could not be used because user's local times was not available, since very few users had geolocation enabled." + ], + [ + "The complete set of features is shown in Table TABREF7 . There are two sets of features: (a) N-gram features, and (b) Stylistic features. We use unigrams and bigrams as N-gram features- considering both presence and count.", + "Table TABREF7 shows the complete set of stylistic features of our prediction system. POS ratios are a set of features that record the proportion of each POS tag in the dataset (for example, the proportion of nouns/adjectives, etc.). The POS tags and named entity mentions are obtained from NLTK BIBREF9 . Discourse connectors are identified based on a manually created list. Spelling errors are identified using a spell checker by enchant. The repeated characters feature captures a situation in which a word contains a letter that is repeated three or more times, as in the case of happpy. Since drunk-texting is often associated with emotional expression, we also incorporate a set of sentiment-based features. These features include: count/presence of emoticons and sentiment ratio. Sentiment ratio is the proportion of positive and negative words in the tweet. To determine positive and negative words, we use the sentiment lexicon in mpqa. To identify a more refined set of words that correspond to the two classes, we also estimated 20 topics for the dataset by estimating an LDA model BIBREF10 . We then consider top 10 words per topic, for both classes. This results in 400 LDA-specific unigrams that are then used as features." + ], + [ + "Using the two sets of features, we train SVM classifiers BIBREF11 . We show the five-fold cross-validation performance of our features on Datasets 1 and 2, in Section SECREF17 , and on Dataset H in Section SECREF21 . Section SECREF22 presents an error analysis. Accuracy, positive/negative precision and positive/negative recall are shown as A, PP/NP and PR/NR respectively. `Drunk' forms the positive class, while `Sober' forms the negative class." + ], + [ + "Table TABREF14 shows the performance for five-fold cross-validation for Datasets 1 and 2. In case of Dataset 1, we observe that N-gram features achieve an accuracy of 85.5%. We see that our stylistic features alone exhibit degraded performance, with an accuracy of 75.6%, in the case of Dataset 1. Table TABREF16 shows top stylistic features, when trained on the two datasets. Spelling errors, POS ratios for nouns (POS_NOUN), length and sentiment ratios appear in both lists, in addition to LDA-based unigrams. However, negative recall reduces to a mere 3.2%. This degradation implies that our features capture a subset of drunk tweets and that there are properties of drunk tweets that may be more subtle. When both N-gram and stylistic features are used, there is negligible improvement. The accuracy for Dataset 2 increases from 77.9% to 78.1%. Precision/Recall metrics do not change significantly either. The best accuracy of our classifier is 78.1% for all features, and 75.6% for stylistic features. This shows that text-based clues can indeed be used for drunk-texting prediction." + ], + [ + "Using held-out dataset H, we evaluate how our system performs in comparison to humans. Three annotators, A1-A3, mark each tweet in the Dataset H as drunk or sober. Table TABREF19 shows a moderate agreement between our annotators (for example, it is 0.42 for A1 and A2). Table TABREF20 compares our classifier with humans. Our human annotators perform the task with an average accuracy of 68.8%, while our classifier (with all features) trained on Dataset 2 reaches 64%. The classifier trained on Dataset 2 is better than which is trained on Dataset 1." + ], + [ + "Some categories of errors that occur are:", + "Incorrect hashtag supervision: The tweet `Can't believe I lost my bag last night, literally had everything in! Thanks god the bar man found it' was marked with`#Drunk'. However, this tweet is not likely to be a drunk tweet, but describes a drunk episode in retrospective. Our classifier predicts it as sober.", + "Seemingly sober tweets: Human annotators as well as our classifier could not identify whether `Will you take her on a date? But really she does like you' was drunk, although the author of the tweet had marked it so. This example also highlights the difficulty of drunk-texting prediction.", + "Pragmatic difficulty: The tweet `National dress of Ireland is one's one vomit.. my family is lovely' was correctly identified by our human annotators as a drunk tweet. This tweet contains an element of humour and topic change, but our classifier could not capture it." + ], + [ + "In this paper, we introduce automatic drunk-texting prediction as the task of predicting a tweet as drunk or sober. First, we justify the need for drunk-texting prediction as means of identifying risky social behavior arising out of alcohol abuse, and the need to build tools that avoid privacy leaks due to drunk-texting. We then highlight the challenges of drunk-texting prediction: one of the challenges is selection of negative examples (sober tweets). Using hashtag-based supervision, we create three datasets annotated with drunk or sober labels. We then present SVM-based classifiers which use two sets of features: N-gram and stylistic features. Our drunk prediction system obtains a best accuracy of 78.1%. We observe that our stylistic features add negligible value to N-gram features. We use our heldout dataset to compare how our system performs against human annotators. While human annotators achieve an accuracy of 68.8%, our system reaches reasonably close and performs with a best accuracy of 64%.", + "Our analysis of the task and experimental findings make a case for drunk-texting prediction as a useful and feasible NLP application." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0278/instruction.md b/qasper-0278/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..0050790c1db4fcaaa5a795bcd1a07588f31e322e --- /dev/null +++ b/qasper-0278/instruction.md @@ -0,0 +1,64 @@ +Name of Paper: A Computational Approach to Automatic Prediction of Drunk Texting + +Question: Is the data acquired under distant supervision verified by humans at any stage? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Motivation", + "Definition and Challenges", + "Dataset Creation", + "Feature Design", + "Evaluation", + "Performance for Datasets 1 and 2", + "Performance for Held-out Dataset H", + "Error Analysis", + "Conclusion & Future Work" + ], + "paragraphs": [ + [ + "The ubiquity of communication devices has made social media highly accessible. The content on these media reflects a user's day-to-day activities. This includes content created under the influence of alcohol. In popular culture, this has been referred to as `drunk-texting'. In this paper, we introduce automatic `drunk-texting prediction' as a computational task. Given a tweet, the goal is to automatically identify if it was written by a drunk user. We refer to tweets written under the influence of alcohol as `drunk tweets', and the opposite as `sober tweets'.", + "A key challenge is to obtain an annotated dataset. We use hashtag-based supervision so that the authors of the tweets mention if they were drunk at the time of posting a tweet. We create three datasets by using different strategies that are related to the use of hashtags. We then present SVM-based classifiers that use N-gram and stylistic features such as capitalisation, spelling errors, etc. Through our experiments, we make subtle points related to: (a) the performance of our features, (b) how our approach compares against human ability to detect drunk-texting, (c) most discriminative stylistic features, and (d) an error analysis that points to future work. To the best of our knowledge, this is a first study that shows the feasibility of text-based analysis for drunk-texting prediction." + ], + [ + "Past studies show the relation between alcohol abuse and unsociable behaviour such as aggression BIBREF0 , crime BIBREF1 , suicide attempts BIBREF2 , drunk driving BIBREF3 , and risky sexual behaviour BIBREF4 . suicide state that \u201cthose responsible for assessing cases of attempted suicide should be adept at detecting alcohol misuse\u201d. Thus, a drunk-texting prediction system can be used to identify individuals susceptible to these behaviours, or for investigative purposes after an incident.", + "Drunk-texting may also cause regret. Mail Goggles prompts a user to solve math questions before sending an email on weekend evenings. Some Android applications avoid drunk-texting by blocking outgoing texts at the click of a button. However, to the best of our knowledge, these tools require a user command to begin blocking. An ongoing text-based analysis will be more helpful, especially since it offers a more natural setting by monitoring stream of social media text and not explicitly seeking user input. Thus, automatic drunk-texting prediction will improve systems aimed to avoid regrettable drunk-texting. To the best of our knowledge, ours is the first study that does a quantitative analysis, in terms of prediction of the drunk state by using textual clues.", + "Several studies have studied linguistic traits associated with emotion expression and mental health issues, suicidal nature, criminal status, etc. BIBREF5 , BIBREF6 . NLP techniques have been used in the past to address social safety and mental health issues BIBREF7 ." + ], + [ + "Drunk-texting prediction is the task of classifying a text as drunk or sober. For example, a tweet `Feeling buzzed. Can't remember how the evening went' must be predicted as `drunk', whereas, `Returned from work late today, the traffic was bad' must be predicted as `sober'. The challenges are:" + ], + [ + "We use hashtag-based supervision to create our datasets, similar to tasks like emotion classification BIBREF8 . The tweets are downloaded using Twitter API (https://dev.twitter.com/). We remove non-Unicode characters, and eliminate tweets that contain hyperlinks and also tweets that are shorter than 6 words in length. Finally, hashtags used to indicate drunk or sober tweets are removed so that they provide labels, but do not act as features. The dataset is available on request. As a result, we create three datasets, each using a different strategy for sober tweets, as follows:", + "The drunk tweets for Datasets 1 and 2 are the same. Figure FIGREF9 shows a word-cloud for these drunk tweets (with stop words and forms of the word `drunk' removed), created using WordItOut. The size of a word indicates its frequency. In addition to topical words such as `bar', `bottle' and `wine', the word-cloud shows sentiment words such as `love' or `damn', along with profane words.", + "Heuristics other than these hashtags could have been used for dataset creation. For example, timestamps were a good option to account for time at which a tweet was posted. However, this could not be used because user's local times was not available, since very few users had geolocation enabled." + ], + [ + "The complete set of features is shown in Table TABREF7 . There are two sets of features: (a) N-gram features, and (b) Stylistic features. We use unigrams and bigrams as N-gram features- considering both presence and count.", + "Table TABREF7 shows the complete set of stylistic features of our prediction system. POS ratios are a set of features that record the proportion of each POS tag in the dataset (for example, the proportion of nouns/adjectives, etc.). The POS tags and named entity mentions are obtained from NLTK BIBREF9 . Discourse connectors are identified based on a manually created list. Spelling errors are identified using a spell checker by enchant. The repeated characters feature captures a situation in which a word contains a letter that is repeated three or more times, as in the case of happpy. Since drunk-texting is often associated with emotional expression, we also incorporate a set of sentiment-based features. These features include: count/presence of emoticons and sentiment ratio. Sentiment ratio is the proportion of positive and negative words in the tweet. To determine positive and negative words, we use the sentiment lexicon in mpqa. To identify a more refined set of words that correspond to the two classes, we also estimated 20 topics for the dataset by estimating an LDA model BIBREF10 . We then consider top 10 words per topic, for both classes. This results in 400 LDA-specific unigrams that are then used as features." + ], + [ + "Using the two sets of features, we train SVM classifiers BIBREF11 . We show the five-fold cross-validation performance of our features on Datasets 1 and 2, in Section SECREF17 , and on Dataset H in Section SECREF21 . Section SECREF22 presents an error analysis. Accuracy, positive/negative precision and positive/negative recall are shown as A, PP/NP and PR/NR respectively. `Drunk' forms the positive class, while `Sober' forms the negative class." + ], + [ + "Table TABREF14 shows the performance for five-fold cross-validation for Datasets 1 and 2. In case of Dataset 1, we observe that N-gram features achieve an accuracy of 85.5%. We see that our stylistic features alone exhibit degraded performance, with an accuracy of 75.6%, in the case of Dataset 1. Table TABREF16 shows top stylistic features, when trained on the two datasets. Spelling errors, POS ratios for nouns (POS_NOUN), length and sentiment ratios appear in both lists, in addition to LDA-based unigrams. However, negative recall reduces to a mere 3.2%. This degradation implies that our features capture a subset of drunk tweets and that there are properties of drunk tweets that may be more subtle. When both N-gram and stylistic features are used, there is negligible improvement. The accuracy for Dataset 2 increases from 77.9% to 78.1%. Precision/Recall metrics do not change significantly either. The best accuracy of our classifier is 78.1% for all features, and 75.6% for stylistic features. This shows that text-based clues can indeed be used for drunk-texting prediction." + ], + [ + "Using held-out dataset H, we evaluate how our system performs in comparison to humans. Three annotators, A1-A3, mark each tweet in the Dataset H as drunk or sober. Table TABREF19 shows a moderate agreement between our annotators (for example, it is 0.42 for A1 and A2). Table TABREF20 compares our classifier with humans. Our human annotators perform the task with an average accuracy of 68.8%, while our classifier (with all features) trained on Dataset 2 reaches 64%. The classifier trained on Dataset 2 is better than which is trained on Dataset 1." + ], + [ + "Some categories of errors that occur are:", + "Incorrect hashtag supervision: The tweet `Can't believe I lost my bag last night, literally had everything in! Thanks god the bar man found it' was marked with`#Drunk'. However, this tweet is not likely to be a drunk tweet, but describes a drunk episode in retrospective. Our classifier predicts it as sober.", + "Seemingly sober tweets: Human annotators as well as our classifier could not identify whether `Will you take her on a date? But really she does like you' was drunk, although the author of the tweet had marked it so. This example also highlights the difficulty of drunk-texting prediction.", + "Pragmatic difficulty: The tweet `National dress of Ireland is one's one vomit.. my family is lovely' was correctly identified by our human annotators as a drunk tweet. This tweet contains an element of humour and topic change, but our classifier could not capture it." + ], + [ + "In this paper, we introduce automatic drunk-texting prediction as the task of predicting a tweet as drunk or sober. First, we justify the need for drunk-texting prediction as means of identifying risky social behavior arising out of alcohol abuse, and the need to build tools that avoid privacy leaks due to drunk-texting. We then highlight the challenges of drunk-texting prediction: one of the challenges is selection of negative examples (sober tweets). Using hashtag-based supervision, we create three datasets annotated with drunk or sober labels. We then present SVM-based classifiers which use two sets of features: N-gram and stylistic features. Our drunk prediction system obtains a best accuracy of 78.1%. We observe that our stylistic features add negligible value to N-gram features. We use our heldout dataset to compare how our system performs against human annotators. While human annotators achieve an accuracy of 68.8%, our system reaches reasonably close and performs with a best accuracy of 64%.", + "Our analysis of the task and experimental findings make a case for drunk-texting prediction as a useful and feasible NLP application." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0279/instruction.md b/qasper-0279/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..4377a01f34477db429676011182fd8c0e5a52339 --- /dev/null +++ b/qasper-0279/instruction.md @@ -0,0 +1,64 @@ +Name of Paper: A Computational Approach to Automatic Prediction of Drunk Texting + +Question: What hashtags are used for distant supervision? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Motivation", + "Definition and Challenges", + "Dataset Creation", + "Feature Design", + "Evaluation", + "Performance for Datasets 1 and 2", + "Performance for Held-out Dataset H", + "Error Analysis", + "Conclusion & Future Work" + ], + "paragraphs": [ + [ + "The ubiquity of communication devices has made social media highly accessible. The content on these media reflects a user's day-to-day activities. This includes content created under the influence of alcohol. In popular culture, this has been referred to as `drunk-texting'. In this paper, we introduce automatic `drunk-texting prediction' as a computational task. Given a tweet, the goal is to automatically identify if it was written by a drunk user. We refer to tweets written under the influence of alcohol as `drunk tweets', and the opposite as `sober tweets'.", + "A key challenge is to obtain an annotated dataset. We use hashtag-based supervision so that the authors of the tweets mention if they were drunk at the time of posting a tweet. We create three datasets by using different strategies that are related to the use of hashtags. We then present SVM-based classifiers that use N-gram and stylistic features such as capitalisation, spelling errors, etc. Through our experiments, we make subtle points related to: (a) the performance of our features, (b) how our approach compares against human ability to detect drunk-texting, (c) most discriminative stylistic features, and (d) an error analysis that points to future work. To the best of our knowledge, this is a first study that shows the feasibility of text-based analysis for drunk-texting prediction." + ], + [ + "Past studies show the relation between alcohol abuse and unsociable behaviour such as aggression BIBREF0 , crime BIBREF1 , suicide attempts BIBREF2 , drunk driving BIBREF3 , and risky sexual behaviour BIBREF4 . suicide state that \u201cthose responsible for assessing cases of attempted suicide should be adept at detecting alcohol misuse\u201d. Thus, a drunk-texting prediction system can be used to identify individuals susceptible to these behaviours, or for investigative purposes after an incident.", + "Drunk-texting may also cause regret. Mail Goggles prompts a user to solve math questions before sending an email on weekend evenings. Some Android applications avoid drunk-texting by blocking outgoing texts at the click of a button. However, to the best of our knowledge, these tools require a user command to begin blocking. An ongoing text-based analysis will be more helpful, especially since it offers a more natural setting by monitoring stream of social media text and not explicitly seeking user input. Thus, automatic drunk-texting prediction will improve systems aimed to avoid regrettable drunk-texting. To the best of our knowledge, ours is the first study that does a quantitative analysis, in terms of prediction of the drunk state by using textual clues.", + "Several studies have studied linguistic traits associated with emotion expression and mental health issues, suicidal nature, criminal status, etc. BIBREF5 , BIBREF6 . NLP techniques have been used in the past to address social safety and mental health issues BIBREF7 ." + ], + [ + "Drunk-texting prediction is the task of classifying a text as drunk or sober. For example, a tweet `Feeling buzzed. Can't remember how the evening went' must be predicted as `drunk', whereas, `Returned from work late today, the traffic was bad' must be predicted as `sober'. The challenges are:" + ], + [ + "We use hashtag-based supervision to create our datasets, similar to tasks like emotion classification BIBREF8 . The tweets are downloaded using Twitter API (https://dev.twitter.com/). We remove non-Unicode characters, and eliminate tweets that contain hyperlinks and also tweets that are shorter than 6 words in length. Finally, hashtags used to indicate drunk or sober tweets are removed so that they provide labels, but do not act as features. The dataset is available on request. As a result, we create three datasets, each using a different strategy for sober tweets, as follows:", + "The drunk tweets for Datasets 1 and 2 are the same. Figure FIGREF9 shows a word-cloud for these drunk tweets (with stop words and forms of the word `drunk' removed), created using WordItOut. The size of a word indicates its frequency. In addition to topical words such as `bar', `bottle' and `wine', the word-cloud shows sentiment words such as `love' or `damn', along with profane words.", + "Heuristics other than these hashtags could have been used for dataset creation. For example, timestamps were a good option to account for time at which a tweet was posted. However, this could not be used because user's local times was not available, since very few users had geolocation enabled." + ], + [ + "The complete set of features is shown in Table TABREF7 . There are two sets of features: (a) N-gram features, and (b) Stylistic features. We use unigrams and bigrams as N-gram features- considering both presence and count.", + "Table TABREF7 shows the complete set of stylistic features of our prediction system. POS ratios are a set of features that record the proportion of each POS tag in the dataset (for example, the proportion of nouns/adjectives, etc.). The POS tags and named entity mentions are obtained from NLTK BIBREF9 . Discourse connectors are identified based on a manually created list. Spelling errors are identified using a spell checker by enchant. The repeated characters feature captures a situation in which a word contains a letter that is repeated three or more times, as in the case of happpy. Since drunk-texting is often associated with emotional expression, we also incorporate a set of sentiment-based features. These features include: count/presence of emoticons and sentiment ratio. Sentiment ratio is the proportion of positive and negative words in the tweet. To determine positive and negative words, we use the sentiment lexicon in mpqa. To identify a more refined set of words that correspond to the two classes, we also estimated 20 topics for the dataset by estimating an LDA model BIBREF10 . We then consider top 10 words per topic, for both classes. This results in 400 LDA-specific unigrams that are then used as features." + ], + [ + "Using the two sets of features, we train SVM classifiers BIBREF11 . We show the five-fold cross-validation performance of our features on Datasets 1 and 2, in Section SECREF17 , and on Dataset H in Section SECREF21 . Section SECREF22 presents an error analysis. Accuracy, positive/negative precision and positive/negative recall are shown as A, PP/NP and PR/NR respectively. `Drunk' forms the positive class, while `Sober' forms the negative class." + ], + [ + "Table TABREF14 shows the performance for five-fold cross-validation for Datasets 1 and 2. In case of Dataset 1, we observe that N-gram features achieve an accuracy of 85.5%. We see that our stylistic features alone exhibit degraded performance, with an accuracy of 75.6%, in the case of Dataset 1. Table TABREF16 shows top stylistic features, when trained on the two datasets. Spelling errors, POS ratios for nouns (POS_NOUN), length and sentiment ratios appear in both lists, in addition to LDA-based unigrams. However, negative recall reduces to a mere 3.2%. This degradation implies that our features capture a subset of drunk tweets and that there are properties of drunk tweets that may be more subtle. When both N-gram and stylistic features are used, there is negligible improvement. The accuracy for Dataset 2 increases from 77.9% to 78.1%. Precision/Recall metrics do not change significantly either. The best accuracy of our classifier is 78.1% for all features, and 75.6% for stylistic features. This shows that text-based clues can indeed be used for drunk-texting prediction." + ], + [ + "Using held-out dataset H, we evaluate how our system performs in comparison to humans. Three annotators, A1-A3, mark each tweet in the Dataset H as drunk or sober. Table TABREF19 shows a moderate agreement between our annotators (for example, it is 0.42 for A1 and A2). Table TABREF20 compares our classifier with humans. Our human annotators perform the task with an average accuracy of 68.8%, while our classifier (with all features) trained on Dataset 2 reaches 64%. The classifier trained on Dataset 2 is better than which is trained on Dataset 1." + ], + [ + "Some categories of errors that occur are:", + "Incorrect hashtag supervision: The tweet `Can't believe I lost my bag last night, literally had everything in! Thanks god the bar man found it' was marked with`#Drunk'. However, this tweet is not likely to be a drunk tweet, but describes a drunk episode in retrospective. Our classifier predicts it as sober.", + "Seemingly sober tweets: Human annotators as well as our classifier could not identify whether `Will you take her on a date? But really she does like you' was drunk, although the author of the tweet had marked it so. This example also highlights the difficulty of drunk-texting prediction.", + "Pragmatic difficulty: The tweet `National dress of Ireland is one's one vomit.. my family is lovely' was correctly identified by our human annotators as a drunk tweet. This tweet contains an element of humour and topic change, but our classifier could not capture it." + ], + [ + "In this paper, we introduce automatic drunk-texting prediction as the task of predicting a tweet as drunk or sober. First, we justify the need for drunk-texting prediction as means of identifying risky social behavior arising out of alcohol abuse, and the need to build tools that avoid privacy leaks due to drunk-texting. We then highlight the challenges of drunk-texting prediction: one of the challenges is selection of negative examples (sober tweets). Using hashtag-based supervision, we create three datasets annotated with drunk or sober labels. We then present SVM-based classifiers which use two sets of features: N-gram and stylistic features. Our drunk prediction system obtains a best accuracy of 78.1%. We observe that our stylistic features add negligible value to N-gram features. We use our heldout dataset to compare how our system performs against human annotators. While human annotators achieve an accuracy of 68.8%, our system reaches reasonably close and performs with a best accuracy of 64%.", + "Our analysis of the task and experimental findings make a case for drunk-texting prediction as a useful and feasible NLP application." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0282/instruction.md b/qasper-0282/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..05b61061d963f5692742baec6fe33bea7820a284 --- /dev/null +++ b/qasper-0282/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Answering Complex Questions Using Open Information Extraction + +Question: What is the accuracy of the proposed technique? \ No newline at end of file diff --git a/qasper-0283/instruction.md b/qasper-0283/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..c1434c25831d0dcb4a482b3dcc7b8b5ffdbe2ee2 --- /dev/null +++ b/qasper-0283/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Answering Complex Questions Using Open Information Extraction + +Question: Is an entity linking process used? \ No newline at end of file diff --git a/qasper-0284/instruction.md b/qasper-0284/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..01cd7661aa417f4fd6ffa840588172ce286ee536 --- /dev/null +++ b/qasper-0284/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Answering Complex Questions Using Open Information Extraction + +Question: Are the OpenIE extractions all triples? \ No newline at end of file diff --git a/qasper-0285/instruction.md b/qasper-0285/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..21fff54715296cfb94f9d9711795f582edb71c57 --- /dev/null +++ b/qasper-0285/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Answering Complex Questions Using Open Information Extraction + +Question: What method was used to generate the OpenIE extractions? \ No newline at end of file diff --git a/qasper-0297/instruction.md b/qasper-0297/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..6e811d25436be190c3eb05897be270d8646c0c8d --- /dev/null +++ b/qasper-0297/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Error Analysis for Vietnamese Named Entity Recognition on Deep Neural Network Models + +Question: How much better was the BLSTM-CNN-CRF than the BLSTM-CRF? \ No newline at end of file diff --git a/qasper-0302/environment/Dockerfile b/qasper-0302/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0302/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/* diff --git a/qasper-0302/instruction.md b/qasper-0302/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..b5c0e2a8a2b4ce145d91d03661404138d5e8fae7 --- /dev/null +++ b/qasper-0302/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Recurrent Neural Network Encoder with Attention for Community Question Answering + +Question: Did they experimnet in other languages? \ No newline at end of file diff --git a/qasper-0303/instruction.md b/qasper-0303/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..6c82ddd573a5118627faced2838c7c14b4e101dc --- /dev/null +++ b/qasper-0303/instruction.md @@ -0,0 +1,120 @@ +Name of Paper: Attentional Encoder Network for Targeted Sentiment Classification + +Question: Do they use multi-attention heads? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Proposed Methodology", + "Embedding Layer", + "Attentional Encoder Layer", + "Target-specific Attention Layer", + "Output Layer", + "Regularization and Model Training", + "Datasets and Experimental Settings", + "Model Comparisons", + "Main Results", + "Model Analysis", + "Conclusion" + ], + "paragraphs": [ + [ + "Targeted sentiment classification is a fine-grained sentiment analysis task, which aims at determining the sentiment polarities (e.g., negative, neutral, or positive) of a sentence over \u201copinion targets\u201d that explicitly appear in the sentence. For example, given a sentence \u201cI hated their service, but their food was great\u201d, the sentiment polarities for the target \u201cservice\u201d and \u201cfood\u201d are negative and positive respectively. A target is usually an entity or an entity aspect.", + "In recent years, neural network models are designed to automatically learn useful low-dimensional representations from targets and contexts and obtain promising results BIBREF0 , BIBREF1 . However, these neural network models are still in infancy to deal with the fine-grained targeted sentiment classification task.", + "Attention mechanism, which has been successfully used in machine translation BIBREF2 , is incorporated to enforce the model to pay more attention to context words with closer semantic relations with the target. There are already some studies use attention to generate target-specific sentence representations BIBREF3 , BIBREF4 , BIBREF5 or to transform sentence representations according to target words BIBREF6 . However, these studies depend on complex recurrent neural networks (RNNs) as sequence encoder to compute hidden semantics of texts.", + "The first problem with previous works is that the modeling of text relies on RNNs. RNNs, such as LSTM, are very expressive, but they are hard to parallelize and backpropagation through time (BPTT) requires large amounts of memory and computation. Moreover, essentially every training algorithm of RNN is the truncated BPTT, which affects the model's ability to capture dependencies over longer time scales BIBREF7 . Although LSTM can alleviate the vanishing gradient problem to a certain extent and thus maintain long distance information, this usually requires a large amount of training data. Another problem that previous studies ignore is the label unreliability issue, since neutral sentiment is a fuzzy sentimental state and brings difficulty for model learning. As far as we know, we are the first to raise the label unreliability issue in the targeted sentiment classification task.", + "This paper propose an attention based model to solve the problems above. Specifically, our model eschews recurrence and employs attention as a competitive alternative to draw the introspective and interactive semantics between target and context words. To deal with the label unreliability issue, we employ a label smoothing regularization to encourage the model to be less confident with fuzzy labels. We also apply pre-trained BERT BIBREF8 to this task and show our model enhances the performance of basic BERT model. Experimental results on three benchmark datasets show that the proposed model achieves competitive performance and is a lightweight alternative of the best RNN based models.", + "The main contributions of this work are presented as follows:" + ], + [ + "The research approach of the targeted sentiment classification task including traditional machine learning methods and neural networks methods.", + "Traditional machine learning methods, including rule-based methods BIBREF9 and statistic-based methods BIBREF10 , mainly focus on extracting a set of features like sentiment lexicons features and bag-of-words features to train a sentiment classifier BIBREF11 . The performance of these methods highly depends on the effectiveness of the feature engineering works, which are labor intensive.", + "In recent years, neural network methods are getting more and more attention as they do not need handcrafted features and can encode sentences with low-dimensional word vectors where rich semantic information stained. In order to incorporate target words into a model, Tang et al. tang2016effective propose TD-LSTM to extend LSTM by using two single-directional LSTM to model the left context and right context of the target word respectively. Tang et al. tang2016aspect design MemNet which consists of a multi-hop attention mechanism with an external memory to capture the importance of each context word concerning the given target. Multiple attention is paid to the memory represented by word embeddings to build higher semantic information. Wang et al. wang2016attention propose ATAE-LSTM which concatenates target embeddings with word representations and let targets participate in computing attention weights. Chen et al. chen2017recurrent propose RAM which adopts multiple-attention mechanism on the memory built with bidirectional LSTM and nonlinearly combines the attention results with gated recurrent units (GRUs). Ma et al. ma2017interactive propose IAN which learns the representations of the target and context with two attention networks interactively." + ], + [ + "Given a context sequence INLINEFORM0 and a target sequence INLINEFORM1 , where INLINEFORM2 is a sub-sequence of INLINEFORM3 . The goal of this model is to predict the sentiment polarity of the sentence INLINEFORM4 over the target INLINEFORM5 .", + "Figure FIGREF9 illustrates the overall architecture of the proposed Attentional Encoder Network (AEN), which mainly consists of an embedding layer, an attentional encoder layer, a target-specific attention layer, and an output layer. Embedding layer has two types: GloVe embedding and BERT embedding. Accordingly, the models are named AEN-GloVe and AEN-BERT." + ], + [ + "Let INLINEFORM0 to be the pre-trained GloVe BIBREF12 embedding matrix, where INLINEFORM1 is the dimension of word vectors and INLINEFORM2 is the vocabulary size. Then we map each word INLINEFORM3 to its corresponding embedding vector INLINEFORM4 , which is a column in the embedding matrix INLINEFORM5 .", + "BERT embedding uses the pre-trained BERT to generate word vectors of sequence. In order to facilitate the training and fine-tuning of BERT model, we transform the given context and target to \u201c[CLS] + context + [SEP]\u201d and \u201c[CLS] + target + [SEP]\u201d respectively." + ], + [ + "The attentional encoder layer is a parallelizable and interactive alternative of LSTM and is applied to compute the hidden states of the input embeddings. This layer consists of two submodules: the Multi-Head Attention (MHA) and the Point-wise Convolution Transformation (PCT).", + "Multi-Head Attention (MHA) is the attention that can perform multiple attention function in parallel. Different from Transformer BIBREF13 , we use Intra-MHA for introspective context words modeling and Inter-MHA for context-perceptive target words modeling, which is more lightweight and target is modeled according to a given context.", + "An attention function maps a key sequence INLINEFORM0 and a query sequence INLINEFORM1 to an output sequence INLINEFORM2 : DISPLAYFORM0 ", + " where INLINEFORM0 denotes the alignment function which learns the semantic relevance between INLINEFORM1 and INLINEFORM2 : DISPLAYFORM0 ", + " where INLINEFORM0 are learnable weights.", + "MHA can learn n_head different scores in parallel child spaces and is very powerful for alignments. The INLINEFORM0 outputs are concatenated and projected to the specified hidden dimension INLINEFORM1 , namely, DISPLAYFORM0 ", + " where \u201c INLINEFORM0 \u201d denotes vector concatenation, INLINEFORM1 , INLINEFORM2 is the output of the INLINEFORM3 -th head attention and INLINEFORM4 .", + "Intra-MHA, or multi-head self-attention, is a special situation for typical attention mechanism that INLINEFORM0 . Given a context embedding INLINEFORM1 , we can get the introspective context representation INLINEFORM2 by: DISPLAYFORM0 ", + " The learned context representation INLINEFORM0 is aware of long-term dependencies.", + "Inter-MHA is the generally used form of attention mechanism that INLINEFORM0 is different from INLINEFORM1 . Given a context embedding INLINEFORM2 and a target embedding INLINEFORM3 , we can get the context-perceptive target representation INLINEFORM4 by: DISPLAYFORM0 ", + "After this interactive procedure, each given target word INLINEFORM0 will have a composed representation selected from context embeddings INLINEFORM1 . Then we get the context-perceptive target words modeling INLINEFORM2 .", + "A Point-wise Convolution T ransformation (PCT) can transform contextual information gathered by the MHA. Point-wise means that the kernel sizes are 1 and the same transformation is applied to every single token belonging to the input. Formally, given a input sequence INLINEFORM0 , PCT is defined as: DISPLAYFORM0 ", + " where INLINEFORM0 stands for the ELU activation, INLINEFORM1 is the convolution operator, INLINEFORM2 and INLINEFORM3 are the learnable weights of the two convolutional kernels, INLINEFORM4 and INLINEFORM5 are biases of the two convolutional kernels.", + "Given INLINEFORM0 and INLINEFORM1 , PCTs are applied to get the output hidden states of the attentional encoder layer INLINEFORM2 and INLINEFORM3 by: DISPLAYFORM0 " + ], + [ + "After we obtain the introspective context representation INLINEFORM0 and the context-perceptive target representation INLINEFORM1 , we employ another MHA to obtain the target-specific context representation INLINEFORM2 by: DISPLAYFORM0 ", + " The multi-head attention function here also has its independent parameters." + ], + [ + "We get the final representations of the previous outputs by average pooling, concatenate them as the final comprehensive representation INLINEFORM0 , and use a full connected layer to project the concatenated vector into the space of the targeted INLINEFORM1 classes. DISPLAYFORM0 ", + " where INLINEFORM0 is the predicted sentiment polarity distribution, INLINEFORM1 and INLINEFORM2 are learnable parameters." + ], + [ + "Since neutral sentiment is a very fuzzy sentimental state, training samples which labeled neutral are unreliable. We employ a Label Smoothing Regularization (LSR) term in the loss function. which penalizes low entropy output distributions BIBREF14 . LSR can reduce overfitting by preventing a network from assigning the full probability to each training example during training, replaces the 0 and 1 targets for a classifier with smoothed values like 0.1 or 0.9.", + "For a training sample INLINEFORM0 with the original ground-truth label distribution INLINEFORM1 , we replace INLINEFORM2 with DISPLAYFORM0 ", + " where INLINEFORM0 is the prior distribution over labels , and INLINEFORM1 is the smoothing parameter. In this paper, we set the prior label distribution to be uniform INLINEFORM2 .", + "LSR is equivalent to the KL divergence between the prior label distribution INLINEFORM0 and the network's predicted distribution INLINEFORM1 . Formally, LSR term is defined as: DISPLAYFORM0 ", + "The objective function (loss function) to be optimized is the cross-entropy loss with INLINEFORM0 and INLINEFORM1 regularization, which is defined as: DISPLAYFORM0 ", + " where INLINEFORM0 is the ground truth represented as a one-hot vector, INLINEFORM1 is the predicted sentiment distribution vector given by the output layer, INLINEFORM2 is the coefficient for INLINEFORM3 regularization term, and INLINEFORM4 is the parameter set." + ], + [ + "We conduct experiments on three datasets: SemEval 2014 Task 4 BIBREF15 dataset composed of Restaurant reviews and Laptop reviews, and ACL 14 Twitter dataset gathered by Dong et al. dong2014adaptive. These datasets are labeled with three sentiment polarities: positive, neutral and negative. Table TABREF31 shows the number of training and test instances in each category.", + "Word embeddings in AEN-GloVe do not get updated in the learning process, but we fine-tune pre-trained BERT in AEN-BERT. Embedding dimension INLINEFORM0 is 300 for GloVe and is 768 for pre-trained BERT. Dimension of hidden states INLINEFORM1 is set to 300. The weights of our model are initialized with Glorot initialization BIBREF16 . During training, we set label smoothing parameter INLINEFORM2 to 0.2 BIBREF14 , the coefficient INLINEFORM3 of INLINEFORM4 regularization item is INLINEFORM5 and dropout rate is 0.1. Adam optimizer BIBREF17 is applied to update all the parameters. We adopt the Accuracy and Macro-F1 metrics to evaluate the performance of the model." + ], + [ + "In order to comprehensively evaluate and analysis the performance of AEN-GloVe, we list 7 baseline models and design 4 ablations of AEN-GloVe. We also design a basic BERT-based model to evaluate the performance of AEN-BERT.", + " ", + "Non-RNN based baselines:", + " INLINEFORM0 Feature-based SVM BIBREF18 is a traditional support vector machine based model with extensive feature engineering.", + " INLINEFORM0 Rec-NN BIBREF0 firstly uses rules to transform the dependency tree and put the opinion target at the root, and then learns the sentence representation toward target via semantic composition using Recursive NNs.", + " INLINEFORM0 MemNet BIBREF19 uses multi-hops of attention layers on the context word embeddings for sentence representation to explicitly captures the importance of each context word.", + " ", + "RNN based baselines:", + " INLINEFORM0 TD-LSTM BIBREF1 extends LSTM by using two LSTM networks to model the left context with target and the right context with target respectively. The left and right target-dependent representations are concatenated for predicting the sentiment polarity of the target.", + " INLINEFORM0 ATAE-LSTM BIBREF3 strengthens the effect of target embeddings, which appends the target embeddings with each word embeddings and use LSTM with attention to get the final representation for classification.", + " INLINEFORM0 IAN BIBREF4 learns the representations of the target and context with two LSTMs and attentions interactively, which generates the representations for targets and contexts with respect to each other.", + " INLINEFORM0 RAM BIBREF5 strengthens MemNet by representing memory with bidirectional LSTM and using a gated recurrent unit network to combine the multiple attention outputs for sentence representation.", + " ", + "AEN-GloVe ablations:", + " INLINEFORM0 AEN-GloVe w/o PCT ablates PCT module.", + " INLINEFORM0 AEN-GloVe w/o MHA ablates MHA module.", + " INLINEFORM0 AEN-GloVe w/o LSR ablates label smoothing regularization.", + " INLINEFORM0 AEN-GloVe-BiLSTM replaces the attentional encoder layer with two bidirectional LSTM.", + " ", + "Basic BERT-based model:", + " INLINEFORM0 BERT-SPC feeds sequence \u201c[CLS] + context + [SEP] + target + [SEP]\u201d into the basic BERT model for sentence pair classification task." + ], + [ + "Table TABREF34 shows the performance comparison of AEN with other models. BERT-SPC and AEN-BERT obtain substantial accuracy improvements, which shows the power of pre-trained BERT on small-data task. The overall performance of AEN-BERT is better than BERT-SPC, which suggests that it is important to design a downstream network customized to a specific task. As the prior knowledge in the pre-trained BERT is not specific to any particular domain, further fine-tuning on the specific task is necessary for releasing the true power of BERT.", + "The overall performance of TD-LSTM is not good since it only makes a rough treatment of the target words. ATAE-LSTM, IAN and RAM are attention based models, they stably exceed the TD-LSTM method on Restaurant and Laptop datasets. RAM is better than other RNN based models, but it does not perform well on Twitter dataset, which might because bidirectional LSTM is not good at modeling small and ungrammatical text.", + "Feature-based SVM is still a competitive baseline, but relying on manually-designed features. Rec-NN gets the worst performances among all neural network baselines as dependency parsing is not guaranteed to work well on ungrammatical short texts such as tweets and comments. Like AEN, MemNet also eschews recurrence, but its overall performance is not good since it does not model the hidden semantic of embeddings, and the result of the last attention is essentially a linear combination of word embeddings." + ], + [ + "As shown in Table TABREF34 , the performances of AEN-GloVe ablations are incomparable with AEN-GloVe in both accuracy and macro-F1 measure. This result shows that all of these discarded components are crucial for a good performance. Comparing the results of AEN-GloVe and AEN-GloVe w/o LSR, we observe that the accuracy of AEN-GloVe w/o LSR drops significantly on all three datasets. We could attribute this phenomenon to the unreliability of the training samples with neutral sentiment. The overall performance of AEN-GloVe and AEN-GloVe-BiLSTM is relatively close, AEN-GloVe performs better on the Restaurant dataset. More importantly, AEN-GloVe has fewer parameters and is easier to parallelize.", + "To figure out whether the proposed AEN-GloVe is a lightweight alternative of recurrent models, we study the model size of each model on the Restaurant dataset. Statistical results are reported in Table TABREF37 . We implement all the compared models base on the same source code infrastructure, use the same hyperparameters, and run them on the same GPU .", + "RNN-based and BERT-based models indeed have larger model size. ATAE-LSTM, IAN, RAM, and AEN-GloVe-BiLSTM are all attention based RNN models, memory optimization for these models will be more difficult as the encoded hidden states must be kept simultaneously in memory in order to perform attention mechanisms. MemNet has the lowest model size as it only has one shared attention layer and two linear layers, it does not calculate hidden states of word embeddings. AEN-GloVe's lightweight level ranks second, since it takes some more parameters than MemNet in modeling hidden states of sequences. As a comparison, the model size of AEN-GloVe-BiLSTM is more than twice that of AEN-GloVe, but does not bring any performance improvements." + ], + [ + "In this work, we propose an attentional encoder network for the targeted sentiment classification task. which employs attention based encoders for the modeling between context and target. We raise the the label unreliability issue add a label smoothing regularization to encourage the model to be less confident with fuzzy labels. We also apply pre-trained BERT to this task and obtain new state-of-the-art results. Experiments and analysis demonstrate the effectiveness and lightweight of the proposed model." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0304/instruction.md b/qasper-0304/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..1a7ceded67c27ba3820e8d281eec68b7802e40f8 --- /dev/null +++ b/qasper-0304/instruction.md @@ -0,0 +1,120 @@ +Name of Paper: Attentional Encoder Network for Targeted Sentiment Classification + +Question: How big is their model? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Proposed Methodology", + "Embedding Layer", + "Attentional Encoder Layer", + "Target-specific Attention Layer", + "Output Layer", + "Regularization and Model Training", + "Datasets and Experimental Settings", + "Model Comparisons", + "Main Results", + "Model Analysis", + "Conclusion" + ], + "paragraphs": [ + [ + "Targeted sentiment classification is a fine-grained sentiment analysis task, which aims at determining the sentiment polarities (e.g., negative, neutral, or positive) of a sentence over \u201copinion targets\u201d that explicitly appear in the sentence. For example, given a sentence \u201cI hated their service, but their food was great\u201d, the sentiment polarities for the target \u201cservice\u201d and \u201cfood\u201d are negative and positive respectively. A target is usually an entity or an entity aspect.", + "In recent years, neural network models are designed to automatically learn useful low-dimensional representations from targets and contexts and obtain promising results BIBREF0 , BIBREF1 . However, these neural network models are still in infancy to deal with the fine-grained targeted sentiment classification task.", + "Attention mechanism, which has been successfully used in machine translation BIBREF2 , is incorporated to enforce the model to pay more attention to context words with closer semantic relations with the target. There are already some studies use attention to generate target-specific sentence representations BIBREF3 , BIBREF4 , BIBREF5 or to transform sentence representations according to target words BIBREF6 . However, these studies depend on complex recurrent neural networks (RNNs) as sequence encoder to compute hidden semantics of texts.", + "The first problem with previous works is that the modeling of text relies on RNNs. RNNs, such as LSTM, are very expressive, but they are hard to parallelize and backpropagation through time (BPTT) requires large amounts of memory and computation. Moreover, essentially every training algorithm of RNN is the truncated BPTT, which affects the model's ability to capture dependencies over longer time scales BIBREF7 . Although LSTM can alleviate the vanishing gradient problem to a certain extent and thus maintain long distance information, this usually requires a large amount of training data. Another problem that previous studies ignore is the label unreliability issue, since neutral sentiment is a fuzzy sentimental state and brings difficulty for model learning. As far as we know, we are the first to raise the label unreliability issue in the targeted sentiment classification task.", + "This paper propose an attention based model to solve the problems above. Specifically, our model eschews recurrence and employs attention as a competitive alternative to draw the introspective and interactive semantics between target and context words. To deal with the label unreliability issue, we employ a label smoothing regularization to encourage the model to be less confident with fuzzy labels. We also apply pre-trained BERT BIBREF8 to this task and show our model enhances the performance of basic BERT model. Experimental results on three benchmark datasets show that the proposed model achieves competitive performance and is a lightweight alternative of the best RNN based models.", + "The main contributions of this work are presented as follows:" + ], + [ + "The research approach of the targeted sentiment classification task including traditional machine learning methods and neural networks methods.", + "Traditional machine learning methods, including rule-based methods BIBREF9 and statistic-based methods BIBREF10 , mainly focus on extracting a set of features like sentiment lexicons features and bag-of-words features to train a sentiment classifier BIBREF11 . The performance of these methods highly depends on the effectiveness of the feature engineering works, which are labor intensive.", + "In recent years, neural network methods are getting more and more attention as they do not need handcrafted features and can encode sentences with low-dimensional word vectors where rich semantic information stained. In order to incorporate target words into a model, Tang et al. tang2016effective propose TD-LSTM to extend LSTM by using two single-directional LSTM to model the left context and right context of the target word respectively. Tang et al. tang2016aspect design MemNet which consists of a multi-hop attention mechanism with an external memory to capture the importance of each context word concerning the given target. Multiple attention is paid to the memory represented by word embeddings to build higher semantic information. Wang et al. wang2016attention propose ATAE-LSTM which concatenates target embeddings with word representations and let targets participate in computing attention weights. Chen et al. chen2017recurrent propose RAM which adopts multiple-attention mechanism on the memory built with bidirectional LSTM and nonlinearly combines the attention results with gated recurrent units (GRUs). Ma et al. ma2017interactive propose IAN which learns the representations of the target and context with two attention networks interactively." + ], + [ + "Given a context sequence INLINEFORM0 and a target sequence INLINEFORM1 , where INLINEFORM2 is a sub-sequence of INLINEFORM3 . The goal of this model is to predict the sentiment polarity of the sentence INLINEFORM4 over the target INLINEFORM5 .", + "Figure FIGREF9 illustrates the overall architecture of the proposed Attentional Encoder Network (AEN), which mainly consists of an embedding layer, an attentional encoder layer, a target-specific attention layer, and an output layer. Embedding layer has two types: GloVe embedding and BERT embedding. Accordingly, the models are named AEN-GloVe and AEN-BERT." + ], + [ + "Let INLINEFORM0 to be the pre-trained GloVe BIBREF12 embedding matrix, where INLINEFORM1 is the dimension of word vectors and INLINEFORM2 is the vocabulary size. Then we map each word INLINEFORM3 to its corresponding embedding vector INLINEFORM4 , which is a column in the embedding matrix INLINEFORM5 .", + "BERT embedding uses the pre-trained BERT to generate word vectors of sequence. In order to facilitate the training and fine-tuning of BERT model, we transform the given context and target to \u201c[CLS] + context + [SEP]\u201d and \u201c[CLS] + target + [SEP]\u201d respectively." + ], + [ + "The attentional encoder layer is a parallelizable and interactive alternative of LSTM and is applied to compute the hidden states of the input embeddings. This layer consists of two submodules: the Multi-Head Attention (MHA) and the Point-wise Convolution Transformation (PCT).", + "Multi-Head Attention (MHA) is the attention that can perform multiple attention function in parallel. Different from Transformer BIBREF13 , we use Intra-MHA for introspective context words modeling and Inter-MHA for context-perceptive target words modeling, which is more lightweight and target is modeled according to a given context.", + "An attention function maps a key sequence INLINEFORM0 and a query sequence INLINEFORM1 to an output sequence INLINEFORM2 : DISPLAYFORM0 ", + " where INLINEFORM0 denotes the alignment function which learns the semantic relevance between INLINEFORM1 and INLINEFORM2 : DISPLAYFORM0 ", + " where INLINEFORM0 are learnable weights.", + "MHA can learn n_head different scores in parallel child spaces and is very powerful for alignments. The INLINEFORM0 outputs are concatenated and projected to the specified hidden dimension INLINEFORM1 , namely, DISPLAYFORM0 ", + " where \u201c INLINEFORM0 \u201d denotes vector concatenation, INLINEFORM1 , INLINEFORM2 is the output of the INLINEFORM3 -th head attention and INLINEFORM4 .", + "Intra-MHA, or multi-head self-attention, is a special situation for typical attention mechanism that INLINEFORM0 . Given a context embedding INLINEFORM1 , we can get the introspective context representation INLINEFORM2 by: DISPLAYFORM0 ", + " The learned context representation INLINEFORM0 is aware of long-term dependencies.", + "Inter-MHA is the generally used form of attention mechanism that INLINEFORM0 is different from INLINEFORM1 . Given a context embedding INLINEFORM2 and a target embedding INLINEFORM3 , we can get the context-perceptive target representation INLINEFORM4 by: DISPLAYFORM0 ", + "After this interactive procedure, each given target word INLINEFORM0 will have a composed representation selected from context embeddings INLINEFORM1 . Then we get the context-perceptive target words modeling INLINEFORM2 .", + "A Point-wise Convolution T ransformation (PCT) can transform contextual information gathered by the MHA. Point-wise means that the kernel sizes are 1 and the same transformation is applied to every single token belonging to the input. Formally, given a input sequence INLINEFORM0 , PCT is defined as: DISPLAYFORM0 ", + " where INLINEFORM0 stands for the ELU activation, INLINEFORM1 is the convolution operator, INLINEFORM2 and INLINEFORM3 are the learnable weights of the two convolutional kernels, INLINEFORM4 and INLINEFORM5 are biases of the two convolutional kernels.", + "Given INLINEFORM0 and INLINEFORM1 , PCTs are applied to get the output hidden states of the attentional encoder layer INLINEFORM2 and INLINEFORM3 by: DISPLAYFORM0 " + ], + [ + "After we obtain the introspective context representation INLINEFORM0 and the context-perceptive target representation INLINEFORM1 , we employ another MHA to obtain the target-specific context representation INLINEFORM2 by: DISPLAYFORM0 ", + " The multi-head attention function here also has its independent parameters." + ], + [ + "We get the final representations of the previous outputs by average pooling, concatenate them as the final comprehensive representation INLINEFORM0 , and use a full connected layer to project the concatenated vector into the space of the targeted INLINEFORM1 classes. DISPLAYFORM0 ", + " where INLINEFORM0 is the predicted sentiment polarity distribution, INLINEFORM1 and INLINEFORM2 are learnable parameters." + ], + [ + "Since neutral sentiment is a very fuzzy sentimental state, training samples which labeled neutral are unreliable. We employ a Label Smoothing Regularization (LSR) term in the loss function. which penalizes low entropy output distributions BIBREF14 . LSR can reduce overfitting by preventing a network from assigning the full probability to each training example during training, replaces the 0 and 1 targets for a classifier with smoothed values like 0.1 or 0.9.", + "For a training sample INLINEFORM0 with the original ground-truth label distribution INLINEFORM1 , we replace INLINEFORM2 with DISPLAYFORM0 ", + " where INLINEFORM0 is the prior distribution over labels , and INLINEFORM1 is the smoothing parameter. In this paper, we set the prior label distribution to be uniform INLINEFORM2 .", + "LSR is equivalent to the KL divergence between the prior label distribution INLINEFORM0 and the network's predicted distribution INLINEFORM1 . Formally, LSR term is defined as: DISPLAYFORM0 ", + "The objective function (loss function) to be optimized is the cross-entropy loss with INLINEFORM0 and INLINEFORM1 regularization, which is defined as: DISPLAYFORM0 ", + " where INLINEFORM0 is the ground truth represented as a one-hot vector, INLINEFORM1 is the predicted sentiment distribution vector given by the output layer, INLINEFORM2 is the coefficient for INLINEFORM3 regularization term, and INLINEFORM4 is the parameter set." + ], + [ + "We conduct experiments on three datasets: SemEval 2014 Task 4 BIBREF15 dataset composed of Restaurant reviews and Laptop reviews, and ACL 14 Twitter dataset gathered by Dong et al. dong2014adaptive. These datasets are labeled with three sentiment polarities: positive, neutral and negative. Table TABREF31 shows the number of training and test instances in each category.", + "Word embeddings in AEN-GloVe do not get updated in the learning process, but we fine-tune pre-trained BERT in AEN-BERT. Embedding dimension INLINEFORM0 is 300 for GloVe and is 768 for pre-trained BERT. Dimension of hidden states INLINEFORM1 is set to 300. The weights of our model are initialized with Glorot initialization BIBREF16 . During training, we set label smoothing parameter INLINEFORM2 to 0.2 BIBREF14 , the coefficient INLINEFORM3 of INLINEFORM4 regularization item is INLINEFORM5 and dropout rate is 0.1. Adam optimizer BIBREF17 is applied to update all the parameters. We adopt the Accuracy and Macro-F1 metrics to evaluate the performance of the model." + ], + [ + "In order to comprehensively evaluate and analysis the performance of AEN-GloVe, we list 7 baseline models and design 4 ablations of AEN-GloVe. We also design a basic BERT-based model to evaluate the performance of AEN-BERT.", + " ", + "Non-RNN based baselines:", + " INLINEFORM0 Feature-based SVM BIBREF18 is a traditional support vector machine based model with extensive feature engineering.", + " INLINEFORM0 Rec-NN BIBREF0 firstly uses rules to transform the dependency tree and put the opinion target at the root, and then learns the sentence representation toward target via semantic composition using Recursive NNs.", + " INLINEFORM0 MemNet BIBREF19 uses multi-hops of attention layers on the context word embeddings for sentence representation to explicitly captures the importance of each context word.", + " ", + "RNN based baselines:", + " INLINEFORM0 TD-LSTM BIBREF1 extends LSTM by using two LSTM networks to model the left context with target and the right context with target respectively. The left and right target-dependent representations are concatenated for predicting the sentiment polarity of the target.", + " INLINEFORM0 ATAE-LSTM BIBREF3 strengthens the effect of target embeddings, which appends the target embeddings with each word embeddings and use LSTM with attention to get the final representation for classification.", + " INLINEFORM0 IAN BIBREF4 learns the representations of the target and context with two LSTMs and attentions interactively, which generates the representations for targets and contexts with respect to each other.", + " INLINEFORM0 RAM BIBREF5 strengthens MemNet by representing memory with bidirectional LSTM and using a gated recurrent unit network to combine the multiple attention outputs for sentence representation.", + " ", + "AEN-GloVe ablations:", + " INLINEFORM0 AEN-GloVe w/o PCT ablates PCT module.", + " INLINEFORM0 AEN-GloVe w/o MHA ablates MHA module.", + " INLINEFORM0 AEN-GloVe w/o LSR ablates label smoothing regularization.", + " INLINEFORM0 AEN-GloVe-BiLSTM replaces the attentional encoder layer with two bidirectional LSTM.", + " ", + "Basic BERT-based model:", + " INLINEFORM0 BERT-SPC feeds sequence \u201c[CLS] + context + [SEP] + target + [SEP]\u201d into the basic BERT model for sentence pair classification task." + ], + [ + "Table TABREF34 shows the performance comparison of AEN with other models. BERT-SPC and AEN-BERT obtain substantial accuracy improvements, which shows the power of pre-trained BERT on small-data task. The overall performance of AEN-BERT is better than BERT-SPC, which suggests that it is important to design a downstream network customized to a specific task. As the prior knowledge in the pre-trained BERT is not specific to any particular domain, further fine-tuning on the specific task is necessary for releasing the true power of BERT.", + "The overall performance of TD-LSTM is not good since it only makes a rough treatment of the target words. ATAE-LSTM, IAN and RAM are attention based models, they stably exceed the TD-LSTM method on Restaurant and Laptop datasets. RAM is better than other RNN based models, but it does not perform well on Twitter dataset, which might because bidirectional LSTM is not good at modeling small and ungrammatical text.", + "Feature-based SVM is still a competitive baseline, but relying on manually-designed features. Rec-NN gets the worst performances among all neural network baselines as dependency parsing is not guaranteed to work well on ungrammatical short texts such as tweets and comments. Like AEN, MemNet also eschews recurrence, but its overall performance is not good since it does not model the hidden semantic of embeddings, and the result of the last attention is essentially a linear combination of word embeddings." + ], + [ + "As shown in Table TABREF34 , the performances of AEN-GloVe ablations are incomparable with AEN-GloVe in both accuracy and macro-F1 measure. This result shows that all of these discarded components are crucial for a good performance. Comparing the results of AEN-GloVe and AEN-GloVe w/o LSR, we observe that the accuracy of AEN-GloVe w/o LSR drops significantly on all three datasets. We could attribute this phenomenon to the unreliability of the training samples with neutral sentiment. The overall performance of AEN-GloVe and AEN-GloVe-BiLSTM is relatively close, AEN-GloVe performs better on the Restaurant dataset. More importantly, AEN-GloVe has fewer parameters and is easier to parallelize.", + "To figure out whether the proposed AEN-GloVe is a lightweight alternative of recurrent models, we study the model size of each model on the Restaurant dataset. Statistical results are reported in Table TABREF37 . We implement all the compared models base on the same source code infrastructure, use the same hyperparameters, and run them on the same GPU .", + "RNN-based and BERT-based models indeed have larger model size. ATAE-LSTM, IAN, RAM, and AEN-GloVe-BiLSTM are all attention based RNN models, memory optimization for these models will be more difficult as the encoded hidden states must be kept simultaneously in memory in order to perform attention mechanisms. MemNet has the lowest model size as it only has one shared attention layer and two linear layers, it does not calculate hidden states of word embeddings. AEN-GloVe's lightweight level ranks second, since it takes some more parameters than MemNet in modeling hidden states of sequences. As a comparison, the model size of AEN-GloVe-BiLSTM is more than twice that of AEN-GloVe, but does not bring any performance improvements." + ], + [ + "In this work, we propose an attentional encoder network for the targeted sentiment classification task. which employs attention based encoders for the modeling between context and target. We raise the the label unreliability issue add a label smoothing regularization to encourage the model to be less confident with fuzzy labels. We also apply pre-trained BERT to this task and obtain new state-of-the-art results. Experiments and analysis demonstrate the effectiveness and lightweight of the proposed model." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0305/environment/Dockerfile b/qasper-0305/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0305/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/* diff --git a/qasper-0305/instruction.md b/qasper-0305/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..5aba1932ab43b4415d23d00a22fe27afd675494c --- /dev/null +++ b/qasper-0305/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Attentional Encoder Network for Targeted Sentiment Classification + +Question: How is their model different from BERT? \ No newline at end of file diff --git a/qasper-0332/instruction.md b/qasper-0332/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..9413d8242d0943186b4c083f2034be4018ab795e --- /dev/null +++ b/qasper-0332/instruction.md @@ -0,0 +1,136 @@ +Name of Paper: The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection + +Question: What were the non-neural baselines used for the task? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Tasks and Evaluation ::: Task 1: Cross-lingual transfer for morphological inflection", + "Tasks and Evaluation ::: Task 1: Cross-lingual transfer for morphological inflection ::: Example", + "Tasks and Evaluation ::: Task 1: Cross-lingual transfer for morphological inflection ::: Evaluation", + "Tasks and Evaluation ::: Task 2: Morphological analysis in context", + "Data ::: Data for Task 1 ::: Language pairs", + "Data ::: Data for Task 1 ::: Data format", + "Data ::: Data for Task 1 ::: Extraction from Wiktionary", + "Data ::: Data for Task 1 ::: Sampling data splits", + "Data ::: Data for Task 1 ::: Other modifications", + "Data ::: Data for Task 2", + "Data ::: Data for Task 2 ::: Data conversion", + "Baselines ::: Task 1 Baseline", + "Baselines ::: Task 2 Baselines ::: Non-neural", + "Baselines ::: Task 2 Baselines ::: Neural", + "Results", + "Results ::: Task 1 Results", + "Results ::: Task 2 Results", + "Future Directions", + "Conclusions", + "Acknowledgments" + ], + "paragraphs": [ + [ + "While producing a sentence, humans combine various types of knowledge to produce fluent output\u2014various shades of meaning are expressed through word selection and tone, while the language is made to conform to underlying structural rules via syntax and morphology. Native speakers are often quick to identify disfluency, even if the meaning of a sentence is mostly clear.", + "Automatic systems must also consider these constraints when constructing or processing language. Strong enough language models can often reconstruct common syntactic structures, but are insufficient to properly model morphology. Many languages implement large inflectional paradigms that mark both function and content words with a varying levels of morphosyntactic information. For instance, Romanian verb forms inflect for person, number, tense, mood, and voice; meanwhile, Archi verbs can take on thousands of forms BIBREF0. Such complex paradigms produce large inventories of words, all of which must be producible by a realistic system, even though a large percentage of them will never be observed over billions of lines of linguistic input. Compounding the issue, good inflectional systems often require large amounts of supervised training data, which is infeasible in many of the world's languages.", + "This year's shared task is concentrated on encouraging the construction of strong morphological systems that perform two related but different inflectional tasks. The first task asks participants to create morphological inflectors for a large number of under-resourced languages, encouraging systems that use highly-resourced, related languages as a cross-lingual training signal. The second task welcomes submissions that invert this operation in light of contextual information: Given an unannotated sentence, lemmatize each word, and tag them with a morphosyntactic description. Both of these tasks extend upon previous morphological competitions, and the best submitted systems now represent the state of the art in their respective tasks." + ], + [ + "Annotated resources for the world's languages are not distributed equally\u2014some languages simply have more as they have more native speakers willing and able to annotate more data. We explore how to transfer knowledge from high-resource languages that are genetically related to low-resource languages.", + "The first task iterates on last year's main task: morphological inflection BIBREF1. Instead of giving some number of training examples in the language of interest, we provided only a limited number in that language. To accompany it, we provided a larger number of examples in either a related or unrelated language. Each test example asked participants to produce some other inflected form when given a lemma and a bundle of morphosyntactic features as input. The goal, thus, is to perform morphological inflection in the low-resource language, having hopefully exploited some similarity to the high-resource language. Models which perform well here can aid downstream tasks like machine translation in low-resource settings. All datasets were resampled from UniMorph, which makes them distinct from past years.", + "The mode of the task is inspired by BIBREF2, who fine-tune a model pre-trained on a high-resource language to perform well on a low-resource language. We do not, though, require that models be trained by fine-tuning. Joint modeling or any number of methods may be explored instead." + ], + [ + "The model will have access to type-level data in a low-resource target language, plus a high-resource source language. We give an example here of Asturian as the target language with Spanish as the source language.", + "" + ], + [ + "We score the output of each system in terms of its predictions' exact-match accuracy and the average Levenshtein distance between the predictions and their corresponding true forms." + ], + [ + "Although inflection of words in a context-agnostic manner is a useful evaluation of the morphological quality of a system, people do not learn morphology in isolation.", + "In 2018, the second task of the CoNLL\u2013SIGMORPHON Shared Task BIBREF1 required submitting systems to complete an inflectional cloze task BIBREF3 given only the sentential context and the desired lemma \u2013 an example of the problem is given in the following lines: A successful system would predict the plural form \u201cdogs\u201d. Likewise, a Spanish word form ayuda may be a feminine noun or a third-person verb form, which must be disambiguated by context.", + "", + "This year's task extends the second task from last year. Rather than inflect a single word in context, the task is to provide a complete morphological tagging of a sentence: for each word, a successful system will need to lemmatize and tag it with a morphsyntactic description (MSD).", + "width=", + "Context is critical\u2014depending on the sentence, identical word forms realize a large number of potential inflectional categories, which will in turn influence lemmatization decisions. If the sentence were instead \u201cThe barking dogs kept us up all night\u201d, \u201cbarking\u201d is now an adjective, and its lemma is also \u201cbarking\u201d." + ], + [ + "We presented data in 100 language pairs spanning 79 unique languages. Data for all but four languages (Basque, Kurmanji, Murrinhpatha, and Sorani) are extracted from English Wiktionary, a large multi-lingual crowd-sourced dictionary with morphological paradigms for many lemmata. 20 of the 100 language pairs are either distantly related or unrelated; this allows speculation into the relative importance of data quantity and linguistic relatedness." + ], + [ + "For each language, the basic data consists of triples of the form (lemma, feature bundle, inflected form), as in tab:sub1data. The first feature in the bundle always specifies the core part of speech (e.g., verb). For each language pair, separate files contain the high- and low-resource training examples.", + "All features in the bundle are coded according to the UniMorph Schema, a cross-linguistically consistent universal morphological feature set BIBREF8, BIBREF9." + ], + [ + "For each of the Wiktionary languages, Wiktionary provides a number of tables, each of which specifies the full inflectional paradigm for a particular lemma. As in the previous iteration, tables were extracted using a template annotation procedure described in BIBREF10." + ], + [ + "From each language's collection of paradigms, we sampled the training, development, and test sets as in 2018. Crucially, while the data were sampled in the same fashion, the datasets are distinct from those used for the 2018 shared task.", + "Our first step was to construct probability distributions over the (lemma, feature bundle, inflected form) triples in our full dataset. For each triple, we counted how many tokens the inflected form has in the February 2017 dump of Wikipedia for that language. To distribute the counts of an observed form over all the triples that have this token as its form, we follow the method used in the previous shared task BIBREF1, training a neural network on unambiguous forms to estimate the distribution over all, even ambiguous, forms. We then sampled 12,000 triples without replacement from this distribution. The first 100 were taken as training data for low-resource settings. The first 10,000 were used as high-resource training sets. As these sets are nested, the highest-count triples tend to appear in the smaller training sets.", + "The final 2000 triples were randomly shuffled and then split in half to obtain development and test sets of 1000 forms each. The final shuffling was performed to ensure that the development set is similar to the test set. By contrast, the development and test sets tend to contain lower-count triples than the training set." + ], + [ + "We further adopted some changes to increase compatibility. Namely, we corrected some annotation errors created while scraping Wiktionary for the 2018 task, and we standardized Romanian t-cedilla and t-comma to t-comma. (The same was done with s-cedilla and s-comma.)" + ], + [ + "Our data for task 2 come from the Universal Dependencies treebanks BIBREF11, which provides pre-defined training, development, and test splits and annotations in a unified annotation schema for morphosyntax and dependency relationships. Unlike the 2018 cloze task which used UD data, we require no manual data preparation and are able to leverage all 107 monolingual treebanks. As is typical, data are presented in CoNLL-U format, although we modify the morphological feature and lemma fields." + ], + [ + "The morphological annotations for the 2019 shared task were converted to the UniMorph schema BIBREF10 according to BIBREF12, who provide a deterministic mapping that increases agreement across languages. This also moves the part of speech into the bundle of morphological features. We do not attempt to individually correct any errors in the UD source material. Further, some languages received additional pre-processing. In the Finnish data, we removed morpheme boundaries that were present in the lemmata (e.g., puhe#kieli $\\mapsto $ puhekieli `spoken+language'). Russian lemmata in the GSD treebank were presented in all uppercase; to match the 2018 shared task, we lowercased these. In development and test data, all fields except for form and index within the sentence were struck." + ], + [ + "We include four neural sequence-to-sequence models mapping lemma into inflected word forms: soft attention BIBREF13, non-monotonic hard attention BIBREF14, monotonic hard attention and a variant with offset-based transition distribution BIBREF15. Neural sequence-to-sequence models with soft attention BIBREF13 have dominated previous SIGMORPHON shared tasks BIBREF16. BIBREF14 instead models the alignment between characters in the lemma and the inflected word form explicitly with hard attention and learns this alignment and transduction jointly. BIBREF15 shows that enforcing strict monotonicity with hard attention is beneficial in tasks such as morphological inflection where the transduction is mostly monotonic. The encoder is a biLSTM while the decoder is a left-to-right LSTM. All models use multiplicative attention and have roughly the same number of parameters. In the model, a morphological tag is fed to the decoder along with target character embeddings to guide the decoding. During the training of the hard attention model, dynamic programming is applied to marginalize all latent alignments exactly." + ], + [ + "BIBREF17: The Lemming model is a log-linear model that performs joint morphological tagging and lemmatization. The model is globally normalized with the use of a second order linear-chain CRF. To efficiently calculate the partition function, the choice of lemmata are pruned with the use of pre-extracted edit trees." + ], + [ + "BIBREF18: This is a state-of-the-art neural model that also performs joint morphological tagging and lemmatization, but also accounts for the exposure bias with the application of maximum likelihood (MLE). The model stitches the tagger and lemmatizer together with the use of jackknifing BIBREF19 to expose the lemmatizer to the errors made by the tagger model during training. The morphological tagger is based on a character-level biLSTM embedder that produces the embedding for a word, and a word-level biLSTM tagger that predicts a morphological tag sequence for each word in the sentence. The lemmatizer is a neural sequence-to-sequence model BIBREF15 that uses the decoded morphological tag sequence from the tagger as an additional attribute. The model uses hard monotonic attention instead of standard soft attention, along with a dynamic programming based training scheme." + ], + [ + "The SIGMORPHON 2019 shared task received 30 submissions\u201414 for task 1 and 16 for task 2\u2014from 23 teams. In addition, the organizers' baseline systems were evaluated." + ], + [ + "Five teams participated in the first Task, with a variety of methods aimed at leveraging the cross-lingual data to improve system performance.", + "The University of Alberta (UAlberta) performed a focused investigation on four language pairs, training cognate-projection systems from external cognate lists. Two methods were considered: one which trained a high-resource neural encoder-decoder, and projected the test data into the HRL, and one that projected the HRL data into the LRL, and trained a combined system. Results demonstrated that certain language pairs may be amenable to such methods.", + "The Tuebingen University submission (Tuebingen) aligned source and target to learn a set of edit-actions with both linear and neural classifiers that independently learned to predict action sequences for each morphological category. Adding in the cross-lingual data only led to modest gains.", + "AX-Semantics combined the low- and high-resource data to train an encoder-decoder seq2seq model; optionally also implementing domain adaptation methods to focus later epochs on the target language.", + "The CMU submission first attends over a decoupled representation of the desired morphological sequence before using the updated decoder state to attend over the character sequence of the lemma. Secondly, in order to reduce the bias of the decoder's language model, they hallucinate two types of data that encourage common affixes and character copying. Simply allowing the model to learn to copy characters for several epochs significantly out-performs the task baseline, while further improvements are obtained through fine-tuning. Making use of an adversarial language discriminator, cross lingual gains are highly-correlated to linguistic similarity, while augmenting the data with hallucinated forms and multiple related target language further improves the model.", + "The system from IT-IST also attends separately to tags and lemmas, using a gating mechanism to interpolate the importance of the individual attentions. By combining the gated dual-head attention with a SparseMax activation function, they are able to jointly learn stem and affix modifications, improving significantly over the baseline system.", + "The relative system performance is described in tab:sub2team, which shows the average per-language accuracy of each system. The table reflects the fact that some teams submitted more than one system (e.g. Tuebingen-1 & Tuebingen-2 in the table)." + ], + [ + "Nine teams submitted system papers for Task 2, with several interesting modifications to either the baseline or other prior work that led to modest improvements.", + "Charles-Saarland achieved the highest overall tagging accuracy by leveraging multi-lingual BERT embeddings fine-tuned on a concatenation of all available languages, effectively transporting the cross-lingual objective of Task 1 into Task 2. Lemmas and tags are decoded separately (with a joint encoder and separate attention); Lemmas are a sequence of edit-actions, while tags are calculated jointly. (There is no splitting of tags into features; tags are atomic.)", + "CBNU instead lemmatize using a transformer network, while performing tagging with a multilayer perceptron with biaffine attention. Input words are first lemmatized, and then pipelined to the tagger, which produces atomic tag sequences (i.e., no splitting of features).", + "The team from Istanbul Technical University (ITU) jointly produces lemmatic edit-actions and morphological tags via a two level encoder (first word embeddings, and then context embeddings) and separate decoders. Their system slightly improves over the baseline lemmatization, but significantly improves tagging accuracy.", + "The team from the University of Groningen (RUG) also uses separate decoders for lemmatization and tagging, but uses ELMo to initialize the contextual embeddings, leading to large gains in performance. Furthermore, joint training on related languages further improves results.", + "CMU approaches tagging differently than the multi-task decoding we've seen so far (baseline is used for lemmatization). Making use of a hierarchical CRF that first predicts POS (that is subsequently looped back into the encoder), they then seek to predict each feature separately. In particular, predicting POS separately greatly improves results. An attempt to leverage gold typological information led to little gain in the results; experiments suggest that the system is already learning the pertinent information.", + "The team from Ohio State University (OHIOSTATE) concentrates on predicting tags; the baseline lemmatizer is used for lemmatization. To that end, they make use of a dual decoder that first predicts features given only the word embedding as input; the predictions are fed to a GRU seq2seq, which then predicts the sequence of tags.", + "The UNT HiLT+Ling team investigates a low-resource setting of the tagging, by using parallel Bible data to learn a translation matrix between English and the target language, learning morphological tags through analogy with English.", + "The UFAL-Prague team extends their submission from the UD shared task (multi-layer LSTM), replacing the pretrained embeddings with BERT, to great success (first in lemmatization, 2nd in tagging). Although they predict complete tags, they use the individual features to regularize the decoder. Small gains are also obtained from joining multi-lingual corpora and ensembling.", + "CUNI\u2013Malta performs lemmatization as operations over edit actions with LSTM and ReLU. Tagging is a bidirectional LSTM augmented by the edit actions (i.e., two-stage decoding), predicting features separately.", + "The Edinburgh system is a character-based LSTM encoder-decoder with attention, implemented in OpenNMT. It can be seen as an extension of the contextual lemmatization system Lematus BIBREF20 to include morphological tagging, or alternatively as an adaptation of the morphological re-inflection system MED BIBREF21 to incorporate context and perform analysis rather than re-inflection. Like these systems it uses a completely generic encoder-decoder architecture with no specific adaptation to the morphological processing task other than the form of the input. In the submitted version of the system, the input is split into short chunks corresponding to the target word plus one word of context on either side, and the system is trained to output the corresponding lemmas and tags for each three-word chunk.", + "Several teams relied on external resources to improve their lemmatization and feature analysis. Several teams made use of pre-trained embeddings. CHARLES-SAARLAND-2 and UFALPRAGUE-1 used pretrained contextual embeddings (BERT) provided by Google BIBREF22. CBNU-1 used a mix of pre-trained embeddings from the CoNLL 2017 shared task and fastText. Further, some teams trained their own embeddings to aid performance." + ], + [ + "In general, the application of typology to natural language processing BIBREF23, BIBREF24 provides an interesting avenue for multilinguality. Further, our shared task was designed to only leverage a single helper language, though many may exist with lexical or morphological overlap with the target language. Techniques like those of BIBREF25 may aid in designing universal inflection architectures. Neither task this year included unannotated monolingual corpora. Using such data is well-motivated from an L1-learning point of view, and may affect the performance of low-resource data settings.", + "In the case of inflection an interesting future topic could involve departing from orthographic representation and using more IPA-like representations, i.e. transductions over pronunciations. Different languages, in particular those with idiosyncratic orthographies, may offer new challenges in this respect.", + "Only one team tried to learn inflection in a multilingual setting\u2014i.e. to use all training data to train one model. Such transfer learning is an interesting avenue of future research, but evaluation could be difficult. Whether any cross-language transfer is actually being learned vs. whether having more data better biases the networks to copy strings is an evaluation step to disentangle.", + "Creating new data sets that accurately reflect learner exposure (whether L1 or L2) is also an important consideration in the design of future shared tasks. One pertinent facet of this is information about inflectional categories\u2014often the inflectional information is insufficiently prescribed by the lemma, as with the Romanian verbal inflection classes or nominal gender in German.", + "As we move toward multilingual models for morphology, it becomes important to understand which representations are critical or irrelevant for adapting to new languages; this may be probed in the style of BIBREF27, and it can be used as a first step toward designing systems that avoid catastrophic forgetting as they learn to inflect new languages BIBREF28.", + "Future directions for Task 2 include exploring cross-lingual analysis\u2014in stride with both Task 1 and BIBREF29\u2014and leveraging these analyses in downstream tasks." + ], + [ + "The SIGMORPHON 2019 shared task provided a type-level evaluation on 100 language pairs in 79 languages and a token-level evaluation on 107 treebanks in 66 languages, of systems for inflection and analysis. On task 1 (low-resource inflection with cross-lingual transfer), 14 systems were submitted, while on task 2 (lemmatization and morphological feature analysis), 16 systems were submitted. All used neural network models, completing a trend in past years' shared tasks and other recent work on morphology.", + "In task 1, gains from cross-lingual training were generally modest, with gains positively correlating with the linguistic similarity of the two languages.", + "In the second task, several methods were implemented by multiple groups, with the most successful systems implementing variations of multi-headed attention, multi-level encoding, multiple decoders, and ELMo and BERT contextual embeddings.", + "We have released the training, development, and test sets, and expect these datasets to provide a useful benchmark for future research into learning of inflectional morphology and string-to-string transduction." + ], + [ + "MS has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 771113)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0333/environment/Dockerfile b/qasper-0333/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0333/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/* diff --git a/qasper-0333/instruction.md b/qasper-0333/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..ee7ab88d9c899a4ca66b56514bfe2c81d1541984 --- /dev/null +++ b/qasper-0333/instruction.md @@ -0,0 +1,112 @@ +Name of Paper: Hierarchical Multi-Task Natural Language Understanding for Cross-domain Conversational AI: HERMIT NLU + +Question: Which publicly available NLU dataset is used? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Introduction ::: Cross-domain NLU", + "Introduction ::: Multi-task NLU", + "Introduction ::: Multi-dialogue act and -intent NLU", + "Related Work", + "Jointly parsing dialogue acts and frame-like structures", + "Jointly parsing dialogue acts and frame-like structures ::: Architecture description", + "Experimental Evaluation", + "Experimental Evaluation ::: Datasets", + "Experimental Evaluation ::: Datasets ::: NLU-Benchmark dataset", + "Experimental Evaluation ::: Datasets ::: ROMULUS dataset", + "Experimental Evaluation ::: Experimental setup", + "Experimental Evaluation ::: Experiments on the NLU-Benchmark", + "Experimental Evaluation ::: Experiments on the NLU-Benchmark ::: Ablation study", + "Experimental Evaluation ::: Experiments on the ROMULUS dataset", + "Experimental Evaluation ::: Discussion", + "Future Work", + "Conclusion", + "Acknowledgement" + ], + "paragraphs": [ + [ + "Research in Conversational AI (also known as Spoken Dialogue Systems) has applications ranging from home devices to robotics, and has a growing presence in industry. A key problem in real-world Dialogue Systems is Natural Language Understanding (NLU) \u2013 the process of extracting structured representations of meaning from user utterances. In fact, the effective extraction of semantics is an essential feature, being the entry point of any Natural Language interaction system. Apart from challenges given by the inherent complexity and ambiguity of human language, other challenges arise whenever the NLU has to operate over multiple domains. In fact, interaction patterns, domain, and language vary depending on the device the user is interacting with. For example, chit-chatting and instruction-giving for executing an action are different processes in terms of language, domain, syntax and interaction schemes involved. And what if the user combines two interaction domains: \u201cplay some music, but first what's the weather tomorrow\u201d?", + "In this work, we present HERMIT, a HiERarchical MultI-Task Natural Language Understanding architecture, designed for effective semantic parsing of domain-independent user utterances, extracting meaning representations in terms of high-level intents and frame-like semantic structures. With respect to previous approaches to NLU for SDS, HERMIT stands out for being a cross-domain, multi-task architecture, capable of recognising multiple intents/frames in an utterance. HERMIT also shows better performance with respect to current state-of-the-art commercial systems. Such a novel combination of requirements is discussed below." + ], + [ + "A cross-domain dialogue agent must be able to handle heterogeneous types of conversation, such as chit-chatting, giving directions, entertaining, and triggering domain/task actions. A domain-independent and rich meaning representation is thus required to properly capture the intent of the user. Meaning is modelled here through three layers of knowledge: dialogue acts, frames, and frame arguments. Frames and arguments can be in turn mapped to domain-dependent intents and slots, or to Frame Semantics' BIBREF0 structures (i.e. semantic frames and frame elements, respectively), which allow handling of heterogeneous domains and language." + ], + [ + "Deriving such a multi-layered meaning representation can be approached through a multi-task learning approach. Multi-task learning has found success in several NLP problems BIBREF1, BIBREF2, especially with the recent rise of Deep Learning. Thanks to the possibility of building complex networks, handling more tasks at once has been proven to be a successful solution, provided that some degree of dependence holds between the tasks. Moreover, multi-task learning allows the use of different datasets to train sub-parts of the network BIBREF3. Following the same trend, HERMIT is a hierarchical multi-task neural architecture which is able to deal with the three tasks of tagging dialogue acts, frame-like structures, and their arguments in parallel. The network, based on self-attention mechanisms, seq2seq bi-directional Long-Short Term Memory (BiLSTM) encoders, and CRF tagging layers, is hierarchical in the sense that information output from earlier layers flows through the network, feeding following layers to solve downstream dependent tasks." + ], + [ + "Another degree of complexity in NLU is represented by the granularity of knowledge that can be extracted from an utterance. Utterance semantics is often rich and expressive: approximating meaning to a single user intent is often not enough to convey the required information. As opposed to the traditional single-dialogue act and single-intent view in previous work BIBREF4, BIBREF5, BIBREF6, HERMIT operates on a meaning representation that is multi-dialogue act and multi-intent. In fact, it is possible to model an utterance's meaning through multiple dialogue acts and intents at the same time. For example, the user would be able both to request tomorrow's weather and listen to his/her favourite music with just a single utterance.", + "A further requirement is that for practical application the system should be competitive with state-of-the-art: we evaluate HERMIT's effectiveness by running several empirical investigations. We perform a robust test on a publicly available NLU-Benchmark (NLU-BM) BIBREF7 containing 25K cross-domain utterances with a conversational agent. The results obtained show a performance higher than well-known off-the-shelf tools (i.e., Rasa, DialogueFlow, LUIS, and Watson). The contribution of the different network components is then highlighted through an ablation study. We also test HERMIT on the smaller Robotics-Oriented MUltitask Language UnderStanding (ROMULUS) corpus, annotated with Dialogue Acts and Frame Semantics. HERMIT produces promising results for the application in a real scenario." + ], + [ + "Much research on Natural (or Spoken, depending on the input) Language Understanding has been carried out in the area of Spoken Dialogue Systems BIBREF8, where the advent of statistical learning has led to the application of many data-driven approaches BIBREF9. In recent years, the rise of deep learning models has further improved the state-of-the-art. Recurrent Neural Networks (RNNs) have proven to be particularly successful, especially uni- and bi-directional LSTMs and Gated Recurrent Units (GRUs). The use of such deep architectures has also fostered the development of joint classification models of intents and slots. Bi-directional GRUs are applied in BIBREF10, where the hidden state of each time step is used for slot tagging in a seq2seq fashion, while the final state of the GRU is used for intent classification. The application of attention mechanisms in a BiLSTM architecture is investigated in BIBREF5, while the work of BIBREF11 explores the use of memory networks BIBREF12 to exploit encoding of historical user utterances to improve the slot-filling task. Seq2seq with self-attention is applied in BIBREF13, where the classified intent is also used to guide a special gated unit that contributes to the slot classification of each token.", + "One of the first attempts to jointly detect domains in addition to intent-slot tagging is the work of BIBREF4. An utterance syntax is encoded through a Recursive NN, and it is used to predict the joined domain-intent classes. Syntactic features extracted from the same network are used in the per-word slot classifier. The work of BIBREF6 applies the same idea of BIBREF10, this time using a context-augmented BiLSTM, and performing domain-intent classification as a single joint task. As in BIBREF11, the history of user utterances is also considered in BIBREF14, in combination with a dialogue context encoder. A two-layer hierarchical structure made of a combination of BiLSTM and BiGRU is used for joint classification of domains and intents, together with slot tagging. BIBREF15 apply multi-task learning to the dialogue domain. Dialogue state tracking, dialogue act and intent classification, and slot tagging are jointly learned. Dialogue states and user utterances are encoded to provide hidden representations, which jointly affect all the other tasks.", + "Many previous systems are trained and compared over the ATIS (Airline Travel Information Systems) dataset BIBREF16, which covers only the flight-booking domain. Some of them also use bigger, not publicly available datasets, which appear to be similar to the NLU-BM in terms of number of intents and slots, but they cover no more than three or four domains. Our work stands out for its more challenging NLU setting, since we are dealing with a higher number of domains/scenarios (18), intents (64) and slots (54) in the NLU-BM dataset, and dialogue acts (11), frames (58) and frame elements (84) in the ROMULUS dataset. Moreover, we propose a multi-task hierarchical architecture, where each layer is trained to solve one of the three tasks. Each of these is tackled with a seq2seq classification using a CRF output layer, as in BIBREF3.", + "The NLU problem has been studied also on the Interactive Robotics front, mostly to support basic dialogue systems, with few dialogue states and tailored for specific tasks, such as semantic mapping BIBREF17, navigation BIBREF18, BIBREF19, or grounded language learning BIBREF20. However, the designed approaches, either based on formal languages or data-driven, have never been shown to scale to real world scenarios. The work of BIBREF21 makes a step forward in this direction. Their model still deals with the single `pick and place' domain, covering no more than two intents, but it is trained on several thousands of examples, making it able to manage more unstructured language. An attempt to manage a higher number of intents, as well as more variable language, is represented by the work of BIBREF22 where the sole Frame Semantics is applied to represent user intents, with no Dialogue Acts." + ], + [ + "The identification of Dialogue Acts (henceforth DAs) is required to drive the dialogue manager to the next dialogue state. General frame structures (FRs) provide a reference framework to capture user intents, in terms of required or desired actions that a conversational agent has to perform. Depending on the level of abstraction required by an application, these can be interpreted as more domain-dependent paradigms like intent, or to shallower representations, such as semantic frames, as conceived in FrameNet BIBREF23. From this perspective, semantic frames represent a versatile abstraction that can be mapped over an agent's capabilities, allowing also the system to be easily extended with new functionalities without requiring the definition of new ad-hoc structures. Similarly, frame arguments (ARs) act as slots in a traditional intent-slots scheme, or to frame elements for semantic frames.", + "In our work, the whole process of extracting a complete semantic interpretation as required by the system is tackled with a multi-task learning approach across DAs, FRs, and ARs. Each of these tasks is modelled as a seq2seq problem, where a task-specific label is assigned to each token of the sentence according to the IOB2 notation BIBREF24, with \u201cB-\u201d marking the Beginning of the chunk, \u201cI-\u201d the tokens Inside the chunk while \u201cO-\u201d is assigned to any token that does not belong to any chunk. Task labels are drawn from the set of classes defined for DAs, FRs, and ARs. Figure TABREF5 shows an example of the tagging layers over the sentence Where can I find Starbucks?, where Frame Semantics has been selected as underlying reference theory." + ], + [ + "The central motivation behind the proposed architecture is that there is a dependence among the three tasks of identifying DAs, FRs, and ARs. The relationship between tagging frame and arguments appears more evident, as also developed in theories like Frame Semantics \u2013 although it is defined independently by each theory. However, some degree of dependence also holds between the DAs and FRs. For example, the FrameNet semantic frame Desiring, expressing a desire of the user for an event to occur, is more likely to be used in the context of an Inform DA, which indicates the state of notifying the agent with an information, other than in an Instruction. This is clearly visible in interactions like \u201cI'd like a cup of hot chocolate\u201d or \u201cI'd like to find a shoe shop\u201d, where the user is actually notifying the agent about a desire of hers/his.", + "In order to reflect such inter-task dependence, the classification process is tackled here through a hierarchical multi-task learning approach. We designed a multi-layer neural network, whose architecture is shown in Figure FIGREF7, where each layer is trained to solve one of the three tasks, namely labelling dialogue acts ($DA$ layer), semantic frames ($FR$ layer), and frame elements ($AR$ layer). The layers are arranged in a hierarchical structure that allows the information produced by earlier layers to be fed to downstream tasks.", + "The network is mainly composed of three BiLSTM BIBREF25 encoding layers. A sequence of input words is initially converted into an embedded representation through an ELMo embeddings layer BIBREF26, and is fed to the $DA$ layer. The embedded representation is also passed over through shortcut connections BIBREF1, and concatenated with both the outputs of the $DA$ and $FR$ layers. Self-attention layers BIBREF27 are placed after the $DA$ and $FR$ BiLSTM encoders. Where $w_t$ is the input word at time step $t$ of the sentence $\\textbf {\\textrm {w}} = (w_1, ..., w_T)$, the architecture can be formalised by:", + "where $\\oplus $ represents the vector concatenation operator, $e_t$ is the embedding of the word at time $t$, and $\\textbf {\\textrm {s}}^{L}$ = ($s_1^L$, ..., $s_T^L$) is the embedded sequence output of each $L$ layer, with $L = \\lbrace DA, FR, AR\\rbrace $. Given an input sentence, the final sequence of labels $\\textbf {y}^L$ for each task is computed through a CRF tagging layer, which operates on the output of the $DA$ and $FR$ self-attention, and of the $AR$ BiLSTM embedding, so that:", + "where a$^{DA}$, a$^{FR}$ are attended embedded sequences. Due to shortcut connections, layers in the upper levels of the architecture can rely both on direct word embeddings as well as the hidden representation $a_t^L$ computed by a previous layer. Operationally, the latter carries task specific information which, combined with the input embeddings, helps in stabilising the classification of each CRF layer, as shown by our experiments. The network is trained by minimising the sum of the individual negative log-likelihoods of the three CRF layers, while at test time the most likely sequence is obtained through the Viterbi decoding over the output scores of the CRF layer." + ], + [ + "In order to assess the effectiveness of the proposed architecture and compare against existing off-the-shelf tools, we run several empirical evaluations." + ], + [ + "We tested the system on two datasets, different in size and complexity of the addressed language." + ], + [ + "The first (publicly available) dataset, NLU-Benchmark (NLU-BM), contains $25,716$ utterances annotated with targeted Scenario, Action, and involved Entities. For example, \u201cschedule a call with Lisa on Monday morning\u201d is labelled to contain a calendar scenario, where the set_event action is instantiated through the entities [event_name: a call with Lisa] and [date: Monday morning]. The Intent is then obtained by concatenating scenario and action labels (e.g., calendar_set_event). This dataset consists of multiple home assistant task domains (e.g., scheduling, playing music), chit-chat, and commands to a robot BIBREF7." + ], + [ + "The second dataset, ROMULUS, is composed of $1,431$ sentences, for each of which dialogue acts, semantic frames, and corresponding frame elements are provided. This dataset is being developed for modelling user utterances to open-domain conversational systems for robotic platforms that are expected to handle different interaction situations/patterns \u2013 e.g., chit-chat, command interpretation. The corpus is composed of different subsections, addressing heterogeneous linguistic phenomena, ranging from imperative instructions (e.g., \u201center the bedroom slowly, turn left and turn the lights off \u201d) to complex requests for information (e.g., \u201cgood morning I want to buy a new mobile phone is there any shop nearby?\u201d) or open-domain chit-chat (e.g., \u201cnope thanks let's talk about cinema\u201d). A considerable number of utterances in the dataset is collected through Human-Human Interaction studies in robotic domain ($\\approx $$70\\%$), though a small portion has been synthetically generated for balancing the frame distribution.", + "Note that while the NLU-BM is designed to have at most one intent per utterance, sentences are here tagged following the IOB2 sequence labelling scheme (see example of Figure TABREF5), so that multiple dialogue acts, frames, and frame elements can be defined at the same time for the same utterance. For example, three dialogue acts are identified within the sentence [good morning]$_{\\textsc {Opening}}$ [I want to buy a new mobile phone]$_{\\textsc {Inform}}$ [is there any shop nearby?]$_{\\textsc {Req\\_info}}$. As a result, though smaller, the ROMULUS dataset provides a richer representation of the sentence's semantics, making the tasks more complex and challenging. These observations are highlighted by the statistics in Table TABREF13, that show an average number of dialogue acts, frames and frame elements always greater than 1 (i.e., $1.33$, $1.41$ and $3.54$, respectively)." + ], + [ + "All the models are implemented with Keras BIBREF28 and Tensorflow BIBREF29 as backend, and run on a Titan Xp. Experiments are performed in a 10-fold setting, using one fold for tuning and one for testing. However, since HERMIT is designed to operate on dialogue acts, semantic frames and frame elements, the best hyperparameters are obtained over the ROMULUS dataset via a grid search using early stopping, and are applied also to the NLU-BM models. This guarantees fairness towards other systems, that do not perform any fine-tuning on the training data. We make use of pre-trained 1024-dim ELMo embeddings BIBREF26 as word vector representations without re-training the weights." + ], + [ + "This section shows the results obtained on the NLU-Benchmark (NLU-BM) dataset provided by BIBREF7, by comparing HERMIT to off-the-shelf NLU services, namely: Rasa, Dialogflow, LUIS and Watson. In order to apply HERMIT to NLU-BM annotations, these have been aligned so that Scenarios are treated as DAs, Actions as FRs and Entities as ARs.", + "To make our model comparable against other approaches, we reproduced the same folds as in BIBREF7, where a resized version of the original dataset is used. Table TABREF11 shows some statistics of the NLU-BM and its reduced version. Moreover, micro-averaged Precision, Recall and F1 are computed following the original paper to assure consistency. TP, FP and FN of intent labels are obtained as in any other multi-class task. An entity is instead counted as TP if there is an overlap between the predicted and the gold span, and their labels match.", + "Experimental results are reported in Table TABREF21. The statistical significance is evaluated through the Wilcoxon signed-rank test. When looking at the intent F1, HERMIT performs significantly better than Rasa $[Z=-2.701, p = .007]$ and LUIS $[Z=-2.807, p = .005]$. On the contrary, the improvements w.r.t. Dialogflow $[Z=-1.173, p = .241]$ do not seem to be significant. This is probably due to the high variance obtained by Dialogflow across the 10 folds. Watson is by a significant margin the most accurate system in recognising intents $[Z=-2.191, p = .028]$, especially due to its Precision score.", + "The hierarchical multi-task architecture of HERMIT seems to contribute strongly to entity tagging accuracy. In fact, in this task it performs significantly better than Rasa $[Z=-2.803, p = .005]$, Dialogflow $[Z=-2.803, p = .005]$, LUIS $[Z=-2.803, p = .005]$ and Watson $[Z=-2.805, p = .005]$, with improvements from $7.08$ to $35.92$ of F1.", + "Following BIBREF7, we then evaluated a metric that combines intent and entities, computed by simply summing up the two confusion matrices (Table TABREF23). Results highlight the contribution of the entity tagging task, where HERMIT outperforms the other approaches. Paired-samples t-tests were conducted to compare the HERMIT combined F1 against the other systems. The statistical analysis shows a significant improvement over Rasa $[Z=-2.803, p = .005]$, Dialogflow $[Z=-2.803, p = .005]$, LUIS $[Z=-2.803, p = .005]$ and Watson $[Z=-2.803, p = .005]$." + ], + [ + "In order to assess the contributions of the HERMIT's components, we performed an ablation study. The results are obtained on the NLU-BM, following the same setup as in Section SECREF16.", + "Results are shown in Table TABREF25. The first row refers to the complete architecture, while \u2013SA shows the results of HERMIT without the self-attention mechanism. Then, from this latter we further remove shortcut connections (\u2013 SA/CN) and CRF taggers (\u2013 SA/CRF). The last row (\u2013 SA/CN/CRF) shows the results of a simple architecture, without self-attention, shortcuts, and CRF. Though not significant, the contribution of the several architectural components can be observed. The contribution of self-attention is distributed across all the tasks, with a small inclination towards the upstream ones. This means that while the entity tagging task is mostly lexicon independent, it is easier to identify pivoting keywords for predicting the intent, e.g. the verb \u201cschedule\u201d triggering the calendar_set_event intent. The impact of shortcut connections is more evident on entity tagging. In fact, the effect provided by shortcut connections is that the information flowing throughout the hierarchical architecture allows higher layers to encode richer representations (i.e., original word embeddings + latent semantics from the previous task). Conversely, the presence of the CRF tagger affects mainly the lower levels of the hierarchical architecture. This is not probably due to their position in the hierarchy, but to the way the tasks have been designed. In fact, while the span of an entity is expected to cover few tokens, in intent recognition (i.e., a combination of Scenario and Action recognition) the span always covers all the tokens of an utterance. CRF therefore preserves consistency of IOB2 sequences structure. However, HERMIT seems to be the most stable architecture, both in terms of standard deviation and task performance, with a good balance between intent and entity recognition." + ], + [ + "In this section we report the experiments performed on the ROMULUS dataset (Table TABREF27). Together with the evaluation metrics used in BIBREF7, we report the span F1, computed using the CoNLL-2000 shared task evaluation script, and the Exact Match (EM) accuracy of the entire sequence of labels. It is worth noticing that the EM Combined score is computed as the conjunction of the three individual predictions \u2013 e.g., a match is when all the three sequences are correct.", + "Results in terms of EM reflect the complexity of the different tasks, motivating their position within the hierarchy. Specifically, dialogue act identification is the easiest task ($89.31\\%$) with respect to frame ($82.60\\%$) and frame element ($79.73\\%$), due to the shallow semantics it aims to catch. However, when looking at the span F1, its score ($89.42\\%$) is lower than the frame element identification task ($92.26\\%$). What happens is that even though the label set is smaller, dialogue act spans are supposed to be longer than frame element ones, sometimes covering the whole sentence. Frame elements, instead, are often one or two tokens long, that contribute in increasing span based metrics. Frame identification is the most complex task for several reasons. First, lots of frame spans are interlaced or even nested; this contributes to increasing the network entropy. Second, while the dialogue act label is highly related to syntactic structures, frame identification is often subject to the inherent ambiguity of language (e.g., get can evoke both Commerce_buy and Arriving). We also report the metrics in BIBREF7 for consistency. For dialogue act and frame tasks, scores provide just the extent to which the network is able to detect those labels. In fact, the metrics do not consider any span information, essential to solve and evaluate our tasks. However, the frame element scores are comparable to the benchmark, since the task is very similar.", + "Overall, getting back to the combined EM accuracy, HERMIT seems to be promising, with the network being able to reproduce all the three gold sequences for almost $70\\%$ of the cases. The importance of this result provides an idea of the architecture behaviour over the entire pipeline." + ], + [ + "The experimental evaluation reported in this section provides different insights. The proposed architecture addresses the problem of NLU in wide-coverage conversational systems, modelling semantics through multiple Dialogue Acts and Frame-like structures in an end-to-end fashion. In addition, its hierarchical structure, which reflects the complexity of the single tasks, allows providing rich representations across the whole network. In this respect, we can affirm that the architecture successfully tackles the multi-task problem, with results that are promising in terms of usability and applicability of the system in real scenarios.", + "However, a thorough evaluation in the wild must be carried out, to assess to what extent the system is able to handle complex spoken language phenomena, such as repetitions, disfluencies, etc. To this end, a real scenario evaluation may open new research directions, by addressing new tasks to be included in the multi-task architecture. This is supported by the scalable nature of the proposed approach. Moreover, following BIBREF3, corpora providing different annotations can be exploited within the same multi-task network.", + "We also empirically showed how the same architectural design could be applied to a dataset addressing similar problems. In fact, a comparison with off-the-shelf tools shows the benefits provided by the hierarchical structure, with better overall performance better than any current solution. An ablation study has been performed, assessing the contribution provided by the different components of the network. The results show how the shortcut connections help in the more fine-grained tasks, successfully encoding richer representations. CRFs help when longer spans are being predicted, more present in the upstream tasks.", + "Finally, the seq2seq design allowed obtaining a multi-label approach, enabling the identification of multiple spans in the same utterance that might evoke different dialogue acts/frames. This represents a novelty for NLU in conversational systems, as such a problem has always been tackled as a single-intent detection. However, the seq2seq approach carries also some limitations, especially on the Frame Semantics side. In fact, label sequences are linear structures, not suitable for representing nested predicates, a tough and common problem in Natural Language. For example, in the sentence \u201cI want to buy a new mobile phone\u201d, the [to buy a new mobile phone] span represents both the Desired_event frame element of the Desiring frame and a Commerce_buy frame at the same time. At the moment of writing, we are working on modeling nested predicates through the application of bilinear models." + ], + [ + "We have started integrating a corpus of 5M sentences of real users chit-chatting with our conversational agent, though at the time of writing they represent only $16\\%$ of the current dataset.", + "As already pointed out in Section SECREF28, there are some limitations in the current approach that need to be addressed. First, we have to assess the network's capability in handling typical phenomena of spontaneous spoken language input, such as repetitions and disfluencies BIBREF30. This may open new research directions, by including new tasks to identify/remove any kind of noise from the spoken input. Second, the seq2seq scheme does not deal with nested predicates, a common aspect of Natural Language. To the best of our knowledge, there is no architecture that implements an end-to-end network for FrameNet based semantic parsing. Following previous work BIBREF2, one of our future goals is to tackle such problems through hierarchical multi-task architectures that rely on bilinear models." + ], + [ + "In this paper we presented HERMIT NLU, a hierarchical multi-task architecture for semantic parsing sentences for cross-domain spoken dialogue systems. The problem is addressed using a seq2seq model employing BiLSTM encoders and self-attention mechanisms and followed by CRF tagging layers. We evaluated HERMIT on a 25K sentences NLU-Benchmark and out-perform state-of-the-art NLU tools such as Rasa, Dialogflow, LUIS and Watson, even without specific fine-tuning of the model." + ], + [ + "This research was partially supported by the European Union's Horizon 2020 research and innovation programme under grant agreement No. 688147 (MuMMER project)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0334/environment/Dockerfile b/qasper-0334/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0334/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/* diff --git a/qasper-0334/instruction.md b/qasper-0334/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..6365e5ddc8d9be5d2b17597cb4f397b118126bdd --- /dev/null +++ b/qasper-0334/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Hierarchical Multi-Task Natural Language Understanding for Cross-domain Conversational AI: HERMIT NLU + +Question: What metrics other than entity tagging are compared? \ No newline at end of file diff --git a/qasper-0335/instruction.md b/qasper-0335/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..4d2f6db35c247beea71b4e256dcdab754bf6ee2e --- /dev/null +++ b/qasper-0335/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Interactive Machine Comprehension with Information Seeking Agents + +Question: Do they provide decision sequences as supervision while training models? \ No newline at end of file diff --git a/qasper-0350/environment/Dockerfile b/qasper-0350/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0350/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/* diff --git a/qasper-0350/instruction.md b/qasper-0350/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..7d3a04d3f14172895fb7d8cc63139f3195b3270e --- /dev/null +++ b/qasper-0350/instruction.md @@ -0,0 +1,134 @@ +Name of Paper: Self-Taught Convolutional Neural Networks for Short Text Clustering + +Question: What were the evaluation metrics used? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Short Text Clustering", + "Deep Neural Networks", + "Methodology", + "Deep Convolutional Neural Networks", + "Unsupervised Dimensionality Reduction", + "Learning", + "K-means for Clustering", + "Datasets", + "Pre-trained Word Vectors", + "Comparisons", + "Evaluation Metrics", + "Hyperparameter Settings", + "Results and Analysis", + "Conclusions", + "Acknowledgments" + ], + "paragraphs": [ + [ + "Short text clustering is of great importance due to its various applications, such as user profiling BIBREF0 and recommendation BIBREF1 , for nowaday's social media dataset emerged day by day. However, short text clustering has the data sparsity problem and most words only occur once in each short text BIBREF2 . As a result, the Term Frequency-Inverse Document Frequency (TF-IDF) measure cannot work well in short text setting. In order to address this problem, some researchers work on expanding and enriching the context of data from Wikipedia BIBREF3 or an ontology BIBREF4 . However, these methods involve solid Natural Language Processing (NLP) knowledge and still use high-dimensional representation which may result in a waste of both memory and computation time. Another way to overcome these issues is to explore some sophisticated models to cluster short texts. For example, Yin and Wang BIBREF5 proposed a Dirichlet multinomial mixture model-based approach for short text clustering. Yet how to design an effective model is an open question, and most of these methods directly trained based on Bag-of-Words (BoW) are shallow structures which cannot preserve the accurate semantic similarities.", + "Recently, with the help of word embedding, neural networks demonstrate their great performance in terms of constructing text representation, such as Recursive Neural Network (RecNN) BIBREF6 , BIBREF7 and Recurrent Neural Network (RNN) BIBREF8 . However, RecNN exhibits high time complexity to construct the textual tree, and RNN, using the hidden layer computed at the last word to represent the text, is a biased model where later words are more dominant than earlier words BIBREF9 . Whereas for the non-biased models, the learned representation of one text can be extracted from all the words in the text with non-dominant learned weights. More recently, Convolution Neural Network (CNN), as the most popular non-biased model and applying convolutional filters to capture local features, has achieved a better performance in many NLP applications, such as sentence modeling BIBREF10 , relation classification BIBREF11 , and other traditional NLP tasks BIBREF12 . Most of the previous works focus CNN on solving supervised NLP tasks, while in this paper we aim to explore the power of CNN on one unsupervised NLP task, short text clustering.", + "We systematically introduce a simple yet surprisingly powerful Self-Taught Convolutional neural network framework for Short Text Clustering, called STC INLINEFORM0 . An overall architecture of our proposed approach is illustrated in Figure FIGREF5 . We, inspired by BIBREF13 , BIBREF14 , utilize a self-taught learning framework into our task. In particular, the original raw text features are first embedded into compact binary codes INLINEFORM1 with the help of one traditional unsupervised dimensionality reduction function. Then text matrix INLINEFORM2 projected from word embeddings are fed into CNN model to learn the deep feature representation INLINEFORM3 and the output units are used to fit the pre-trained binary codes INLINEFORM4 . After obtaining the learned features, K-means algorithm is employed on them to cluster texts into clusters INLINEFORM5 . Obviously, we call our approach \u201cself-taught\u201d because the CNN model is learnt from the pseudo labels generated from the previous stage, which is quite different from the term \u201cself-taught\u201d in BIBREF15 . Our main contributions can be summarized as follows:", + "This work is an extension of our conference paper BIBREF16 , and they differ in the following aspects. First, we put forward a general a self-taught CNN framework in this paper which can flexibly couple various semantic features, whereas the conference version can be seen as a specific example of this work. Second, in this paper we use a new short text dataset, Biomedical, in the experiment to verify the effectiveness of our approach. Third, we put much effort on studying the influence of various different semantic features integrated in our self-taught CNN framework, which is not involved in the conference paper.", + "For the purpose of reproducibility, we make the datasets and software used in our experiments publicly available at the website.", + "The remainder of this paper is organized as follows: In Section SECREF2 , we first briefly survey several related works. In Section SECREF3 , we describe the proposed approach STC INLINEFORM0 and implementation details. Experimental results and analyses are presented in Section SECREF4 . Finally, conclusions are given in the last Section." + ], + [ + "In this section, we review the related work from the following two perspectives: short text clustering and deep neural networks." + ], + [ + "There have been several studies that attempted to overcome the sparseness of short text representation. One way is to expand and enrich the context of data. For example, Banerjee et al. BIBREF3 proposed a method of improving the accuracy of short text clustering by enriching their representation with additional features from Wikipedia, and Fodeh et al. BIBREF4 incorporate semantic knowledge from an ontology into text clustering. However, these works need solid NLP knowledge and still use high-dimensional representation which may result in a waste of both memory and computation time. Another direction is to map the original features into reduced space, such as Latent Semantic Analysis (LSA) BIBREF17 , Laplacian Eigenmaps (LE) BIBREF18 , and Locality Preserving Indexing (LPI) BIBREF19 . Even some researchers explored some sophisticated models to cluster short texts. For example, Yin and Wang BIBREF5 proposed a Dirichlet multinomial mixture model-based approach for short text clustering. Moreover, some studies even focus the above both two streams. For example, Tang et al. BIBREF20 proposed a novel framework which enrich the text features by employing machine translation and reduce the original features simultaneously through matrix factorization techniques.", + "Despite the above clustering methods can alleviate sparseness of short text representation to some extent, most of them ignore word order in the text and belong to shallow structures which can not fully capture accurate semantic similarities." + ], + [ + "Recently, there is a revival of interest in DNN and many researchers have concentrated on using Deep Learning to learn features. Hinton and Salakhutdinov BIBREF21 use DAE to learn text representation. During the fine-tuning procedure, they use backpropagation to find codes that are good at reconstructing the word-count vector.", + "More recently, researchers propose to use external corpus to learn a distributed representation for each word, called word embedding BIBREF22 , to improve DNN performance on NLP tasks. The Skip-gram and continuous bag-of-words models of Word2vec BIBREF23 propose a simple single-layer architecture based on the inner product between two word vectors, and Pennington et al. BIBREF24 introduce a new model for word representation, called GloVe, which captures the global corpus statistics.", + "In order to learn the compact representation vectors of sentences, Le and Mikolov BIBREF25 directly extend the previous Word2vec BIBREF23 by predicting words in the sentence, which is named Paragraph Vector (Para2vec). Para2vec is still a shallow window-based method and need a larger corpus to yield better performance. More neural networks utilize word embedding to capture true meaningful syntactic and semantic regularities, such as RecNN BIBREF6 , BIBREF7 and RNN BIBREF8 . However, RecNN exhibits high time complexity to construct the textual tree, and RNN, using the layer computed at the last word to represent the text, is a biased model. Recently, Long Short-Term Memory (LSTM) BIBREF26 and Gated Recurrent Unit (GRU) BIBREF27 , as sophisticated recurrent hidden units of RNN, has presented its advantages in many sequence generation problem, such as machine translation BIBREF28 , speech recognition BIBREF29 , and text conversation BIBREF30 . While, CNN is better to learn non-biased implicit features which has been successfully exploited for many supervised NLP learning tasks as described in Section SECREF1 , and various CNN based variants are proposed in the recent works, such as Dynamic Convolutional Neural Network (DCNN) BIBREF10 , Gated Recursive Convolutional Neural Network (grConv) BIBREF31 and Self-Adaptive Hierarchical Sentence model (AdaSent) BIBREF32 .", + "In the past few days, Visin et al. BIBREF33 have attempted to replace convolutional layer in CNN to learn non-biased features for object recognition with four RNNs, called ReNet, that sweep over lower-layer features in different directions: (1) bottom to top, (2) top to bottom, (3) left to right and (4) right to left. However, ReNet does not outperform state-of-the-art convolutional neural networks on any of the three benchmark datasets, and it is also a supervised learning model for classification. Inspired by Skip-gram of word2vec BIBREF34 , BIBREF23 , Skip-thought model BIBREF35 describe an approach for unsupervised learning of a generic, distributed sentence encoder. Similar as Skip-gram model, Skip-thought model trains an encoder-decoder model that tries to reconstruct the surrounding sentences of an encoded sentence and released an off-the-shelf encoder to extract sentence representation. Even some researchers introduce continuous Skip-gram and negative sampling to CNN for learning visual representation in an unsupervised manner BIBREF36 . This paper, from a new perspective, puts forward a general self-taught CNN framework which can flexibly couple various semantic features and achieve a good performance on one unsupervised learning task, short text clustering." + ], + [ + "Assume that we are given a dataset of INLINEFORM0 training texts denoted as: INLINEFORM1 , where INLINEFORM2 is the dimensionality of the original BoW representation. Denote its tag set as INLINEFORM3 and the pre-trained word embedding set as INLINEFORM4 , where INLINEFORM5 is the dimensionality of word vectors and INLINEFORM6 is the vocabulary size. In order to learn the INLINEFORM7 -dimensional deep feature representation INLINEFORM8 from CNN in an unsupervised manner, some unsupervised dimensionality reduction methods INLINEFORM9 are employed to guide the learning of CNN model. Our goal is to cluster these texts INLINEFORM10 into clusters INLINEFORM11 based on the learned deep feature representation while preserving the semantic consistency.", + "As depicted in Figure FIGREF5 , the proposed framework consist of three components, deep convolutional neural network (CNN), unsupervised dimensionality reduction function and K-means module. In the rest sections, we first present the first two components respectively, and then give the trainable parameters and the objective function to learn the deep feature representation. Finally, the last section describe how to perform clustering on the learned features." + ], + [ + "In this section, we briefly review one popular deep convolutional neural network, Dynamic Convolutional Neural Network (DCNN) BIBREF10 as an instance of CNN in the following sections, which as the foundation of our proposed method has been successfully proposed for the completely supervised learning task, text classification.", + "Taking a neural network with two convolutional layers in Figure FIGREF9 as an example, the network transforms raw input text to a powerful representation. Particularly, each raw text vector INLINEFORM0 is projected into a matrix representation INLINEFORM1 by looking up a word embedding INLINEFORM2 , where INLINEFORM3 is the length of one text. We also let INLINEFORM4 and INLINEFORM5 denote the weights of the neural networks. The network defines a transformation INLINEFORM6 INLINEFORM7 which transforms an input raw text INLINEFORM8 to a INLINEFORM9 -dimensional deep representation INLINEFORM10 . There are three basic operations described as follows:", + "Wide one-dimensional convolution This operation INLINEFORM0 is applied to an individual row of the sentence matrix INLINEFORM1 , and yields a resulting matrix INLINEFORM2 , where INLINEFORM3 is the width of convolutional filter.", + "Folding In this operation, every two rows in a feature map are simply summed component-wisely. For a map of INLINEFORM0 rows, folding returns a map of INLINEFORM1 rows, thus halving the size of the representation and yielding a matrix feature INLINEFORM2 . Note that folding operation does not introduce any additional parameters.", + "Dynamic INLINEFORM0 -max pooling Assuming the pooling parameter as INLINEFORM1 , INLINEFORM2 -max pooling selects the sub-matrix INLINEFORM3 of the INLINEFORM4 highest values in each row of the matrix INLINEFORM5 . For dynamic INLINEFORM6 -max pooling, the pooling parameter INLINEFORM7 is dynamically selected in order to allow for a smooth extraction of higher-order and longer-range features BIBREF10 . Given a fixed pooling parameter INLINEFORM8 for the topmost convolutional layer, the parameter INLINEFORM9 of INLINEFORM10 -max pooling in the INLINEFORM11 -th convolutional layer can be computed as follows: DISPLAYFORM0 ", + "where INLINEFORM0 is the total number of convolutional layers in the network." + ], + [ + "As described in Figure FIGREF5 , the dimensionality reduction function is defined as follows: DISPLAYFORM0 ", + "where, INLINEFORM0 are the INLINEFORM1 -dimensional reduced latent space representations. Here, we take four popular dimensionality reduction methods as examples in our framework.", + "Average Embedding (AE): This method directly averages the word embeddings which are respectively weighted with TF and TF-IDF. Huang et al. BIBREF37 used this strategy as the global context in their task, and Socher et al. BIBREF7 and Lai et al. BIBREF9 used this method for text classification. The weighted average of all word vectors in one text can be computed as follows: DISPLAYFORM0 ", + "where INLINEFORM0 can be any weighting function that captures the importance of word INLINEFORM1 in the text INLINEFORM2 .", + "Latent Semantic Analysis (LSA): LSA BIBREF17 is the most popular global matrix factorization method, which applies a dimension reducing linear projection, Singular Value Decomposition (SVD), of the corresponding term/document matrix. Suppose the rank of INLINEFORM0 is INLINEFORM1 , LSA decompose INLINEFORM2 into the product of three other matrices: DISPLAYFORM0 ", + "where INLINEFORM0 and INLINEFORM1 are the singular values of INLINEFORM2 , INLINEFORM3 is a set of left singular vectors and INLINEFORM4 is a set of right singular vectors. LSA uses the top INLINEFORM5 vectors in INLINEFORM6 as the transformation matrix to embed the original text features into a INLINEFORM7 -dimensional subspace INLINEFORM8 BIBREF17 .", + "Laplacian Eigenmaps (LE): The top eigenvectors of graph Laplacian, defined on the similarity matrix of texts, are used in the method, which can discover the manifold structure of the text space BIBREF18 . In order to avoid storing the dense similarity matrix, many approximation techniques are proposed to reduce the memory usage and computational complexity for LE. There are two representative approximation methods, sparse similarity matrix and Nystr INLINEFORM0 m approximation. Following previous studies BIBREF38 , BIBREF13 , we select the former technique to construct the INLINEFORM1 local similarity matrix INLINEFORM2 by using heat kernel as follows: DISPLAYFORM0 ", + "where, INLINEFORM0 is a tuning parameter (default is 1) and INLINEFORM1 represents the set of INLINEFORM2 -nearest-neighbors of INLINEFORM3 . By introducing a diagonal INLINEFORM4 matrix INLINEFORM5 whose entries are given by INLINEFORM6 , the graph Laplacian INLINEFORM7 can be computed by ( INLINEFORM8 ). The optimal INLINEFORM9 real-valued matrix INLINEFORM10 can be obtained by solving the following objective function: DISPLAYFORM0 ", + "where INLINEFORM0 is the trace function, INLINEFORM1 requires the different dimensions to be uncorrelated, and INLINEFORM2 requires each dimension to achieve equal probability as positive or negative).", + "Locality Preserving Indexing (LPI): This method extends LE to deal with unseen texts by approximating the linear function INLINEFORM0 BIBREF13 , and the subspace vectors are obtained by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the Riemannian manifold BIBREF19 . Similar as LE, we first construct the local similarity matrix INLINEFORM1 , then the graph Laplacian INLINEFORM2 can be computed by ( INLINEFORM3 ), where INLINEFORM4 measures the local density around INLINEFORM5 and is equal to INLINEFORM6 . Compute the eigenvectors INLINEFORM7 and eigenvalues INLINEFORM8 of the following generalized eigen-problem: DISPLAYFORM0 ", + "The mapping function INLINEFORM0 can be obtained and applied to the unseen data BIBREF38 .", + "All of the above methods claim a better performance in capturing semantic similarity between texts in the reduced latent space representation INLINEFORM0 than in the original representation INLINEFORM1 , while the performance of short text clustering can be further enhanced with the help of our framework, self-taught CNN." + ], + [ + "The last layer of CNN is an output layer as follows: DISPLAYFORM0 ", + "where, INLINEFORM0 is the deep feature representation, INLINEFORM1 is the output vector and INLINEFORM2 is weight matrix.", + "In order to incorporate the latent semantic features INLINEFORM0 , we first binary the real-valued vectors INLINEFORM1 to the binary codes INLINEFORM2 by setting the threshold to be the media vector INLINEFORM3 . Then, the output vector INLINEFORM4 is used to fit the binary codes INLINEFORM5 via INLINEFORM6 logistic operations as follows: DISPLAYFORM0 ", + "All parameters to be trained are defined as INLINEFORM0 . DISPLAYFORM0 ", + "Given the training text collection INLINEFORM0 , and the pre-trained binary codes INLINEFORM1 , the log likelihood of the parameters can be written down as follows: DISPLAYFORM0 ", + "Following the previous work BIBREF10 , we train the network with mini-batches by back-propagation and perform the gradient-based optimization using the Adagrad update rule BIBREF39 . For regularization, we employ dropout with 50% rate to the penultimate layer BIBREF10 , BIBREF40 ." + ], + [ + "With the given short texts, we first utilize the trained deep neural network to obtain the semantic representations INLINEFORM0 , and then employ traditional K-means algorithm to perform clustering." + ], + [ + "We test our proposed approach on three public short text datasets. The summary statistics and semantic topics of these datasets are described in Table TABREF24 and Table TABREF25 .", + "SearchSnippets. This dataset was selected from the results of web search transaction using predefined phrases of 8 different domains by Phan et al. BIBREF41 .", + "StackOverflow. We use the challenge data published in Kaggle.com. The raw dataset consists 3,370,528 samples through July 31st, 2012 to August 14, 2012. In our experiments, we randomly select 20,000 question titles from 20 different tags as in Table TABREF25 .", + "Biomedical. We use the challenge data published in BioASQ's official website. In our experiments, we randomly select 20, 000 paper titles from 20 different MeSH major topics as in Table TABREF25 . As described in Table TABREF24 , the max length of selected paper titles is 53.", + "For these datasets, we randomly select 10% of data as the development set. Since SearchSnippets has been pre-processed by Phan et al. BIBREF41 , we do not further process this dataset. In StackOverflow, texts contain lots of computer terminology, and symbols and capital letters are meaningful, thus we do not do any pre-processed procedures. For Biomedical, we remove the symbols and convert letters into lower case." + ], + [ + "We use the publicly available word2vec tool to train word embeddings, and the most parameters are set as same as Mikolov et al. BIBREF23 to train word vectors on Google News setting, except of vector dimensionality using 48 and minimize count using 5. For SearchSnippets, we train word vectors on Wikipedia dumps. For StackOverflow, we train word vectors on the whole corpus of the StackOverflow dataset described above which includes the question titles and post contents. For Biomedical, we train word vectors on all titles and abstracts of 2014 training articles. The coverage of these learned vectors on three datasets are listed in Table TABREF32 , and the words not present in the set of pre-trained words are initialized randomly." + ], + [ + "In our experiment, some widely used text clustering methods are compared with our approach. Besides K-means, Skip-thought Vectors, Recursive Neural Network and Paragraph Vector based clustering methods, four baseline clustering methods are directly based on the popular unsupervised dimensionality reduction methods as described in Section SECREF11 . We further compare our approach with some other non-biased neural networks, such as bidirectional RNN. More details are listed as follows:", + "K-means K-means BIBREF42 on original keyword features which are respectively weighted with term frequency (TF) and term frequency-inverse document frequency (TF-IDF).", + "Skip-thought Vectors (SkipVec) This baseline BIBREF35 gives an off-the-shelf encoder to produce highly generic sentence representations. The encoder is trained using a large collection of novels and provides three encoder modes, that are unidirectional encoder (SkipVec (Uni)) with 2,400 dimensions, bidirectional encoder (SkipVec (Bi)) with 2,400 dimensions and combined encoder (SkipVec (Combine)) with SkipVec (Uni) and SkipVec (Bi) of 2,400 dimensions each. K-means is employed on the these vector representations respectively.", + "Recursive Neural Network (RecNN) In BIBREF6 , the tree structure is firstly greedy approximated via unsupervised recursive autoencoder. Then, semi-supervised recursive autoencoders are used to capture the semantics of texts based on the predicted structure. In order to make this recursive-based method completely unsupervised, we remove the cross-entropy error in the second phrase to learn vector representation and subsequently employ K-means on the learned vectors of the top tree node and the average of all vectors in the tree.", + "Paragraph Vector (Para2vec) K-means on the fixed size feature vectors generated by Paragraph Vector (Para2vec) BIBREF25 which is an unsupervised method to learn distributed representation of words and paragraphs. In our experiments, we use the open source software released by Mesnil et al. BIBREF43 .", + "Average Embedding (AE) K-means on the weighted average vectors of the word embeddings which are respectively weighted with TF and TF-IDF. The dimension of average vectors is equal to and decided by the dimension of word vectors used in our experiments.", + "Latent Semantic Analysis (LSA) K-means on the reduced subspace vectors generated by Singular Value Decomposition (SVD) method. The dimension of subspace is default set to the number of clusters, we also iterate the dimensions ranging from 10:10:200 to get the best performance, that is 10 on SearchSnippets, 20 on StackOverflow and 20 on Biomedical in our experiments.", + "Laplacian Eigenmaps (LE) This baseline, using Laplacian Eigenmaps and subsequently employing K-means algorithm, is well known as spectral clustering BIBREF44 . The dimension of subspace is default set to the number of clusters BIBREF18 , BIBREF38 , we also iterate the dimensions ranging from 10:10:200 to get the best performance, that is 20 on SearchSnippets, 70 on StackOverflow and 30 on Biomedical in our experiments.", + "Locality Preserving Indexing (LPI) This baseline, projecting the texts into a lower dimensional semantic space, can discover both the geometric and discriminating structures of the original feature space BIBREF38 . The dimension of subspace is default set to the number of clusters BIBREF38 , we also iterate the dimensions ranging from 10:10:200 to get the best performance, that is 20 on SearchSnippets, 80 on StackOverflow and 30 on Biomedical in our experiments.", + "bidirectional RNN (bi-RNN) We replace the CNN model in our framework as in Figure FIGREF5 with some bi-RNN models. Particularly, LSTM and GRU units are used in the experiments. In order to generate the fixed-length document representation from the variable-length vector sequences, for both bi-LSTM and bi-GRU based clustering methods, we further utilize three pooling methods: last pooling (using the last hidden state), mean pooling and element-wise max pooling. These pooling methods are respectively used in the previous works BIBREF45 , BIBREF27 , BIBREF46 and BIBREF9 . For regularization, the training gradients of all parameters with an INLINEFORM0 2 norm larger than 40 are clipped to 40, as the previous work BIBREF47 ." + ], + [ + "The clustering performance is evaluated by comparing the clustering results of texts with the tags/labels provided by the text corpus. Two metrics, the accuracy (ACC) and the normalized mutual information metric (NMI), are used to measure the clustering performance BIBREF38 , BIBREF48 . Given a text INLINEFORM0 , let INLINEFORM1 and INLINEFORM2 be the obtained cluster label and the label provided by the corpus, respectively. Accuracy is defined as: DISPLAYFORM0 ", + "where, INLINEFORM0 is the total number of texts, INLINEFORM1 is the indicator function that equals one if INLINEFORM2 and equals zero otherwise, and INLINEFORM3 is the permutation mapping function that maps each cluster label INLINEFORM4 to the equivalent label from the text data by Hungarian algorithm BIBREF49 .", + "Normalized mutual information BIBREF50 between tag/label set INLINEFORM0 and cluster set INLINEFORM1 is a popular metric used for evaluating clustering tasks. It is defined as follows: DISPLAYFORM0 ", + "where, INLINEFORM0 is the mutual information between INLINEFORM1 and INLINEFORM2 , INLINEFORM3 is entropy and the denominator INLINEFORM4 is used for normalizing the mutual information to be in the range of [0, 1]." + ], + [ + "The most of parameters are set uniformly for these datasets. Following previous study BIBREF38 , the number of nearest neighbors in Eqn. ( EQREF15 ) is fixed to 15 when constructing the graph structures for LE and LPI. For CNN model, the networks has two convolutional layers. The widths of the convolutional filters are both 3. The value of INLINEFORM0 for the top INLINEFORM1 -max pooling in Eqn. ( EQREF10 ) is 5. The number of feature maps at the first convolutional layer is 12, and 8 feature maps at the second convolutional layer. Both those two convolutional layers are followed by a folding layer. We further set the dimension of word embeddings INLINEFORM2 as 48. Finally, the dimension of the deep feature representation INLINEFORM3 is fixed to 480. Moreover, we set the learning rate INLINEFORM4 as 0.01 and the mini-batch training size as 200. The output size INLINEFORM5 in Eqn. ( EQREF19 ) is set same as the best dimensions of subspace in the baseline method, as described in Section SECREF37 .", + "For initial centroids have significant impact on clustering results when utilizing the K-means algorithms, we repeat K-means for multiple times with random initial centroids (specifically, 100 times for statistical significance) as Huang BIBREF48 . The all subspace vectors are normalized to 1 before applying K-means and the final results reported are the average of 5 trials with all clustering methods on three text datasets." + ], + [ + "In Table TABREF43 and Table TABREF44 , we report the ACC and NMI performance of our proposed approaches and four baseline methods, K-means, SkipVec, RecNN and Para2vec based clustering methods. Intuitively, we get a general observation that (1) BoW based approaches, including K-means (TF) and K-means (TF-IDF), and SkipVec based approaches perform not well; (2) RecNN based approaches, both RecNN (Ave.) and RecNN (Top+Ave.), do better; (3) Para2vec makes a comparable performance with the most baselines; and (4) the evaluation clearly demonstrate the superiority of our proposed methods STC INLINEFORM0 . It is an expected results. For SkipVec based approaches, the off-the-shelf encoders are trained on the BookCorpus datasets BIBREF51 , and then applied to our datasets to extract the sentence representations. The SkipVec encoders can produce generic sentence representations but may not perform well for specific datasets, in our experiments, StackOverflow and Biomedical datasets consist of many computer terms and medical terms, such as \u201cASP.NET\u201d, \u201cXML\u201d, \u201cC#\u201d, \u201cserum\u201d and \u201cglycolytic\u201d. When we take a more careful look, we find that RecNN (Top) does poorly, even worse than K-means (TF-IDF). The reason maybe that although recursive neural models introduce tree structure to capture compositional semantics, the vector of the top node mainly captures a biased semantic while the average of all vectors in the tree nodes, such as RecNN (Ave.), can be better to represent sentence level semantic. And we also get another observation that, although our proposed STC INLINEFORM1 -LE and STC INLINEFORM2 -LPI outperform both BoW based and RecNN based approaches across all three datasets, STC INLINEFORM3 -AE and STC INLINEFORM4 -LSA do just exhibit some similar performances as RecNN (Ave.) and RecNN (Top+Ave.) do in the datasets of StackOverflow and Biomedical.", + "We further replace the CNN model in our framework as in Figure FIGREF5 with some other non-biased models, such as bi-LSTM and bi-GRU, and report the results in Table TABREF46 and Table TABREF47 . As an instance, the binary codes generated from LPI are used to guide the learning of bi-LSTM/bi-GRU models. From the results, we can see that bi-GRU and bi-LSTM based clustering methods do equally well, no clear winner, and both achieve great enhancements compared with LPI (best). Compared with these bi-LSTM/bi-GRU based models, the evaluation results still demonstrate the superiority of our approach methods, CNN based clustering model, in the most cases. As the results reported by Visin et al. BIBREF33 , despite bi-directional or multi-directional RNN models perform a good non-biased feature extraction, they yet do not outperform state-of-the-art CNN on some tasks.", + "In order to make clear what factors make our proposed method work, we report the bar chart results of ACC and MNI of our proposed methods and the corresponding baseline methods in Figure FIGREF49 and Figure FIGREF53 . It is clear that, although AE and LSA does well or even better than LE and LPI, especially in dataset of both StackOverflow and Biomedical, STC INLINEFORM0 -LE and STC INLINEFORM1 -LPI achieve a much larger performance enhancements than STC INLINEFORM2 -AE and STC INLINEFORM3 -LSA do. The possible reason is that the information the pseudo supervision used to guide the learning of CNN model that make difference. Especially, for AE case, the input features fed into CNN model and the pseudo supervision employed to guide the learning of CNN model are all come from word embeddings. There are no different semantic features to be used into our proposed method, thus the performance enhancements are limited in STC INLINEFORM4 -AE. For LSA case, as we known, LSA is to make matrix factorization to find the best subspace approximation of the original feature space to minimize the global reconstruction error. And as BIBREF24 , BIBREF52 recently point out that word embeddings trained with word2vec or some variances, is essentially to do an operation of matrix factorization. Therefore, the information between input and the pseudo supervision in CNN is not departed very largely from each other, and the performance enhancements of STC INLINEFORM5 -AE is also not quite satisfactory. For LE and LPI case, as we known that LE extracts the manifold structure of the original feature space, and LPI extracts both geometric and discriminating structure of the original feature space BIBREF38 . We guess that our approach STC INLINEFORM6 -LE and STC INLINEFORM7 -LPI achieve enhancements compared with both LE and LPI by a large margin, because both of LE and LPI get useful semantic features, and these features are also different from word embeddings used as input of CNN. From this view, we say that our proposed STC has potential to behave more effective when the pseudo supervision is able to get semantic meaningful features, which is different enough from the input of CNN.", + "Furthermore, from the results of K-means and AE in Table TABREF43 - TABREF44 and Figure FIGREF49 - FIGREF53 , we note that TF-IDF weighting gives a more remarkable improvement for K-means, while TF weighting works better than TF-IDF weighting for Average Embedding. Maybe the reason is that pre-trained word embeddings encode some useful information from external corpus and are able to get even better results without TF-IDF weighting. Meanwhile, we find that LE get quite unusual good performance than LPI, LSA and AE in SearchSnippets dataset, which is not found in the other two datasets. To get clear about this, and also to make a much better demonstration about our proposed approaches and other baselines, we further report 2-dimensional text embeddings on SearchSnippets in Figure FIGREF58 , using t-SNE BIBREF53 to get distributed stochastic neighbor embedding of the feature representations used in the clustering methods. We can see that the results of from AE and LSA seem to be fairly good or even better than the ones from LE and LPI, which is not the same as the results from ACC and NMI in Figure FIGREF49 - FIGREF53 . Meanwhile, RecNN (Ave.) performs better than BoW (both TF and TF-IDF) while RecNN (Top) does not, which is the same as the results from ACC and NMI in Table TABREF43 and Table TABREF44 . Then we guess that both \u201dthe same as\u201d and \u201dnot the same as\u201d above, is just a good example to illustrate that visualization tool, such as t-SNE, get some useful information for measuring results, which is different from the ones of ACC and NMI. Moreover, from this complementary view of t-SNE, we can see that our STC INLINEFORM0 -AE, STC INLINEFORM1 -LSA, STC INLINEFORM2 -LE, and STC INLINEFORM3 -LPI show more clear-cut margins among different semantic topics (that is, tags/labels), compared with AE, LSA, LE and LPI, respectively, as well as compared with both baselines, BoW and RecNN based ones.", + "From all these results, with three measures of ACC, NMI and t-SNE under three datasets, we can get a solid conclusion that our proposed approaches is an effective approaches to get useful semantic features for short text clustering." + ], + [ + "With the emergence of social media, short text clustering has become an increasing important task. This paper explores a new perspective to cluster short texts based on deep feature representation learned from the proposed self-taught convolutional neural networks. Our framework can be successfully accomplished without using any external tags/labels and complicated NLP pre-processing, and and our approach is a flexible framework, in which the traditional dimension reduction approaches could be used to get performance enhancement. Our extensive experimental study on three short text datasets shows that our approach can achieve a significantly better performance. In the future, how to select and incorporate more effective semantic features into the proposed framework would call for more research." + ], + [ + "We would like to thank reviewers for their comments, and acknowledge Kaggle and BioASQ for making the datasets available. This work is supported by the National Natural Science Foundation of China (No. 61602479, No. 61303172, No. 61403385) and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB02070005)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0351/environment/Dockerfile b/qasper-0351/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0351/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/* diff --git a/qasper-0351/instruction.md b/qasper-0351/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..19c78cfbe65ceac108b090977822922b681a6ba6 --- /dev/null +++ b/qasper-0351/instruction.md @@ -0,0 +1,134 @@ +Name of Paper: Self-Taught Convolutional Neural Networks for Short Text Clustering + +Question: What were their performance results? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Short Text Clustering", + "Deep Neural Networks", + "Methodology", + "Deep Convolutional Neural Networks", + "Unsupervised Dimensionality Reduction", + "Learning", + "K-means for Clustering", + "Datasets", + "Pre-trained Word Vectors", + "Comparisons", + "Evaluation Metrics", + "Hyperparameter Settings", + "Results and Analysis", + "Conclusions", + "Acknowledgments" + ], + "paragraphs": [ + [ + "Short text clustering is of great importance due to its various applications, such as user profiling BIBREF0 and recommendation BIBREF1 , for nowaday's social media dataset emerged day by day. However, short text clustering has the data sparsity problem and most words only occur once in each short text BIBREF2 . As a result, the Term Frequency-Inverse Document Frequency (TF-IDF) measure cannot work well in short text setting. In order to address this problem, some researchers work on expanding and enriching the context of data from Wikipedia BIBREF3 or an ontology BIBREF4 . However, these methods involve solid Natural Language Processing (NLP) knowledge and still use high-dimensional representation which may result in a waste of both memory and computation time. Another way to overcome these issues is to explore some sophisticated models to cluster short texts. For example, Yin and Wang BIBREF5 proposed a Dirichlet multinomial mixture model-based approach for short text clustering. Yet how to design an effective model is an open question, and most of these methods directly trained based on Bag-of-Words (BoW) are shallow structures which cannot preserve the accurate semantic similarities.", + "Recently, with the help of word embedding, neural networks demonstrate their great performance in terms of constructing text representation, such as Recursive Neural Network (RecNN) BIBREF6 , BIBREF7 and Recurrent Neural Network (RNN) BIBREF8 . However, RecNN exhibits high time complexity to construct the textual tree, and RNN, using the hidden layer computed at the last word to represent the text, is a biased model where later words are more dominant than earlier words BIBREF9 . Whereas for the non-biased models, the learned representation of one text can be extracted from all the words in the text with non-dominant learned weights. More recently, Convolution Neural Network (CNN), as the most popular non-biased model and applying convolutional filters to capture local features, has achieved a better performance in many NLP applications, such as sentence modeling BIBREF10 , relation classification BIBREF11 , and other traditional NLP tasks BIBREF12 . Most of the previous works focus CNN on solving supervised NLP tasks, while in this paper we aim to explore the power of CNN on one unsupervised NLP task, short text clustering.", + "We systematically introduce a simple yet surprisingly powerful Self-Taught Convolutional neural network framework for Short Text Clustering, called STC INLINEFORM0 . An overall architecture of our proposed approach is illustrated in Figure FIGREF5 . We, inspired by BIBREF13 , BIBREF14 , utilize a self-taught learning framework into our task. In particular, the original raw text features are first embedded into compact binary codes INLINEFORM1 with the help of one traditional unsupervised dimensionality reduction function. Then text matrix INLINEFORM2 projected from word embeddings are fed into CNN model to learn the deep feature representation INLINEFORM3 and the output units are used to fit the pre-trained binary codes INLINEFORM4 . After obtaining the learned features, K-means algorithm is employed on them to cluster texts into clusters INLINEFORM5 . Obviously, we call our approach \u201cself-taught\u201d because the CNN model is learnt from the pseudo labels generated from the previous stage, which is quite different from the term \u201cself-taught\u201d in BIBREF15 . Our main contributions can be summarized as follows:", + "This work is an extension of our conference paper BIBREF16 , and they differ in the following aspects. First, we put forward a general a self-taught CNN framework in this paper which can flexibly couple various semantic features, whereas the conference version can be seen as a specific example of this work. Second, in this paper we use a new short text dataset, Biomedical, in the experiment to verify the effectiveness of our approach. Third, we put much effort on studying the influence of various different semantic features integrated in our self-taught CNN framework, which is not involved in the conference paper.", + "For the purpose of reproducibility, we make the datasets and software used in our experiments publicly available at the website.", + "The remainder of this paper is organized as follows: In Section SECREF2 , we first briefly survey several related works. In Section SECREF3 , we describe the proposed approach STC INLINEFORM0 and implementation details. Experimental results and analyses are presented in Section SECREF4 . Finally, conclusions are given in the last Section." + ], + [ + "In this section, we review the related work from the following two perspectives: short text clustering and deep neural networks." + ], + [ + "There have been several studies that attempted to overcome the sparseness of short text representation. One way is to expand and enrich the context of data. For example, Banerjee et al. BIBREF3 proposed a method of improving the accuracy of short text clustering by enriching their representation with additional features from Wikipedia, and Fodeh et al. BIBREF4 incorporate semantic knowledge from an ontology into text clustering. However, these works need solid NLP knowledge and still use high-dimensional representation which may result in a waste of both memory and computation time. Another direction is to map the original features into reduced space, such as Latent Semantic Analysis (LSA) BIBREF17 , Laplacian Eigenmaps (LE) BIBREF18 , and Locality Preserving Indexing (LPI) BIBREF19 . Even some researchers explored some sophisticated models to cluster short texts. For example, Yin and Wang BIBREF5 proposed a Dirichlet multinomial mixture model-based approach for short text clustering. Moreover, some studies even focus the above both two streams. For example, Tang et al. BIBREF20 proposed a novel framework which enrich the text features by employing machine translation and reduce the original features simultaneously through matrix factorization techniques.", + "Despite the above clustering methods can alleviate sparseness of short text representation to some extent, most of them ignore word order in the text and belong to shallow structures which can not fully capture accurate semantic similarities." + ], + [ + "Recently, there is a revival of interest in DNN and many researchers have concentrated on using Deep Learning to learn features. Hinton and Salakhutdinov BIBREF21 use DAE to learn text representation. During the fine-tuning procedure, they use backpropagation to find codes that are good at reconstructing the word-count vector.", + "More recently, researchers propose to use external corpus to learn a distributed representation for each word, called word embedding BIBREF22 , to improve DNN performance on NLP tasks. The Skip-gram and continuous bag-of-words models of Word2vec BIBREF23 propose a simple single-layer architecture based on the inner product between two word vectors, and Pennington et al. BIBREF24 introduce a new model for word representation, called GloVe, which captures the global corpus statistics.", + "In order to learn the compact representation vectors of sentences, Le and Mikolov BIBREF25 directly extend the previous Word2vec BIBREF23 by predicting words in the sentence, which is named Paragraph Vector (Para2vec). Para2vec is still a shallow window-based method and need a larger corpus to yield better performance. More neural networks utilize word embedding to capture true meaningful syntactic and semantic regularities, such as RecNN BIBREF6 , BIBREF7 and RNN BIBREF8 . However, RecNN exhibits high time complexity to construct the textual tree, and RNN, using the layer computed at the last word to represent the text, is a biased model. Recently, Long Short-Term Memory (LSTM) BIBREF26 and Gated Recurrent Unit (GRU) BIBREF27 , as sophisticated recurrent hidden units of RNN, has presented its advantages in many sequence generation problem, such as machine translation BIBREF28 , speech recognition BIBREF29 , and text conversation BIBREF30 . While, CNN is better to learn non-biased implicit features which has been successfully exploited for many supervised NLP learning tasks as described in Section SECREF1 , and various CNN based variants are proposed in the recent works, such as Dynamic Convolutional Neural Network (DCNN) BIBREF10 , Gated Recursive Convolutional Neural Network (grConv) BIBREF31 and Self-Adaptive Hierarchical Sentence model (AdaSent) BIBREF32 .", + "In the past few days, Visin et al. BIBREF33 have attempted to replace convolutional layer in CNN to learn non-biased features for object recognition with four RNNs, called ReNet, that sweep over lower-layer features in different directions: (1) bottom to top, (2) top to bottom, (3) left to right and (4) right to left. However, ReNet does not outperform state-of-the-art convolutional neural networks on any of the three benchmark datasets, and it is also a supervised learning model for classification. Inspired by Skip-gram of word2vec BIBREF34 , BIBREF23 , Skip-thought model BIBREF35 describe an approach for unsupervised learning of a generic, distributed sentence encoder. Similar as Skip-gram model, Skip-thought model trains an encoder-decoder model that tries to reconstruct the surrounding sentences of an encoded sentence and released an off-the-shelf encoder to extract sentence representation. Even some researchers introduce continuous Skip-gram and negative sampling to CNN for learning visual representation in an unsupervised manner BIBREF36 . This paper, from a new perspective, puts forward a general self-taught CNN framework which can flexibly couple various semantic features and achieve a good performance on one unsupervised learning task, short text clustering." + ], + [ + "Assume that we are given a dataset of INLINEFORM0 training texts denoted as: INLINEFORM1 , where INLINEFORM2 is the dimensionality of the original BoW representation. Denote its tag set as INLINEFORM3 and the pre-trained word embedding set as INLINEFORM4 , where INLINEFORM5 is the dimensionality of word vectors and INLINEFORM6 is the vocabulary size. In order to learn the INLINEFORM7 -dimensional deep feature representation INLINEFORM8 from CNN in an unsupervised manner, some unsupervised dimensionality reduction methods INLINEFORM9 are employed to guide the learning of CNN model. Our goal is to cluster these texts INLINEFORM10 into clusters INLINEFORM11 based on the learned deep feature representation while preserving the semantic consistency.", + "As depicted in Figure FIGREF5 , the proposed framework consist of three components, deep convolutional neural network (CNN), unsupervised dimensionality reduction function and K-means module. In the rest sections, we first present the first two components respectively, and then give the trainable parameters and the objective function to learn the deep feature representation. Finally, the last section describe how to perform clustering on the learned features." + ], + [ + "In this section, we briefly review one popular deep convolutional neural network, Dynamic Convolutional Neural Network (DCNN) BIBREF10 as an instance of CNN in the following sections, which as the foundation of our proposed method has been successfully proposed for the completely supervised learning task, text classification.", + "Taking a neural network with two convolutional layers in Figure FIGREF9 as an example, the network transforms raw input text to a powerful representation. Particularly, each raw text vector INLINEFORM0 is projected into a matrix representation INLINEFORM1 by looking up a word embedding INLINEFORM2 , where INLINEFORM3 is the length of one text. We also let INLINEFORM4 and INLINEFORM5 denote the weights of the neural networks. The network defines a transformation INLINEFORM6 INLINEFORM7 which transforms an input raw text INLINEFORM8 to a INLINEFORM9 -dimensional deep representation INLINEFORM10 . There are three basic operations described as follows:", + "Wide one-dimensional convolution This operation INLINEFORM0 is applied to an individual row of the sentence matrix INLINEFORM1 , and yields a resulting matrix INLINEFORM2 , where INLINEFORM3 is the width of convolutional filter.", + "Folding In this operation, every two rows in a feature map are simply summed component-wisely. For a map of INLINEFORM0 rows, folding returns a map of INLINEFORM1 rows, thus halving the size of the representation and yielding a matrix feature INLINEFORM2 . Note that folding operation does not introduce any additional parameters.", + "Dynamic INLINEFORM0 -max pooling Assuming the pooling parameter as INLINEFORM1 , INLINEFORM2 -max pooling selects the sub-matrix INLINEFORM3 of the INLINEFORM4 highest values in each row of the matrix INLINEFORM5 . For dynamic INLINEFORM6 -max pooling, the pooling parameter INLINEFORM7 is dynamically selected in order to allow for a smooth extraction of higher-order and longer-range features BIBREF10 . Given a fixed pooling parameter INLINEFORM8 for the topmost convolutional layer, the parameter INLINEFORM9 of INLINEFORM10 -max pooling in the INLINEFORM11 -th convolutional layer can be computed as follows: DISPLAYFORM0 ", + "where INLINEFORM0 is the total number of convolutional layers in the network." + ], + [ + "As described in Figure FIGREF5 , the dimensionality reduction function is defined as follows: DISPLAYFORM0 ", + "where, INLINEFORM0 are the INLINEFORM1 -dimensional reduced latent space representations. Here, we take four popular dimensionality reduction methods as examples in our framework.", + "Average Embedding (AE): This method directly averages the word embeddings which are respectively weighted with TF and TF-IDF. Huang et al. BIBREF37 used this strategy as the global context in their task, and Socher et al. BIBREF7 and Lai et al. BIBREF9 used this method for text classification. The weighted average of all word vectors in one text can be computed as follows: DISPLAYFORM0 ", + "where INLINEFORM0 can be any weighting function that captures the importance of word INLINEFORM1 in the text INLINEFORM2 .", + "Latent Semantic Analysis (LSA): LSA BIBREF17 is the most popular global matrix factorization method, which applies a dimension reducing linear projection, Singular Value Decomposition (SVD), of the corresponding term/document matrix. Suppose the rank of INLINEFORM0 is INLINEFORM1 , LSA decompose INLINEFORM2 into the product of three other matrices: DISPLAYFORM0 ", + "where INLINEFORM0 and INLINEFORM1 are the singular values of INLINEFORM2 , INLINEFORM3 is a set of left singular vectors and INLINEFORM4 is a set of right singular vectors. LSA uses the top INLINEFORM5 vectors in INLINEFORM6 as the transformation matrix to embed the original text features into a INLINEFORM7 -dimensional subspace INLINEFORM8 BIBREF17 .", + "Laplacian Eigenmaps (LE): The top eigenvectors of graph Laplacian, defined on the similarity matrix of texts, are used in the method, which can discover the manifold structure of the text space BIBREF18 . In order to avoid storing the dense similarity matrix, many approximation techniques are proposed to reduce the memory usage and computational complexity for LE. There are two representative approximation methods, sparse similarity matrix and Nystr INLINEFORM0 m approximation. Following previous studies BIBREF38 , BIBREF13 , we select the former technique to construct the INLINEFORM1 local similarity matrix INLINEFORM2 by using heat kernel as follows: DISPLAYFORM0 ", + "where, INLINEFORM0 is a tuning parameter (default is 1) and INLINEFORM1 represents the set of INLINEFORM2 -nearest-neighbors of INLINEFORM3 . By introducing a diagonal INLINEFORM4 matrix INLINEFORM5 whose entries are given by INLINEFORM6 , the graph Laplacian INLINEFORM7 can be computed by ( INLINEFORM8 ). The optimal INLINEFORM9 real-valued matrix INLINEFORM10 can be obtained by solving the following objective function: DISPLAYFORM0 ", + "where INLINEFORM0 is the trace function, INLINEFORM1 requires the different dimensions to be uncorrelated, and INLINEFORM2 requires each dimension to achieve equal probability as positive or negative).", + "Locality Preserving Indexing (LPI): This method extends LE to deal with unseen texts by approximating the linear function INLINEFORM0 BIBREF13 , and the subspace vectors are obtained by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the Riemannian manifold BIBREF19 . Similar as LE, we first construct the local similarity matrix INLINEFORM1 , then the graph Laplacian INLINEFORM2 can be computed by ( INLINEFORM3 ), where INLINEFORM4 measures the local density around INLINEFORM5 and is equal to INLINEFORM6 . Compute the eigenvectors INLINEFORM7 and eigenvalues INLINEFORM8 of the following generalized eigen-problem: DISPLAYFORM0 ", + "The mapping function INLINEFORM0 can be obtained and applied to the unseen data BIBREF38 .", + "All of the above methods claim a better performance in capturing semantic similarity between texts in the reduced latent space representation INLINEFORM0 than in the original representation INLINEFORM1 , while the performance of short text clustering can be further enhanced with the help of our framework, self-taught CNN." + ], + [ + "The last layer of CNN is an output layer as follows: DISPLAYFORM0 ", + "where, INLINEFORM0 is the deep feature representation, INLINEFORM1 is the output vector and INLINEFORM2 is weight matrix.", + "In order to incorporate the latent semantic features INLINEFORM0 , we first binary the real-valued vectors INLINEFORM1 to the binary codes INLINEFORM2 by setting the threshold to be the media vector INLINEFORM3 . Then, the output vector INLINEFORM4 is used to fit the binary codes INLINEFORM5 via INLINEFORM6 logistic operations as follows: DISPLAYFORM0 ", + "All parameters to be trained are defined as INLINEFORM0 . DISPLAYFORM0 ", + "Given the training text collection INLINEFORM0 , and the pre-trained binary codes INLINEFORM1 , the log likelihood of the parameters can be written down as follows: DISPLAYFORM0 ", + "Following the previous work BIBREF10 , we train the network with mini-batches by back-propagation and perform the gradient-based optimization using the Adagrad update rule BIBREF39 . For regularization, we employ dropout with 50% rate to the penultimate layer BIBREF10 , BIBREF40 ." + ], + [ + "With the given short texts, we first utilize the trained deep neural network to obtain the semantic representations INLINEFORM0 , and then employ traditional K-means algorithm to perform clustering." + ], + [ + "We test our proposed approach on three public short text datasets. The summary statistics and semantic topics of these datasets are described in Table TABREF24 and Table TABREF25 .", + "SearchSnippets. This dataset was selected from the results of web search transaction using predefined phrases of 8 different domains by Phan et al. BIBREF41 .", + "StackOverflow. We use the challenge data published in Kaggle.com. The raw dataset consists 3,370,528 samples through July 31st, 2012 to August 14, 2012. In our experiments, we randomly select 20,000 question titles from 20 different tags as in Table TABREF25 .", + "Biomedical. We use the challenge data published in BioASQ's official website. In our experiments, we randomly select 20, 000 paper titles from 20 different MeSH major topics as in Table TABREF25 . As described in Table TABREF24 , the max length of selected paper titles is 53.", + "For these datasets, we randomly select 10% of data as the development set. Since SearchSnippets has been pre-processed by Phan et al. BIBREF41 , we do not further process this dataset. In StackOverflow, texts contain lots of computer terminology, and symbols and capital letters are meaningful, thus we do not do any pre-processed procedures. For Biomedical, we remove the symbols and convert letters into lower case." + ], + [ + "We use the publicly available word2vec tool to train word embeddings, and the most parameters are set as same as Mikolov et al. BIBREF23 to train word vectors on Google News setting, except of vector dimensionality using 48 and minimize count using 5. For SearchSnippets, we train word vectors on Wikipedia dumps. For StackOverflow, we train word vectors on the whole corpus of the StackOverflow dataset described above which includes the question titles and post contents. For Biomedical, we train word vectors on all titles and abstracts of 2014 training articles. The coverage of these learned vectors on three datasets are listed in Table TABREF32 , and the words not present in the set of pre-trained words are initialized randomly." + ], + [ + "In our experiment, some widely used text clustering methods are compared with our approach. Besides K-means, Skip-thought Vectors, Recursive Neural Network and Paragraph Vector based clustering methods, four baseline clustering methods are directly based on the popular unsupervised dimensionality reduction methods as described in Section SECREF11 . We further compare our approach with some other non-biased neural networks, such as bidirectional RNN. More details are listed as follows:", + "K-means K-means BIBREF42 on original keyword features which are respectively weighted with term frequency (TF) and term frequency-inverse document frequency (TF-IDF).", + "Skip-thought Vectors (SkipVec) This baseline BIBREF35 gives an off-the-shelf encoder to produce highly generic sentence representations. The encoder is trained using a large collection of novels and provides three encoder modes, that are unidirectional encoder (SkipVec (Uni)) with 2,400 dimensions, bidirectional encoder (SkipVec (Bi)) with 2,400 dimensions and combined encoder (SkipVec (Combine)) with SkipVec (Uni) and SkipVec (Bi) of 2,400 dimensions each. K-means is employed on the these vector representations respectively.", + "Recursive Neural Network (RecNN) In BIBREF6 , the tree structure is firstly greedy approximated via unsupervised recursive autoencoder. Then, semi-supervised recursive autoencoders are used to capture the semantics of texts based on the predicted structure. In order to make this recursive-based method completely unsupervised, we remove the cross-entropy error in the second phrase to learn vector representation and subsequently employ K-means on the learned vectors of the top tree node and the average of all vectors in the tree.", + "Paragraph Vector (Para2vec) K-means on the fixed size feature vectors generated by Paragraph Vector (Para2vec) BIBREF25 which is an unsupervised method to learn distributed representation of words and paragraphs. In our experiments, we use the open source software released by Mesnil et al. BIBREF43 .", + "Average Embedding (AE) K-means on the weighted average vectors of the word embeddings which are respectively weighted with TF and TF-IDF. The dimension of average vectors is equal to and decided by the dimension of word vectors used in our experiments.", + "Latent Semantic Analysis (LSA) K-means on the reduced subspace vectors generated by Singular Value Decomposition (SVD) method. The dimension of subspace is default set to the number of clusters, we also iterate the dimensions ranging from 10:10:200 to get the best performance, that is 10 on SearchSnippets, 20 on StackOverflow and 20 on Biomedical in our experiments.", + "Laplacian Eigenmaps (LE) This baseline, using Laplacian Eigenmaps and subsequently employing K-means algorithm, is well known as spectral clustering BIBREF44 . The dimension of subspace is default set to the number of clusters BIBREF18 , BIBREF38 , we also iterate the dimensions ranging from 10:10:200 to get the best performance, that is 20 on SearchSnippets, 70 on StackOverflow and 30 on Biomedical in our experiments.", + "Locality Preserving Indexing (LPI) This baseline, projecting the texts into a lower dimensional semantic space, can discover both the geometric and discriminating structures of the original feature space BIBREF38 . The dimension of subspace is default set to the number of clusters BIBREF38 , we also iterate the dimensions ranging from 10:10:200 to get the best performance, that is 20 on SearchSnippets, 80 on StackOverflow and 30 on Biomedical in our experiments.", + "bidirectional RNN (bi-RNN) We replace the CNN model in our framework as in Figure FIGREF5 with some bi-RNN models. Particularly, LSTM and GRU units are used in the experiments. In order to generate the fixed-length document representation from the variable-length vector sequences, for both bi-LSTM and bi-GRU based clustering methods, we further utilize three pooling methods: last pooling (using the last hidden state), mean pooling and element-wise max pooling. These pooling methods are respectively used in the previous works BIBREF45 , BIBREF27 , BIBREF46 and BIBREF9 . For regularization, the training gradients of all parameters with an INLINEFORM0 2 norm larger than 40 are clipped to 40, as the previous work BIBREF47 ." + ], + [ + "The clustering performance is evaluated by comparing the clustering results of texts with the tags/labels provided by the text corpus. Two metrics, the accuracy (ACC) and the normalized mutual information metric (NMI), are used to measure the clustering performance BIBREF38 , BIBREF48 . Given a text INLINEFORM0 , let INLINEFORM1 and INLINEFORM2 be the obtained cluster label and the label provided by the corpus, respectively. Accuracy is defined as: DISPLAYFORM0 ", + "where, INLINEFORM0 is the total number of texts, INLINEFORM1 is the indicator function that equals one if INLINEFORM2 and equals zero otherwise, and INLINEFORM3 is the permutation mapping function that maps each cluster label INLINEFORM4 to the equivalent label from the text data by Hungarian algorithm BIBREF49 .", + "Normalized mutual information BIBREF50 between tag/label set INLINEFORM0 and cluster set INLINEFORM1 is a popular metric used for evaluating clustering tasks. It is defined as follows: DISPLAYFORM0 ", + "where, INLINEFORM0 is the mutual information between INLINEFORM1 and INLINEFORM2 , INLINEFORM3 is entropy and the denominator INLINEFORM4 is used for normalizing the mutual information to be in the range of [0, 1]." + ], + [ + "The most of parameters are set uniformly for these datasets. Following previous study BIBREF38 , the number of nearest neighbors in Eqn. ( EQREF15 ) is fixed to 15 when constructing the graph structures for LE and LPI. For CNN model, the networks has two convolutional layers. The widths of the convolutional filters are both 3. The value of INLINEFORM0 for the top INLINEFORM1 -max pooling in Eqn. ( EQREF10 ) is 5. The number of feature maps at the first convolutional layer is 12, and 8 feature maps at the second convolutional layer. Both those two convolutional layers are followed by a folding layer. We further set the dimension of word embeddings INLINEFORM2 as 48. Finally, the dimension of the deep feature representation INLINEFORM3 is fixed to 480. Moreover, we set the learning rate INLINEFORM4 as 0.01 and the mini-batch training size as 200. The output size INLINEFORM5 in Eqn. ( EQREF19 ) is set same as the best dimensions of subspace in the baseline method, as described in Section SECREF37 .", + "For initial centroids have significant impact on clustering results when utilizing the K-means algorithms, we repeat K-means for multiple times with random initial centroids (specifically, 100 times for statistical significance) as Huang BIBREF48 . The all subspace vectors are normalized to 1 before applying K-means and the final results reported are the average of 5 trials with all clustering methods on three text datasets." + ], + [ + "In Table TABREF43 and Table TABREF44 , we report the ACC and NMI performance of our proposed approaches and four baseline methods, K-means, SkipVec, RecNN and Para2vec based clustering methods. Intuitively, we get a general observation that (1) BoW based approaches, including K-means (TF) and K-means (TF-IDF), and SkipVec based approaches perform not well; (2) RecNN based approaches, both RecNN (Ave.) and RecNN (Top+Ave.), do better; (3) Para2vec makes a comparable performance with the most baselines; and (4) the evaluation clearly demonstrate the superiority of our proposed methods STC INLINEFORM0 . It is an expected results. For SkipVec based approaches, the off-the-shelf encoders are trained on the BookCorpus datasets BIBREF51 , and then applied to our datasets to extract the sentence representations. The SkipVec encoders can produce generic sentence representations but may not perform well for specific datasets, in our experiments, StackOverflow and Biomedical datasets consist of many computer terms and medical terms, such as \u201cASP.NET\u201d, \u201cXML\u201d, \u201cC#\u201d, \u201cserum\u201d and \u201cglycolytic\u201d. When we take a more careful look, we find that RecNN (Top) does poorly, even worse than K-means (TF-IDF). The reason maybe that although recursive neural models introduce tree structure to capture compositional semantics, the vector of the top node mainly captures a biased semantic while the average of all vectors in the tree nodes, such as RecNN (Ave.), can be better to represent sentence level semantic. And we also get another observation that, although our proposed STC INLINEFORM1 -LE and STC INLINEFORM2 -LPI outperform both BoW based and RecNN based approaches across all three datasets, STC INLINEFORM3 -AE and STC INLINEFORM4 -LSA do just exhibit some similar performances as RecNN (Ave.) and RecNN (Top+Ave.) do in the datasets of StackOverflow and Biomedical.", + "We further replace the CNN model in our framework as in Figure FIGREF5 with some other non-biased models, such as bi-LSTM and bi-GRU, and report the results in Table TABREF46 and Table TABREF47 . As an instance, the binary codes generated from LPI are used to guide the learning of bi-LSTM/bi-GRU models. From the results, we can see that bi-GRU and bi-LSTM based clustering methods do equally well, no clear winner, and both achieve great enhancements compared with LPI (best). Compared with these bi-LSTM/bi-GRU based models, the evaluation results still demonstrate the superiority of our approach methods, CNN based clustering model, in the most cases. As the results reported by Visin et al. BIBREF33 , despite bi-directional or multi-directional RNN models perform a good non-biased feature extraction, they yet do not outperform state-of-the-art CNN on some tasks.", + "In order to make clear what factors make our proposed method work, we report the bar chart results of ACC and MNI of our proposed methods and the corresponding baseline methods in Figure FIGREF49 and Figure FIGREF53 . It is clear that, although AE and LSA does well or even better than LE and LPI, especially in dataset of both StackOverflow and Biomedical, STC INLINEFORM0 -LE and STC INLINEFORM1 -LPI achieve a much larger performance enhancements than STC INLINEFORM2 -AE and STC INLINEFORM3 -LSA do. The possible reason is that the information the pseudo supervision used to guide the learning of CNN model that make difference. Especially, for AE case, the input features fed into CNN model and the pseudo supervision employed to guide the learning of CNN model are all come from word embeddings. There are no different semantic features to be used into our proposed method, thus the performance enhancements are limited in STC INLINEFORM4 -AE. For LSA case, as we known, LSA is to make matrix factorization to find the best subspace approximation of the original feature space to minimize the global reconstruction error. And as BIBREF24 , BIBREF52 recently point out that word embeddings trained with word2vec or some variances, is essentially to do an operation of matrix factorization. Therefore, the information between input and the pseudo supervision in CNN is not departed very largely from each other, and the performance enhancements of STC INLINEFORM5 -AE is also not quite satisfactory. For LE and LPI case, as we known that LE extracts the manifold structure of the original feature space, and LPI extracts both geometric and discriminating structure of the original feature space BIBREF38 . We guess that our approach STC INLINEFORM6 -LE and STC INLINEFORM7 -LPI achieve enhancements compared with both LE and LPI by a large margin, because both of LE and LPI get useful semantic features, and these features are also different from word embeddings used as input of CNN. From this view, we say that our proposed STC has potential to behave more effective when the pseudo supervision is able to get semantic meaningful features, which is different enough from the input of CNN.", + "Furthermore, from the results of K-means and AE in Table TABREF43 - TABREF44 and Figure FIGREF49 - FIGREF53 , we note that TF-IDF weighting gives a more remarkable improvement for K-means, while TF weighting works better than TF-IDF weighting for Average Embedding. Maybe the reason is that pre-trained word embeddings encode some useful information from external corpus and are able to get even better results without TF-IDF weighting. Meanwhile, we find that LE get quite unusual good performance than LPI, LSA and AE in SearchSnippets dataset, which is not found in the other two datasets. To get clear about this, and also to make a much better demonstration about our proposed approaches and other baselines, we further report 2-dimensional text embeddings on SearchSnippets in Figure FIGREF58 , using t-SNE BIBREF53 to get distributed stochastic neighbor embedding of the feature representations used in the clustering methods. We can see that the results of from AE and LSA seem to be fairly good or even better than the ones from LE and LPI, which is not the same as the results from ACC and NMI in Figure FIGREF49 - FIGREF53 . Meanwhile, RecNN (Ave.) performs better than BoW (both TF and TF-IDF) while RecNN (Top) does not, which is the same as the results from ACC and NMI in Table TABREF43 and Table TABREF44 . Then we guess that both \u201dthe same as\u201d and \u201dnot the same as\u201d above, is just a good example to illustrate that visualization tool, such as t-SNE, get some useful information for measuring results, which is different from the ones of ACC and NMI. Moreover, from this complementary view of t-SNE, we can see that our STC INLINEFORM0 -AE, STC INLINEFORM1 -LSA, STC INLINEFORM2 -LE, and STC INLINEFORM3 -LPI show more clear-cut margins among different semantic topics (that is, tags/labels), compared with AE, LSA, LE and LPI, respectively, as well as compared with both baselines, BoW and RecNN based ones.", + "From all these results, with three measures of ACC, NMI and t-SNE under three datasets, we can get a solid conclusion that our proposed approaches is an effective approaches to get useful semantic features for short text clustering." + ], + [ + "With the emergence of social media, short text clustering has become an increasing important task. This paper explores a new perspective to cluster short texts based on deep feature representation learned from the proposed self-taught convolutional neural networks. Our framework can be successfully accomplished without using any external tags/labels and complicated NLP pre-processing, and and our approach is a flexible framework, in which the traditional dimension reduction approaches could be used to get performance enhancement. Our extensive experimental study on three short text datasets shows that our approach can achieve a significantly better performance. In the future, how to select and incorporate more effective semantic features into the proposed framework would call for more research." + ], + [ + "We would like to thank reviewers for their comments, and acknowledge Kaggle and BioASQ for making the datasets available. This work is supported by the National Natural Science Foundation of China (No. 61602479, No. 61303172, No. 61403385) and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB02070005)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0356/environment/Dockerfile b/qasper-0356/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0356/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/* diff --git a/qasper-0356/instruction.md b/qasper-0356/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..8b10f9cbcd17a134c8b1a353da147c4e0b0a065c --- /dev/null +++ b/qasper-0356/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Solving Arithmetic Word Problems Automatically Using Transformer and Unambiguous Representations + +Question: What neural configurations are explored? \ No newline at end of file diff --git a/qasper-0357/environment/Dockerfile b/qasper-0357/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0357/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/* diff --git a/qasper-0357/instruction.md b/qasper-0357/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..8b0aeb3e02cb3de65b26d1c3dabb25a4f1cad760 --- /dev/null +++ b/qasper-0357/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Solving Arithmetic Word Problems Automatically Using Transformer and Unambiguous Representations + +Question: Are the Transformers masked? \ No newline at end of file diff --git a/qasper-0358/environment/Dockerfile b/qasper-0358/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0358/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/* diff --git a/qasper-0358/instruction.md b/qasper-0358/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..647ed0f59fd898a1d9be3935e7e118f05c14b115 --- /dev/null +++ b/qasper-0358/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Solving Arithmetic Word Problems Automatically Using Transformer and Unambiguous Representations + +Question: How is this problem evaluated? \ No newline at end of file diff --git a/qasper-0359/instruction.md b/qasper-0359/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..33481db0c65bda8c920dc14f093c565e2142faa2 --- /dev/null +++ b/qasper-0359/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Solving Arithmetic Word Problems Automatically Using Transformer and Unambiguous Representations + +Question: What datasets do they use? \ No newline at end of file diff --git a/qasper-0360/environment/Dockerfile b/qasper-0360/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0360/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/* diff --git a/qasper-0360/instruction.md b/qasper-0360/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..1db58557b0f24308dece6f885c386e4e69a06758 --- /dev/null +++ b/qasper-0360/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: What Do You Mean I'm Funny? Personalizing the Joke Skill of a Voice-Controlled Virtual Assistant + +Question: What evaluation metrics were used? \ No newline at end of file diff --git a/qasper-0361/instruction.md b/qasper-0361/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..a16a58cd1ecdd9df85a721bb2a37955bb66bba6c --- /dev/null +++ b/qasper-0361/instruction.md @@ -0,0 +1,65 @@ +Name of Paper: What Do You Mean I'm Funny? Personalizing the Joke Skill of a Voice-Controlled Virtual Assistant + +Question: Where did the real production data come from? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Method ::: Labelling Strategies", + "Method ::: Features", + "Method ::: NLP-based: LR-Model", + "Method ::: Deep-Learning-based: DL-Models", + "Validation", + "Validation ::: Online Results: A/B Testing", + "Validation ::: Offline Results", + "Conclusions and Future Work" + ], + "paragraphs": [ + [ + "Voice-controlled virtual assistants (VVA) such as Siri and Alexa have experienced an exponential growth in terms of number of users and provided capabilities. They are used by millions for a variety of tasks including shopping, playing music, and even telling jokes. Arguably, their success is due in part to the emotional and personalized experience they provide. One important aspect of this emotional interaction is humor, a fundamental element of communication. Not only can it create in the user a sense of personality, but also be used as fallback technique for out-of-domain queries BIBREF0. Usually, a VVA's humorous responses are invoked by users with the phrase \"Tell me a joke\". In order to improve the joke experience and overall user satisfaction with a VVA, we propose to personalize the response to each request. To achieve this, a method should be able to recognize and evaluate humor, a challenging task that has been the focus of extensive work. Some authors have applied traditional NLP techniques BIBREF1, while others deep learning models BIBREF2. Moreover, BIBREF3 follows a semantic-based approach, while BIBREF4 and BIBREF5 tackle the challenge from a cognitive and linguistic perspective respectively.", + "To this end, we have developed two methods. The first one is based on traditional NLP techniques. Although relatively simple, it is robust, scalable, and has low latency, a fundamental property for real-time VVA systems. The other approaches combine multi-task learning BIBREF6 and self-attentional networks BIBREF7 to obtain better results, at the cost of added complexity. Both BERT BIBREF8 and an adapted transformer BIBREF7 architecture are considered. This choice of architecture was motivated by the advantages it presents over traditional RNN and CNN models, including better performance BIBREF9, faster training/inference (important for real-time systems), and better sense disambiguation BIBREF10 (an important component of computational humor BIBREF3).", + "The proposed models use binary classifiers to perform point-wise ranking, and therefore require a labelled dataset. To generate it, we explore two implicit user-feedback labelling strategies: five-minute reuse and one-day return. Online A/B testing is used to determine if these labelling strategies are suited to optimize the desired user-satisfaction metrics, and offline data to evaluated and compared the system's performance." + ], + [ + "Generating labels for this VVA skill is challenging. Label generation through explicit user feedback is unavailable since asking users for feedback creates friction and degrade the user experience. In addition, available humor datasets such as BIBREF3, BIBREF11 only contain jokes and corresponding labels, but not the additional features we need to personalize the jokes.", + "To overcome this difficulty, it is common to resort to implicit feedback. In particular, many VVA applications use interruptions as negative labels, the rationale being that unhappy users will stop the VVA. This strategy, however, is not suitable for our use-case since responses are short and users need to hear the entire joke to decide if it is funny. Instead, we explore two other implicit feedback labelling strategies: five-minute reuse and 1-day return. Five-minute reuse labels an instance positive if it was followed by a new joke request within five-minutes. Conversely, 1-day return marks as positive all joke requests that were followed by a new one within the following 1 to 25-hour interval. Both strategies assume that if a user returns, he is happy with the jokes. This is clearly an approximation, since a returning user might be overall satisfied with the experience, but not with all the jokes. The same is true for the implied negatives; the user might have been satisfied with some or all of the jokes. Therefore, these labels are noisy and only provide weak supervision to the models.", + "Table TABREF2 shows an example of the labels' values for a set of joke requests from one user." + ], + [ + "All models have access to the same raw features, which we conceptually separate into user, item and contextual features. Examples of features in each of these categories are shown in Table TABREF4. Some of these are used directly by the models, while others need to be pre-processed. The manner in which each model consumes them is explained next." + ], + [ + "To favor simplicity over accuracy, a logistic regression (LR) model is first proposed. Significant effort was put into finding expressive features. Categorical features are one-hot encoded and numerical ones are normalized. The raw Joke Text and Timestamp features require special treatment. The Joke Text is tokenized and the stop-words are removed. We can then compute computational humor features on the clean text such as sense combination BIBREF3 and ambiguity BIBREF12. In addition, since many jokes in our corpus are related to specific events (Christmas, etc), we check for keywords that relate the jokes to them. For example, if \"Santa\" is included, we infer it is a Christmas joke. Finally, pre-computed word embeddings with sub-word information are used to represent jokes by taking the average and maximum vectors over the token representations. Sub-word information is important when encoding jokes since many can contain out-of-vocabulary tokens. The joke's vector representations are also used to compute a summarized view of the user's past liked and disliked jokes. We consider that a user liked a joke when the assigned label is 1, an approximation given the noisy nature of the labels. The user's liked/disliked joke vectors are also combined with the candidate joke vector by taking the cosine similarity between them.", + "For the raw Timestamp feature, we first extract simple time/date features such as month, day and isWeekend. We then compute binary features that mark if the timestamp occurred near one of the special events mentioned before. Some of these events occur the same day every year, while others change (for example, the Super Bowl). In addition, many events are country dependent. The timestamp's event features are combined with the joke's event features to allow the model to capture if an event-related joke occurs at the right time of the year.", + "The LR classifier is trained on the processed features and one of the labels. The model's posterior probability is used to sort the candidates, which are chosen randomly from a pool of unheard jokes. Although useful (see Validation section), this model has several shortcomings. In particular, many of the used features require significant feature engineering and/or are country/language dependent, limiting the extensibility of the model." + ], + [ + "To overcome the LR-model's limitations, we propose the following model (see Figure FIGREF7). In the input layer, features are separated into context, item and user features. Unlike the LR-model, time and text features do not require extensive feature engineering. Instead, simple features (day, month and year) are extracted from the timestamp. After tokenization and stop-word removal, text features are passed through a pre-trained word embeding layer, and later, input into the joke encoder block.", + "The basis of the joke encoder is a modified transformer. Firstly, only the encoder is needed. Moreover, since studies suggest that humor is subjective and conditioned on the user's context BIBREF13, we add an additional sub-layer in the transformer encoder that performs attention over the user's features. This sub-layer, inserted between the two typical transformer sub-layers at certain depths of the network, allows the encoder to adapt the representations of the jokes to different user contexts. Thus, the same joke can be encoded differently depending on the user's features. In practice, this additional sub-layer works like the normal self-attention sub-layer, except it creates its query matrix Q from the sub-layer below, and its K and V matrices from the user features. As an alternative, we also test encoding the jokes using a pre-trained BERT model.", + "Regardless of the used encoder, we average the token representations to obtain a global encoding of the jokes. The same encoder is used to represent the item's (the joke to rank) and the user's (liked and disliked jokes) textual features through weight sharing, and the cosine similarity between both representations are computed. The processed features are then concatenated and passed through a final block of fully connected layers that contains the output layers. Since experiments determined (see Validation section) that both labeling strategies can improve the desired business metrics, instead of optimizing for only one of them, we take a multi-task learning approach. Thus, we have two softmax outputs.", + "Finally, we use a loss function that considers label uncertainty, class imbalance and the different labeling functions. We start from the traditional cross-entropy loss for one labelling function. We then apply uniform label smoothing BIBREF14, which converts the one-hot-encoded label vectors into smoothed label vectors towards $0.5$:", + "with $\\epsilon $ a hyper-parameter. Label smoothing provides a way of considering the uncertainty on the labels by encouraging the model to be less confident. We have also experimented with other alternatives, including specialized losses such as BIBREF15. However, they did not produce a significant increase in performance in our tests. To further model the possible uncertainty in the feedback, we apply sample weights calculated using an exponential decay function on the time difference between the current and the following training instance of the same customer:", + "where $w_i$ is the weight of sample $i$, $t_i$ is the time difference between instances $i$ and $i+1$ for the same user, and $a,b$ are hyper-parameters such that $a>0$ and $0$ , a forward language model allows us to predict the probability of the sequence as: ", + "$$p(t_1,t_2,...,t_N) = \\prod _{i=1}^{N}p(t_i|t_1, t_2,..., t_{i-1}).$$ (Eq. 8) ", + "Similarly, a backward language model allows us to predict the probability of the sentence as: ", + "$$p(t_1,t_2,...,t_N) = \\prod _{i=1}^{N}p(t_i|t_{i+1}, t_{i+2},..., t_N).$$ (Eq. 9) ", + "Traditionally, a bidirectional language model a shallow concatenation of independently trained forward and backward LMs.", + "In order to train a deep bidirectional language model, BERT proposed Masked Language Model (MLM) task, which was also referred to Cloze Task BIBREF23 . MLM task randomly masks some percentage of the input tokens, and then predicts only those masked tokens according to their context. Given a masked token ${t_i}$ , the context is the tokens surrounding token ${t_i}$ in the sequence $\\textbf {\\textit {S}}$ , i.e. cloze sentence ${\\textbf {\\textit {S}}\\backslash \\lbrace t_i \\rbrace }$ . The final hidden vectors corresponding to the mask tokens are fed into an output softmax over the vocabulary to produce words with a probability distribution ${p(\\cdot |\\textbf {\\textit {S}}\\backslash \\lbrace t_i \\rbrace )}$ . MLM task only predicts the masked words rather than reconstructing the entire input, which suggests that more pre-training steps are required for the model to converge. Pre-trained BERT can augment sentences through MLM task, by predicting new words in masked positions according to their context." + ], + [ + "As shown in Fig 1 , our conditional BERT shares the same model architecture with the original BERT. The differences are the input representation and training procedure.", + "The input embeddings of BERT are the sum of the token embeddings, the segmentation embeddings and the position embeddings. For the segmentation embeddings in BERT, a learned sentence A embedding is added to every token of the first sentence, and if a second sentence exists, a sentence B embedding will be added to every token of the second sentence. However, the segmentation embeddings has no connection to the actual annotated labels of a sentence, like sense, sentiment or subjectivity, so predicted word is not always compatible with annotated labels. For example, given a positive movie remark \u201cthis actor is good\", we have the word \u201cgood\" masked. Through the Masked Language Model task by BERT, the predicted word in the masked position has potential to be negative word likes \"bad\" or \"boring\". Such new generated sentences by substituting masked words are implausible with respect to their original labels, which will be harmful if added to the corpus to apply augmentation. In order to address this issue, we propose a new task: \u201cconditional masked language model\".", + "The conditional masked language model randomly masks some of the tokens from the labeled sentence, and the objective is to predict the original vocabulary index of the masked word based on both its context and its label. Given a masked token ${t_i}$ , the context ${\\textbf {\\textit {S}}\\backslash \\lbrace t_i \\rbrace }$ and label ${y}$ are both considered, aiming to calculate ${p(\\cdot |y,\\textbf {\\textit {S}}\\backslash \\lbrace t_i \\rbrace )}$ , instead of calculating ${p(\\cdot |\\textbf {\\textit {S}}\\backslash \\lbrace t_i \\rbrace )}$ . Unlike MLM pre-training, the conditional MLM objective allows the representation to fuse the context information and the label information, which allows us to further train a label-conditional deep bidirectional representations.", + "To perform conditional MLM task, we fine-tune on pre-trained BERT. We alter the segmentation embeddings to label embeddings, which are learned corresponding to their annotated labels on labeled datasets. Note that the BERT are designed with segmentation embedding being embedding A or embedding B, so when a downstream task dataset with more than two labels, we have to adapt the size of embedding to label size compatible. We train conditional BERT using conditional MLM task on labeled dataset. After the model has converged, it is expected to be able to predict words in masked position both considering the context and the label." + ], + [ + "After the conditional BERT is well-trained, we utilize it to augment sentences. Given a labeled sentence from the corpus, we randomly mask a few words in the sentence. Through conditional BERT, various words compatibly with the label of the sentence are predicted by conditional BERT. After substituting the masked words with predicted words, a new sentences is generated, which shares similar context and same label with original sentence. Then new sentences are added to original corpus. We elaborate the entire process in algorithm \"Conditional BERT Contextual Augmentation\" .", + "Conditional BERT contextual augmentation algorithm. Fine-tuning on the pre-trained BERT , we retrofit BERT to conditional BERT using conditional MLM task on labeled dataset. After the model converged, we utilize it to augment sentences. New sentences are added into dataset to augment the dataset. [1] Alter the segmentation embeddings to label embeddings Fine-tune the pre-trained BERT using conditional MLM task on labeled dataset D until convergence each iteration i=1,2,...,M Sample a sentence $s$ from D Randomly mask $k$ words Using fine-tuned conditional BERT to predict label-compatible words on masked positions to generate a new sentence $S^{\\prime }$ Add new sentences into dataset $D$ to get augmented dataset $D^{\\prime }$ Perform downstream task on augmented dataset $D^{\\prime }$ " + ], + [ + "In this section, we present conditional BERT parameter settings and, following Kobayashi BIBREF6 , we apply different augmentation methods on two types of neural models through six text classification tasks. The pre-trained BERT model we used in our experiment is BERT $_{BASE}$ , with number of layers (i.e., Transformer blocks) $L = 12$ , the hidden size $ H = 768$ , and the number of self-attention heads $A = 12$ , total parameters $= 110M$ . Detailed pre-train parameters setting can be found in original paper BIBREF11 . For each task, we perform the following steps independently. First, we evaluate the augmentation ability of original BERT model pre-trained on MLM task. We use pre-trained BERT to augment dataset, by predicted masked words only condition on context for each sentence. Second, we fine-tune the original BERT model to a conditional BERT. Well-trained conditional BERT augments each sentence in dataset by predicted masked words condition on both context and label. Third, we compare the performance of the two methods with Kobayashi's BIBREF6 contextual augmentation results. Note that the original BERT\u2019s segmentation embeddings layer is compatible with two-label dataset. When the task-specific dataset is with more than two different labels, we should re-train a label size compatible label embeddings layer instead of directly fine-tuning the pre-trained one." + ], + [ + "Six benchmark classification datasets are listed in table 1 . Following Kim BIBREF24 , for a dataset without validation data, we use 10% of its training set for the validation set. Summary statistics of six classification datasets are shown in table 1.", + "SST BIBREF25 SST (Stanford Sentiment Treebank) is a dataset for sentiment classification on movie reviews, which are annotated with five labels (SST5: very positive, positive, neutral, negative, or very negative) or two labels (SST2: positive or negative).", + "Subj BIBREF26 Subj (Subjectivity dataset) is annotated with whether a sentence is subjective or objective.", + "MPQA BIBREF27 MPQA Opinion Corpus is an opinion polarity detection dataset of short phrases rather than sentences, which contains news articles from a wide variety of news sources manually annotated for opinions and other private states (i.e., beliefs, emotions, sentiments, speculations, etc.).", + "RT BIBREF28 RT is another movie review sentiment dataset contains a collection of short review excerpts from Rotten Tomatoes collected by Bo Pang and Lillian Lee.", + "TREC BIBREF29 TREC is a dataset for classification of the six question types (whether the question is about person, location, numeric information, etc.)." + ], + [ + "We evaluate the performance improvement brought by conditional BERT contextual augmentation on sentence classification tasks, so we need to prepare two common sentence classifiers beforehand. For comparison, following Kobayashi BIBREF6 , we adopt two typical classifier architectures: CNN or LSTM-RNN. The CNN-based classifier BIBREF24 has convolutional filters of size 3, 4, 5 and word embeddings. All outputs of each filter are concatenated before applied with a max-pooling over time, then fed into a two-layer feed-forward network with ReLU, followed by the softmax function. An RNN-based classifier has a single layer LSTM and word embeddings, whose output is fed into an output affine layer with the softmax function. For both the architectures, dropout BIBREF30 and Adam optimization BIBREF31 are applied during training. The train process is finish by early stopping with validation at each epoch.", + "Sentence classifier hyper-parameters including learning rate, embedding dimension, unit or filter size, and dropout ratio, are selected using grid-search for each task-specific dataset. We refer to Kobayashi's implementation in the released code. For BERT, all hyper-parameters are kept the same as Devlin BIBREF11 , codes in Tensorflow and PyTorch are all available on github and pre-trained BERT model can also be downloaded. The number of conditional BERT training epochs ranges in [1-50] and number of masked words ranges in [1-2].", + "We compare the performance improvements obtained by our proposed method with the following baseline methods, \u201cw/\" means \u201cwith\":", + "w/synonym: Words are randomly replaced with synonyms from WordNet BIBREF5 .", + "w/context: Proposed by Kobayashi BIBREF6 , which used a bidirectional language model to apply contextual augmentation, each word was replaced with a probability.", + "w/context+label: Kobayashi\u2019s contextual augmentation method BIBREF6 in a label-conditional LM architecture.", + "Table 2 lists the accuracies of the all methods on two classifier architectures. The results show that, for various datasets on different classifier architectures, our conditional BERT contextual augmentation improves the model performances most. BERT can also augments sentences to some extent, but not as much as conditional BERT does. For we masked words randomly, the masked words may be label-sensitive or label-insensitive. If label-insensitive words are masked, words predicted through BERT may not be compatible with original labels. The improvement over all benchmark datasets also shows that conditional BERT is a general augmentation method for multi-labels sentence classification tasks.", + "We also explore the effect of number of training steps to the performance of conditional BERT data augmentation. The fine-tuning epoch setting ranges in [1-50], we list the fine-tuning epoch of conditional BERT to outperform BERT for various benchmarks in table 3 . The results show that our conditional BERT contextual augmentation can achieve obvious performance improvement after only a few fine-tuning epochs, which is very convenient to apply to downstream tasks." + ], + [ + "In this section, we further deep into the connection to style transfer and apply our well trained conditional BERT to style transfer task. Style transfer is defined as the task of rephrasing the text to contain specific stylistic properties without changing the intent or affect within the context BIBREF32 . Our conditional MLM task changes words in the text condition on given label without changing the context. View from this point, the two tasks are very close. So in order to apply conditional BERT to style transfer task, given a specific stylistic sentence, we break it into two steps: first, we find the words relevant to the style; second, we mask the style-relevant words, then use conditional BERT to predict new substitutes with sentence context and target style property. In order to find style-relevant words in a sentence, we refer to Xu BIBREF33 , which proposed an attention-based method to extract the contribution of each word to the sentence sentimental label. For example, given a positive movie remark \u201cThis movie is funny and interesting\", we filter out the words contributes largely to the label and mask them. Then through our conditional BERT contextual augmentation method, we fill in the masked position by predicting words conditioning on opposite label and sentence context, resulting in \u201cThis movie is boring and dull\". The words \u201cboring\" and \u201cdull\" contribute to the new sentence being labeled as negative style. We sample some sentences from dataset SST2, transferring them to the opposite label, as listed in table 4 ." + ], + [ + "In this paper, we fine-tune BERT to conditional BERT by introducing a novel conditional MLM task. After being well trained, the conditional BERT can be applied to data augmentation for sentence classification tasks. Experiment results show that our model outperforms several baseline methods obviously. Furthermore, we demonstrate that our conditional BERT can also be applied to style transfer task. In the future, (1)We will explore how to perform text data augmentation on imbalanced datasets with pre-trained language model, (2) we believe the idea of conditional BERT contextual augmentation is universal and will be applied to paragraph or document level data augmentation." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0420/instruction.md b/qasper-0420/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..e16cfe4e81358abe3c4850a1899bca99c136ef13 --- /dev/null +++ b/qasper-0420/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Recent Advances in Neural Question Generation + +Question: Do they cover data augmentation papers? \ No newline at end of file diff --git a/qasper-0421/instruction.md b/qasper-0421/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..abb0cb9398d849b0e96013bf3a79a7b05a1c43dc --- /dev/null +++ b/qasper-0421/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Recent Advances in Neural Question Generation + +Question: What is the latest paper covered by this survey? \ No newline at end of file diff --git a/qasper-0426/instruction.md b/qasper-0426/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..b13ebb226f14789606263330b01d8ac7e4be73be --- /dev/null +++ b/qasper-0426/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Recent Advances in Neural Question Generation + +Question: Do they survey non-neural methods for question generation? \ No newline at end of file diff --git a/qasper-0427/instruction.md b/qasper-0427/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..c792464fb54ae2e6461c641d782f9bd09687b758 --- /dev/null +++ b/qasper-0427/instruction.md @@ -0,0 +1,94 @@ +Name of Paper: Open Named Entity Modeling from Embedding Distribution + +Question: What is their model? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Word Embeddings", + "Model", + "Open Monolingual NE Modeling", + " Embedding Distribution Mapping", + "Hypersphere features for NE Recognition ", + "Experiment", + "Setup", + "Monolingual Embedding Distribution", + " Hypersphere Mapping", + "Off-the-shelf NE Recognition Systems", + "Related Work", + "Conclusion" + ], + "paragraphs": [ + [ + "Named Entity Recognition is a major natural language processing task that recognizes the proper labels such as LOC (Location), PER (Person), ORG (Organization), etc. Like words or phrase, being a sort of language constituent, named entities also benefit from better representation for better processing. Continuous word representations, known as word embeddings, well capture semantic and syntactic regularities of words BIBREF0 and perform well in monolingual NE recognition BIBREF1 , BIBREF2 . Word embeddings also exhibit isomorphism structure across languages BIBREF3 . On account of these characteristics above, we attempt to utilize word embeddings to improve NE recognition for resource-poor languages with the help of richer ones. The state-of-the-art cross-lingual NE recognition methods are mainly based on annotation projection methods according to parallel corpora, translations BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 and Wikipedia methods BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 .", + "Most annotated corpus based NE recognition tasks can benefit a great deal from a known NE dictionary, as NEs are those words which carry common sense knowledge quite differ from the rest ones in any language vocabulary. This work will focus on the NE recognition from plain text instead of corpus based NE recognition. For a purpose of learning from limited annotated linguistic resources, our preliminary discovery shows that it is possible to build a geometric space projection between embedding spaces to help cross-lingual NE recognition. Our study contains two main steps: First, we explore the NE distribution in monolingual case. Next, we learn a hypersphere mapping between embedding spaces of languages with minimal supervision.", + "Despite the simplicity of our model, we make the following contributions. First, for word embeddings generated by different dimensions and objective functions, all common NE types (PER, LOC, ORG) tend to be densely distributed in a hypersphere, which gives a better solution to characterize the general NE distribution rather than existing closed dictionary definition for NE. Second, with the help of the hypersphere mapping, it is possible to capture the NE distribution of resource-poor languages with only a small amount of annotated data. Third, our method is highly friendly to unregistered NEs, as the distance to each hypersphere center is the only factor needed to determine their NE categories. Finally, by adding hypersphere features we can significantly improve the performance of off-the-shelf named entity recognition (NER) systems." + ], + [ + "Seok BIBREF2 proposed that similar words are more likely to occupy close spatial positions, since their word embeddings carries syntactical and semantical informative clues. For an intuitive understanding, they listed the nearest neighbors of words included in the PER and ORG tags under cosine similarity metric. To empirically verify this observation and explore the performance of this property in Euclidean space , we list Top-5 nearest neighbors under Euclidean distance metric in Table 1 and illustrate a standard t-SNE BIBREF12 2- $D$ projection of the embeddings of three entity types with a sample of 500 words for each type.", + "Nearest neighbors are calculated by comparing the Euclidean distance between the embedding of each word (such as Fohnsdorf, Belgian, and Ltd.) and the embeddings of all other words in the vocabulary. We pre-train word embeddings using the continuous skip-gram model BIBREF13 with the tool, and obtain multi-word and single-word phrases with a maximum length of 8, and a minimum word frequency cutoff of 3. The examples in Table 1 and visualization in Figure 1 demonstrate that the above observation suits well under Euclidean distance metric for NE recognition either for monolingual or multilingual situations." + ], + [ + "Encouraged by the verification of nearest neighbors of NEs still being NEs, we attempt to build a model which can represent this property with least parameters. Namely, given an NE dictionary on a monolingual, we build a model to describe the distribution of the word embeddings of these entities, then we can easily use these parameters as a decoder for any word to directly determine whether it belongs to a certain type of entity. In this section, we first introduce the open modeling from embedding distribution in monolingual cases, and then put forward the mapping of the distribution model between languages, and then use the mapping to build named entity dataset for resource-poor languages. Finally, we use the proposed named entity model to improve the performance of state-of-the-art NE recognition systems." + ], + [ + "As illustrated is Figure 1, the embedding distribution of NEs is aggregated, and there exists a certain boundary between different types of NEs. We construct an open representation for each type of NEs \u2013 hypersphere, the NE type of any entity can be easily judged by checking whether it is inside a hypersphere, which makes a difference from the defining way of any limited and insufficient NE dictionary. The hypersphere can be expressed as follows: ", + "$$E( X, O) \\le r$$ (Eq. 9) ", + "where E represents the adopted Euclidean distance, X is referred to any point in the hypersphere, $ O $ and $ r $ are the center vector and radius. For each entity type, we attempt to construct a hypersphere which encompass as many congeneric NEs as possible, and as few as possible inhomogeneous NEs, we use $F_1$ score as a trade-off between these two concerns. We carefully tune the center and radius of the hypersphere to maximize its $F_1$ score: we first fix the center as the average of all NE embeddings from known NE dictionaries, and search the best radius in $[minDist, maxDist]$ , where $minDist/maxDist$ refers to the distance between the center and its nearest/farthest neighbors; Then, we kick NEs which are far from the center with the distance threshold $q$ (much larger than the radius) to generate a new center; Finally, we tune the threshold $q$ and repeat the above steps to find the most suitable center and radius.", + "The mathematical intuition for using a hypersphere can be interpreted in a manner similar to support vector machine (SVM) BIBREF14 , which uses the kernel to obtain the optimal margin in very high dimensional spaces through linear hyperplane separation in Descartes coordination. We transfer the idea to the separation of NE distributions. The only difference is about boundary shape, what we need is a closed surface instead of an open hyperplane, and hypersphere is such a smooth, closed boundary (with least parameters as well) in polar coordinates as counterpart of hyperplane in Descartes coordinates. Using the least principle to model the mathematical objective also follows the Occam razor principle.", + "Figure 1 also reveals that the distribution of PER NEs is compact, while ORG NE distribution is relatively sparse. Syntactically, PER NEs are more stable in terms of position and length in sentences compared to ORG NEs, so that they have a more accurate embedding representation with strong strong syntax and semantics, making the corresponding word embeddings closer to central region of the hypersphere." + ], + [ + "As the isomorphism characteristic exists between languages BIBREF3 , BIBREF15 , we can apply the distributional modeling for every languages in the same way. For a target language without an NE dictionary, its NE distribution can be obtained from a source language with known NE distributions by learning the transforming function between these two languages. We construct the transformation matrix $W$ via a set of parallel word pairs (the set will be referred to seed pairs hereafter) and their word embeddings $\\lbrace X^{(i)}, Z^{(i)}\\rbrace _{i=1}^m$ BIBREF3 , $\\lbrace X^{(i)}\\rbrace _{i=1}^m$ , $\\lbrace Z^{(i)}\\rbrace _{i=1}^m$ are the source and target word embeddings respectively. $W$ can be learned by solving the matrix equation $XW = Z$ . Then, given the source center vector ${ O_1}$ , the mapping center vector ${O_2}$ can be expressed as: ", + "$${ O_2} = W^T{O_1}$$ (Eq. 11) ", + "Actually, the isomorphism (mapping) between embedding spaces is the type of affine isomorphism by furthermore considering embedding in continuous space. The invariant characteristics of relative position BIBREF16 , BIBREF17 , BIBREF18 , BIBREF19 in affine transformation is applied to correct transformation matrix errors caused by limited amount of parallel word pairs (the set will be referred to seed pairs hereafter). As shown in Figure 2, the ratio of the line segments keep constant when the distance is linearly enlarged or shortened. Recall that point $Q$ is an affine combination of two other noncoincident points $Q_1$ and $Q_2$ on the line: $Q = (1-t)Q_1 + tQ_2 $ .", + "We apply the affine mapping $f$ and get: $f(Q) = f((1-t)Q_1 + tQ_2) = (1-t)f(Q_1) + tf(Q_2)$ Obviously, the constant ratio $t$ is not affected by the affine transformation $f$ . That is, $Q$ has the same relative distances between it and $Q_1$ and $Q_2$ during the process of transformation. Based on the above characteristic, for any point $X^{(i)}$ in the source space and its mapping point $Z^{(i)}$ , $X^{(i)}$ and $f(Q) = f((1-t)Q_1 + tQ_2) = (1-t)f(Q_1) + tf(Q_2)$0 cut off radiuses with the same ratio, namely, the ratio of the distance of these two points to their centers and their radiuses remains unchanged. ", + "$$\\frac{E( O_1, X^{(i)})}{r_1} = \\frac{E( O_2, Z^{(i)})}{r_2}$$ (Eq. 15) ", + "where $E$ represents the adopted Euclidean distance, ${O_1, O_2, r_1, r_2}$ are the centers and radii of hyperspheres. We convert the equation and learn the optimized mapping center ${O_2}$ and ratio $K$ via the seed pairs: ", + "$${K = \\frac{r_2}{r_1} = \\frac{E( O_2, Z^{(i)})}{E( O_1, X^{(i)})}}$$ (Eq. 16) ", + "$$\\begin{aligned}\nE( O_2, Z^{(i)}) &= K * E( O_1, X^{(i)}) \\quad r_2 &= K * r_1 \\\\\n\\end{aligned}$$ (Eq. 17) ", + "Given the seed pairs $\\lbrace X^{(i)}, Z^{(i)}\\rbrace _{i=1}^m$ , the initialized center $O_2$ in Equation (3), the center $ O_1 $ and radius $ r_1 $ of the hypersphere in source language space, we may work out the optimized ratio $K$ , the mapping center $ O_2 $ and radius $ r_2 $ in target language space by solving the linear equation group (5)." + ], + [ + "The Euclidean distance between word and hypersphere centers can be pre-computed as its NE likelihood, which may provide informative clues for NE recognition. We only consider three entity types in our experiment, and the Euclidean distance which is represented as a 3- $D$ vector and referred to HS vector hereafter) is added to four representative off-the-shelf NER systems to verify its effectiveness. We feed HS vector into different layers of the neural network: (1) input layer $[x_k; c_k; HS]$ ; (2) output layer of LSTM $[h_k; HS]$ , where $x_k$ , $w_k$ and $h_k$ represent word embeddings, char embeddings and the output of LSTM, respectively. All of these models are based on classical BiLSTM-CRF architecture BIBREF20 , except that BIBREF21 replaces CRF layer with softmax. These four baseline systems are introduced as follows.", + " BIBREF22 concatenates ELMo with word embeddings as the input of LSTM to enhance word representations as it carries both syntactic and semantic information.", + " BIBREF21 uses distant supervision for NER task and propose a new Tie or Break tagging scheme, where entity spans and entity types are encoded into two folds. They first build a binary classifier to distinguish Break from Tie, and then learn the entity types according to their occurrence and frequency in NE dictionary. The authors conduct their experiments on biomedical datasets rather than standard benchmark, so we extract the NEs in training data as the domain-specific dictionary. This work creates a promising prospect for using dictionary to replace the role of training data.", + " BIBREF23 takes advantage of the power of the 120 entity types from annotated data in Wikipedia. Cosine similarity between the word embedding and the embedding of each entity type is concatenated as the 120- $D$ feature vector (which is called LS vector in their paper) and then fed into the input layer of LSTM. Lexical feature has been shown a key factor to NE recognition.", + " BIBREF24 passes sentences as sequences of characters into a character-level language model to produce a novel type of word embedding, contextual string embeddings, where one word may have different embeddings as the embeddings are computed both on the characters of a word and its surrounding context. Such embeddings are then fed into the input layer of LSTM." + ], + [ + "In this section, we evaluate the hypersphere model based on the three models introduced above: open monolingual NE modeling, embedding distribution mapping and refinement NE recognition." + ], + [ + "In this experiment, we adopt pre-trained word embeddings from Wikipedia corpus. Our preliminary experiments will be conducted on English and Chinese. For the former, we use NLTK toolkit and LANGID toolkit to perform the pre-processing. For the latter, we first use OpenCC to simplify characters, and then use THULAC to perform word segmentation.", + "In order to make the experimental results more accurate and credible, we manually annotate two large enough Chinese and English NE dictionaries for training and test. Table 2 lists the statistics of Wikipedia corpus and the annotated data. Our dictionary contains many multi-word NEs in LOC and ORG types as accounted in the second column for each language in Table 2, while we only include single-word PER NEs in our dictionary, since the English first name and last name are separated, and Chinese word segmentation cuts most of the PER entities together. We pre-train quality multi-word and single-word embeddings and aim to maximize the coverage of the NEs in the dictionary. The pre-trained word embeddings cover 82.3% / 82.51% of LOC NEs and 70.2% / 63.61% of ORG NEs in English and Chinese, respectively. For other multi-word NEs, we simply calculate the average vector of each word embedding as their representations." + ], + [ + "The NE distribution is closely correlated to the dimension of the embedding space, we train the word embeddings from 2- $D$ to 300- $D$ and search for the most suitable dimension for each NE type. For each dimension, we carefully tune the center and radius of the hypersphere using the method introduced in section 3.1 for maximize $F_1$ score, and select the dimension with maximize $F_1$ score. The most suitable dimension for ORG, PER, LOC are 16- ${D}$ /16- ${D}$ /24- ${D}$ (these dimensions will be used as parameters in the following experiments), respectively . We discover that in low-dimensional space, the distributions of NEs are better. In high dimensions, the curse of dimension could be the main reason to limit the performance.", + "Table 3 lists the final maximum $F_1$ score of three NE types. The results of the three types of NE are almost 50%, and PER type performs best. The main factor may be that PER NEs are represented as single-word in our dictionary, and word embeddings can better represents their meanings. The result also states that better representations for multi-word NEs which are not covered by the dictionary instead of the average of each word may help bring better results. Besides, the incompleteness of NE dictionaries and noises during pre-processing may cause a decrease on the performance. Overall, hypersphere model has shown been effectively used as the open modeling for NEs." + ], + [ + "The following preparations were made for the mapping: $(i)$ A large enough NE dictionary in source (resource-rich) corpus; $(ii)$ A small amount of annotated seed pairs. We use $s$ to represent the number of seed pairs and $d$ to represent the number of unknown variables. With seed pair size $s < d$ , the matrix can be solved with much loose constraints, and $F_1$ score remarkably increases with more seed pairs. Once $s > d$ , the linear equation group will be always determined by strong enough constraints, which leads to a stable solution. Based on the characteristics, we only take two dozen of seed pairs on each type in following experiments. We combine human translation and online translation together for double verification for this small set of seed pairs. In this part, we utilize English and Chinese as the corpus of known NEs in turn, and predict the NE distribution of the other language.", + "Evaluation In order to quantitatively represent the mapping effect, we present a new evaluation method to judge the hypersphere mapping between English and Chinese: ", + "$$\\begin{aligned}\nP = \\frac{V_i}{V_m} \\quad R = \\frac{V_i}{V_t} \\quad F_1 = \\frac{2 * P * R}{P + R}\n\\end{aligned}$$ (Eq. 29) ", + "where ${V_t, V_m, V_i}$ represent the volumes of the target, mapping and intersection hyperspheres. Due to the difficulty of calculating the volume of hyperspheres in high dimensions, we adopt Monte Carlo methods to simulate the volume BIBREF25 . we generate a great quantity of points in the embedding spaces, and take the amount of the points falling in each hypersphere as its volume.", + "Mapping between English and Chinese Table 4 shows the comparisons of cross-lingual named entity extraction performance. We use the unsupervised method proposed in BIBREF26 to generate cross-lingual embeddings. $k$ -NN and SVM are the same as monolingual cases in Table 3 except for the training set. $k$ -NN $_{150}$ and SVM $_{150}$ use 20% of the NEs in source language and 150 NEs (50 LOC, PER and ORG) in target language for training, while $k$ -NN $_{2500}$ and SVM $_{2500}$ use 20% of the NEs in source language and 2500 NEs (1000 LOC and PER, 500 ORG) in target language. $k$ -NN and SVM depend much on the annotated training set, requiring more than $1K$ training samples to provide a performance as our model offers. Due to the instability of ORG type in length, taking the average of each word embedding may disobey the syntactic and semantic regularities of ORG NEs, thereby undermines the multilingual isomorphism characteristics, which causes the inferior performance of our model on this type of NEs. This suggests that build better representations NEs for multi-word NEs may contribute to a better performance in our model.", + "Mapping to truly Low-resource Language We build named entity dataset for a truly resource-poor language, Indonesian, and manually examine the nearest words to the hypersphere center for 'gold-standard' evaluation. We take English as the source language, the settings of the dimension $D$ and the number of seed pairs $s$ are the same as the above experiments between Chinese and English. From the results listed in Table 5, we can see that even the precision of the top-100 NEs are 0.350 $F_1$ /0.440 $F_1$ /0.310 $F_1$ , respectively, which proves the this distribution can indeed serves as a candidate NE dictionary for Indonesian.", + "[9] The authors of BIBREF24 publish an updated results (92.98) on CoNLL-2003 dataset in https://github.com/zalandoresearch/flair/issues/206 on their 0.3.2 version, and this is the best result at our most try. [10] This is the reported state-of-the-art result in their github. [11]We use the same parameters as the authors release in https://github.com/zalandoresearch/flair/issues/173 and obtain the result of 89.45 on ONTONOTES 5.0 dataset." + ], + [ + "To evaluate the influence of our hypersphere feature for off-the-shelf NER systems, we perform the NE recognition on two standard NER benchmark datasets, CoNLL2003 and ONTONOTES 5.0. Our results in Table 6 and Table 7 demonstrate the power of hypersphere features, which contribute to nearly all of the three types of entities as shown in Table 6, except for a slight drop in the PER type of BIBREF22 on a strong baseline. HS features stably enhance all strong state-of-the-art baselines, BIBREF22 , BIBREF21 and BIBREF23 by 0.33/0.72/0.23 $F_1$ point and 0.13/0.3/0.1 $F_1$ point on both benchmark datasets, CoNLL-2003 and ONTONOTES 5.0. We show that our HS feature is also comparable with previous much more complicated LS feature, and our model surpasses their baseline (without LS feature) by 0.58/0.78 $F_1$ point with only HS features. We establish a new state-of-the-art $F_1$ score of 89.75 on ONTONOTES 5.0, while matching state-of-the-art performance with a $F_1$ score of 92.95 on CoNLL-2003 dataset." + ], + [ + "In recent years, word embeddings have also been used as a feature to enhance the NE recognition, with the revealing of linguistic features in morphological, syntactic and semantic perspective. BIBREF1 clustered the word embeddings and combined multiple cluster granularities to improve the NE recognition performance. Our work likewise use word embeddings to help NE recognition, we make use of the characteristic that syntactically and semantically s are more likely to be neighbors in embedding spaces and construct a hypersphere model to encompass NEs.", + "Cross-lingual knowledge transfer is a highly promising work for resource-poor languages, annotation projection and representation projection are widely used in NE recognition BIBREF27 , BIBREF5 , BIBREF4 , BIBREF28 , BIBREF29 , BIBREF30 . These works put forward inconvenient requirements for parallel or comparable corpora, a large amount of annotated or translation data or bilingual lexicon. Different from any existing work to the best of our knowledge, this is the first work that merely uses isomorphic mappings in low-dimensional embedding spaces to recognize NEs, and we introduce a mathematically simple model to describe NE embedding distribution from visualization results, showing it works for both monolingual and cross-lingual situations." + ], + [ + "Named entities being an open set which keeps expanding are difficult to represent through a closed NE dictionary. This work mitigates significant defects in previous closed NE definitions and proposes a new open definition for NEs by modeling their embedding distributions with least parameters. We visualize NE distributions in monolingual case and perform an effective isomorphism spaces mapping in cross-lingual case. According to our work, we demonstrate that common named entity types (PER, LOC, ORG) tend to be densely distributed in a hypersphere and it is possible to build a mapping between the NE distributions in embedding spaces to help cross-lingual NE recognition. Experimental results show that the distribution of named entities via mapping can be used as a good enough replacement for the original distribution. Then the discovery is used to build an NE dictionary for Indonesian being a truly low-resource language, which also gives satisfactory precision. Finally, our simple hypersphere features being the representation of NE likelihood can be used for enhancing off-the-shelf NER systems by concatenating with word embeddings and the output of BiLSTM in the input layer and encode layer, respectively, and we achieve a new state-of-the-art $F_1$ score of 89.75 on ONTONOTES 5.0 benchmark. In this work, we also give a better solution for unregistered NEs. For any newly emerged NE together with its embedding, in case we obtain the hypersphere of each named entity, the corresponding named entity category can be determined by calculating the distance between its word embedding and the center of each hypersphere." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0428/instruction.md b/qasper-0428/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..a72b88fa742bad83095d8c3bc5c50558960943d8 --- /dev/null +++ b/qasper-0428/instruction.md @@ -0,0 +1,94 @@ +Name of Paper: Open Named Entity Modeling from Embedding Distribution + +Question: Do they evaluate on NER data sets? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Word Embeddings", + "Model", + "Open Monolingual NE Modeling", + " Embedding Distribution Mapping", + "Hypersphere features for NE Recognition ", + "Experiment", + "Setup", + "Monolingual Embedding Distribution", + " Hypersphere Mapping", + "Off-the-shelf NE Recognition Systems", + "Related Work", + "Conclusion" + ], + "paragraphs": [ + [ + "Named Entity Recognition is a major natural language processing task that recognizes the proper labels such as LOC (Location), PER (Person), ORG (Organization), etc. Like words or phrase, being a sort of language constituent, named entities also benefit from better representation for better processing. Continuous word representations, known as word embeddings, well capture semantic and syntactic regularities of words BIBREF0 and perform well in monolingual NE recognition BIBREF1 , BIBREF2 . Word embeddings also exhibit isomorphism structure across languages BIBREF3 . On account of these characteristics above, we attempt to utilize word embeddings to improve NE recognition for resource-poor languages with the help of richer ones. The state-of-the-art cross-lingual NE recognition methods are mainly based on annotation projection methods according to parallel corpora, translations BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 and Wikipedia methods BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 .", + "Most annotated corpus based NE recognition tasks can benefit a great deal from a known NE dictionary, as NEs are those words which carry common sense knowledge quite differ from the rest ones in any language vocabulary. This work will focus on the NE recognition from plain text instead of corpus based NE recognition. For a purpose of learning from limited annotated linguistic resources, our preliminary discovery shows that it is possible to build a geometric space projection between embedding spaces to help cross-lingual NE recognition. Our study contains two main steps: First, we explore the NE distribution in monolingual case. Next, we learn a hypersphere mapping between embedding spaces of languages with minimal supervision.", + "Despite the simplicity of our model, we make the following contributions. First, for word embeddings generated by different dimensions and objective functions, all common NE types (PER, LOC, ORG) tend to be densely distributed in a hypersphere, which gives a better solution to characterize the general NE distribution rather than existing closed dictionary definition for NE. Second, with the help of the hypersphere mapping, it is possible to capture the NE distribution of resource-poor languages with only a small amount of annotated data. Third, our method is highly friendly to unregistered NEs, as the distance to each hypersphere center is the only factor needed to determine their NE categories. Finally, by adding hypersphere features we can significantly improve the performance of off-the-shelf named entity recognition (NER) systems." + ], + [ + "Seok BIBREF2 proposed that similar words are more likely to occupy close spatial positions, since their word embeddings carries syntactical and semantical informative clues. For an intuitive understanding, they listed the nearest neighbors of words included in the PER and ORG tags under cosine similarity metric. To empirically verify this observation and explore the performance of this property in Euclidean space , we list Top-5 nearest neighbors under Euclidean distance metric in Table 1 and illustrate a standard t-SNE BIBREF12 2- $D$ projection of the embeddings of three entity types with a sample of 500 words for each type.", + "Nearest neighbors are calculated by comparing the Euclidean distance between the embedding of each word (such as Fohnsdorf, Belgian, and Ltd.) and the embeddings of all other words in the vocabulary. We pre-train word embeddings using the continuous skip-gram model BIBREF13 with the tool, and obtain multi-word and single-word phrases with a maximum length of 8, and a minimum word frequency cutoff of 3. The examples in Table 1 and visualization in Figure 1 demonstrate that the above observation suits well under Euclidean distance metric for NE recognition either for monolingual or multilingual situations." + ], + [ + "Encouraged by the verification of nearest neighbors of NEs still being NEs, we attempt to build a model which can represent this property with least parameters. Namely, given an NE dictionary on a monolingual, we build a model to describe the distribution of the word embeddings of these entities, then we can easily use these parameters as a decoder for any word to directly determine whether it belongs to a certain type of entity. In this section, we first introduce the open modeling from embedding distribution in monolingual cases, and then put forward the mapping of the distribution model between languages, and then use the mapping to build named entity dataset for resource-poor languages. Finally, we use the proposed named entity model to improve the performance of state-of-the-art NE recognition systems." + ], + [ + "As illustrated is Figure 1, the embedding distribution of NEs is aggregated, and there exists a certain boundary between different types of NEs. We construct an open representation for each type of NEs \u2013 hypersphere, the NE type of any entity can be easily judged by checking whether it is inside a hypersphere, which makes a difference from the defining way of any limited and insufficient NE dictionary. The hypersphere can be expressed as follows: ", + "$$E( X, O) \\le r$$ (Eq. 9) ", + "where E represents the adopted Euclidean distance, X is referred to any point in the hypersphere, $ O $ and $ r $ are the center vector and radius. For each entity type, we attempt to construct a hypersphere which encompass as many congeneric NEs as possible, and as few as possible inhomogeneous NEs, we use $F_1$ score as a trade-off between these two concerns. We carefully tune the center and radius of the hypersphere to maximize its $F_1$ score: we first fix the center as the average of all NE embeddings from known NE dictionaries, and search the best radius in $[minDist, maxDist]$ , where $minDist/maxDist$ refers to the distance between the center and its nearest/farthest neighbors; Then, we kick NEs which are far from the center with the distance threshold $q$ (much larger than the radius) to generate a new center; Finally, we tune the threshold $q$ and repeat the above steps to find the most suitable center and radius.", + "The mathematical intuition for using a hypersphere can be interpreted in a manner similar to support vector machine (SVM) BIBREF14 , which uses the kernel to obtain the optimal margin in very high dimensional spaces through linear hyperplane separation in Descartes coordination. We transfer the idea to the separation of NE distributions. The only difference is about boundary shape, what we need is a closed surface instead of an open hyperplane, and hypersphere is such a smooth, closed boundary (with least parameters as well) in polar coordinates as counterpart of hyperplane in Descartes coordinates. Using the least principle to model the mathematical objective also follows the Occam razor principle.", + "Figure 1 also reveals that the distribution of PER NEs is compact, while ORG NE distribution is relatively sparse. Syntactically, PER NEs are more stable in terms of position and length in sentences compared to ORG NEs, so that they have a more accurate embedding representation with strong strong syntax and semantics, making the corresponding word embeddings closer to central region of the hypersphere." + ], + [ + "As the isomorphism characteristic exists between languages BIBREF3 , BIBREF15 , we can apply the distributional modeling for every languages in the same way. For a target language without an NE dictionary, its NE distribution can be obtained from a source language with known NE distributions by learning the transforming function between these two languages. We construct the transformation matrix $W$ via a set of parallel word pairs (the set will be referred to seed pairs hereafter) and their word embeddings $\\lbrace X^{(i)}, Z^{(i)}\\rbrace _{i=1}^m$ BIBREF3 , $\\lbrace X^{(i)}\\rbrace _{i=1}^m$ , $\\lbrace Z^{(i)}\\rbrace _{i=1}^m$ are the source and target word embeddings respectively. $W$ can be learned by solving the matrix equation $XW = Z$ . Then, given the source center vector ${ O_1}$ , the mapping center vector ${O_2}$ can be expressed as: ", + "$${ O_2} = W^T{O_1}$$ (Eq. 11) ", + "Actually, the isomorphism (mapping) between embedding spaces is the type of affine isomorphism by furthermore considering embedding in continuous space. The invariant characteristics of relative position BIBREF16 , BIBREF17 , BIBREF18 , BIBREF19 in affine transformation is applied to correct transformation matrix errors caused by limited amount of parallel word pairs (the set will be referred to seed pairs hereafter). As shown in Figure 2, the ratio of the line segments keep constant when the distance is linearly enlarged or shortened. Recall that point $Q$ is an affine combination of two other noncoincident points $Q_1$ and $Q_2$ on the line: $Q = (1-t)Q_1 + tQ_2 $ .", + "We apply the affine mapping $f$ and get: $f(Q) = f((1-t)Q_1 + tQ_2) = (1-t)f(Q_1) + tf(Q_2)$ Obviously, the constant ratio $t$ is not affected by the affine transformation $f$ . That is, $Q$ has the same relative distances between it and $Q_1$ and $Q_2$ during the process of transformation. Based on the above characteristic, for any point $X^{(i)}$ in the source space and its mapping point $Z^{(i)}$ , $X^{(i)}$ and $f(Q) = f((1-t)Q_1 + tQ_2) = (1-t)f(Q_1) + tf(Q_2)$0 cut off radiuses with the same ratio, namely, the ratio of the distance of these two points to their centers and their radiuses remains unchanged. ", + "$$\\frac{E( O_1, X^{(i)})}{r_1} = \\frac{E( O_2, Z^{(i)})}{r_2}$$ (Eq. 15) ", + "where $E$ represents the adopted Euclidean distance, ${O_1, O_2, r_1, r_2}$ are the centers and radii of hyperspheres. We convert the equation and learn the optimized mapping center ${O_2}$ and ratio $K$ via the seed pairs: ", + "$${K = \\frac{r_2}{r_1} = \\frac{E( O_2, Z^{(i)})}{E( O_1, X^{(i)})}}$$ (Eq. 16) ", + "$$\\begin{aligned}\nE( O_2, Z^{(i)}) &= K * E( O_1, X^{(i)}) \\quad r_2 &= K * r_1 \\\\\n\\end{aligned}$$ (Eq. 17) ", + "Given the seed pairs $\\lbrace X^{(i)}, Z^{(i)}\\rbrace _{i=1}^m$ , the initialized center $O_2$ in Equation (3), the center $ O_1 $ and radius $ r_1 $ of the hypersphere in source language space, we may work out the optimized ratio $K$ , the mapping center $ O_2 $ and radius $ r_2 $ in target language space by solving the linear equation group (5)." + ], + [ + "The Euclidean distance between word and hypersphere centers can be pre-computed as its NE likelihood, which may provide informative clues for NE recognition. We only consider three entity types in our experiment, and the Euclidean distance which is represented as a 3- $D$ vector and referred to HS vector hereafter) is added to four representative off-the-shelf NER systems to verify its effectiveness. We feed HS vector into different layers of the neural network: (1) input layer $[x_k; c_k; HS]$ ; (2) output layer of LSTM $[h_k; HS]$ , where $x_k$ , $w_k$ and $h_k$ represent word embeddings, char embeddings and the output of LSTM, respectively. All of these models are based on classical BiLSTM-CRF architecture BIBREF20 , except that BIBREF21 replaces CRF layer with softmax. These four baseline systems are introduced as follows.", + " BIBREF22 concatenates ELMo with word embeddings as the input of LSTM to enhance word representations as it carries both syntactic and semantic information.", + " BIBREF21 uses distant supervision for NER task and propose a new Tie or Break tagging scheme, where entity spans and entity types are encoded into two folds. They first build a binary classifier to distinguish Break from Tie, and then learn the entity types according to their occurrence and frequency in NE dictionary. The authors conduct their experiments on biomedical datasets rather than standard benchmark, so we extract the NEs in training data as the domain-specific dictionary. This work creates a promising prospect for using dictionary to replace the role of training data.", + " BIBREF23 takes advantage of the power of the 120 entity types from annotated data in Wikipedia. Cosine similarity between the word embedding and the embedding of each entity type is concatenated as the 120- $D$ feature vector (which is called LS vector in their paper) and then fed into the input layer of LSTM. Lexical feature has been shown a key factor to NE recognition.", + " BIBREF24 passes sentences as sequences of characters into a character-level language model to produce a novel type of word embedding, contextual string embeddings, where one word may have different embeddings as the embeddings are computed both on the characters of a word and its surrounding context. Such embeddings are then fed into the input layer of LSTM." + ], + [ + "In this section, we evaluate the hypersphere model based on the three models introduced above: open monolingual NE modeling, embedding distribution mapping and refinement NE recognition." + ], + [ + "In this experiment, we adopt pre-trained word embeddings from Wikipedia corpus. Our preliminary experiments will be conducted on English and Chinese. For the former, we use NLTK toolkit and LANGID toolkit to perform the pre-processing. For the latter, we first use OpenCC to simplify characters, and then use THULAC to perform word segmentation.", + "In order to make the experimental results more accurate and credible, we manually annotate two large enough Chinese and English NE dictionaries for training and test. Table 2 lists the statistics of Wikipedia corpus and the annotated data. Our dictionary contains many multi-word NEs in LOC and ORG types as accounted in the second column for each language in Table 2, while we only include single-word PER NEs in our dictionary, since the English first name and last name are separated, and Chinese word segmentation cuts most of the PER entities together. We pre-train quality multi-word and single-word embeddings and aim to maximize the coverage of the NEs in the dictionary. The pre-trained word embeddings cover 82.3% / 82.51% of LOC NEs and 70.2% / 63.61% of ORG NEs in English and Chinese, respectively. For other multi-word NEs, we simply calculate the average vector of each word embedding as their representations." + ], + [ + "The NE distribution is closely correlated to the dimension of the embedding space, we train the word embeddings from 2- $D$ to 300- $D$ and search for the most suitable dimension for each NE type. For each dimension, we carefully tune the center and radius of the hypersphere using the method introduced in section 3.1 for maximize $F_1$ score, and select the dimension with maximize $F_1$ score. The most suitable dimension for ORG, PER, LOC are 16- ${D}$ /16- ${D}$ /24- ${D}$ (these dimensions will be used as parameters in the following experiments), respectively . We discover that in low-dimensional space, the distributions of NEs are better. In high dimensions, the curse of dimension could be the main reason to limit the performance.", + "Table 3 lists the final maximum $F_1$ score of three NE types. The results of the three types of NE are almost 50%, and PER type performs best. The main factor may be that PER NEs are represented as single-word in our dictionary, and word embeddings can better represents their meanings. The result also states that better representations for multi-word NEs which are not covered by the dictionary instead of the average of each word may help bring better results. Besides, the incompleteness of NE dictionaries and noises during pre-processing may cause a decrease on the performance. Overall, hypersphere model has shown been effectively used as the open modeling for NEs." + ], + [ + "The following preparations were made for the mapping: $(i)$ A large enough NE dictionary in source (resource-rich) corpus; $(ii)$ A small amount of annotated seed pairs. We use $s$ to represent the number of seed pairs and $d$ to represent the number of unknown variables. With seed pair size $s < d$ , the matrix can be solved with much loose constraints, and $F_1$ score remarkably increases with more seed pairs. Once $s > d$ , the linear equation group will be always determined by strong enough constraints, which leads to a stable solution. Based on the characteristics, we only take two dozen of seed pairs on each type in following experiments. We combine human translation and online translation together for double verification for this small set of seed pairs. In this part, we utilize English and Chinese as the corpus of known NEs in turn, and predict the NE distribution of the other language.", + "Evaluation In order to quantitatively represent the mapping effect, we present a new evaluation method to judge the hypersphere mapping between English and Chinese: ", + "$$\\begin{aligned}\nP = \\frac{V_i}{V_m} \\quad R = \\frac{V_i}{V_t} \\quad F_1 = \\frac{2 * P * R}{P + R}\n\\end{aligned}$$ (Eq. 29) ", + "where ${V_t, V_m, V_i}$ represent the volumes of the target, mapping and intersection hyperspheres. Due to the difficulty of calculating the volume of hyperspheres in high dimensions, we adopt Monte Carlo methods to simulate the volume BIBREF25 . we generate a great quantity of points in the embedding spaces, and take the amount of the points falling in each hypersphere as its volume.", + "Mapping between English and Chinese Table 4 shows the comparisons of cross-lingual named entity extraction performance. We use the unsupervised method proposed in BIBREF26 to generate cross-lingual embeddings. $k$ -NN and SVM are the same as monolingual cases in Table 3 except for the training set. $k$ -NN $_{150}$ and SVM $_{150}$ use 20% of the NEs in source language and 150 NEs (50 LOC, PER and ORG) in target language for training, while $k$ -NN $_{2500}$ and SVM $_{2500}$ use 20% of the NEs in source language and 2500 NEs (1000 LOC and PER, 500 ORG) in target language. $k$ -NN and SVM depend much on the annotated training set, requiring more than $1K$ training samples to provide a performance as our model offers. Due to the instability of ORG type in length, taking the average of each word embedding may disobey the syntactic and semantic regularities of ORG NEs, thereby undermines the multilingual isomorphism characteristics, which causes the inferior performance of our model on this type of NEs. This suggests that build better representations NEs for multi-word NEs may contribute to a better performance in our model.", + "Mapping to truly Low-resource Language We build named entity dataset for a truly resource-poor language, Indonesian, and manually examine the nearest words to the hypersphere center for 'gold-standard' evaluation. We take English as the source language, the settings of the dimension $D$ and the number of seed pairs $s$ are the same as the above experiments between Chinese and English. From the results listed in Table 5, we can see that even the precision of the top-100 NEs are 0.350 $F_1$ /0.440 $F_1$ /0.310 $F_1$ , respectively, which proves the this distribution can indeed serves as a candidate NE dictionary for Indonesian.", + "[9] The authors of BIBREF24 publish an updated results (92.98) on CoNLL-2003 dataset in https://github.com/zalandoresearch/flair/issues/206 on their 0.3.2 version, and this is the best result at our most try. [10] This is the reported state-of-the-art result in their github. [11]We use the same parameters as the authors release in https://github.com/zalandoresearch/flair/issues/173 and obtain the result of 89.45 on ONTONOTES 5.0 dataset." + ], + [ + "To evaluate the influence of our hypersphere feature for off-the-shelf NER systems, we perform the NE recognition on two standard NER benchmark datasets, CoNLL2003 and ONTONOTES 5.0. Our results in Table 6 and Table 7 demonstrate the power of hypersphere features, which contribute to nearly all of the three types of entities as shown in Table 6, except for a slight drop in the PER type of BIBREF22 on a strong baseline. HS features stably enhance all strong state-of-the-art baselines, BIBREF22 , BIBREF21 and BIBREF23 by 0.33/0.72/0.23 $F_1$ point and 0.13/0.3/0.1 $F_1$ point on both benchmark datasets, CoNLL-2003 and ONTONOTES 5.0. We show that our HS feature is also comparable with previous much more complicated LS feature, and our model surpasses their baseline (without LS feature) by 0.58/0.78 $F_1$ point with only HS features. We establish a new state-of-the-art $F_1$ score of 89.75 on ONTONOTES 5.0, while matching state-of-the-art performance with a $F_1$ score of 92.95 on CoNLL-2003 dataset." + ], + [ + "In recent years, word embeddings have also been used as a feature to enhance the NE recognition, with the revealing of linguistic features in morphological, syntactic and semantic perspective. BIBREF1 clustered the word embeddings and combined multiple cluster granularities to improve the NE recognition performance. Our work likewise use word embeddings to help NE recognition, we make use of the characteristic that syntactically and semantically s are more likely to be neighbors in embedding spaces and construct a hypersphere model to encompass NEs.", + "Cross-lingual knowledge transfer is a highly promising work for resource-poor languages, annotation projection and representation projection are widely used in NE recognition BIBREF27 , BIBREF5 , BIBREF4 , BIBREF28 , BIBREF29 , BIBREF30 . These works put forward inconvenient requirements for parallel or comparable corpora, a large amount of annotated or translation data or bilingual lexicon. Different from any existing work to the best of our knowledge, this is the first work that merely uses isomorphic mappings in low-dimensional embedding spaces to recognize NEs, and we introduce a mathematically simple model to describe NE embedding distribution from visualization results, showing it works for both monolingual and cross-lingual situations." + ], + [ + "Named entities being an open set which keeps expanding are difficult to represent through a closed NE dictionary. This work mitigates significant defects in previous closed NE definitions and proposes a new open definition for NEs by modeling their embedding distributions with least parameters. We visualize NE distributions in monolingual case and perform an effective isomorphism spaces mapping in cross-lingual case. According to our work, we demonstrate that common named entity types (PER, LOC, ORG) tend to be densely distributed in a hypersphere and it is possible to build a mapping between the NE distributions in embedding spaces to help cross-lingual NE recognition. Experimental results show that the distribution of named entities via mapping can be used as a good enough replacement for the original distribution. Then the discovery is used to build an NE dictionary for Indonesian being a truly low-resource language, which also gives satisfactory precision. Finally, our simple hypersphere features being the representation of NE likelihood can be used for enhancing off-the-shelf NER systems by concatenating with word embeddings and the output of BiLSTM in the input layer and encode layer, respectively, and we achieve a new state-of-the-art $F_1$ score of 89.75 on ONTONOTES 5.0 benchmark. In this work, we also give a better solution for unregistered NEs. For any newly emerged NE together with its embedding, in case we obtain the hypersphere of each named entity, the corresponding named entity category can be determined by calculating the distance between its word embedding and the center of each hypersphere." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0429/instruction.md b/qasper-0429/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..6195b3a46994a55c7952a737ea3721f4a2a250f3 --- /dev/null +++ b/qasper-0429/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Efficient Twitter Sentiment Classification using Subjective Distant Supervision + +Question: What previously proposed methods is this method compared against? \ No newline at end of file diff --git a/qasper-0435/instruction.md b/qasper-0435/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..ad5b523333038006196fc383a6f113f6e444be0d --- /dev/null +++ b/qasper-0435/instruction.md @@ -0,0 +1,142 @@ +Name of Paper: Dynamic Memory Networks for Visual and Textual Question Answering + +Question: What improvements they did for DMN? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Dynamic Memory Networks", + "Improved Dynamic Memory Networks: DMN+", + "Input Module for Text QA", + "Input Module for VQA", + "The Episodic Memory Module", + "Related Work", + "Datasets", + "bAbI-10k", + "DAQUAR-ALL visual dataset", + "Visual Question Answering", + "Model Analysis", + "Comparison to state of the art using bAbI-10k", + "Comparison to state of the art using VQA", + "Conclusion" + ], + "paragraphs": [ + [ + "Neural network based methods have made tremendous progress in image and text classification BIBREF0 , BIBREF1 . However, only recently has progress been made on more complex tasks that require logical reasoning. This success is based in part on the addition of memory and attention components to complex neural networks. For instance, memory networks BIBREF2 are able to reason over several facts written in natural language or (subject, relation, object) triplets. Attention mechanisms have been successful components in both machine translation BIBREF3 , BIBREF4 and image captioning models BIBREF5 .", + "The dynamic memory network BIBREF6 (DMN) is one example of a neural network model that has both a memory component and an attention mechanism. The DMN yields state of the art results on question answering with supporting facts marked during training, sentiment analysis, and part-of-speech tagging.", + "We analyze the DMN components, specifically the input module and memory module, to improve question answering. We propose a new input module which uses a two level encoder with a sentence reader and input fusion layer to allow for information flow between sentences. For the memory, we propose a modification to gated recurrent units (GRU) BIBREF7 . The new GRU formulation incorporates attention gates that are computed using global knowledge over the facts. Unlike before, the new DMN+ model does not require that supporting facts (i.e. the facts that are relevant for answering a particular question) are labeled during training. The model learns to select the important facts from a larger set.", + "In addition, we introduce a new input module to represent images. This module is compatible with the rest of the DMN architecture and its output is fed into the memory module. We show that the changes in the memory module that improved textual question answering also improve visual question answering. Both tasks are illustrated in Fig. 1 ." + ], + [ + "We begin by outlining the DMN for question answering and the modules as presented in BIBREF6 .", + "The DMN is a general architecture for question answering (QA). It is composed of modules that allow different aspects such as input representations or memory components to be analyzed and improved independently. The modules, depicted in Fig. 1 , are as follows:", + "Input Module: This module processes the input data about which a question is being asked into a set of vectors termed facts, represented as $F=[f_1,\\hdots ,f_N]$ , where $N$ is the total number of facts. These vectors are ordered, resulting in additional information that can be used by later components. For text QA in BIBREF6 , the module consists of a GRU over the input words.", + "As the GRU is used in many components of the DMN, it is useful to provide the full definition. For each time step $i$ with input $x_i$ and previous hidden state $h_{i-1}$ , we compute the updated hidden state $h_i = GRU(x_i,h_{i-1})$ by ", + "$$u_i &=& \\sigma \\left(W^{(u)}x_{i} + U^{(u)} h_{i-1} + b^{(u)} \\right)\\\\\nr_i &=& \\sigma \\left(W^{(r)}x_{i} + U^{(r)} h_{i-1} + b^{(r)} \\right)\\\\\n\\tilde{h}_i &=& \\tanh \\left(Wx_{i} + r_i \\circ U h_{i-1} + b^{(h)}\\right)\\\\\nh_i &=& u_i\\circ \\tilde{h}_i + (1-u_i) \\circ h_{i-1}$$ (Eq. 2) ", + "where $\\sigma $ is the sigmoid activation function, $\\circ $ is an element-wise product, $W^{(z)}, W^{(r)}, W \\in \\mathbb {R}^{n_H \\times n_I}$ , $U^{(z)}, U^{(r)}, U \\in \\mathbb {R}^{n_H \\times n_H}$ , $n_H$ is the hidden size, and $n_I$ is the input size.", + "Question Module: This module computes a vector representation $q$ of the question, where $q \\in \\mathbb {R}^{n_H}$ is the final hidden state of a GRU over the words in the question.", + "Episodic Memory Module: Episode memory aims to retrieve the information required to answer the question $q$ from the input facts. To improve our understanding of both the question and input, especially if questions require transitive reasoning, the episode memory module may pass over the input multiple times, updating episode memory after each pass. We refer to the episode memory on the $t^{th}$ pass over the inputs as $m^t$ , where $m^t \\in \\mathbb {R}^{n_H}$ , the initial memory vector is set to the question vector: $m^0 = q$ .", + "The episodic memory module consists of two separate components: the attention mechanism and the memory update mechanism. The attention mechanism is responsible for producing a contextual vector $c^t$ , where $c^t \\in \\mathbb {R}^{n_H}$ is a summary of relevant input for pass $t$ , with relevance inferred by the question $q$ and previous episode memory $m^{t-1}$ . The memory update mechanism is responsible for generating the episode memory $m^t$ based upon the contextual vector $c^t$ and previous episode memory $m^{t-1}$ . By the final pass $T$ , the episodic memory $m^T$ should contain all the information required to answer the question $c^t \\in \\mathbb {R}^{n_H}$0 .", + "Answer Module: The answer module receives both $q$ and $m^T$ to generate the model's predicted answer. For simple answers, such as a single word, a linear layer with softmax activation may be used. For tasks requiring a sequence output, an RNN may be used to decode $a = [q ; m^T]$ , the concatenation of vectors $q$ and $m^T$ , to an ordered set of tokens. The cross entropy error on the answers is used for training and backpropagated through the entire network." + ], + [ + "We propose and compare several modeling choices for two crucial components: input representation, attention mechanism and memory update. The final DMN+ model obtains the highest accuracy on the bAbI-10k dataset without supporting facts and the VQA dataset BIBREF8 . Several design choices are motivated by intuition and accuracy improvements on that dataset." + ], + [ + "In the DMN specified in BIBREF6 , a single GRU is used to process all the words in the story, extracting sentence representations by storing the hidden states produced at the end of sentence markers. The GRU also provides a temporal component by allowing a sentence to know the content of the sentences that came before them. Whilst this input module worked well for bAbI-1k with supporting facts, as reported in BIBREF6 , it did not perform well on bAbI-10k without supporting facts (Sec. \"Model Analysis\" ).", + "We speculate that there are two main reasons for this performance disparity, all exacerbated by the removal of supporting facts. First, the GRU only allows sentences to have context from sentences before them, but not after them. This prevents information propagation from future sentences. Second, the supporting sentences may be too far away from each other on a word level to allow for these distant sentences to interact through the word level GRU.", + "Input Fusion Layer", + "For the DMN+, we propose replacing this single GRU with two different components. The first component is a sentence reader, responsible only for encoding the words into a sentence embedding. The second component is the input fusion layer, allowing for interactions between sentences. This resembles the hierarchical neural auto-encoder architecture of BIBREF9 and allows content interaction between sentences. We adopt the bi-directional GRU for this input fusion layer because it allows information from both past and future sentences to be used. As gradients do not need to propagate through the words between sentences, the fusion layer also allows for distant supporting sentences to have a more direct interaction.", + "Fig. 2 shows an illustration of an input module, where a positional encoder is used for the sentence reader and a bi-directional GRU is adopted for the input fusion layer. Each sentence encoding $f_i$ is the output of an encoding scheme taking the word tokens $[w^i_1, \\hdots , w^i_{M_i}]$ , where $M_i$ is the length of the sentence.", + "The sentence reader could be based on any variety of encoding schemes. We selected positional encoding described in BIBREF10 to allow for a comparison to their work. GRUs and LSTMs were also considered but required more computational resources and were prone to overfitting if auxiliary tasks, such as reconstructing the original sentence, were not used.", + "For the positional encoding scheme, the sentence representation is produced by $f_i = \\sum ^{j=1}_M l_j \\circ w^i_j$ , where $\\circ $ is element-wise multiplication and $l_j$ is a column vector with structure $l_{jd} = (1 - j / M) - (d / D) (1 - 2j / M)$ , where $d$ is the embedding index and $D$ is the dimension of the embedding.", + "The input fusion layer takes these input facts and enables an information exchange between them by applying a bi-directional GRU. ", + "$$\\overrightarrow{f_i} = GRU_{fwd}(f_i, \\overrightarrow{f_{i-1}}) \\\\\n\\overleftarrow{f_{i}} = GRU_{bwd}(f_{i}, \\overleftarrow{f_{i+1}}) \\\\\n\\overleftrightarrow{f_i} = \\overleftarrow{f_i} + \\overrightarrow{f_i}$$ (Eq. 5) ", + "where $f_i$ is the input fact at timestep $i$ , $ \\overrightarrow{f_i}$ is the hidden state of the forward GRU at timestep $i$ , and $\\overleftarrow{f_i}$ is the hidden state of the backward GRU at timestep $i$ . This allows contextual information from both future and past facts to impact $\\overleftrightarrow{f_i}$ .", + "We explored a variety of encoding schemes for the sentence reader, including GRUs, LSTMs, and the positional encoding scheme described in BIBREF10 . For simplicity and speed, we selected the positional encoding scheme. GRUs and LSTMs were also considered but required more computational resources and were prone to overfitting if auxiliary tasks, such as reconstructing the original sentence, were not used." + ], + [ + "To apply the DMN to visual question answering, we introduce a new input module for images. The module splits an image into small local regions and considers each region equivalent to a sentence in the input module for text. The input module for VQA is composed of three parts, illustrated in Fig. 3 : local region feature extraction, visual feature embedding, and the input fusion layer introduced in Sec. \"Input Module for Text QA\" .", + "Local region feature extraction: To extract features from the image, we use a convolutional neural network BIBREF0 based upon the VGG-19 model BIBREF11 . We first rescale the input image to $448 \\times 448$ and take the output from the last pooling layer which has dimensionality $d = 512 \\times 14 \\times 14$ . The pooling layer divides the image into a grid of $14 \\times 14$ , resulting in 196 local regional vectors of $d = 512$ .", + "Visual feature embedding: As the VQA task involves both image features and text features, we add a linear layer with tanh activation to project the local regional vectors to the textual feature space used by the question vector $q$ .", + "Input fusion layer: The local regional vectors extracted from above do not yet have global information available to them. Without global information, their representational power is quite limited, with simple issues like object scaling or locational variance causing accuracy problems.", + "To solve this, we add an input fusion layer similar to that of the textual input module described in Sec. \"Input Module for Text QA\" . First, to produce the input facts $F$ , we traverse the image in a snake like fashion, as seen in Figure 3 . We then apply a bi-directional GRU over these input facts $F$ to produce the globally aware input facts $\\overleftrightarrow{F}$ . The bi-directional GRU allows for information propagation from neighboring image patches, capturing spatial information." + ], + [ + "The episodic memory module, as depicted in Fig. 4 , retrieves information from the input facts $\\overleftrightarrow{F} = [\\overleftrightarrow{f_1}, \\hdots , \\overleftrightarrow{f_N}]$ provided to it by focusing attention on a subset of these facts. We implement this attention by associating a single scalar value, the attention gate $g^t_i$ , with each fact $\\overleftrightarrow{f}_i$ during pass $t$ . This is computed by allowing interactions between the fact and both the question representation and the episode memory state. ", + "$$z^t_i &=& [\\overleftrightarrow{f_i} \\circ q; \\overleftrightarrow{f_i} \\circ m^{t-1}; \\vert \\overleftrightarrow{f_i} - q \\vert ; \\vert \\overleftrightarrow{f_i} - m^{t-1} \\vert ] \\\\\nZ^t_i &=& W^{(2)} \\tanh \\left(W^{(1)}z^t_i + b^{(1)} \\right)+ b^{(2)} \\\\\ng^t_i &=& \\frac{\\exp (Z^t_i)}{\\sum _{k=1}^{M_i} \\exp (Z^t_k)} $$ (Eq. 10) ", + "where $\\overleftrightarrow{f_i}$ is the $i^{th}$ fact, $m^{t-1}$ is the previous episode memory, $q$ is the original question, $\\circ $ is the element-wise product, $|\\cdot |$ is the element-wise absolute value, and $;$ represents concatenation of the vectors.", + "The DMN implemented in BIBREF6 involved a more complex set of interactions within $z$ , containing the additional terms $[f; m^{t-1}; q; f^T W^{(b)} q; f^T W^{(b)} m^{t-1}]$ . After an initial analysis, we found these additional terms were not required.", + "Attention Mechanism", + "Once we have the attention gate $g^t_i$ we use an attention mechanism to extract a contextual vector $c^t$ based upon the current focus. We focus on two types of attention: soft attention and a new attention based GRU. The latter improves performance and is hence the final modeling choice for the DMN+.", + "Soft attention: Soft attention produces a contextual vector $c^t$ through a weighted summation of the sorted list of vectors $\\overleftrightarrow{F}$ and corresponding attention gates $g_i^t$ : $c^t = \\sum _{i=1}^N g^t_i \\overleftrightarrow{f}_i$ This method has two advantages. First, it is easy to compute. Second, if the softmax activation is spiky it can approximate a hard attention function by selecting only a single fact for the contextual vector whilst still being differentiable. However the main disadvantage to soft attention is that the summation process loses both positional and ordering information. Whilst multiple attention passes can retrieve some of this information, this is inefficient.", + "Attention based GRU: For more complex queries, we would like for the attention mechanism to be sensitive to both the position and ordering of the input facts $\\overleftrightarrow{F}$ . An RNN would be advantageous in this situation except they cannot make use of the attention gate from Equation .", + "We propose a modification to the GRU architecture by embedding information from the attention mechanism. The update gate $u_i$ in Equation 2 decides how much of each dimension of the hidden state to retain and how much should be updated with the transformed input $x_i$ from the current timestep. As $u_i$ is computed using only the current input and the hidden state from previous timesteps, it lacks any knowledge from the question or previous episode memory.", + "By replacing the update gate $u_i$ in the GRU (Equation 2 ) with the output of the attention gate $g^t_i$ (Equation ) in Equation , the GRU can now use the attention gate for updating its internal state. This change is depicted in Fig 5 . ", + "$$h_i &=& g^t_i \\circ \\tilde{h}_i + (1-g^t_i) \\circ h_{i-1}$$ (Eq. 12) ", + "An important consideration is that $g^t_i$ is a scalar, generated using a softmax activation, as opposed to the vector $u_i \\in \\mathbb {R}^{n_H}$ , generated using a sigmoid activation. This allows us to easily visualize how the attention gates activate over the input, later shown for visual QA in Fig. 6 . Though not explored, replacing the softmax activation in Equation with a sigmoid activation would result in $g^t_i \\in \\mathbb {R}^{n_H}$ . To produce the contextual vector $c^t$ used for updating the episodic memory state $m^t$ , we use the final hidden state of the attention based GRU.", + "Episode Memory Updates", + "After each pass through the attention mechanism, we wish to update the episode memory $m^{t-1}$ with the newly constructed contextual vector $c^t$ , producing $m^t$ . In the DMN, a GRU with the initial hidden state set to the question vector $q$ is used for this purpose. The episodic memory for pass $t$ is computed by ", + "$$m^t = GRU(c^t, m^{t-1})$$ (Eq. 13) ", + "The work of BIBREF10 suggests that using different weights for each pass through the episodic memory may be advantageous. When the model contains only one set of weights for all episodic passes over the input, it is referred to as a tied model, as in the \u201cMem Weights\u201d row in Table 1 .", + "Following the memory update component used in BIBREF10 and BIBREF12 we experiment with using a ReLU layer for the memory update, calculating the new episode memory state by ", + "$$m^t = ReLU\\left(W^t [m^{t-1} ; c^t ; q] + b\\right)$$ (Eq. 14) ", + "where $;$ is the concatenation operator, $W^t \\in \\mathbb {R}^{n_H \\times n_H}$ , $b \\in \\mathbb {R}^{n_H}$ , and $n_H$ is the hidden size. The untying of weights and using this ReLU formulation for the memory update improves accuracy by another 0.5% as shown in Table 1 in the last column. The final output of the memory network is passed to the answer module as in the original DMN." + ], + [ + "The DMN is related to two major lines of recent work: memory and attention mechanisms. We work on both visual and textual question answering which have, until now, been developed in separate communities.", + "Neural Memory Models The earliest recent work with a memory component that is applied to language processing is that of memory networks BIBREF2 which adds a memory component for question answering over simple facts. They are similar to DMNs in that they also have input, scoring, attention and response mechanisms. However, unlike the DMN their input module computes sentence representations independently and hence cannot easily be used for other tasks such as sequence labeling. Like the original DMN, this memory network requires that supporting facts are labeled during QA training. End-to-end memory networks BIBREF10 do not have this limitation. In contrast to previous memory models with a variety of different functions for memory attention retrieval and representations, DMNs BIBREF6 have shown that neural sequence models can be used for input representation, attention and response mechanisms. Sequence models naturally capture position and temporality of both the inputs and transitive reasoning steps.", + "Neural Attention Mechanisms Attention mechanisms allow neural network models to use a question to selectively pay attention to specific inputs. They can benefit image classification BIBREF13 , generating captions for images BIBREF5 , among others mentioned below, and machine translation BIBREF14 , BIBREF3 , BIBREF4 . Other recent neural architectures with memory or attention which have proposed include neural Turing machines BIBREF15 , neural GPUs BIBREF16 and stack-augmented RNNs BIBREF17 .", + "Question Answering in NLP Question answering involving natural language can be solved in a variety of ways to which we cannot all do justice. If the potential input is a large text corpus, QA becomes a combination of information retrieval and extraction BIBREF18 . Neural approaches can include reasoning over knowledge bases, BIBREF19 , BIBREF20 or directly via sentences for trivia competitions BIBREF21 .", + "Visual Question Answering (VQA) In comparison to QA in NLP, VQA is still a relatively young task that is feasible only now that objects can be identified with high accuracy. The first large scale database with unconstrained questions about images was introduced by BIBREF8 . While VQA datasets existed before they did not include open-ended, free-form questions about general images BIBREF22 . Others are were too small to be viable for a deep learning approach BIBREF23 . The only VQA model which also has an attention component is the stacked attention network BIBREF24 . Their work also uses CNN based features. However, unlike our input fusion layer, they use a single layer neural network to map the features of each patch to the dimensionality of the question vector. Hence, the model cannot easily incorporate adjacency of local information in its hidden state. A model that also uses neural modules, albeit logically inspired ones, is that by BIBREF25 who evaluate on knowledgebase reasoning and visual question answering. We compare directly to their method on the latter task and dataset.", + "Related to visual question answering is the task of describing images with sentences BIBREF26 . BIBREF27 used deep learning methods to map images and sentences into the same space in order to describe images with sentences and to find images that best visualize a sentence. This was the first work to map both modalities into a joint space with deep learning methods, but it could only select an existing sentence to describe an image. Shortly thereafter, recurrent neural networks were used to generate often novel sentences based on images BIBREF28 , BIBREF29 , BIBREF30 , BIBREF5 ." + ], + [ + "To analyze our proposed model changes and compare our performance with other architectures, we use three datasets." + ], + [ + "For evaluating the DMN on textual question answering, we use bAbI-10k English BIBREF31 , a synthetic dataset which features 20 different tasks. Each example is composed of a set of facts, a question, the answer, and the supporting facts that lead to the answer. The dataset comes in two sizes, referring to the number of training examples each task has: bAbI-1k and bAbI-10k. The experiments in BIBREF10 found that their lowest error rates on the smaller bAbI-1k dataset were on average three times higher than on bAbI-10k." + ], + [ + "The DAtaset for QUestion Answering on Real-world images (DAQUAR) BIBREF23 consists of 795 training images and 654 test images. Based upon these images, 6,795 training questions and 5,673 test questions were generated. Following the previously defined experimental method, we exclude multiple word answers BIBREF32 , BIBREF33 . The resulting dataset covers 90% of the original data. The evaluation method uses classification accuracy over the single words. We use this as a development dataset for model analysis (Sec. \"Model Analysis\" )." + ], + [ + "The Visual Question Answering (VQA) dataset was constructed using the Microsoft COCO dataset BIBREF34 which contained 123,287 training/validation images and 81,434 test images. Each image has several related questions with each question answered by multiple people. This dataset contains 248,349 training questions, 121,512 validation questions, and 244,302 for testing. The testing data was split into test-development, test-standard and test-challenge in BIBREF8 .", + "Evaluation on both test-standard and test-challenge are implemented via a submission system. test-standard may only be evaluated 5 times and test-challenge is only evaluated at the end of the competition. To the best of our knowledge, VQA is the largest and most complex image dataset for the visual question answering task." + ], + [ + "To understand the impact of the proposed module changes, we analyze the performance of a variety of DMN models on textual and visual question answering datasets.", + "The original DMN (ODMN) is the architecture presented in BIBREF6 without any modifications. DMN2 only replaces the input module with the input fusion layer (Sec. \"Input Module for Text QA\" ). DMN3, based upon DMN2, replaces the soft attention mechanism with the attention based GRU proposed in Sec. \"The Episodic Memory Module\" . Finally, DMN+, based upon DMN3, is an untied model, using a unique set of weights for each pass and a linear layer with a ReLU activation to compute the memory update. We report the performance of the model variations in Table 1 .", + "A large improvement to accuracy on both the bAbI-10k textual and DAQUAR visual datasets results from updating the input module, seen when comparing ODMN to DMN2. On both datasets, the input fusion layer improves interaction between distant facts. In the visual dataset, this improvement is purely from providing contextual information from neighboring image patches, allowing it to handle objects of varying scale or questions with a locality aspect. For the textual dataset, the improved interaction between sentences likely helps the path finding required for logical reasoning when multiple transitive steps are required.", + "The addition of the attention GRU in DMN3 helps answer questions where complex positional or ordering information may be required. This change impacts the textual dataset the most as few questions in the visual dataset are likely to require this form of logical reasoning. Finally, the untied model in the DMN+ overfits on some tasks compared to DMN3, but on average the error rate decreases.", + "From these experimental results, we find that the combination of all the proposed model changes results, culminating in DMN+, achieves the highest performance across both the visual and textual datasets." + ], + [ + "We trained our models using the Adam optimizer BIBREF35 with a learning rate of 0.001 and batch size of 128. Training runs for up to 256 epochs with early stopping if the validation loss had not improved within the last 20 epochs. The model from the epoch with the lowest validation loss was then selected. Xavier initialization was used for all weights except for the word embeddings, which used random uniform initialization with range $[-\\sqrt{3}, \\sqrt{3}]$ . Both the embedding and hidden dimensions were of size $d = 80$ . We used $\\ell _2$ regularization on all weights except bias and used dropout on the initial sentence encodings and the answer module, keeping the input with probability $p=0.9$ . The last 10% of the training data on each task was chosen as the validation set. For all tasks, three passes were used for the episodic memory module, allowing direct comparison to other state of the art methods. Finally, we limited the input to the last 70 sentences for all tasks except QA3 for which we limited input to the last 130 sentences, similar to BIBREF10 .", + "On some tasks, the accuracy was not stable across multiple runs. This was particularly problematic on QA3, QA17, and QA18. To solve this, we repeated training 10 times using random initializations and evaluated the model that achieved the lowest validation set loss.", + "Text QA Results", + "We compare our best performing approach, DMN+, to two state of the art question answering architectures: the end to end memory network (E2E) BIBREF10 and the neural reasoner framework (NR) BIBREF12 . Neither approach use supporting facts for training.", + "The end-to-end memory network is a form of memory network BIBREF2 tested on both textual question answering and language modeling. The model features both explicit memory and a recurrent attention mechanism. We select the model from the paper that achieves the lowest mean error over the bAbI-10k dataset. This model utilizes positional encoding for input, RNN-style tied weights for the episode module, and a ReLU non-linearity for the memory update component.", + "The neural reasoner framework is an end-to-end trainable model which features a deep architecture for logical reasoning and an interaction-pooling mechanism for allowing interaction over multiple facts. While the neural reasoner framework was only tested on QA17 and QA19, these were two of the most challenging question types at the time.", + "In Table 2 we compare the accuracy of these question answering architectures, both as mean error and error on individual tasks. The DMN+ model reduces mean error by 1.4% compared to the the end-to-end memory network, achieving a new state of the art for the bAbI-10k dataset.", + "One notable deficiency in our model is that of QA16: Basic Induction. In BIBREF10 , an untied model using only summation for memory updates was able to achieve a near perfect error rate of $0.4$ . When the memory update was replaced with a linear layer with ReLU activation, the end-to-end memory network's overall mean error decreased but the error for QA16 rose sharply. Our model experiences the same difficulties, suggesting that the more complex memory update component may prevent convergence on certain simpler tasks.", + "The neural reasoner model outperforms both the DMN and end-to-end memory network on QA17: Positional Reasoning. This is likely as the positional reasoning task only involves minimal supervision - two sentences for input, yes/no answers for supervision, and only 5,812 unique examples after removing duplicates from the initial 10,000 training examples. BIBREF12 add an auxiliary task of reconstructing both the original sentences and question from their representations. This auxiliary task likely improves performance by preventing overfitting." + ], + [ + "For the VQA dataset, each question is answered by multiple people and the answers may not be the same, the generated answers are evaluated using human consensus. For each predicted answer $a_i$ for the $i_{th}$ question with target answer set $T^{i}$ , the accuracy of VQA: $Acc_{VQA} = \\frac{1}{N}\\sum _{i=1}^Nmin(\\frac{\\sum _{t\\in T^i}{1}_{(a_i==t)}}{3},1)$ where ${1}_{(\\cdot )}$ is the indicator function. Simply put, the answer $a_i$ is only 100 $\\%$ accurate if at least 3 people provide that exact answer.", + "Training Details We use the Adam optimizer BIBREF35 with a learning rate of 0.003 and batch size of 100. Training runs for up to 256 epochs with early stopping if the validation loss has not improved in the last 10 epochs. For weight initialization, we sampled from a random uniform distribution with range $[-0.08, 0.08]$ . Both the word embedding and hidden layers were vectors of size $d=512$ . We apply dropout on the initial image output from the VGG convolutional neural network BIBREF11 as well as the input to the answer module, keeping input with probability $p=0.5$ .", + "Results and Analysis", + "The VQA dataset is composed of three question domains: Yes/No, Number, and Other. This enables us to analyze the performance of the models on various tasks that require different reasoning abilities.", + "The comparison models are separated into two broad classes: those that utilize a full connected image feature for classification and those that perform reasoning over multiple small image patches. Only the SAN and DMN approach use small image patches, while the rest use the fully-connected whole image feature approach.", + "Here, we show the quantitative and qualitative results in Table 3 and Fig. 6 , respectively. The images in Fig. 6 illustrate how the attention gate $g^t_i$ selectively activates over relevant portions of the image according to the query. In Table 3 , our method outperforms baseline and other state-of-the-art methods across all question domains (All) in both test-dev and test-std, and especially for Other questions, achieves a wide margin compared to the other architectures, which is likely as the small image patches allow for finely detailed reasoning over the image.", + "However, the granularity offered by small image patches does not always offer an advantage. The Number questions may be not solvable for both the SAN and DMN architectures, potentially as counting objects is not a simple task when an object crosses image patch boundaries." + ], + [ + "We have proposed new modules for the DMN framework to achieve strong results without supervision of supporting facts. These improvements include the input fusion layer to allow interactions between input facts and a novel attention based GRU that allows for logical reasoning over ordered inputs. Our resulting model obtains state of the art results on both the VQA dataset and the bAbI-10k text question-answering dataset, proving the framework can be generalized across input domains." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0442/instruction.md b/qasper-0442/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..86775b2c12ae4172f95a906025d0d54dbe1422a2 --- /dev/null +++ b/qasper-0442/instruction.md @@ -0,0 +1,145 @@ +Name of Paper: Low-Level Linguistic Controls for Style Transfer and Content Preservation + +Question: How they model style as a suite of low-level linguistic controls, such as frequency of pronouns, prepositions, and subordinate clause constructions? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work ::: Style Transfer with Parallel Data", + "Related Work ::: Style Transfer without Parallel Data", + "Related Work ::: Controlling Linguistic Features", + "Related Work ::: Stylometry and the Digital Humanities", + "Models ::: Preliminary Classification Experiments", + "Models ::: Formal Model of Style", + "Models ::: Formal Model of Style ::: Reconstruction Task", + "Models ::: Neural Architecture", + "Models ::: Neural Architecture ::: Baseline Genre Model", + "Models ::: Neural Architecture ::: Training", + "Models ::: Neural Architecture ::: Selecting Controls for Style Transfer", + "Automatic Evaluations ::: BLEU Scores & Perplexity", + "Automatic Evaluations ::: Feature Control", + "Automatic Evaluations ::: Automatic Classification", + "Human Evaluation", + "Human Evaluation ::: Fluency Evaluation", + "Human Evaluation ::: Human Classification", + "Human Evaluation ::: The `Vampires in Space' Problem", + "Conclusion and Future Work", + "Acknowledgements" + ], + "paragraphs": [ + [ + "All text has style, whether it be formal or informal, polite or aggressive, colloquial, persuasive, or even robotic. Despite the success of style transfer in image processing BIBREF0, BIBREF1, there has been limited progress in the text domain, where disentangling style from content is particularly difficult.", + "To date, most work in style transfer relies on the availability of meta-data, such as sentiment, authorship, or formality. While meta-data can provide insight into the style of a text, it often conflates style with content, limiting the ability to perform style transfer while preserving content. Generalizing style transfer requires separating style from the meaning of the text itself. The study of literary style can guide us. For example, in the digital humanities and its subfield of stylometry, content doesn't figure prominently in practical methods of discriminating authorship and genres, which can be thought of as style at the level of the individual and population, respectively. Rather, syntactic and functional constructions are the most salient features.", + "In this work, we turn to literary style as a test-bed for style transfer, and build on work from literature scholars using computational techniques for analysis. In particular we draw on stylometry: the use of surface level features, often counts of function words, to discriminate between literary styles. Stylometry first saw success in attributing authorship to the disputed Federalist Papers BIBREF2, but is recently used by scholars to study things such as the birth of genres BIBREF3 and the change of author styles over time BIBREF4. The use of function words is likely not the way writers intend to express style, but they appear to be downstream realizations of higher-level stylistic decisions.", + "We hypothesize that surface-level linguistic features, such as counts of personal pronouns, prepositions, and punctuation, are an excellent definition of literary style, as borne out by their use in the digital humanities, and our own style classification experiments. We propose a controllable neural encoder-decoder model in which these features are modelled explicitly as decoder feature embeddings. In training, the model learns to reconstruct a text using only the content words and the linguistic feature embeddings. We can then transfer arbitrary content words to a new style without parallel data by setting the low-level style feature embeddings to be indicative of the target style.", + "This paper makes the following contributions:", + "A formal model of style as a suite of controllable, low-level linguistic features that are independent of content.", + "An automatic evaluation showing that our model fools a style classifier 84% of the time.", + "A human evaluation with English literature experts, including recommendations for dealing with the entanglement of content with style." + ], + [ + "Following in the footsteps of machine translation, style transfer in text has seen success by using parallel data. BIBREF5 use modern translations of Shakespeare plays to build a modern-to-Shakespearan model. BIBREF6 compile parallel data for formal and informal sentences, allowing them to successfully use various machine translation techniques. While parallel data may work for very specific styles, the difficulty of finding parallel texts dramatically limits this approach." + ], + [ + "There has been a decent amount of work on this approach in the past few years BIBREF7, BIBREF8, mostly focusing on variations of an encoder-decoder framework in which style is modeled as a monolithic style embedding. The main obstacle is often to disentangle style and content. However, it remains a challenging problem.", + "Perhaps the most successful is BIBREF9, who use a de-noising auto encoder and back translation to learn style without parallel data. BIBREF10 outline the benefits of automatically extracting style, and suggest there is a formal weakness of using linguistic heuristics. In contrast, we believe that monolithic style embeddings don't capture the existing knowledge we have about style, and will struggle to disentangle content." + ], + [ + "Several papers have worked on controlling style when generating sentences from restaurant meaning representations BIBREF11, BIBREF12. In each of these cases, the diversity in outputs is quite small given the constraints of the meaning representation, style is often constrained to interjections (like \u201cyeah\u201d), and there is no original style from which to transfer.", + "BIBREF13 investigate using stylistic parameters and content parameters to control text generation using a movie review dataset. Their stylistic parameters are created using word-level heuristics and they are successful in controlling these parameters in the outputs. Their success bodes well for our related approach in a style transfer setting, in which the content (not merely content parameters) is held fixed." + ], + [ + "Style, in literary research, is anything but a stable concept, but it nonetheless has a long tradition of study in the digital humanities. In a remarkably early quantitative study of literature, BIBREF14 charts sentence-level stylistic attributes specific to a number of novelists. Half a century later, BIBREF15 builds on earlier work in information theory by BIBREF16, and defines a literary text as consisting of two \u201cmaterials\": \u201cthe vocabulary, and some structural properties, the style, of its author.\"", + "Beginning with BIBREF2, statistical approaches to style, or stylometry, join the already-heated debates over the authorship of literary works. A noteable example of this is the \u201cDelta\" measure, which uses z-scores of function word frequencies BIBREF17. BIBREF18 find that Shakespeare added some material to a later edition of Thomas Kyd's The Spanish Tragedy, and that Christopher Marlowe collaborated with Shakespeare on Henry VI." + ], + [ + "The stylometric research cited above suggests that the most frequently used words, e.g. function words, are most discriminating of authorship and literary style. We investigate these claims using three corpora that have distinctive styles in the literary community: gothic novels, philosophy books, and pulp science fiction, hereafter sci-fi.", + "We retrieve gothic novels and philosophy books from Project Gutenberg and pulp sci-fi from Internet Archive's Pulp Magazine Archive. We partition this corpus into train, validation, and test sets the sizes of which can be found in Table TABREF12.", + "In order to validate the above claims, we train five different classifiers to predict the literary style of sentences from our corpus. Each classifier has gradually more content words replaced with part-of-speech (POS) tag placeholder tokens. The All model is trained on sentences with all proper nouns replaced by `PROPN'. The models Ablated N, Ablated NV, and Ablated NVA replace nouns, nouns & verbs, and nouns, verbs, & adjectives with the corresponding POS tag respectively. Finally, Content-only is trained on sentences with all words that are not tagged as NOUN, VERB, ADJ removed; the remaining words are not ablated.", + "We train the classifiers on the training set, balancing the class distribution to make sure there are the same number of sentences from each style. Classifiers are trained using fastText BIBREF19, using tri-gram features with all other settings as default. table:classifiers shows the accuracies of the classifiers.", + "The styles are highly distinctive: the All classifier has an accuracy of 86%. Additionally, even the Ablated NVA is quite successful, with 75% accuracy, even without access to any content words. The Content only classifier is also quite successful, at 80% accuracy. This indicates that these stylistic genres are distinctive at both the content level and at the syntactic level." + ], + [ + "Given that non-content words are distinctive enough for a classifier to determine style, we propose a suite of low-level linguistic feature counts (henceforth, controls) as our formal, content-blind definition of style. The style of a sentence is represented as a vector of counts of closed word classes (like personal pronouns) as well as counts of syntactic features like the number of SBAR non-terminals in its constituency parse, since clause structure has been shown to be indicative of style BIBREF20. Controls are extracted heuristically, and almost all rely on counts of pre-defined word lists. For constituency parses we use the Stanford Parser BIBREF21. table:controlexamples lists all the controls along with examples." + ], + [ + "Models are trained with a reconstruction task, in which a distorted version of a reference sentence is input and the goal is to output the original reference.", + "fig:sentenceinput illustrates the process. Controls are calculated heuristically. All words found in the control word lists are then removed from the reference sentence. The remaining words, which represent the content, are used as input into the model, along with their POS tags and lemmas.", + "In this way we encourage models to construct a sentence using content and style independently. This will allow us to vary the stylistic controls while keeping the content constant, and successfully perform style transfer. When generating a new sentence, the controls correspond to the counts of the corresponding syntactic features that we expect to be realized in the output." + ], + [ + "We implement our feature controlled language model using a neural encoder-decoder with attention BIBREF22, using 2-layer uni-directional gated recurrent units (GRUs) for the encoder and decoder BIBREF23.", + "The input to the encoder is a sequence of $M$ content words, along with their lemmas, and fine and coarse grained part-of-speech (POS) tags, i.e. $X_{.,j} = (x_{1,j},\\ldots ,x_{M,j})$ for $j \\in \\mathcal {T} = \\lbrace \\textrm {word, lemma, fine-pos, coarse-pos}\\rbrace $. We embed each token (and its lemma and POS) before concatenating, and feeding into the encoder GRU to obtain encoder hidden states, $ c_i = \\operatorname{gru}(c_{i-1}, \\left[E_j(X_{i,j}), \\; j\\in \\mathcal {T} \\right]; \\omega _{enc}) $ for $i \\in {1,\\ldots ,M},$ where initial state $c_0$, encoder GRU parameters $\\omega _{enc}$ and embedding matrices $E_j$ are learned parameters.", + "The decoder sequentially generates the outputs, i.e. a sequence of $N$ tokens $y =(y_1,\\ldots ,y_N)$, where all tokens $y_i$ are drawn from a finite output vocabulary $\\mathcal {V}$. To generate the each token we first embed the previously generated token $y_{i-1}$ and a vector of $K$ control features $z = ( z_1,\\ldots , z_K)$ (using embedding matrices $E_{dec}$ and $E_{\\textrm {ctrl-1}}, \\ldots , E_{\\textrm {ctrl-K}}$ respectively), before concatenating them into a vector $\\rho _i,$ and feeding them into the decoder side GRU along with the previous decoder state $h_{i-1}$:", + "where $\\omega _{dec}$ are the decoder side GRU parameters.", + "Using the decoder hidden state $h_i$ we then attend to the encoder context vectors $c_j$, computing attention scores $\\alpha _{i,j}$, where", + "before passing $h_i$ and the attention weighted context $\\bar{c}_i=\\sum _{j=1}^M \\alpha _{i,j} c_j$ into a single hidden-layer perceptron with softmax output to compute the next token prediction probability,", + "where $W,U,V$ and $u,v, \\nu $ are parameter matrices and vectors respectively.", + "Crucially, the controls $z$ remain fixed for all input decoder steps. Each $z_k$ represents the frequency of one of the low-level features described in sec:formalstyle. During training on the reconstruction task, we can observe the full output sequence $y,$ and so we can obtain counts for each control feature directly. Controls receive a different embedding depending on their frequency, where counts of 0-20 each get a unique embedding, and counts greater than 20 are assigned to the same embedding. At test time, we set the values of the controls according to procedure described in Section SECREF25.", + "We use embedding sizes of 128, 128, 64, and 32 for token, lemma, fine, and coarse grained POS embedding matrices respectively. Output token embeddings $E_{dec}$ have size 512, and 50 for the control feature embeddings. We set 512 for all GRU and perceptron output sizes. We refer to this model as the StyleEQ model. See fig:model for a visual depiction of the model." + ], + [ + "We compare the above model to a similar model, where rather than explicitly represent $K$ features as input, we have $K$ features in the form of a genre embedding, i.e. we learn a genre specific embedding for each of the gothic, scifi, and philosophy genres, as studied in BIBREF8 and BIBREF7. To generate in a specific style, we simply set the appropriate embedding. We use genre embeddings of size 850 which is equivalent to the total size of the $K$ feature embeddings in the StyleEQ model." + ], + [ + "We train both models with minibatch stochastic gradient descent with a learning rate of 0.25, weight decay penalty of 0.0001, and batch size of 64. We also apply dropout with a drop rate of 0.25 to all embedding layers, the GRUs, and preceptron hidden layer. We train for a maximum of 200 epochs, using validation set BLEU score BIBREF26 to select the final model iteration for evaluation." + ], + [ + "In the Baseline model, style transfer is straightforward: given an input sentence in one style, fix the encoder content features while selecting a different genre embedding. In contrast, the StyleEQ model requires selecting the counts for each control. Although there are a variety of ways to do this, we use a method that encourages a diversity of outputs.", + "In order to ensure the controls match the reference sentence in magnitude, we first find all sentences in the target style with the same number of words as the reference sentence. Then, we add the following constraints: the same number of proper nouns, the same number of nouns, the same number of verbs, and the same number of adjectives. We randomly sample $n$ of the remaining sentences, and for each of these `sibling' sentences, we compute the controls. For each of the new controls, we generate a sentence using the original input sentence content features. The generated sentences are then reranked using the length normalized log-likelihood under the model. We can then select the highest scoring sentence as our style-transferred output, or take the top-$k$ when we need a diverse set of outputs.", + "The reason for this process is that although there are group-level distinctive controls for each style, e.g. the high use of punctuation in philosophy books or of first person pronouns in gothic novels, at the sentence level it can understandably be quite varied. This method matches sentences between styles, capturing the natural distribution of the corpora." + ], + [ + "In tab:blueperpl we report BLEU scores for the reconstruction of test set sentences from their content and feature representations, as well as the model perplexities of the reconstruction. For both models, we use beam decoding with a beam size of eight. Beam candidates are ranked according to their length normalized log-likelihood. On these automatic measures we see that StyleEQ is better able to reconstruct the original sentences. In some sense this evaluation is mostly a sanity check, as the feature controls contain more locally specific information than the genre embeddings, which say very little about how many specific function words one should expect to see in the output." + ], + [ + "Designing controllable language models is often difficult because of the various dependencies between tokens; when changing one control value it may effect other aspects of the surface realization. For example, increasing the number of conjunctions may effect how the generator places prepositions to compensate for structural changes in the sentence. Since our features are deterministically recoverable, we can perturb an individual control value and check to see that the desired change was realized in the output. Moreover, we can check the amount of change in the other non-perturbed features to measure the independence of the controls.", + "We sample 50 sentences from each genre from the test set. For each sample, we create a perturbed control setting for each control by adding $\\delta $ to the original control value. This is done for $\\delta \\in \\lbrace -3, -2, -1, 0, 1, 2, 3\\rbrace $, skipping any settings where the new control value would be negative.", + "table:autoeval:ctrl shows the results of this experiment. The Exact column displays the percentage of generated texts that realize the exact number of control features specified by the perturbed control. High percentages in the Exact column indicate greater one-to-one correspondence between the control and surface realization. For example, if the input was \u201cDracula and Frankenstein and the mummy,\u201d and we change the conjunction feature by $\\delta =-1$, an output of \u201cDracula, Frankenstein and the mummy,\u201d would count towards the Exact category, while \u201cDracula, Frankenstein, the mummy,\u201d would not.", + "The Direction column specifies the percentage of cases where the generated text produces a changed number of the control features that, while not exactly matching the specified value of the perturbed control, does change from the original in the correct direction. For example, if the input again was \u201cDracula and Frankenstein and the mummy,\u201d and we change the conjunction feature by $\\delta =-1$, both outputs of \u201cDracula, Frankenstein and the mummy,\u201d and \u201cDracula, Frankenstein, the mummy,\u201d would count towards Direction. High percentages in Direction mean that we could roughly ensure desired surface realizations by modifying the control by a larger $\\delta $.", + "Finally, the Atomic column specifies the percentage of cases where the generated text with the perturbed control only realizes changes to that specific control, while other features remain constant. For example, if the input was \u201cDracula and Frankenstein in the castle,\u201d and we set the conjunction feature to $\\delta =-1$, an output of \u201cDracula near Frankenstein in the castle,\u201d would not count as Atomic because, while the number of conjunctions did decrease by one, the number of simple preposition changed. An output of \u201cDracula, Frankenstein in the castle,\u201d would count as Atomic. High percentages in the Atomic column indicate this feature is only loosely coupled to the other features and can be changed without modifying other aspects of the sentence.", + "Controls such as conjunction, determiner, and punctuation are highly controllable, with Exact rates above 80%. But with the exception of the constituency parse features, all controls have high Direction rates, many in the 90s. These results indicate our model successfully controls these features. The fact that the Atomic rates are relatively low is to be expected, as controls are highly coupled \u2013 e.g. to increase 1stPer, it is likely another pronoun control will have to decrease." + ], + [ + "For each model we look at the classifier prediction accuracy of reconstructed and transferred sentences. In particular we use the Ablated NVA classifier, as this is the most content-blind one.", + "We produce 16 outputs from both the Baseline and StyleEq models. For the Baseline, we use a beam search of size 16. For the StyleEQ model, we use the method described in Section SECREF25 to select 16 `sibling' sentences in the target style, and generated a transferred sentence for each. We look at three different methods for selection: all, which uses all output sentences; top, which selects the top ranked sentence based on the score from the model; and oracle, which selects the sentence with the highest classifier likelihood for the intended style.", + "The reason for the third method, which indeed acts as an oracle, is that using the score from the model didn't always surface a transferred sentence that best reflected the desired style. Partially this was because the model score was mostly a function of how well a transferred sentence reflected the distribution of the training data. But additionally, some control settings are more indicative of a target style than others. The use of the classifier allows us to identify the most suitable control setting for a target style that was roughly compatible with the number of content words.", + "In table:fasttext-results we see the results. Note that for both models, the all and top classification accuracy tends to be quite similar, though for the Baseline they are often almost exactly the same when the Baseline has little to no diversity in the outputs.", + "However, the oracle introduces a huge jump in accuracy for the StyleEQ model, especially compared to the Baseline, partially because the diversity of outputs from StyleEQ is much higher; often the Baseline model produces no diversity \u2013 the 16 output sentences may be nearly identical, save a single word or two. It's important to note that neither model uses the classifier in any way except to select the sentence from 16 candidate outputs.", + "What this implies is that lurking within the StyleEQ model outputs are great sentences, even if they are hard to find. In many cases, the StyleEQ model has a classification accuracy above the base rate from the test data, which is 75% (see table:classifiers)." + ], + [ + "table:cherrypicking shows example outputs for the StyleEQ and Baseline models. Through inspection we see that the StyleEQ model successfully changes syntactic constructions in stylistically distinctive ways, such as increasing syntactic complexity when transferring to philosophy, or changing relevant pronouns when transferring to sci-fi. In contrast, the Baseline model doesn't create outputs that move far from the reference sentence, making only minor modifications such changing the type of a single pronoun.", + "To determine how readers would classify our transferred sentences, we recruited three English Literature PhD candidates, all of whom had passed qualifying exams that included determining both genre and era of various literary texts." + ], + [ + "To evaluate the fluency of our outputs, we had the annotators score reference sentences, reconstructed sentences, and transferred sentences on a 0-5 scale, where 0 was incoherent and 5 was a well-written human sentence.", + "table:fluency shows the average fluency of various conditions from all three annotators. Both models have fluency scores around 3. Upon inspection of the outputs, it is clear that many have fluency errors, resulting in ungrammatical sentences.", + "Notably the Baseline often has slightly higher fluency scores than the StyleEQ model. This is likely because the Baseline model is far less constrained in how to construct the output sentence, and upon inspection often reconstructs the reference sentence even when performing style transfer. In contrast, the StyleEQ is encouraged to follow the controls, but can struggle to incorporate these controls into a fluent sentence.", + "The fluency of all outputs is lower than desired. We expect that incorporating pre-trained language models would increase the fluency of all outputs without requiring larger datasets." + ], + [ + "Each annotator annotated 90 reference sentences (i.e. from the training corpus) with which style they thought the sentence was from. The accuracy on this baseline task for annotators A1, A2, and A3 was 80%, 88%, and 80% respectively, giving us an upper expected bound on the human evaluation.", + "In discussing this task with the annotators, they noted that content is a heavy predictor of genre, and that would certainly confound their annotations. To attempt to mitigate this, we gave them two annotation tasks: which-of-3 where they simply marked which style they thought a sentence was from, and which-of-2 where they were given the original style and marked which style they thought the sentence was transferred into.", + "For each task, each annotator marked 180 sentences: 90 from each model, with an even split across the three genres. Annotators were presented the sentences in a random order, without information about the models. In total, each marked 270 sentences. (Note there were no reconstructions in this annotation task.)", + "table:humanclassifiers shows the results. In both tasks, accuracy of annotators classifying the sentence as its intended style was low. In which-of-3, scores were around 20%, below the chance rate of 33%. In which-of-2, scores were in the 50s, slightly above the chance rate of 50%. This was the case for both models. There was a slight increase in accuracy for the StyleEQ model over the Baseline for which-of-3, but the opposite trend for which-of-2, suggesting these differences are not significant.", + "It's clear that it's hard to fool the annotators. Introspecting on their approach, the annotators expressed having immediate responses based on key words \u2013 for instance any references of `space' implied `sci-fi'. We call this the `vampires in space' problem, because no matter how well a gothic sentence is rewritten as a sci-fi one, it's impossible to ignore the fact that there is a vampire in space. The transferred sentences, in the eyes of the Ablated NVA classifier (with no access to content words), did quite well transferring into their intended style. But people are not blind to content." + ], + [ + "Working with the annotators, we regularly came up against the 'vampires in space' problem: while syntactic constructions account for much of the distinction of literary styles, these constructions often co-occur with distinctive content.", + "Stylometrics finds syntactic constructions are great at fingerprinting, but suggests that these constructions are surface realizations of higher-level stylistic decisions. The number and type of personal pronouns is a reflection of how characters feature in a text. A large number of positional prepositions may be the result of a writer focusing on physical descriptions of scenes. In our attempt to decouple these, we create Frankenstein sentences, which piece together features of different styles \u2013 we are putting vampires in space.", + "Another way to validate our approach would be to select data that is stylistically distinctive but with similar content: perhaps genres in which content is static but language use changes over time, stylistically distinct authors within a single genre, or parodies of a distinctive genre." + ], + [ + "We present a formal, extendable model of style that can add control to any neural text generation system. We model style as a suite of low-level linguistic controls, and train a neural encoder-decoder model to reconstruct reference sentences given only content words and the setting of the controls. In automatic evaluations, we show that our model can fool a style classifier 84% of the time and outperforms a baseline genre-embedding model. In human evaluations, we encounter the `vampires in space' problem in which content and style are equally discriminative but people focus more on the content.", + "In future work we would like to model higher-level syntactic controls. BIBREF20 show that differences in clausal constructions, for instance having a dependent clause before an independent clause or vice versa, is a marker of style appreciated by the reader. Such features would likely interact with our lower-level controls in an interesting way, and provide further insight into style transfer in text." + ], + [ + "Katy Gero is supported by an NSF GRF (DGE - 1644869). We would also like to thank Elsbeth Turcan for her helpful comments." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0443/instruction.md b/qasper-0443/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..2330804ebb3dbb958ef1f1082915a01249690bf0 --- /dev/null +++ b/qasper-0443/instruction.md @@ -0,0 +1,105 @@ +Name of Paper: Fusing Visual, Textual and Connectivity Clues for Studying Mental Health + +Question: Do they report results only on English data? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + null, + "Introduction", + "Related Work", + "Dataset", + "Data Modality Analysis", + "Demographic Prediction", + "Multi-modal Prediction Framework" + ], + "paragraphs": [ + [ + "0pt*0*0", + "0pt*0*0", + "0pt*0*0 0.95", + "1]Amir Hossein Yazdavar 1]Mohammad Saeid Mahdavinejad 2]Goonmeet Bajaj", + " 3]William Romine 1]Amirhassan Monadjemi 1]Krishnaprasad Thirunarayan", + " 1]Amit Sheth 4]Jyotishman Pathak [1]Department of Computer Science & Engineering, Wright State University, OH, USA [2]Ohio State University, Columbus, OH, USA [3]Department of Biological Science, Wright State University, OH, USA [4] Division of Health Informatics, Weill Cornell University, New York, NY, USA", + "[1] yazdavar.2@wright.edu", + "With ubiquity of social media platforms, millions of people are sharing their online persona by expressing their thoughts, moods, emotions, feelings, and even their daily struggles with mental health issues voluntarily and publicly on social media. Unlike the most existing efforts which study depression by analyzing textual content, we examine and exploit multimodal big data to discern depressive behavior using a wide variety of features including individual-level demographics. By developing a multimodal framework and employing statistical techniques for fusing heterogeneous sets of features obtained by processing visual, textual and user interaction data, we significantly enhance the current state-of-the-art approaches for identifying depressed individuals on Twitter (improving the average F1-Score by 5 percent) as well as facilitate demographic inference from social media for broader applications. Besides providing insights into the relationship between demographics and mental health, our research assists in the design of a new breed of demographic-aware health interventions." + ], + [ + "Depression is a highly prevalent public health challenge and a major cause of disability worldwide. Depression affects 6.7% (i.e., about 16 million) Americans each year . According to the World Mental Health Survey conducted in 17 countries, on average, about 5% of people reported having an episode of depression in 2011 BIBREF0 . Untreated or under-treated clinical depression can lead to suicide and other chronic risky behaviors such as drug or alcohol addiction.", + "Global efforts to curb clinical depression involve identifying depression through survey-based methods employing phone or online questionnaires. These approaches suffer from under-representation as well as sampling bias (with very small group of respondents.) In contrast, the widespread adoption of social media where people voluntarily and publicly express their thoughts, moods, emotions, and feelings, and even share their daily struggles with mental health problems has not been adequately tapped into studying mental illnesses, such as depression. The visual and textual content shared on different social media platforms like Twitter offer new opportunities for a deeper understanding of self-expressed depression both at an individual as well as community-level. Previous research efforts have suggested that language style, sentiment, users' activities, and engagement expressed in social media posts can predict the likelihood of depression BIBREF1 , BIBREF2 . However, except for a few attempts BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , these investigations have seldom studied extraction of emotional state from visual content of images in posted/profile images. Visual content can express users' emotions more vividly, and psychologists noted that imagery is an effective medium for communicating difficult emotions.", + "According to eMarketer, photos accounted for 75% of content posted on Facebook worldwide and they are the most engaging type of content on Facebook (87%). Indeed, \"a picture is worth a thousand words\" and now \"photos are worth a million likes.\" Similarly, on Twitter, the tweets with image links get twice as much attention as those without , and video-linked tweets drive up engagement . The ease and naturalness of expression through visual imagery can serve to glean depression-indicators in vulnerable individuals who often seek social support through social media BIBREF7 . Further, as psychologist Carl Rogers highlights, we often pursue and promote our Ideal-Self . In this regard, the choice of profile image can be a proxy for the online persona BIBREF8 , providing a window into an individual's mental health status. For instance, choosing emaciated legs of girls covered with several cuts as profile image portrays negative self-view BIBREF9 .", + "Inferring demographic information like gender and age can be crucial for stratifying our understanding of population-level epidemiology of mental health disorders. Relying on electronic health records data, previous studies explored gender differences in depressive behavior from different angles including prevalence, age at onset, comorbidities, as well as biological and psychosocial factors. For instance, women have been diagnosed with depression twice as often as men BIBREF10 and national psychiatric morbidity survey in Britain has shown higher risk of depression in women BIBREF11 . On the other hand, suicide rates for men are three to five times higher compared to that of the women BIBREF12 .", + "Although depression can affect anyone at any age, signs and triggers of depression vary for different age groups . Depression triggers for children include parental depression, domestic violence, and loss of a pet, friend or family member. For teenagers (ages 12-18), depression may arise from hormonal imbalance, sexuality concerns and rejection by peers. Young adults (ages 19-29) may develop depression due to life transitions, poverty, trauma, and work issues. Adult (ages 30-60) depression triggers include caring simultaneously for children and aging parents, financial burden, work and relationship issues. Senior adults develop depression from common late-life issues, social isolation, major life loses such as the death of a spouse, financial stress and other chronic health problems (e.g., cardiac disease, dementia). Therefore, inferring demographic information while studying depressive behavior from passively sensed social data, can shed better light on the population-level epidemiology of depression.", + "The recent advancements in deep neural networks, specifically for image analysis task, can lead to determining demographic features such as age and gender BIBREF13 . We show that by determining and integrating heterogeneous set of features from different modalities \u2013 aesthetic features from posted images (colorfulness, hue variance, sharpness, brightness, blurriness, naturalness), choice of profile picture (for gender, age, and facial expression), the screen name, the language features from both textual content and profile's description (n-gram, emotion, sentiment), and finally sociability from ego-network, and user engagement \u2013 we can reliably detect likely depressed individuals in a data set of 8,770 human-annotated Twitter users.", + "We address and derive answers to the following research questions: 1) How well do the content of posted images (colors, aesthetic and facial presentation) reflect depressive behavior? 2) Does the choice of profile picture show any psychological traits of depressed online persona? Are they reliable enough to represent the demographic information such as age and gender? 3) Are there any underlying common themes among depressed individuals generated using multimodal content that can be used to detect depression reliably?" + ], + [ + "Mental Health Analysis using Social Media:", + "Several efforts have attempted to automatically detect depression from social media content utilizing machine/deep learning and natural language processing approaches. Conducting a retrospective study over tweets, BIBREF14 characterizes depression based on factors such as language, emotion, style, ego-network, and user engagement. They built a classifier to predict the likelihood of depression in a post BIBREF14 , BIBREF15 or in an individual BIBREF1 , BIBREF16 , BIBREF17 , BIBREF18 . Moreover, there have been significant advances due to the shared task BIBREF19 focusing on methods for identifying depressed users on Twitter at the Computational Linguistics and Clinical Psychology Workshop (CLP 2015). A corpus of nearly 1,800 Twitter users was built for evaluation, and the best models employed topic modeling BIBREF20 , Linguistic Inquiry and Word Count (LIWC) features, and other metadata BIBREF21 . More recently, a neural network architecture introduced by BIBREF22 combined posts into a representation of user's activities for detecting depressed users. Another active line of research has focused on capturing suicide and self-harm signals BIBREF23 , BIBREF24 , BIBREF25 , BIBREF26 , BIBREF2 , BIBREF27 . Moreover, the CLP 2016 BIBREF28 defined a shared task on detecting the severity of the mental health from forum posts. All of these studies derive discriminative features to classify depression in user-generated content at message-level, individual-level or community-level. Recent emergence of photo-sharing platforms such as Instagram, has attracted researchers attention to study people's behavior from their visual narratives \u2013 ranging from mining their emotions BIBREF29 , and happiness trend BIBREF30 , to studying medical concerns BIBREF31 . Researchers show that people use Instagram to engage in social exchange and storytelling about their difficult experiences BIBREF4 . The role of visual imagery as a mechanism of self-disclosure by relating visual attributes to mental health disclosures on Instagram was highlighted by BIBREF3 , BIBREF5 where individual Instagram profiles were utilized to build a prediction framework for identifying markers of depression. The importance of data modality to understand user behavior on social media was highlighted by BIBREF32 . More recently, a deep neural network sequence modeling approach that marries audio and text data modalities to analyze question-answer style interviews between an individual and an agent has been developed to study mental health BIBREF32 . Similarly, a multimodal depressive dictionary learning was proposed to detect depressed users on Twitter BIBREF33 . They provide a sparse user representations by defining a feature set consisting of social network features, user profile features, visual features, emotional features BIBREF34 , topic-level features, and domain-specific features. Particularly, our choice of multi-model prediction framework is intended to improve upon the prior works involving use of images in multimodal depression analysis BIBREF33 and prior works on studying Instagram photos BIBREF6 , BIBREF35 .", + "Demographic information inference on Social Media: ", + "There is a growing interest in understanding online user's demographic information due to its numerous applications in healthcare BIBREF36 , BIBREF37 . A supervised model developed by BIBREF38 for determining users' gender by employing features such as screen-name, full-name, profile description and content on external resources (e.g., personal blog). Employing features including emoticons, acronyms, slangs, punctuations, capitalization, sentence length and included links/images, along with online behaviors such as number of friends, post time, and commenting activity, a supervised model was built for predicting user's age group BIBREF39 . Utilizing users life stage information such as secondary school student, college student, and employee, BIBREF40 builds age inference model for Dutch Twitter users. Similarly, relying on profile descriptions while devising a set of rules and patterns, a novel model introduced for extracting age for Twitter users BIBREF41 . They also parse description for occupation by consulting the SOC2010 list of occupations and validating it through social surveys. A novel age inference model was developed while relying on homophily interaction information and content for predicting age of Twitter users BIBREF42 . The limitations of textual content for predicting age and gender was highlighted by BIBREF43 . They distinguish language use based on social gender, age identity, biological sex and chronological age by collecting crowdsourced signals using a game in which players (crowd) guess the biological sex and age of a user based only on their tweets. Their findings indicate how linguistic markers can misguide (e.g., a heart represented as <3 can be misinterpreted as feminine when the writer is male.) Estimating age and gender from facial images by training a convolutional neural networks (CNN) for face recognition is an active line of research BIBREF44 , BIBREF13 , BIBREF45 ." + ], + [ + "Self-disclosure clues have been extensively utilized for creating ground-truth data for numerous social media analytic studies e.g., for predicting demographics BIBREF36 , BIBREF41 , and user's depressive behavior BIBREF46 , BIBREF47 , BIBREF48 . For instance, vulnerable individuals may employ depressive-indicative terms in their Twitter profile descriptions. Others may share their age and gender, e.g., \"16 years old suicidal girl\"(see Figure FIGREF15 ). We employ a huge dataset of 45,000 self-reported depressed users introduced in BIBREF46 where a lexicon of depression symptoms consisting of 1500 depression-indicative terms was created with the help of psychologist clinician and employed for collecting self-declared depressed individual's profiles. A subset of 8,770 users (24 million time-stamped tweets) containing 3981 depressed and 4789 control users (that do not show any depressive behavior) were verified by two human judges BIBREF46 . This dataset INLINEFORM0 contains the metadata values of each user such as profile descriptions, followers_count, created_at, and profile_image_url.", + "Age Enabled Ground-truth Dataset: We extract user's age by applying regular expression patterns to profile descriptions (such as \"17 years old, self-harm, anxiety, depression\") BIBREF41 . We compile \"age prefixes\" and \"age suffixes\", and use three age-extraction rules: 1. I am X years old 2. Born in X 3. X years old, where X is a \"date\" or age (e.g., 1994). We selected a subset of 1061 users among INLINEFORM0 as gold standard dataset INLINEFORM1 who disclose their age. From these 1061 users, 822 belong to depressed class and 239 belong to control class. From 3981 depressed users, 20.6% disclose their age in contrast with only 4% (239/4789) among control group. So self-disclosure of age is more prevalent among vulnerable users. Figure FIGREF18 depicts the age distribution in INLINEFORM2 . The general trend, consistent with the results in BIBREF42 , BIBREF49 , is biased toward young people. Indeed, according to Pew, 47% of Twitter users are younger than 30 years old BIBREF50 . Similar data collection procedure with comparable distribution have been used in many prior efforts BIBREF51 , BIBREF49 , BIBREF42 . We discuss our approach to mitigate the impact of the bias in Section 4.1. The median age is 17 for depressed class versus 19 for control class suggesting either likely depressed-user population is younger, or depressed youngsters are more likely to disclose their age for connecting to their peers (social homophily.) BIBREF51 ", + "Gender Enabled Ground-truth Dataset: We selected a subset of 1464 users INLINEFORM0 from INLINEFORM1 who disclose their gender in their profile description. From 1464 users 64% belonged to the depressed group, and the rest (36%) to the control group. 23% of the likely depressed users disclose their gender which is considerably higher (12%) than that for the control class. Once again, gender disclosure varies among the two gender groups. For statistical significance, we performed chi-square test (null hypothesis: gender and depression are two independent variables). Figure FIGREF19 illustrates gender association with each of the two classes. Blue circles (positive residuals, see Figure FIGREF19 -A,D) show positive association among corresponding row and column variables while red circles (negative residuals, see Figure FIGREF19 -B,C) imply a repulsion. Our findings are consistent with the medical literature BIBREF10 as according to BIBREF52 more women than men were given a diagnosis of depression. In particular, the female-to-male ratio is 2.1 and 1.9 for Major Depressive Disorder and Dysthymic Disorder respectively. Our findings from Twitter data indicate there is a strong association (Chi-square: 32.75, p-value:1.04e-08) between being female and showing depressive behavior on Twitter." + ], + [ + "We now provide an in-depth analysis of visual and textual content of vulnerable users.", + "Visual Content Analysis: We show that the visual content in images from posts as well as profiles provide valuable psychological cues for understanding a user's depression status. Profile/posted images can surface self-stigmatization BIBREF53 . Additionally, as opposed to typical computer vision framework for object recognition that often relies on thousands of predetermined low-level features, what matters more for assessing user's online behavior is the emotions reflected in facial expressions BIBREF54 , attributes contributing to the computational aesthetics BIBREF55 , and sentimental quotes they may subscribe to (Figure FIGREF15 ) BIBREF8 .", + "Facial Presence: ", + "For capturing facial presence, we rely on BIBREF56 's approach that uses multilevel convolutional coarse-to-fine network cascade to tackle facial landmark localization. We identify facial presentation, emotion from facial expression, and demographic features from profile/posted images . Table TABREF21 illustrates facial presentation differences in both profile and posted images (media) for depressed and control users in INLINEFORM0 . With control class showing significantly higher in both profile and media (8%, 9% respectively) compared to that for the depressed class. In contrast with age and gender disclosure, vulnerable users are less likely to disclose their facial identity, possibly due to lack of confidence or fear of stigma.", + "Facial Expression:", + "Following BIBREF8 's approach, we adopt Ekman's model of six emotions: anger, disgust, fear, joy, sadness and surprise, and use the Face++ API to automatically capture them from the shared images. Positive emotions are joy and surprise, and negative emotions are anger, disgust, fear, and sadness. In general, for each user u in INLINEFORM0 , we process profile/shared images for both the depressed and the control groups with at least one face from the shared images (Table TABREF23 ). For the photos that contain multiple faces, we measure the average emotion.", + "Figure FIGREF27 illustrates the inter-correlation of these features. Additionally, we observe that emotions gleaned from facial expressions correlated with emotional signals captured from textual content utilizing LIWC. This indicates visual imagery can be harnessed as a complementary channel for measuring online emotional signals.", + "General Image Features:", + "The importance of interpretable computational aesthetic features for studying users' online behavior has been highlighted by several efforts BIBREF55 , BIBREF8 , BIBREF57 . Color, as a pillar of the human vision system, has a strong association with conceptual ideas like emotion BIBREF58 , BIBREF59 . We measured the normalized red, green, blue and the mean of original colors, and brightness and contrast relative to variations of luminance. We represent images in Hue-Saturation-Value color space that seems intuitive for humans, and measure mean and variance for saturation and hue. Saturation is defined as the difference in the intensities of the different light wavelengths that compose the color. Although hue is not interpretable, high saturation indicates vividness and chromatic purity which are more appealing to the human eye BIBREF8 . Colorfulness is measured as a difference against gray background BIBREF60 . Naturalness is a measure of the degree of correspondence between images and the human perception of reality BIBREF60 . In color reproduction, naturalness is measured from the mental recollection of the colors of familiar objects. Additionally, there is a tendency among vulnerable users to share sentimental quotes bearing negative emotions. We performed optical character recognition (OCR) with python-tesseract to extract text and their sentiment score. As illustrated in Table TABREF26 , vulnerable users tend to use less colorful (higher grayscale) profile as well as shared images to convey their negative feelings, and share images that are less natural (Figure FIGREF15 ). With respect to the aesthetic quality of images (saturation, brightness, and hue), depressed users use images that are less appealing to the human eye. We employ independent t-test, while adopting Bonferroni Correction as a conservative approach to adjust the confidence intervals. Overall, we have 223 features, and choose Bonferroni-corrected INLINEFORM0 level of INLINEFORM1 (*** INLINEFORM2 , ** INLINEFORM3 ).", + "** alpha= 0.05, *** alpha = 0.05/223", + "Demographics Inference & Language Cues: LIWC has been used extensively for examining the latent dimensions of self-expression for analyzing personality BIBREF61 , depressive behavior, demographic differences BIBREF43 , BIBREF40 , etc. Several studies highlight that females employ more first-person singular pronouns BIBREF62 , and deictic language BIBREF63 , while males tend to use more articles BIBREF64 which characterizes concrete thinking, and formal, informational and affirmation words BIBREF65 . For age analysis, the salient findings include older individuals using more future tense verbs BIBREF62 triggering a shift in focus while aging. They also show positive emotions BIBREF66 and employ fewer self-references (i.e. 'I', 'me') with greater first person plural BIBREF62 . Depressed users employ first person pronouns more frequently BIBREF67 , repeatedly use negative emotions and anger words. We analyzed psycholinguistic cues and language style to study the association between depressive behavior as well as demographics. Particularly, we adopt Levinson's adult development grouping that partitions users in INLINEFORM0 into 5 age groups: (14,19],(19,23], (23,34],(34,46], and (46,60]. Then, we apply LIWC for characterizing linguistic styles for each age group for users in INLINEFORM1 .", + "Qualitative Language Analysis: The recent LIWC version summarizes textual content in terms of language variables such as analytical thinking, clout, authenticity, and emotional tone. It also measures other linguistic dimensions such as descriptors categories (e.g., percent of target words gleaned by dictionary, or longer than six letters - Sixltr) and informal language markers (e.g., swear words, netspeak), and other linguistic aspects (e.g., 1st person singular pronouns.)", + "Thinking Style:", + "Measuring people's natural ways of trying to analyze, and organize complex events have strong association with analytical thinking. LIWC relates higher analytic thinking to more formal and logical reasoning whereas a lower value indicates focus on narratives. Also, cognitive processing measures problem solving in mind. Words such as \"think,\" \"realize,\" and \"know\" indicates the degree of \"certainty\" in communications. Critical thinking ability relates to education BIBREF68 , and is impacted by different stages of cognitive development at different ages . It has been shown that older people communicate with greater cognitive complexity while comprehending nuances and subtle differences BIBREF62 . We observe a similar pattern in our data (Table TABREF40 .) A recent study highlights how depression affects brain and thinking at molecular level using a rat model BIBREF69 . Depression can promote cognitive dysfunction including difficulty in concentrating and making decisions. We observed a notable differences in the ability to think analytically in depressed and control users in different age groups (see Figure FIGREF39 - A, F and Table TABREF40 ). Overall, vulnerable younger users are not logical thinkers based on their relative analytical score and cognitive processing ability.", + "Authenticity:", + "Authenticity measures the degree of honesty. Authenticity is often assessed by measuring present tense verbs, 1st person singular pronouns (I, me, my), and by examining the linguistic manifestations of false stories BIBREF70 . Liars use fewer self-references and fewer complex words. Psychologists often see a child's first successfull lie as a mental growth. There is a decreasing trend of the Authenticity with aging (see Figure FIGREF39 -B.) Authenticity for depressed youngsters is strikingly higher than their control peers. It decreases with age (Figure FIGREF39 -B.)", + "Clout:", + "People with high clout speak more confidently and with certainty, employing more social words with fewer negations (e.g., no, not) and swear words. In general, midlife is relatively stable w.r.t. relationships and work. A recent study shows that age 60 to be best for self-esteem BIBREF71 as people take on managerial roles at work and maintain a satisfying relationship with their spouse. We see the same pattern in our data (see Figure FIGREF39 -C and Table TABREF40 ). Unsurprisingly, lack of confidence (the 6th PHQ-9 symptom) is a distinguishable characteristic of vulnerable users, leading to their lower clout scores, especially among depressed users before middle age (34 years old).", + "Self-references:", + "First person singular words are often seen as indicating interpersonal involvement and their high usage is associated with negative affective states implying nervousness and depression BIBREF66 . Consistent with prior studies, frequency of first person singular for depressed people is significantly higher compared to that of control class. Similarly to BIBREF66 , youngsters tend to use more first-person (e.g. I) and second person singular (e.g. you) pronouns (Figure FIGREF39 -G).", + "Informal Language Markers; Swear, Netspeak:", + "Several studies highlighted the use of profanity by young adults has significantly increased over the last decade BIBREF72 . We observed the same pattern in both the depressed and the control classes (Table TABREF40 ), although it's rate is higher for depressed users BIBREF1 . Psychologists have also shown that swearing can indicate that an individual is not a fragmented member of a society. Depressed youngsters, showing higher rate of interpersonal involvement and relationships, have a higher rate of cursing (Figure FIGREF39 -E). Also, Netspeak lexicon measures the frequency of terms such as lol and thx.", + "Sexual, Body: ", + "Sexual lexicon contains terms like \"horny\", \"love\" and \"incest\", and body terms like \"ache\", \"heart\", and \"cough\". Both start with a higher rate for depressed users while decreasing gradually while growing up, possibly due to changes in sexual desire as we age (Figure FIGREF39 -H,I and Table TABREF40 .)", + "Quantitative Language Analysis:", + "We employ one-way ANOVA to compare the impact of various factors and validate our findings above. Table TABREF40 illustrates our findings, with a degree of freedom (df) of 1055. The null hypothesis is that the sample means' for each age group are similar for each of the LIWC features.", + "*** alpha = 0.001, ** alpha = 0.01, * alpha = 0.05" + ], + [ + "We leverage both the visual and textual content for predicting age and gender.", + "Prediction with Textual Content:", + "We employ BIBREF73 's weighted lexicon of terms that uses the dataset of 75,394 Facebook users who shared their status, age and gender. The predictive power of this lexica was evaluated on Twitter, blog, and Facebook, showing promising results BIBREF73 . Utilizing these two weighted lexicon of terms, we are predicting the demographic information (age or gender) of INLINEFORM0 (denoted by INLINEFORM1 ) using following equation: INLINEFORM2 ", + "where INLINEFORM0 is the lexicon weight of the term, and INLINEFORM1 represents the frequency of the term in the user generated INLINEFORM2 , and INLINEFORM3 measures total word count in INLINEFORM4 . As our data is biased toward young people, we report age prediction performance for each age group separately (Table TABREF42 ). Moreover, to measure the average accuracy of this model, we build a balanced dataset (keeping all the users above 23 -416 users), and then randomly sampling the same number of users from the age ranges (11,19] and (19,23]. The average accuracy of this model is 0.63 for depressed users and 0.64 for control class. Table TABREF44 illustrates the performance of gender prediction for each class. The average accuracy is 0.82 on INLINEFORM5 ground-truth dataset.", + "Prediction with Visual Imagery:", + "Inspired by BIBREF56 's approach for facial landmark localization, we use their pretrained CNN consisting of convolutional layers, including unshared and fully-connected layers, to predict gender and age from both the profile and shared images. We evaluate the performance for gender and age prediction task on INLINEFORM0 and INLINEFORM1 respectively as shown in Table TABREF42 and Table TABREF44 .", + "Demographic Prediction Analysis:", + "We delve deeper into the benefits and drawbacks of each data modality for demographic information prediction. This is crucial as the differences between language cues between age groups above age 35 tend to become smaller (see Figure FIGREF39 -A,B,C) and making the prediction harder for older people BIBREF74 . In this case, the other data modality (e.g., visual content) can play integral role as a complementary source for age inference. For gender prediction (see Table TABREF44 ), on average, the profile image-based predictor provides a more accurate prediction for both the depressed and control class (0.92 and 0.90) compared to content-based predictor (0.82). For age prediction (see Table TABREF42 ), textual content-based predictor (on average 0.60) outperforms both of the visual-based predictors (on average profile:0.51, Media:0.53).", + "However, not every user provides facial identity on his account (see Table TABREF21 ). We studied facial presentation for each age-group to examine any association between age-group, facial presentation and depressive behavior (see Table TABREF43 ). We can see youngsters in both depressed and control class are not likely to present their face on profile image. Less than 3% of vulnerable users between 11-19 years reveal their facial identity. Although content-based gender predictor was not as accurate as image-based one, it is adequate for population-level analysis." + ], + [ + "We use the above findings for predicting depressive behavior. Our model exploits early fusion BIBREF32 technique in feature space and requires modeling each user INLINEFORM0 in INLINEFORM1 as vector concatenation of individual modality features. As opposed to computationally expensive late fusion scheme where each modality requires a separate supervised modeling, this model reduces the learning effort and shows promising results BIBREF75 . To develop a generalizable model that avoids overfitting, we perform feature selection using statistical tests and all relevant ensemble learning models. It adds randomness to the data by creating shuffled copies of all features (shadow feature), and then trains Random Forest classifier on the extended data. Iteratively, it checks whether the actual feature has a higher Z-score than its shadow feature (See Algorithm SECREF6 and Figure FIGREF45 ) BIBREF76 .", + "Main each Feature INLINEFORM0 INLINEFORM1 ", + "RndForrest( INLINEFORM0 ) Calculate Imp INLINEFORM1 INLINEFORM2 Generate next hypothesis , INLINEFORM3 Once all hypothesis generated Perform Statistical Test INLINEFORM4 //Binomial Distribution INLINEFORM5 Feature is important Feature is important", + " Ensemble Feature Selection", + "Next, we adopt an ensemble learning method that integrates the predictive power of multiple learners with two main advantages; its interpretability with respect to the contributions of each feature and its high predictive power. For prediction we have INLINEFORM0 where INLINEFORM1 is a weak learner and INLINEFORM2 denotes the final prediction.", + "In particular, we optimize the loss function: INLINEFORM0 where INLINEFORM1 incorporates INLINEFORM2 and INLINEFORM3 regularization. In each iteration, the new INLINEFORM4 is obtained by fitting weak learner to the negative gradient of loss function. Particularly, by estimating the loss function with Taylor expansion : INLINEFORM5 where its first expression is constant, the second and the third expressions are first ( INLINEFORM6 ) and second order derivatives ( INLINEFORM7 ) of the loss. INLINEFORM8 ", + "For exploring the weak learners, assume INLINEFORM0 has k leaf nodes, INLINEFORM1 be subset of users from INLINEFORM2 belongs to the node INLINEFORM3 , and INLINEFORM4 denotes the prediction for node INLINEFORM5 . Then, for each user INLINEFORM6 belonging to INLINEFORM7 , INLINEFORM8 and INLINEFORM9 INLINEFORM10 ", + "Next, for each leaf node INLINEFORM0 , deriving w.r.t INLINEFORM1 : INLINEFORM2 ", + "and by substituting weights: INLINEFORM0 ", + "which represents the loss for fixed weak learners with INLINEFORM0 nodes. The trees are built sequentially such that each subsequent tree aims to reduce the errors of its predecessor tree. Although, the weak learners have high bias, the ensemble model produces a strong learner that effectively integrate the weak learners by reducing bias and variance (the ultimate goal of supervised models) BIBREF77 . Table TABREF48 illustrates our multimodal framework outperform the baselines for identifying depressed users in terms of average specificity, sensitivity, F-Measure, and accuracy in 10-fold cross-validation setting on INLINEFORM1 dataset. Figure FIGREF47 shows how the likelihood of being classified into the depressed class varies with each feature addition to the model for a sample user in the dataset. The prediction bar (the black bar) shows that the log-odds of prediction is 0.31, that is, the likelihood of this person being a depressed user is 57% (1 / (1 + exp(-0.3))). The figure also sheds light on the impact of each contributing feature. The waterfall charts represent how the probability of being depressed changes with the addition of each feature variable. For instance, the \"Analytic thinking\" of this user is considered high 48.43 (Median:36.95, Mean: 40.18) and this decreases the chance of this person being classified into the depressed group by the log-odds of -1.41. Depressed users have significantly lower \"Analytic thinking\" score compared to control class. Moreover, the 40.46 \"Clout\" score is a low value (Median: 62.22, Mean: 57.17) and it decreases the chance of being classified as depressed. With respect to the visual features, for instance, the mean and the median of 'shared_colorfulness' is 112.03 and 113 respectively. The value of 136.71 would be high; thus, it decreases the chance of being depressed for this specific user by log-odds of -0.54. Moreover, the 'profile_naturalness' of 0.46 is considered high compared to 0.36 as the mean for the depressed class which justifies pull down of the log-odds by INLINEFORM2 . For network features, for instance, 'two_hop_neighborhood' for depressed users (Mean : 84) are less than that of control users (Mean: 154), and is reflected in pulling down the log-odds by -0.27.", + "Baselines:", + "To test the efficacy of our multi-modal framework for detecting depressed users, we compare it against existing content, content-network, and image-based models (based on the aforementioned general image feature, facial presence, and facial expressions.)" + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0444/instruction.md b/qasper-0444/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..00b3db0646c851f2f9400645a7f8aa104628d961 --- /dev/null +++ b/qasper-0444/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Fusing Visual, Textual and Connectivity Clues for Studying Mental Health + +Question: What insights into the relationship between demographics and mental health are provided? \ No newline at end of file diff --git a/qasper-0445/instruction.md b/qasper-0445/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..741e73ac1621e3ad92b64a3faa327f993d00c8df --- /dev/null +++ b/qasper-0445/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Fusing Visual, Textual and Connectivity Clues for Studying Mental Health + +Question: What model is used to achieve 5% improvement on F1 for identifying depressed individuals on Twitter? \ No newline at end of file diff --git a/qasper-0450/instruction.md b/qasper-0450/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..da4aef76c56922e30ff1b7db44803022325b703a --- /dev/null +++ b/qasper-0450/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Fusing Visual, Textual and Connectivity Clues for Studying Mental Health + +Question: What is the source of the user interaction data? \ No newline at end of file diff --git a/qasper-0451/instruction.md b/qasper-0451/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..aa4e1aa455f2a5f1e9a143da92a1e03500a38aff --- /dev/null +++ b/qasper-0451/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Fusing Visual, Textual and Connectivity Clues for Studying Mental Health + +Question: What is the source of the textual data? \ No newline at end of file diff --git a/qasper-0456/instruction.md b/qasper-0456/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..37dd1531c1b20f5741d9edd6d9c0249365fed869 --- /dev/null +++ b/qasper-0456/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Incorporating Sememes into Chinese Definition Modeling + +Question: What is a sememe? \ No newline at end of file diff --git a/qasper-0458/instruction.md b/qasper-0458/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..ac32324bab9a53ee1b61a2a18fe3bfa9e139f9b8 --- /dev/null +++ b/qasper-0458/instruction.md @@ -0,0 +1,74 @@ +Name of Paper: RobBERT: a Dutch RoBERTa-based Language Model + +Question: What is the state of the art? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Pre-training RobBERT", + "Pre-training RobBERT ::: Data", + "Pre-training RobBERT ::: Training", + "Evaluation", + "Evaluation ::: Sentiment Analysis", + "Evaluation ::: Die/Dat Disambiguation", + "Code", + "Future Work", + "Conclusion", + "Acknowledgements" + ], + "paragraphs": [ + [ + "The advent of neural networks in natural language processing (NLP) has significantly improved state-of-the-art results within the field. While recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) initially dominated the field, recent models started incorporating attention mechanisms and then later dropped the recurrent part and just kept the attention mechanisms in so-called transformer models BIBREF0. This latter type of model caused a new revolution in NLP and led to popular language models like GPT-2 BIBREF1, BIBREF2 and ELMo BIBREF3. BERT BIBREF4 improved over previous transformer models and recurrent networks by allowing the system to learn from input text in a bidirectional way, rather than only from left-to-right or the other way around. This model was later re-implemented, critically evaluated and improved in the RoBERTa model BIBREF5.", + "These large-scale transformer models provide the advantage of being able to solve NLP tasks by having a common, expensive pre-training phase, followed by a smaller fine-tuning phase. The pre-training happens in an unsupervised way by providing large corpora of text in the desired language. The second phase only needs a relatively small annotated data set for fine-tuning to outperform previous popular approaches in one of a large number of possible language tasks.", + "While language models are usually trained on English data, some multilingual models also exist. These are usually trained on a large quantity of text in different languages. For example, Multilingual-BERT is trained on a collection of corpora in 104 different languages BIBREF4, and generalizes language components well across languages BIBREF6. However, models trained on data from one specific language usually improve the performance of multilingual models for this particular language BIBREF7, BIBREF8. Training a RoBERTa model BIBREF5 on a Dutch dataset thus has a lot of potential for increasing performance for many downstream Dutch NLP tasks. In this paper, we introduce RobBERT, a Dutch RoBERTa-based pre-trained language model, and critically test its performance using natural language tasks against other Dutch languages models." + ], + [ + "Transformer models have been successfully used for a wide range of language tasks. Initially, transformers were introduced for use in machine translation, where they vastly improved state-of-the-art results for English to German in an efficient manner BIBREF0. This transformer model architecture resulted in a new paradigm in NLP with the migration from sequence-to-sequence recurrent neural networks to transformer-based models by removing the recurrent component and only keeping attention. This cornerstone was used for BERT, a transformer model that obtained state-of-the-art results for eleven natural language processing tasks, such as question answering and natural language inference BIBREF4. BERT is pre-trained with large corpora of text using two unsupervised tasks. The first task is word masking (also called the Cloze task BIBREF9 or masked language model (MLM)), where the model has to guess which word is masked in certain position in the text. The second task is next sentence prediction. This is done by predicting if two sentences are subsequent in the corpus, or if they are randomly sampled from the corpus. These tasks allowed the model to create internal representations about a language, which could thereafter be reused for different language tasks. This architecture has been shown to be a general language model that could be fine-tuned with little data in a relatively efficient way for a very distinct range of tasks and still outperform previous architectures BIBREF4.", + "Transformer models are also capable of generating contextualized word embeddings. These contextualized embeddings were presented by BIBREF3 and addressed the well known issue with a word's meaning being defined by its context (e.g. \u201ca stick\u201d versus \u201clet's stick to\u201d). This lack of context is something that traditional word embeddings like word2vec BIBREF10 or GloVe BIBREF11 lack, whereas BERT automatically incorporates the context a word occurs in.", + "Another advantage of transformer models is that attention allows them to better resolve coreferences between words BIBREF12. A typical example for the importance of coreference resolution is \u201cThe trophy doesn\u2019t fit in the brown suitcase because it\u2019s too big.\u201d, where the word \u201cit\u201d would refer to the the suitcase instead of the trophy if the last word was changed to \u201csmall\u201d BIBREF13. Being able to resolve these coreferences is for example important for translating to languages with gender, as suitcase and trophy have different genders in French.", + "Although BERT has been shown to be a useful language model, it has also received some scrutiny on the training and pre-processing of the language model. As mentioned before, BERT uses next sentence prediction (NSP) as one of its two training tasks. In NSP, the model has to predict whether two sentences follow each other in the training text, or are just randomly selected from the corpora. The authors of RoBERTa BIBREF5 showed that while this task made the model achieve a better performance, it was not due to its intended reason, as it might merely predict relatedness rather than subsequent sentences. That BIBREF4 trained a better model when using NSP than without NSP is likely due to the model learning long-range dependencies in text from its inputs, which are longer than just the single sentence on itself. As such, the RoBERTa model uses only the MLM task, and uses multiple full sentences in every input. Other research improved the NSP task by instead making the model predict the correct order of two sentences, where the model thus has to predict whether the sentences occur in the given order in the corpus, or occur in flipped order BIBREF14.", + "BIBREF4 also presented a multilingual model (mBERT) with the same architecture as BERT, but trained on Wikipedia corpora in 104 languages. Unfortunately, the quality of these multilingual embeddings is often considered worse than their monolingual counterparts. BIBREF15 illustrated this difference in quality for German and English models in a generative setting. The monolingual French CamemBERT model BIBREF7 also compared their model to mBERT, which performed poorer on all tasks. More recently, BIBREF8 also showed similar results for Dutch using their BERTje model, outperforming multilingual BERT in a wide range of tasks, such as sentiment analysis and part-of-speech tagging. Since this work is concurrent with ours, we compare our results with BERTje in this paper." + ], + [ + "This section describes the data and training regime we used to train our Dutch RoBERTa-based language model called RobBERT." + ], + [ + "We pre-trained our model on the Dutch section of the OSCAR corpus, a large multilingual corpus which was obtained by language classification in the Common Crawl corpus BIBREF16. This Dutch corpus has 6.6 billion words, totalling 39 GB of text. It contains 126,064,722 lines of text, where each line can contain multiple sentences. Subsequent lines are however not related to each other, due to the shuffled nature of the OSCAR data set. For comparison, the French RoBERTa-based language model CamemBERT BIBREF7 has been trained on the French portion of OSCAR, which consists of 138 GB of scraped text.", + "Our data differs in several ways from the data used to train BERTje, a BERT-based Dutch language model BIBREF8. Firstly, they trained the model on an assembly of multiple Dutch corpora totalling only 12 GB. Secondly, they used WordPiece as subword embeddings, since this is what the original BERT architecture uses. RobBERT on the other hand uses Byte Pair Encoding (BPE), which is also used by GPT-2 BIBREF2 and RoBERTa BIBREF5." + ], + [ + "RobBERT shares its architecture with RoBERTa's base model, which itself is a replication and improvement over BERT BIBREF5. The architecture of our language model is thus equal to the original BERT model with 12 self-attention layers with 12 heads BIBREF4. One difference with the original BERT is due to the different pre-training task specified by RoBERTa, using only the MLM task and not the NSP task. The training thus only uses word masking, where the model has to predict which words were masked in certain positions of a given line of text. The training process uses the Adam optimizer BIBREF17 with polynomial decay of the learning rate $l_r=10^{-6}$ and a ramp-up period of 1000 iterations, with parameters $\\beta _1=0.9$ (a common default) and RoBERTa's default $\\beta _2=0.98$. Additionally, we also used a weight decay of 0.1 as well as a small dropout of 0.1 to help prevent the model from overfitting BIBREF18.", + "We used a computing cluster in order to efficiently pre-train our model. More specifically, the pre-training was executed on a computing cluster with 20 nodes with 4 Nvidia Tesla P100 GPUs (16 GB VRAM each) and 2 nodes with 8 Nvidia V100 GPUs (having 32 GB VRAM each). This pre-training happened in fixed batches of 8192 sentences by rescaling each GPUs batch size depending on the number of GPUs available, in order to maximally utilize the cluster without blocking it entirely for other users. The model trained for two epochs, which is over 16k batches in total. With the large batch size of 8192, this equates to 0.5M updates for a traditional BERT model. At this point, the perplexity did not decrease any further." + ], + [ + "We evaluated RobBERT in several different settings on multiple downstream tasks. First, we compare its performance with other BERT-models and state-of-the-art systems in sentiment analysis, to show its performance for classification tasks. Second, we compare its performance in a recent Dutch language task, namely the disambiguation of demonstrative pronouns, which allows us to additionally compare the zero-shot performance of our and other BERT models, i.e. using only the pre-trained model without any fine-tuning." + ], + [ + "We replicated the high-level sentiment analysis task used to evaluate BERTje BIBREF8 to be able to compare our methods. This task uses a dataset called Dutch Book Reviews Dataset (DBRD), in which book reviews scraped from hebban.nl are labeled as positive or negative BIBREF19. Although the dataset contains 118,516 reviews, only 22,252 of these reviews are actually labeled as positive or negative. The DBRD dataset is already split in a balanced 10% test and 90% train split, allowing us to easily compare to other models trained for solving this task. This dataset was released in a paper analysing the performance of an ULMFiT model (Universal Language Model Fine-tuning for Text Classification model) BIBREF19.", + "We fine-tuned RobBERT on the first 10,000 training examples as well as on the full data set. While the ULMFiT model is first fine-tuned using the unlabeled reviews before training the classifier BIBREF19, it is unclear whether BERTje also first fine-tuned on the unlabeled reviews or only used the labeled data for fine-tuning the pretrained model. It is also unclear how it dealt with reviews being longer than the maximum number of tokens allowed as input in BERT models, as the average book review length is 547 tokens, with 40% of the documents being longer than our RobBERT model can handle. For a safe comparison, we thus decided to discard the unlabeled data and only use the labeled data for training and test purposes (20,028 and 2,224 examples respectively), and compare approaches for dealing with too long input sequences. We trained our model for 2000 iterations with a batch size of 128 and a warm-up of 500 iterations, reaching a learning rate of $10^{-5}$. We found that our model performed better when trained on the last part of the book reviews than on the first part. This is likely due to this part containing concluding remarks summarizing the overall sentiment. While BERTje was slightly outperformed by ULMFiT BIBREF8, BIBREF19, we can see that RobBERT achieves better performance than both on the test set, although the performance difference is not statistically significantly better than the ULMFiT model, as can be seen in Table TABREF4." + ], + [ + "Aside from classic natural language processing tasks in previous subsections, we also evaluated its performance on a task that is specific to Dutch, namely disambiguating \u201cdie\u201d and \u201cdat\u201d (= \u201cthat\u201d in English). In Dutch, depending on the sentence, both terms can be either demonstrative or relative pronouns; in addition they can also be used in a subordinating conjunction, i.e. to introduce a clause. The use of either of these words depends on the gender of the word it refers to. Distinguishing these words is a task introduced by BIBREF20, who presented multiple models trained on the Europarl BIBREF21 and SoNaR corpora BIBREF22. The results ranged from an accuracy of 75.03% on Europarl to 84.56% on SoNaR.", + "For this task, we use the Dutch version of the Europarl corpus BIBREF21, which we split in 1.3M utterances for training, 319k for validation, and 399k for testing. We then process every sentence by checking if it contains \u201cdie\u201d or \u201cdat\u201d, and if so, add a training example for every occurrence of this word in the sentence, where a single occurrence is masked. For the test set for example, this resulted in about 289k masked sentences. We then test two different approaches for solving this task on this dataset. The first approach is making the BERT models use their MLM task and guess which word should be filled in this spot, and check if it has more confidence in either \u201cdie\u201d or \u201cdat\u201d (by checking the first 2,048 guesses at most, as this seemed sufficiently large). This allows us to compare the zero-shot BERT models, i.e. without any fine-tuning after pre-training, for which the results can be seen in Table TABREF7. The second approach uses the same data, but creates two sentences by filling in the mask with both \u201cdie\u201d and \u201cdat\u201d, appending both with the [SEP] token and making the model predict which of the two sentences is correct. The fine-tuning was performed using 4 Nvidia GTX 1080 Ti GPUs and evaluated against the same test set of 399k utterances. As before, we fine-tuned the model twice: once with the full training set and once with a subset of 10k utterances from the training set for illustrating the benefits of pre-training on low-resource tasks.", + "RobBERT outperforms previous models as well as other BERT models both with as well as without fine-tuning (see Table TABREF4 and Table TABREF7). It is also able to reach similar performance using less data. The fact that zero-shot RobBERT outperforms other zero-shot BERT models is also an indication that the base model has internalised more knowledge about Dutch than the other two have. The reason RobBERT and other BERT models outperform the previous RNN-based approach is likely the transformers ability to deal better with coreference resolution BIBREF12, and by extension better in deciding which word the \u201cdie\u201d or \u201cdat\u201d belongs to." + ], + [ + "The training and evaluation code of this paper as well as the RobBERT model and the fine-tuned models are publicly available for download on https://github.com/iPieter/RobBERT." + ], + [ + "There are several possible improvements as well as interesting future directions for this research, for example in training similar models. First, as BERT-based models are a very active field of research, it is interesting to experiment with change the pre-training tasks with new unsupervised tasks when they are discovered, such as the sentence order prediction BIBREF14. Second, while RobBERT is trained on lines that contain multiple sentences, it does not put subsequent lines of the corpus after each other due to the shuffled nature of the OSCAR corpus BIBREF16. This is unlike RoBERTa, which does put full sentences next to each other if they fit, in order to learn the long-range dependencies between words that the original BERT learned using its controversial NSP task. It could be interesting to use the processor used to create OSCAR in order to create an unshuffled version to train on, such that this technique can be used on the data set. Third, RobBERT uses the same tokenizer as RoBERTa, meaning it uses a tokenizer built for the English language. Training a new model using a custom Dutch tokenizer, e.g. using the newly released HuggingFace tokenizers library BIBREF23, could increase the performance even further. On the same note, incorporating more Unicode glyphs as separate tokens can also be beneficial for example for tasks related to conversational agents BIBREF24.", + "RobBERT itself could also be used in new settings to help future research. First, RobBERT could be used in different settings thanks to the renewed interest of sequence-to-sequence models due to their results on a vast range of language tasks BIBREF25, BIBREF26. These models use a BERT-like transformer stack for the encoder and depending on the task a generative model as a decoder. These advances once again highlight the flexibility of the self-attention mechanism and it might be interesting to research the re-usability of RobBERT in these type of architectures. Second, there are many Dutch language tasks that we did not examine in this paper, for which it may also be possible to achieve state-of-the-art results when fine-tuned on this pre-trained model." + ], + [ + "We introduced a new language model for Dutch based on RoBERTa, called RobBERT, and showed that it outperforms earlier approaches for Dutch language tasks, as well as other BERT-based language models. We thus hope this model can serve as a base for fine-tuning on other tasks, and thus help foster new models that might advance results for Dutch language tasks." + ], + [ + "Pieter Delobelle was supported by the Research Foundation - Flanders under EOS No. 30992574 and received funding from the Flemish Government under the \u201cOnderzoeksprogramma Artifici\u00eble Intelligentie (AI) Vlaanderen\u201d programme. Thomas Winters is a fellow of the Research Foundation-Flanders (FWO-Vlaanderen). Most computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation - Flanders (FWO) and the Flemish Government \u2013 department EWI. We are especially grateful to Luc De Raedt for his guidance as well as for providing the facilities to complete this project. We are thankful to Liesbeth Allein and her supervisors for inspiring us to use the die/dat task. We are also grateful to BIBREF27, BIBREF28, BIBREF29, BIBREF23 for their software packages." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0459/instruction.md b/qasper-0459/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..e00f3d73d0cbfb577d17b7deba7270f0e81029af --- /dev/null +++ b/qasper-0459/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: RobBERT: a Dutch RoBERTa-based Language Model + +Question: What language tasks did they experiment on? \ No newline at end of file diff --git a/qasper-0460/instruction.md b/qasper-0460/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..9e5aa3a0544449b2ec2cda18c551d79b303fbc73 --- /dev/null +++ b/qasper-0460/instruction.md @@ -0,0 +1,100 @@ +Name of Paper: Natural Language State Representation for Reinforcement Learning + +Question: What result from experiments suggest that natural language based agents are more robust? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Preliminaries ::: Reinforcement Learning", + "Preliminaries ::: Deep Learning for NLP", + "Semantic Representation Methods", + "Semantic State Representations in the Doom Environment", + "Semantic State Representations in the Doom Environment ::: Experiments", + "Related Work", + "Discussion and Future Work", + "Appendix ::: VizDoom", + "Appendix ::: Natural language State Space", + "Appendix ::: Language model implementation", + "Appendix ::: Model implementation" + ], + "paragraphs": [ + [ + "\u201cThe world of our experiences must be enormously simplified and generalized before it is possible to make a symbolic inventory of all our experiences of things and relations.\"", + "(Edward Sapir, Language: An Introduction to the Study of Speech, 1921)", + "Deep Learning based algorithms use neural networks in order to learn feature representations that are good for solving high dimensional Machine Learning (ML) tasks. Reinforcement Learning (RL) is a subfield of ML that has been greatly affected by the use of deep neural networks as universal function approximators BIBREF0, BIBREF1. These deep neural networks are used in RL to estimate value functions, state-action value functions, policy mappings, next-state predictions, rewards, and more BIBREF2, BIBREF3, BIBREF4, thus combating the \u201ccurse of dimensionality\".", + "The term representation is used differently in different contexts. For the purpose of this paper we define a semantic representation of a state as one that reflects its meaning as it is understood by an expert. The semantic representation of a state should thus be paired with a reliable and computationally efficient method for extracting information from it. Previous success in RL has mainly focused on representing the state in its raw form (e.g., visual input in Atari-based games BIBREF2). This approach stems from the belief that neural networks (specifically convolutional networks) can extract meaningful features from complex inputs. In this work, we challenge current representation techniques and suggest to represent the state using natural language, similar to the way we, as humans, summarize and transfer information efficiently from one to the other BIBREF5.", + "The ability to associate states with natural language sentences that describe them is a hallmark of understanding representations for reinforcement learning. Humans use rich natural language to describe and communicate their visual perceptions, feelings, beliefs, strategies, and more. The semantics inherent to natural language carry knowledge and cues of complex types of content, including: events, spatial relations, temporal relations, semantic roles, logical structures, support for inference and entailment, as well as predicates and arguments BIBREF6. The expressive nature of language can thus act as an alternative semantic state representation.", + "Over the past few years, Natural Language Processing (NLP) has shown an acceleration in progress on a wide range of downstream applications ranging from Question Answering BIBREF7, BIBREF8, to Natural Language Inference BIBREF9, BIBREF10, BIBREF11 through Syntactic Parsing BIBREF12, BIBREF13, BIBREF14. Recent work has shown the ability to learn flexible, hierarchical, contextualized representations, obtaining state-of-the-art results on various natural language processing tasks BIBREF15. A basic observation of our work is that natural language representations are also beneficial for solving problems in which natural language is not the underlying source of input. Moreover, our results indicate that natural language is a strong alternative to current complementary methods for semantic representations of a state.", + "In this work we assume a state can be described using natural language sentences. We use distributional embedding methods in order to represent sentences, processed with a standard Convolutional Neural Network for feature extraction. In Section SECREF2 we describe the basic frameworks we rely on. We discuss possible semantic representations in Section SECREF3, namely, raw visual inputs, semantic segmentation, feature vectors, and natural language representations. Then, in Section SECREF4 we compare NLP representations with their alternatives. Our results suggest that representation of the state using natural language can achieve better performance, even on difficult tasks, or tasks in which the description of the state is saturated with task-nuisances BIBREF17. Moreover, we observe that NLP representations are more robust to transfer and changes in the environment. We conclude the paper with a short discussion and related work." + ], + [ + "In Reinforcement Learning the goal is to learn a policy $\\pi (s)$, which is a mapping from state $s$ to a probability distribution over actions $\\mathcal {A}$, with the objective to maximize a reward $r(s)$ that is provided by the environment. This is often solved by formulating the problem as a Markov Decision Process (MDP) BIBREF19. Two common quantities used to estimate the performance in MDPs are the value $v (s)$ and action-value $Q (s, a)$ functions, which are defined as follows: ${v(s) = \\mathbb {E}^{\\pi } [\\sum _t \\gamma ^t r_t | s_0 = s ]}$ and ${Q(s, a) = \\mathbb {E}^{\\pi } [\\sum _t \\gamma ^t r_t | s_0 = s, a_0 = a ]}$. Two prominent algorithms for solving RL tasks, which we use in this paper, are the value-based DQN BIBREF2 and the policy-based PPO BIBREF3.", + "Deep Q Networks (DQN): The DQN algorithm is an extension of the classical Q-learning approach, to a deep learning regime. Q-learning learns the optimal policy by directly learning the value function, i.e., the action-value function. A neural network is used to estimate the $Q$-values and is trained to minimize the Bellman error, namely", + "Proximal Policy Optimization (PPO): While the DQN learns the optimal behavioral policy using a dynamic programming approach, PPO takes a different route. PPO builds upon the policy gradient theorem, which optimizes the policy directly, with an addition of a trust-region update rule. The policy gradient theorem updates the policy by" + ], + [ + "A word embedding is a mapping from a word $w$ to a vector $\\mathbf {w} \\in \\mathbb {R}^d$. A simple form of word embedding is the Bag of Words (BoW), a vector $\\mathbf {w} \\in \\mathbb {N}^{|D|}$ ($|D|$ is the dictionary size), in which each word receives a unique 1-hot vector representation. Recently, more efficient methods have been proposed, in which the embedding vector is smaller than the dictionary size, $d \\ll |D|$. These methods are also known as distributional embeddings.", + "The distributional hypothesis in linguistics is derived from the semantic theory of language usage (i.e. words that are used and occur in the same contexts tend to have similar meanings). Distributional word representations are a fundamental building block for representing natural language sentences. Word embeddings such as Word2vec BIBREF20 and GloVe BIBREF21 build upon the distributional hypothesis, improving efficiency of state-of-the-art language models.", + "Convolutional Neural Networks (CNNs), originally invented for computer vision, have been shown to achieve strong performance on text classification tasks BIBREF22, BIBREF23, as well as other traditional NLP tasks BIBREF24. In this paper we consider a common architecture BIBREF25, in which each word in a sentence is represented as an embedding vector, a single convolutional layer with $m$ filters is applied, producing an $m$-dimensional vector for each $n$-gram. The vectors are combined using max-pooling followed by a ReLU activation. The result is then passed through multiple hidden linear layers with ReLU activation, eventually generating the final output." + ], + [ + "Contemporary methods for semantic representation of states currently follow one of three approaches: (1) raw visual inputs BIBREF2, BIBREF26, in which raw sensory values of pixels are used from one or multiple sources, (2) feature vectors BIBREF27, BIBREF28, in which general features of the problem are chosen, with no specific structure, and (3) semantic segmentation maps BIBREF29, BIBREF30, in which discrete or logical values are used in one or many channels to represent the general features of the state.", + "The common approach is to derive decisions (e.g., classification, action, etc.) based on information in its raw form. In RL, the raw form is often the pixels representing an image \u2013 however the image is only one form of a semantic representation. In Semantic Segmentation, the image is converted from a 3-channel (RGB) matrix into an $N$-channel matrix, where $N$ is the number of classes. In this case, each channel represents a class, and a binary value at each coordinate denotes whether or not this class is present in the image at this location. For instance, fig: semantic segmentation example considers an autonomous vehicle task. The raw image and segmentation maps are both sufficient for the task (i.e., both contain a sufficient semantic representation). Nevertheless, the semantic segmentation maps contain less task-nuisances BIBREF17, which are random variables that affect the observed data, but are not informative to the task we are trying to solve.", + "In this paper we propose a forth method for representing a state, namely using natural language descriptions. One method to achieve such a representation is through Image Captioning BIBREF31, BIBREF32. Natural language is both rich as well as flexible. This flexibility enables the algorithm designer to represent the information present in the state as efficiently and compactly as possible. As an example, the top image in fig: semantic segmentation example can be represented using natural language as \u201cThere is a car in your lane two meters in front of you, a bicycle rider on your far left in the negative lane, a car in your direction in the opposite lane which is twenty meters away, and trees and pedestrians on the side walk.\u201d or compactly by \u201cThere is a car two meters in front of you a pedestrian on the sidewalk to your right and a car inbound in the negative lane which is far away.\u201d. Language also allows us to efficiently compress information. As an example, the segmentation map in the bottom image of fig: semantic segmentation example can be compactly described by \u201cThere are 13 pedestrians crossing the road in front of you\u201d. In the next section we will demonstrate the benefits of using natural-language-based semantic state representation in a first person shooter enviornment." + ], + [ + "In this section we compare the different types of semantic representations for representing states in the ViZDoom environment BIBREF26, as described in the previous section. More specifically, we use a semantic natural language parser in order to describe a state, over numerous instances of levels varying in difficulty, task-nuisances, and objectives. Our results show that, though semantic segmentation and feature vector representation techniques express a similar statistic of the state, natural language representation offers better performance, faster convergence, more robust solutions, as well as better transfer.", + "The ViZDoom environment involves a 3D world that is significantly more real-world-like than Atari 2600 games, with a relatively realistic physics model. An agent in the ViZDoom environment must effectively perceive, interpret, and learn the 3D world in order to make tactical and strategic decisions of where to go and how to act. There are three types of state representations that are provided by the environment. The first, which is also most commonly used, is raw visual inputs, in which the state is represented by an image from a first person view of the agent. A feature vector representation is an additional state representation provided by the environment. The feature vector representation includes positions as well as labels of all objects and creatures in the vicinity of the agent. Lastly, the environment provides a semantic segmentation map based on the aforementioned feature vector. An example of the visual representations in VizDoom is shown in fig: representations in vizdoom.", + "In order to incorporate natural language representation to the VizDoom environment we've constructed a semantic parser of the semantic segmentation maps provided by the environment. Each state of the environment was converted into a natural language sentence based on positions and labels of objects in the frame. To implement this, the screen was divided into several vertical and horizontal patches, as depicted in fig: patches. These patches describe relational aspects of the state, such as distance of objects and their direction with respect to the agent's point of view. In each patch, objects were counted, and a natural language description of the patch was constructed. This technique was repeated for all patches to form the final state representation. fig: nlp state rep depicts examples of natural language sentences of different states in the enviornment." + ], + [ + "We tested the natural language representation against the visual-based and feature representations on several tasks, with varying difficulty. In these tasks, the agent could navigate, shoot, and collect items such as weapons and medipacks. Often, enemies of different types attacked the agent, and a positive reward was given when an enemy was killed. Occasionally, the agent also suffered from health degeneration. The tasks included a basic scenario, a health gathering scenario, a scenario in which the agent must take cover from fireballs, a scenario in which the agent must defend itself from charging enemies, and a super scenario, where a mixture of the above scenarios was designed to challenge the agent.", + "More specifically, in the basic scenario, a single monster is spawned in front of the agent. The purpose of this scenario is to teach the agent to aim at the enemy and shoot at it. In the health gathering scenario, the floor of the room is covered in toxin, causing the agent to gradually lose health. Medipacks are spawned randomly in the room and the agent's objective is to keep itself alive by collecting them. In the take cover scenario, multiple fireball shooting monsters are spawned in front of the agent. The goal of the agent is to stay alive as long as possible, dodging inbound fireballs. The difficulty of the task increases over time, as additional monsters are spawned. In the defend the center scenario, melee attacking monsters are randomly spawned in the room, and charge towards the agent. As opposed to other scenarios, the agent is incapable of moving, aside from turning left and right and shooting. In the defend the line scenario, both melee and fireball shooting monsters are spawned near the opposing wall. The agent can only step right, left or shoot. Finally, in the \u201csuper\" scenario both melee and fireball shooting monsters are repeatably spawned all over the room. the room contains various items the agent can pick up and use, such as medipacks, shotguns, ammunition and armor. Furthermore, the room is filled with unusable objects, various types of trees, pillars and other decorations. The agent can freely move and turn in any direction, as well as shoot. This scenario combines elements from all of the previous scenarios.", + "Our agent was implemented using a Convolutional Neural Network as described in Section SECREF4. We converted the parsed state into embedded representations of fixed length. We tested both a DQN and a PPO based agent, and compared the natural language representation to the other representation techniques, namely the raw image, feature vector, and semantic segmentation representations.", + "In order to effectively compare the performance of the different representation methods, we conducted our experiments under similar conditions for all agents. The same hyper-parameters were used under all tested representations. Moreover, to rule out effects of architectural expressiveness, the number of weights in all neural networks was approximately matched, regardless of the input type. Finally, we ensured the \u201csuper\" scenario was positively biased toward image-based representations. This was done by adding a large amount items to the game level, thereby filling the state with nuisances (these tests are denoted by `nuisance' in the scenario name). This was especially evident in the NLP representations, as sentences became extensively longer (average of over 250 words). This is contrary to image-based representations, which did not change in dimension.", + "Results of the DQN-based agent are presented in fig: scenario comparison. Each plot depicts the average reward (across 5 seeds) of all representations methods. It can be seen that the NLP representation outperforms the other methods. This is contrary to the fact that it contains the same information as the semantic segmentation maps. More interestingly, comparing the vision-based and feature-based representations render inconsistent conclusions with respect to their relative performance. NLP representations remain robust to changes in the environment as well as task-nuisances in the state. As depicted in fig: nuisance scenarios, inflating the state space with task-nuisances impairs the performance of all representations. There, a large amount of unnecessary objects were spawned in the level, increasing the state's description length to over 250 words, whilst retaining the same amount of useful information. Nevertheless, the NLP representation outperformed the vision and feature based representations, with high robustness to the applied noise.", + "In order to verify the performance of the natural language representation was not due to extensive discretization of patches, we've conducted experiments increasing the number of horizontal patches - ranging from 3 to 31 patches in the extreme case. Our results, as depicted in fig: patch count, indicate that the amount of discretization of patches did not affect the performance of the NLP agent, remaining a superior representation compared to the rest.", + "To conclude, our experiments suggest that NLP representations, though they describe the same raw information of the semantic segmentation maps, are more robust to task-nuisances, allow for better transfer, and achieve higher performance in complex tasks, even when their description is long and convoluted. While we've only presented results for DQN agents, we include plots for a PPO agent in the Appendix, showing similar trends and conclusions. We thus deduce that NLP-based semantic state representations are a preferable choice for training VizDoom agents." + ], + [ + "Work on representation learning is concerned with finding an appropriate representation of data in order to perform a machine learning task BIBREF33. In particular, deep learning exploits this concept by its very nature BIBREF2. Work on representation learning include Predictive State Representations (PSR) BIBREF34, BIBREF35, which capture the state as a vector of predictions of future outcomes, and a Heuristic Embedding of Markov Processes (HEMP) BIBREF36, which learns to embed transition probabilities using an energy-based optimization problem.", + "There has been extensive work attempting to use natural language in RL. Efforts that integrate language in RL develop tools, approaches, and insights that are valuable for improving the generalization and sample efficiency of learning agents. Previous work on language-conditioned RL has considered the use of natural language in the observation and action space. Environments such as Zork and TextWorld BIBREF37 have been the standard benchmarks for testing text-based games. Nevertheless, these environments do not search for semantic state representations, in which an RL algorithm can be better evaluated and controlled.", + "BIBREF38 use high-level semantic abstractions of documents in a representation to facilitate relational learning using Inductive Logic Programming and a generative language model. BIBREF39 use high-level guidance expressed in text to enrich a stochastic agent, playing against the built-in AI of Civilization II. They train an agent with the Monte-Carlo search framework in order to jointly learn to identify text that is relevant to a given game state as well as game strategies based only on environment feedback. BIBREF40 utilize natural language in a model-based approach to describe the dynamics and rewards of an environment, showing these can facilitate transfer between different domains.", + "More recently, the structure and compositionality of natural language has been used for representing policies in hierarchical RL. In a paper by BIBREF41, instructions given in natural language were used in order to break down complex problems into high-level plans and lower-level actions. Their suggested framework leverages the structure inherent to natural language, allowing for transfer to unfamiliar tasks and situations. This use of semantic structure has also been leveraged by BIBREF42, where abstract actions (not necessarily words) were recognized as symbols of a natural and expressive language, improving performance and transfer of RL agents.", + "Outside the context of RL, previous work has also shown that high-quality linguistic representations can assist in cross-modal transfer, such as using semantic relationships between labels for zero-shot transfer in image classification BIBREF43, BIBREF44." + ], + [ + "Our results indicate that natural language can outperform, and sometime even replace, vision-based representations. Nevertheless, natural language representations can also have disadvantages in various scenarios. For one, they require the designer to be able to describe the state exactly, whether by a rule-based or learned parser. Second, they abstract notions of the state space that the designer may not realize are necessary for solving the problem. As such, semantic representations should be carefully chosen, similar to the process of reward shaping or choosing a training algorithm. Here, we enumerate three instances in which we believe natural language representations are beneficial:", + "Natural use-case: Information contained in both generic and task-specific textual corpora may be highly valuable for decision making. This case assumes the state can either be easily described using natural language or is already in a natural language state. This includes examples such as user-based domains, in which user profiles and comments are part of the state, or the stock market, in which stocks are described by analysts and other readily available text. 3D physical environments such as VizDoom also fall into this category, as semantic segmentation maps can be easily described using natural language.", + "Subjective information: Subjectivity refers to aspects used to express opinions, evaluations, and speculations. These may include strategies for a game, the way a doctor feels about her patient, the mood of a driver, and more.", + "Unstructured information: In these cases, features might be measured by different units, with an arbitrary position in the state's feature vector, rendering them sensitive to permutations. Such state representations are thus hard to process using neural networks. As an example, the medical domain may contain numerous features describing the vitals of a patient. These raw features, when observed by an expert, can be efficiently described using natural language. Moreover, they allow an expert to efficiently add subjective information.", + "An orthogonal line of research considers automating the process of image annotation. The noise added from the supervised or unsupervised process serves as a great challenge for natural language representation. We suspect the noise accumulated by this procedure would require additional information to be added to the state (e.g., past information). Nevertheless, as we have shown in this paper, such information can be compressed using natural language. In addition, while we have only considered spatial features of the state, information such as movement directions and transient features can be efficiently encoded as well.", + "Natural language representations help abstract information and interpret the state of an agent, improving its overall performance. Nevertheless, it is imperative to choose a representation that best fits the domain at hand. Designers of RL algorithms should consider searching for a semantic representation that fits their needs. While this work only takes a first step toward finding better semantic state representations, we believe the structure inherent in natural language can be considered a favorable candidate for achieving this goal." + ], + [ + "VizDoom is a \"Doom\" based research environment that was developed at the Pozna\u0144 University of Technology. It is based on \"ZDoom\" game executable, and includes a Python based API. The API offers the user the ability to run game instances, query the game state, and execute actions. The original purpose of VizDoom is to provide a research platform for vision based reinforcement learning. Thus, a natural language representation for the game was needed to be implemented. ViZDoom emulates the \"Doom\" game and enables us to access data within a certain frame using Python dictionaries. This makes it possible to extract valuable data including player health, ammo, enemy locations etc. Each game frame contains \"labels\", which contain data on visible objects in the game (the player, enemies, medkits, etc). We used \"Doom Builder\" in order to edit some of the scenarios and design a new one. Enviroment rewards are presented in doom-scenarios-table." + ], + [ + "A semantic representation using natural language should contain information which can be deduced by a human playing the game. For example, even though a human does not know the exact distance between objects, it can classify them as \"close\" or \"far\". However, objects that are outside the player's field of vision can not be a part of the state. Furthermore, a human would most likely refer to an object's location relative to itself, using directions such as \"right\" or \"left\"." + ], + [ + "To convert each frame to a natural language representation state, the list of available labels is iterated, and a string is built accordingly. The main idea of our implementation is to divide the screen into multiple vertical patches, count the amount of different objects inside by their types, and parse it as a sentence. The decision as to whether an object is close or far can be determined by calculating the distance from it to the player, and using two threshold levels. Object descriptions can be concise or detailed, as needed. We experimented with the following mechanics:", + "the screen can be divided between patches equally, or by determined ratios. Here, our main guideline was to keep the \"front\" patch narrow enough so it can be used as \"sights\".", + "our initial experiment was with 3 patches, and later we added 2 more patches classified as \"outer left\" and \"outer right\". In our experiments we have tested up to 51 patches, referred to as left or right patch with corresponding numbers.", + "we used 2 thresholds, which allowed us to classify the distance of an object from the player as \"close\",\"mid\", and \"far. Depending on the task, the value of the threshold can be changed, as well as adding more thresholds.", + "different states might generate sentence with different size. A maximum sentence length is another parameter that was tested. sentences-length-table presents some data regarding the average word count in some of the game sceanrios.", + "After the sentence describing the state is generated, it is transformed to an embedding vector. Words that were not found in the vocabulary were replaced with an \u201cOOV\" vector. All words were then concatenated to a NxDx1 matrix, representing the state. We experimented with both Word2Vec and GloVe pretrained embedding vectors. Eventually, we used the latter, as it consumes less memory and speeds up the training process. The length of the state sentence is one of the hyperparameters of the agents; shorter sentences are zero padded, where longer ones are trimmed." + ], + [ + "All of our models were implemented using PyTorch. The DQN agents used a single network that outputs the Q-Values of the available actions. The PPO agents used an Actor-Critic model with two networks; the first outputs the policy distribution for the input state, and the second network outputs it's value. As mentioned earlier, we used three common neural network architectures:", + "used for the raw image and semantic segmentation based agents. VizDoom's raw output image resolution is 640X480X3 RGB image. We experimented with both the original image and its down-sampled version. The semantic segmentation image was of resolution 640X480X1, where the pixel value represents the object's class, generated using the VizDoom label API. the network consisted of two convolutional layers, two hidden linear layers and an output layer. The first convolutional layer has 8 6X6 filters with stride 3 and ReLU activation. The second convolutional layer has 16 3X3 filters with stride 2 and ReLU activation. The fully connected layers has 32 and 16 units, both of them are followed by ReLU activation. The output layer's size is the amount of actions the agent has available in the trained scenario.", + "Used in the feature vector based agent. Naturally, some discretization is needed in order to build a feature vector, so some of the state data is lost. the feature vector was made using features we extracted from the VizDoom API, and its dimensions was 90 X 1. The network is made up of two fully connected layers, each of them followed by a ReLU activation. The first layer has 32 units, and the second one one has 16 units. The output layer's size was the amount of actions available to the agent.", + "Used in the natural language based agent. As previously mentioned, each word in the natural language state is transformed into a 200X50X1 matrix. The first layers of the TextCNN are convolutional layers with 8 filter which are designed to scan input sentence, and return convolution outputs of sequences of varying lengths. The filters vary in width, such that each of them learns to identify different lengths of sequences in words. Longer filters have higher capability of extracting features from longer word sequences. The filters we have chosen have the following dimensions: 3X50X1, 4X50X1, 5X50X1, 8X50X1,11X50X1. Following the convolution layer there is a ReLU activation and a max pool layer. Finally, there are two fully connected layers; The first layer has 32 units, and second one has 16 units. Both of them are followed by ReLU activation.", + "All architectures have the same output, regardless of the input type. The DQN network is a regression network, with its output size the number of available actions. The PPO agent has 2 networks; actor and critic. The actor network has a Softmax activation with size equal to the available amount of actions. The critic network is a regression model with a single output representing the state's value. Reward plots for the PPO agent can be found in Figure FIGREF47." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0461/instruction.md b/qasper-0461/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..49a6eb1da2e93f5b23f43c0b51c98719cec47d55 --- /dev/null +++ b/qasper-0461/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Natural Language State Representation for Reinforcement Learning + +Question: How better is performance of natural language based agents in experiments? \ No newline at end of file diff --git a/qasper-0466/instruction.md b/qasper-0466/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..842db8d973ef7d87c1273c2c9a18a502d48f977a --- /dev/null +++ b/qasper-0466/instruction.md @@ -0,0 +1,113 @@ +Name of Paper: Text-based inference of moral sentiment change + +Question: Does the paper discuss previous models which have been applied to the same task? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Moral sentiment change and language", + "Emerging NLP research on morality", + "A three-tier modelling framework", + "A three-tier modelling framework ::: Lexical data for moral sentiment", + "A three-tier modelling framework ::: Models", + "Historical corpus data", + "Model evaluations", + "Model evaluations ::: Moral sentiment inference of seed words", + "Model evaluations ::: Alignment with human valence ratings", + "Applications to diachronic morality", + "Applications to diachronic morality ::: Moral change in individual concepts ::: Historical time courses.", + "Applications to diachronic morality ::: Moral change in individual concepts ::: Prediction of human judgments.", + "Applications to diachronic morality ::: Retrieval of morally changing concepts", + "Applications to diachronic morality ::: Broad-scale investigation of moral change", + "Discussion and conclusion", + "Acknowledgments" + ], + "paragraphs": [ + [ + "People's moral sentiment\u2014our feelings toward right or wrong\u2014can change over time. For instance, the public's views toward slavery have shifted substantially over the past centuries BIBREF0. How society's moral views evolve has been a long-standing issue and a constant source of controversy subject to interpretations from social scientists, historians, philosophers, among others. Here we ask whether natural language processing has the potential to inform moral sentiment change in society at scale, involving minimal human labour or intervention.", + "The topic of moral sentiment has been thus far considered a traditional inquiry in philosophy BIBREF1, BIBREF2, BIBREF3, with contemporary development of this topic represented in social psychology BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, cognitive linguistics BIBREF9, and more recently, the advent of Moral Foundations Theory BIBREF10, BIBREF11, BIBREF12. Despite the fundamental importance and interdisciplinarity of this topic, large-scale formal treatment of moral sentiment, particularly its evolution, is still in infancy from the natural language processing (NLP) community (see overview in Section SECREF2).", + "We believe that there is a tremendous potential to bring NLP methodologies to bear on the problem of moral sentiment change. We build on extensive recent work showing that word embeddings reveal implicit human biases BIBREF13, BIBREF14 and social stereotypes BIBREF15. Differing from this existing work, we demonstrate that moral sentiment change can be revealed by moral biases implicitly learned from diachronic text corpora. Accordingly, we present to our knowledge the first text-based framework for probing moral sentiment change at a large scale with support for different levels of analysis concerning moral relevance, moral polarity, and fine-grained moral dimensions. As such, for any query item such as slavery, our goal is to automatically infer its moral trajectories from sentiments at each of these levels over a long period of time.", + "Our approach is based on the premise that people's moral sentiments are reflected in natural language, and more specifically, in text BIBREF16. In particular, we know that books are highly effective tools for conveying moral views to the public. For example, Uncle Tom's Cabin BIBREF17 was central to the anti-slavery movement in the United States. The framework that we develop builds on this premise to explore changes in moral sentiment reflected in longitudinal or historical text.", + "Figure FIGREF1 offers a preview of our framework by visualizing the evolution trajectories of the public's moral sentiment toward concepts signified by the probe words slavery, democracy, and gay. Each of these concepts illustrates a piece of \u201cmoral history\u201d tracked through a period of 200 years (1800 to 2000), and our framework is able to capture nuanced moral changes. For instance, slavery initially lies at the border of moral virtue (positive sentiment) and vice (negative sentiment) in the 1800s yet gradually moves toward the center of moral vice over the 200-year period; in contrast, democracy considered morally negative (e.g., subversion and anti-authority under monarchy) in the 1800s is now perceived as morally positive, as a mechanism for fairness; gay, which came to denote homosexuality only in the 1930s BIBREF18, is inferred to be morally irrelevant until the modern day. We will describe systematic evaluations and applications of our framework that extend beyond these anecdotal cases of moral sentiment change.", + "The general text-based framework that we propose consists of a parameter-free approach that facilitates the prediction of public moral sentiment toward individual concepts, automated retrieval of morally changing concepts, and broad-scale psycholinguistic analyses of historical rates of moral sentiment change. We provide a description of the probabilistic models and data used, followed by comprehensive evaluations of our methodology." + ], + [ + "An emerging body of work in natural language processing and computational social science has investigated how NLP systems can detect moral sentiment in online text. For example, moral rhetoric in social media and political discourse BIBREF19, BIBREF20, BIBREF21, the relation between moralization in social media and violent protests BIBREF22, and bias toward refugees in talk radio shows BIBREF23 have been some of the topics explored in this line of inquiry. In contrast to this line of research, the development of a formal framework for moral sentiment change is still under-explored, with no existing systematic and formal treatment of this topic BIBREF16.", + "While there is emerging awareness of ethical issues in NLP BIBREF24, BIBREF25, work exploiting NLP techniques to study principles of moral sentiment change is scarce. Moreover, since morality is variable across cultures and time BIBREF12, BIBREF16, developing systems that capture the diachronic nature of moral sentiment will be a pivotal research direction. Our work leverages and complements existing research that finds implicit human biases from word embeddings BIBREF13, BIBREF14, BIBREF19 by developing a novel perspective on using NLP methodology to discover principles of moral sentiment change in human society." + ], + [ + "Our framework treats the moral sentiment toward a concept at three incremental levels, as illustrated in Figure FIGREF3. First, we consider moral relevance, distinguishing between morally irrelevant and morally relevant concepts. At the second tier, moral polarity, we further split morally relevant concepts into those that are positively or negatively perceived in the moral domain. Finally, a third tier classifies these concepts into fine-grained categories of human morality.", + "We draw from research in social psychology to inform our methodology, most prominently Moral Foundations Theory BIBREF26. MFT seeks to explain the structure and variation of human morality across cultures, and proposes five moral foundations: Care / Harm, Fairness / Cheating, Loyalty / Betrayal, Authority / Subversion, and Sanctity / Degradation. Each foundation is summarized by a positive and a negative pole, resulting in ten fine-grained moral categories." + ], + [ + "To ground moral sentiment in text, we leverage the Moral Foundations Dictionary BIBREF27. The MFD is a psycholinguistic resource that associates each MFT category with a set of seed words, which are words that provide evidence for the corresponding moral category in text. We use the MFD for moral polarity classification by dividing seed words into positive and negative sets, and for fine-grained categorization by splitting them into the 10 MFT categories.", + "To implement the first tier of our framework and detect moral relevance, we complement our morally relevant seed words with a corresponding set of seed words approximating moral irrelevance based on the notion of valence, i.e., the degree of pleasantness or unpleasantness of a stimulus. We refer to the emotional valence ratings collected by BIBREF28 for approximately 14,000 English words, and choose the words with most neutral valence rating that do not occur in the MFD as our set of morally irrelevant seed words, for an equal total number of morally relevant and morally irrelevant words." + ], + [ + "We propose and evaluate a set of probabilistic models to classify concepts in the three tiers of morality specified above. Our models exploit the semantic structure of word embeddings BIBREF29 to perform tiered moral classification of query concepts. In each tier, the model receives a query word embedding vector $\\mathbf {q}$ and a set of seed words for each class in that tier, and infers the posterior probabilities over the set of classes $c$ to which the query concept is associated with.", + "The seed words function as \u201clabelled examples\u201d that guide the moral classification of novel concepts, and are organized per classification tier as follows. In moral relevance classification, sets $\\mathbf {S}_0$ and $\\mathbf {S}_1$ contain the morally irrelevant and morally relevant seed words, respectively; for moral polarity, $\\mathbf {S}_+$ and $\\mathbf {S}_-$ contain the positive and negative seed words; and for fine-grained moral categories, $\\mathbf {S}_1, \\ldots , \\mathbf {S}_{10}$ contain the seed words for the 10 categories of MFT. Then our general problem is to estimate $p(c\\,|\\,\\mathbf {q})$, where $\\mathbf {q}$ is a query vector and $c$ is a moral category in the desired tier.", + "We evaluate the following four models:", + "A Centroid model summarizes each set of seed words by its expected vector in embedding space, and classifies concepts into the class of closest expected embedding in Euclidean distance following a softmax rule;", + "A Na\u00efve Bayes model considers both mean and variance, under the assumption of independence among embedding dimensions, by fitting a normal distribution with mean vector and diagonal covariance matrix to the set of seed words of each class;", + "A $k$-Nearest Neighbors ($k$NN) model exploits local density estimation and classifies concepts according to the majority vote of the $k$ seed words closest to the query vector;", + "A Kernel Density Estimation (KDE) model performs density estimation at a broader scale by considering the contribution of each seed word toward the total likelihood of each class, regulated by a bandwidth parameter $h$ that controls the sensitivity of the model to distance in embedding space.", + "Table TABREF2 specifies the formulation of each model. Note that we adopt a parsimonious design principle in our modelling: both Centroid and Na\u00efve Bayes are parameter-free models, $k$NN only depends on the choice of $k$, and KDE uses a single bandwidth parameter $h$." + ], + [ + "To apply our models diachronically, we require a word embedding space that captures the meanings of words at different points in time and reflects changes pertaining to a particular word as diachronic shifts in a common embedding space.", + "Following BIBREF30, we combine skip-gram word embeddings BIBREF29 trained on longitudinal corpora of English with rotational alignments of embedding spaces to obtain diachronic word embeddings that are aligned through time.", + "We divide historical time into decade-long bins, and use two sets of embeddings provided by BIBREF30, each trained on a different historical corpus of English:", + "Google N-grams BIBREF31: a corpus of $8.5 \\times 10^{11}$ tokens collected from the English literature (Google Books, all-genres) spanning the period 1800\u20131999.", + "COHA BIBREF32: a smaller corpus of $4.1 \\times 10^8$ tokens from works selected so as to be genre-balanced and representative of American English in the period 1810\u20132009." + ], + [ + "We evaluated our models in two ways: classification of moral seed words on all three tiers (moral relevance, polarity, and fine-grained categories), and correlation of model predictions with human judgments." + ], + [ + "In this evaluation, we assessed the ability of our models to classify the seed words that compose our moral environment in a leave-one-out classification task. We performed the evaluation for all three classification tiers: 1) moral relevance, where seed words are split into morally relevant and morally irrelevant; 2) moral polarity, where moral seed words are split into positive and negative; 3) fine-grained categories, where moral seed words are split into the 10 MFT categories. In each test, we removed one seed word from the training set at a time to obtain cross-validated model predictions.", + "Table TABREF14 shows classification accuracy for all models and corpora on each tier for the 1990\u20131999 period. We observe that all models perform substantially better than chance, confirming the efficacy of our methodology in capturing moral dimensions of words. We also observe that models using word embeddings trained on Google N-grams perform better than those trained on COHA, which could be expected given the larger corpus size of the former.", + "In the remaining analyses, we employ the Centroid model, which offers competitive accuracy and a simple, parameter-free specification." + ], + [ + "We evaluated the approximate agreement between our methodology and human judgments using valence ratings, i.e., the degree of pleasantness or unpleasantness of a stimulus. Our assumption is that the valence of a concept should correlate with its perceived moral polarity, e.g., morally repulsive ideas should evoke an unpleasant feeling. However, we do not expect this correspondence to be perfect; for example, the concept of dessert evokes a pleasant reaction without being morally relevant.", + "In this analysis, we took the valence ratings for the nearly 14,000 English nouns collected by BIBREF28 and, for each query word $q$, we generated a corresponding prediction of positive moral polarity from our model, $P(c_+\\,|\\,\\mathbf {q})$. Table TABREF16 shows the correlations between human valence ratings and predictions of positive moral polarity generated by models trained on each of our corpora. We observe that the correlations are significant, suggesting the ability of our methodology to capture relevant features of moral sentiment from text.", + "In the remaining applications, we use the diachronic embeddings trained on the Google N-grams corpus, which enabled superior model performance throughout our evaluations." + ], + [ + "We applied our framework in three ways: 1) evaluation of selected concepts in historical time courses and prediction of human judgments; 2) automatic detection of moral sentiment change; and 3) broad-scale study of the relations between psycholinguistic variables and historical change of moral sentiment toward concepts." + ], + [ + "We applied our models diachronically to predict time courses of moral relevance, moral polarity, and fine-grained moral categories toward two historically relevant topics: slavery and democracy. By grounding our model in word embeddings for each decade and querying concepts at the three tiers of classification, we obtained the time courses shown in Figure FIGREF21.", + "We note that these trajectories illustrate actual historical trends. Predictions for democracy show a trend toward morally positive sentiment, consistent with the adoption of democratic regimes in Western societies. On the other hand, predictions for slavery trend down and suggest a drop around the 1860s, coinciding with the American Civil War. We also observe changes in the dominant fine-grained moral categories, such as the perception of democracy as a fair concept, suggesting potential mechanisms behind the polarity changes and providing further insight into the public sentiment toward these concepts as evidenced by text." + ], + [ + "We explored the predictive potential of our framework by comparing model predictions with human judgments of moral relevance and acceptability. We used data from the Pew Research Center's 2013 Global Attitudes survey BIBREF33, in which participants from 40 countries judged 8 topics such as abortion and homosexuality as one of \u201cacceptable\", \u201cunacceptable\", and \u201cnot a moral issue\".", + "We compared human ratings with model predictions at two tiers: for moral relevance, we paired the proportion of \u201cnot a moral issue\u201d human responses with irrelevance predictions $p(c_0\\,|\\,\\mathbf {q})$ for each topic, and for moral acceptability, we paired the proportion of \u201cacceptable\u201d responses with positive predictions $p(c_+\\,|\\,\\mathbf {q})$. We used 1990s word embeddings, and obtained predictions for two-word topics by querying the model with their averaged embeddings.", + "Figure FIGREF23 shows plots of relevance and polarity predictions against survey proportions, and we observe a visible correspondence between model predictions and human judgments despite the difficulty of this task and limited number of topics." + ], + [ + "Beyond analyzing selected concepts, we applied our framework predictively on a large repertoire of words to automatically discover the concepts that have exhibited the greatest change in moral sentiment at two tiers, moral relevance and moral polarity.", + "We selected the 10,000 nouns with highest total frequency in the 1800\u20131999 period according to data from BIBREF30, restricted to words labelled as nouns in WordNet BIBREF34 for validation. For each such word $\\mathbf {q}$, we computed diachronic moral relevance scores $R_i = p(c_1\\,|\\,\\mathbf {q}), i=1,\\ldots ,20$ for the 20 decades in our time span. Then, we performed a linear regression of $R$ on $T = 1,\\ldots ,n$ and took the fitted slope as a measure of moral relevance change. We repeated the same procedure for moral polarity. Finally, we removed words with average relevance score below $0.5$ to focus on morally relevant retrievals.", + "Table TABREF17 shows the words with steepest predicted change toward moral relevance, along with their predicted fine-grained moral categories in modern times (i.e., 1900\u20131999). Table TABREF18 shows the words with steepest predicted change toward the positive and negative moral poles. To further investigate the moral sentiment that may have led to such polarity shifts, we also show the predicted fine-grained moral categories of each word at its earliest time of predicted moral relevance and in modern times. Although we do not have access to ground truth for this application, these results offer initial insight into the historical moral landscape of the English language at scale." + ], + [ + "In this application, we investigated the hypothesis that concept concreteness is inversely related to change in moral relevance, i.e., that concepts considered more abstract might become morally relevant at a higher rate than concepts considered more concrete. To test this hypothesis, we performed a multiple linear regression analysis on rate of change toward moral relevance of a large repertoire of words against concept concreteness ratings, word frequency BIBREF35, and word length BIBREF36.", + "We obtained norms of concreteness ratings from BIBREF28. We collected the same set of high-frequency nouns as in the previous analysis, along with their fitted slopes of moral relevance change. Since we were interested in moral relevance change within this large set of words, we restricted our analysis to those words whose model predictions indicate change in moral relevance, in either direction, from the 1800s to the 1990s.", + "We performed a multiple linear regression under the following model:", + "Here $\\rho (w)$ is the slope of moral relevance change for word $w$; $f(w$) is its average frequency; $l(w)$ is its character length; $c(w)$ is its concreteness rating; $\\beta _f$, $\\beta _l$, $\\beta _c$, and $\\beta _0$ are the corresponding factor weights and intercept, respectively; and $\\epsilon \\sim \\mathcal {N}(0, \\sigma )$ is the regression error term.", + "Table TABREF27 shows the results of multiple linear regression. We observe that concreteness is a significant negative predictor of change toward moral relevance, suggesting that abstract concepts are more strongly associated with increasing moral relevance over time than concrete concepts. This significance persists under partial correlation test against the control factors ($p < 0.01$).", + "We further verified the diachronic component of this effect in a random permutation analysis. We generated 1,000 control time courses by randomly shuffling the 20 decades in our data, and repeated the regression analysis to obtain a control distribution for each regression coefficient. All effects became non-significant under the shuffled condition, suggesting the relevance of concept concreteness for diachronic change in moral sentiment (see Supplementary Material)." + ], + [ + "We presented a text-based framework for exploring the socio-scientific problem of moral sentiment change. Our methodology uses minimal parameters and exploits implicit moral biases learned from diachronic word embeddings to reveal the public's moral perception toward a large concept repertoire over a long historical period.", + "Differing from existing work in NLP that treats moral sentiment as a flat classification problem BIBREF19, BIBREF20, our framework probes moral sentiment change at multiple levels and captures moral dynamics concerning relevance, polarity, and fine-grained categories informed by Moral Foundations Theory BIBREF12. We applied our methodology to the automated analyses of moral change both in individual concepts and at a broad scale, thus providing insights into psycholinguistic variables that associate with rates of moral change in the public.", + "Our current work focuses on exploring moral sentiment change in English-speaking cultures. Future research should evaluate the appropriateness of the framework to probing moral change from a diverse range of cultures and linguistic backgrounds, and the extent to which moral sentiment change interacts and crisscrosses with linguistic meaning change and lexical coinage. Our work creates opportunities for applying natural language processing toward characterizing moral sentiment change in society." + ], + [ + "We would like to thank Nina Wang, Nicola Lacerata, Dan Jurafsky, Paul Bloom, Dzmitry Bahdanau, and the Computational Linguistics Group at the University of Toronto for helpful discussion. We would also like to thank Ben Prystawski for his feedback on the manuscript. JX is supported by an NSERC USRA Fellowship and YX is funded through a SSHRC Insight Grant, an NSERC Discovery Grant, and a Connaught New Researcher Award." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0467/instruction.md b/qasper-0467/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..f4eb2f227c6ad08f02e8f691aed1e0964b0eed97 --- /dev/null +++ b/qasper-0467/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Text-based inference of moral sentiment change + +Question: Which datasets are used in the paper? \ No newline at end of file diff --git a/qasper-0469/instruction.md b/qasper-0469/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..5157238cef63f68b24d1355807c0a96fb1afb903 --- /dev/null +++ b/qasper-0469/instruction.md @@ -0,0 +1,113 @@ +Name of Paper: Text-based inference of moral sentiment change + +Question: How do they quantify moral relevance? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Moral sentiment change and language", + "Emerging NLP research on morality", + "A three-tier modelling framework", + "A three-tier modelling framework ::: Lexical data for moral sentiment", + "A three-tier modelling framework ::: Models", + "Historical corpus data", + "Model evaluations", + "Model evaluations ::: Moral sentiment inference of seed words", + "Model evaluations ::: Alignment with human valence ratings", + "Applications to diachronic morality", + "Applications to diachronic morality ::: Moral change in individual concepts ::: Historical time courses.", + "Applications to diachronic morality ::: Moral change in individual concepts ::: Prediction of human judgments.", + "Applications to diachronic morality ::: Retrieval of morally changing concepts", + "Applications to diachronic morality ::: Broad-scale investigation of moral change", + "Discussion and conclusion", + "Acknowledgments" + ], + "paragraphs": [ + [ + "People's moral sentiment\u2014our feelings toward right or wrong\u2014can change over time. For instance, the public's views toward slavery have shifted substantially over the past centuries BIBREF0. How society's moral views evolve has been a long-standing issue and a constant source of controversy subject to interpretations from social scientists, historians, philosophers, among others. Here we ask whether natural language processing has the potential to inform moral sentiment change in society at scale, involving minimal human labour or intervention.", + "The topic of moral sentiment has been thus far considered a traditional inquiry in philosophy BIBREF1, BIBREF2, BIBREF3, with contemporary development of this topic represented in social psychology BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, cognitive linguistics BIBREF9, and more recently, the advent of Moral Foundations Theory BIBREF10, BIBREF11, BIBREF12. Despite the fundamental importance and interdisciplinarity of this topic, large-scale formal treatment of moral sentiment, particularly its evolution, is still in infancy from the natural language processing (NLP) community (see overview in Section SECREF2).", + "We believe that there is a tremendous potential to bring NLP methodologies to bear on the problem of moral sentiment change. We build on extensive recent work showing that word embeddings reveal implicit human biases BIBREF13, BIBREF14 and social stereotypes BIBREF15. Differing from this existing work, we demonstrate that moral sentiment change can be revealed by moral biases implicitly learned from diachronic text corpora. Accordingly, we present to our knowledge the first text-based framework for probing moral sentiment change at a large scale with support for different levels of analysis concerning moral relevance, moral polarity, and fine-grained moral dimensions. As such, for any query item such as slavery, our goal is to automatically infer its moral trajectories from sentiments at each of these levels over a long period of time.", + "Our approach is based on the premise that people's moral sentiments are reflected in natural language, and more specifically, in text BIBREF16. In particular, we know that books are highly effective tools for conveying moral views to the public. For example, Uncle Tom's Cabin BIBREF17 was central to the anti-slavery movement in the United States. The framework that we develop builds on this premise to explore changes in moral sentiment reflected in longitudinal or historical text.", + "Figure FIGREF1 offers a preview of our framework by visualizing the evolution trajectories of the public's moral sentiment toward concepts signified by the probe words slavery, democracy, and gay. Each of these concepts illustrates a piece of \u201cmoral history\u201d tracked through a period of 200 years (1800 to 2000), and our framework is able to capture nuanced moral changes. For instance, slavery initially lies at the border of moral virtue (positive sentiment) and vice (negative sentiment) in the 1800s yet gradually moves toward the center of moral vice over the 200-year period; in contrast, democracy considered morally negative (e.g., subversion and anti-authority under monarchy) in the 1800s is now perceived as morally positive, as a mechanism for fairness; gay, which came to denote homosexuality only in the 1930s BIBREF18, is inferred to be morally irrelevant until the modern day. We will describe systematic evaluations and applications of our framework that extend beyond these anecdotal cases of moral sentiment change.", + "The general text-based framework that we propose consists of a parameter-free approach that facilitates the prediction of public moral sentiment toward individual concepts, automated retrieval of morally changing concepts, and broad-scale psycholinguistic analyses of historical rates of moral sentiment change. We provide a description of the probabilistic models and data used, followed by comprehensive evaluations of our methodology." + ], + [ + "An emerging body of work in natural language processing and computational social science has investigated how NLP systems can detect moral sentiment in online text. For example, moral rhetoric in social media and political discourse BIBREF19, BIBREF20, BIBREF21, the relation between moralization in social media and violent protests BIBREF22, and bias toward refugees in talk radio shows BIBREF23 have been some of the topics explored in this line of inquiry. In contrast to this line of research, the development of a formal framework for moral sentiment change is still under-explored, with no existing systematic and formal treatment of this topic BIBREF16.", + "While there is emerging awareness of ethical issues in NLP BIBREF24, BIBREF25, work exploiting NLP techniques to study principles of moral sentiment change is scarce. Moreover, since morality is variable across cultures and time BIBREF12, BIBREF16, developing systems that capture the diachronic nature of moral sentiment will be a pivotal research direction. Our work leverages and complements existing research that finds implicit human biases from word embeddings BIBREF13, BIBREF14, BIBREF19 by developing a novel perspective on using NLP methodology to discover principles of moral sentiment change in human society." + ], + [ + "Our framework treats the moral sentiment toward a concept at three incremental levels, as illustrated in Figure FIGREF3. First, we consider moral relevance, distinguishing between morally irrelevant and morally relevant concepts. At the second tier, moral polarity, we further split morally relevant concepts into those that are positively or negatively perceived in the moral domain. Finally, a third tier classifies these concepts into fine-grained categories of human morality.", + "We draw from research in social psychology to inform our methodology, most prominently Moral Foundations Theory BIBREF26. MFT seeks to explain the structure and variation of human morality across cultures, and proposes five moral foundations: Care / Harm, Fairness / Cheating, Loyalty / Betrayal, Authority / Subversion, and Sanctity / Degradation. Each foundation is summarized by a positive and a negative pole, resulting in ten fine-grained moral categories." + ], + [ + "To ground moral sentiment in text, we leverage the Moral Foundations Dictionary BIBREF27. The MFD is a psycholinguistic resource that associates each MFT category with a set of seed words, which are words that provide evidence for the corresponding moral category in text. We use the MFD for moral polarity classification by dividing seed words into positive and negative sets, and for fine-grained categorization by splitting them into the 10 MFT categories.", + "To implement the first tier of our framework and detect moral relevance, we complement our morally relevant seed words with a corresponding set of seed words approximating moral irrelevance based on the notion of valence, i.e., the degree of pleasantness or unpleasantness of a stimulus. We refer to the emotional valence ratings collected by BIBREF28 for approximately 14,000 English words, and choose the words with most neutral valence rating that do not occur in the MFD as our set of morally irrelevant seed words, for an equal total number of morally relevant and morally irrelevant words." + ], + [ + "We propose and evaluate a set of probabilistic models to classify concepts in the three tiers of morality specified above. Our models exploit the semantic structure of word embeddings BIBREF29 to perform tiered moral classification of query concepts. In each tier, the model receives a query word embedding vector $\\mathbf {q}$ and a set of seed words for each class in that tier, and infers the posterior probabilities over the set of classes $c$ to which the query concept is associated with.", + "The seed words function as \u201clabelled examples\u201d that guide the moral classification of novel concepts, and are organized per classification tier as follows. In moral relevance classification, sets $\\mathbf {S}_0$ and $\\mathbf {S}_1$ contain the morally irrelevant and morally relevant seed words, respectively; for moral polarity, $\\mathbf {S}_+$ and $\\mathbf {S}_-$ contain the positive and negative seed words; and for fine-grained moral categories, $\\mathbf {S}_1, \\ldots , \\mathbf {S}_{10}$ contain the seed words for the 10 categories of MFT. Then our general problem is to estimate $p(c\\,|\\,\\mathbf {q})$, where $\\mathbf {q}$ is a query vector and $c$ is a moral category in the desired tier.", + "We evaluate the following four models:", + "A Centroid model summarizes each set of seed words by its expected vector in embedding space, and classifies concepts into the class of closest expected embedding in Euclidean distance following a softmax rule;", + "A Na\u00efve Bayes model considers both mean and variance, under the assumption of independence among embedding dimensions, by fitting a normal distribution with mean vector and diagonal covariance matrix to the set of seed words of each class;", + "A $k$-Nearest Neighbors ($k$NN) model exploits local density estimation and classifies concepts according to the majority vote of the $k$ seed words closest to the query vector;", + "A Kernel Density Estimation (KDE) model performs density estimation at a broader scale by considering the contribution of each seed word toward the total likelihood of each class, regulated by a bandwidth parameter $h$ that controls the sensitivity of the model to distance in embedding space.", + "Table TABREF2 specifies the formulation of each model. Note that we adopt a parsimonious design principle in our modelling: both Centroid and Na\u00efve Bayes are parameter-free models, $k$NN only depends on the choice of $k$, and KDE uses a single bandwidth parameter $h$." + ], + [ + "To apply our models diachronically, we require a word embedding space that captures the meanings of words at different points in time and reflects changes pertaining to a particular word as diachronic shifts in a common embedding space.", + "Following BIBREF30, we combine skip-gram word embeddings BIBREF29 trained on longitudinal corpora of English with rotational alignments of embedding spaces to obtain diachronic word embeddings that are aligned through time.", + "We divide historical time into decade-long bins, and use two sets of embeddings provided by BIBREF30, each trained on a different historical corpus of English:", + "Google N-grams BIBREF31: a corpus of $8.5 \\times 10^{11}$ tokens collected from the English literature (Google Books, all-genres) spanning the period 1800\u20131999.", + "COHA BIBREF32: a smaller corpus of $4.1 \\times 10^8$ tokens from works selected so as to be genre-balanced and representative of American English in the period 1810\u20132009." + ], + [ + "We evaluated our models in two ways: classification of moral seed words on all three tiers (moral relevance, polarity, and fine-grained categories), and correlation of model predictions with human judgments." + ], + [ + "In this evaluation, we assessed the ability of our models to classify the seed words that compose our moral environment in a leave-one-out classification task. We performed the evaluation for all three classification tiers: 1) moral relevance, where seed words are split into morally relevant and morally irrelevant; 2) moral polarity, where moral seed words are split into positive and negative; 3) fine-grained categories, where moral seed words are split into the 10 MFT categories. In each test, we removed one seed word from the training set at a time to obtain cross-validated model predictions.", + "Table TABREF14 shows classification accuracy for all models and corpora on each tier for the 1990\u20131999 period. We observe that all models perform substantially better than chance, confirming the efficacy of our methodology in capturing moral dimensions of words. We also observe that models using word embeddings trained on Google N-grams perform better than those trained on COHA, which could be expected given the larger corpus size of the former.", + "In the remaining analyses, we employ the Centroid model, which offers competitive accuracy and a simple, parameter-free specification." + ], + [ + "We evaluated the approximate agreement between our methodology and human judgments using valence ratings, i.e., the degree of pleasantness or unpleasantness of a stimulus. Our assumption is that the valence of a concept should correlate with its perceived moral polarity, e.g., morally repulsive ideas should evoke an unpleasant feeling. However, we do not expect this correspondence to be perfect; for example, the concept of dessert evokes a pleasant reaction without being morally relevant.", + "In this analysis, we took the valence ratings for the nearly 14,000 English nouns collected by BIBREF28 and, for each query word $q$, we generated a corresponding prediction of positive moral polarity from our model, $P(c_+\\,|\\,\\mathbf {q})$. Table TABREF16 shows the correlations between human valence ratings and predictions of positive moral polarity generated by models trained on each of our corpora. We observe that the correlations are significant, suggesting the ability of our methodology to capture relevant features of moral sentiment from text.", + "In the remaining applications, we use the diachronic embeddings trained on the Google N-grams corpus, which enabled superior model performance throughout our evaluations." + ], + [ + "We applied our framework in three ways: 1) evaluation of selected concepts in historical time courses and prediction of human judgments; 2) automatic detection of moral sentiment change; and 3) broad-scale study of the relations between psycholinguistic variables and historical change of moral sentiment toward concepts." + ], + [ + "We applied our models diachronically to predict time courses of moral relevance, moral polarity, and fine-grained moral categories toward two historically relevant topics: slavery and democracy. By grounding our model in word embeddings for each decade and querying concepts at the three tiers of classification, we obtained the time courses shown in Figure FIGREF21.", + "We note that these trajectories illustrate actual historical trends. Predictions for democracy show a trend toward morally positive sentiment, consistent with the adoption of democratic regimes in Western societies. On the other hand, predictions for slavery trend down and suggest a drop around the 1860s, coinciding with the American Civil War. We also observe changes in the dominant fine-grained moral categories, such as the perception of democracy as a fair concept, suggesting potential mechanisms behind the polarity changes and providing further insight into the public sentiment toward these concepts as evidenced by text." + ], + [ + "We explored the predictive potential of our framework by comparing model predictions with human judgments of moral relevance and acceptability. We used data from the Pew Research Center's 2013 Global Attitudes survey BIBREF33, in which participants from 40 countries judged 8 topics such as abortion and homosexuality as one of \u201cacceptable\", \u201cunacceptable\", and \u201cnot a moral issue\".", + "We compared human ratings with model predictions at two tiers: for moral relevance, we paired the proportion of \u201cnot a moral issue\u201d human responses with irrelevance predictions $p(c_0\\,|\\,\\mathbf {q})$ for each topic, and for moral acceptability, we paired the proportion of \u201cacceptable\u201d responses with positive predictions $p(c_+\\,|\\,\\mathbf {q})$. We used 1990s word embeddings, and obtained predictions for two-word topics by querying the model with their averaged embeddings.", + "Figure FIGREF23 shows plots of relevance and polarity predictions against survey proportions, and we observe a visible correspondence between model predictions and human judgments despite the difficulty of this task and limited number of topics." + ], + [ + "Beyond analyzing selected concepts, we applied our framework predictively on a large repertoire of words to automatically discover the concepts that have exhibited the greatest change in moral sentiment at two tiers, moral relevance and moral polarity.", + "We selected the 10,000 nouns with highest total frequency in the 1800\u20131999 period according to data from BIBREF30, restricted to words labelled as nouns in WordNet BIBREF34 for validation. For each such word $\\mathbf {q}$, we computed diachronic moral relevance scores $R_i = p(c_1\\,|\\,\\mathbf {q}), i=1,\\ldots ,20$ for the 20 decades in our time span. Then, we performed a linear regression of $R$ on $T = 1,\\ldots ,n$ and took the fitted slope as a measure of moral relevance change. We repeated the same procedure for moral polarity. Finally, we removed words with average relevance score below $0.5$ to focus on morally relevant retrievals.", + "Table TABREF17 shows the words with steepest predicted change toward moral relevance, along with their predicted fine-grained moral categories in modern times (i.e., 1900\u20131999). Table TABREF18 shows the words with steepest predicted change toward the positive and negative moral poles. To further investigate the moral sentiment that may have led to such polarity shifts, we also show the predicted fine-grained moral categories of each word at its earliest time of predicted moral relevance and in modern times. Although we do not have access to ground truth for this application, these results offer initial insight into the historical moral landscape of the English language at scale." + ], + [ + "In this application, we investigated the hypothesis that concept concreteness is inversely related to change in moral relevance, i.e., that concepts considered more abstract might become morally relevant at a higher rate than concepts considered more concrete. To test this hypothesis, we performed a multiple linear regression analysis on rate of change toward moral relevance of a large repertoire of words against concept concreteness ratings, word frequency BIBREF35, and word length BIBREF36.", + "We obtained norms of concreteness ratings from BIBREF28. We collected the same set of high-frequency nouns as in the previous analysis, along with their fitted slopes of moral relevance change. Since we were interested in moral relevance change within this large set of words, we restricted our analysis to those words whose model predictions indicate change in moral relevance, in either direction, from the 1800s to the 1990s.", + "We performed a multiple linear regression under the following model:", + "Here $\\rho (w)$ is the slope of moral relevance change for word $w$; $f(w$) is its average frequency; $l(w)$ is its character length; $c(w)$ is its concreteness rating; $\\beta _f$, $\\beta _l$, $\\beta _c$, and $\\beta _0$ are the corresponding factor weights and intercept, respectively; and $\\epsilon \\sim \\mathcal {N}(0, \\sigma )$ is the regression error term.", + "Table TABREF27 shows the results of multiple linear regression. We observe that concreteness is a significant negative predictor of change toward moral relevance, suggesting that abstract concepts are more strongly associated with increasing moral relevance over time than concrete concepts. This significance persists under partial correlation test against the control factors ($p < 0.01$).", + "We further verified the diachronic component of this effect in a random permutation analysis. We generated 1,000 control time courses by randomly shuffling the 20 decades in our data, and repeated the regression analysis to obtain a control distribution for each regression coefficient. All effects became non-significant under the shuffled condition, suggesting the relevance of concept concreteness for diachronic change in moral sentiment (see Supplementary Material)." + ], + [ + "We presented a text-based framework for exploring the socio-scientific problem of moral sentiment change. Our methodology uses minimal parameters and exploits implicit moral biases learned from diachronic word embeddings to reveal the public's moral perception toward a large concept repertoire over a long historical period.", + "Differing from existing work in NLP that treats moral sentiment as a flat classification problem BIBREF19, BIBREF20, our framework probes moral sentiment change at multiple levels and captures moral dynamics concerning relevance, polarity, and fine-grained categories informed by Moral Foundations Theory BIBREF12. We applied our methodology to the automated analyses of moral change both in individual concepts and at a broad scale, thus providing insights into psycholinguistic variables that associate with rates of moral change in the public.", + "Our current work focuses on exploring moral sentiment change in English-speaking cultures. Future research should evaluate the appropriateness of the framework to probing moral change from a diverse range of cultures and linguistic backgrounds, and the extent to which moral sentiment change interacts and crisscrosses with linguistic meaning change and lexical coinage. Our work creates opportunities for applying natural language processing toward characterizing moral sentiment change in society." + ], + [ + "We would like to thank Nina Wang, Nicola Lacerata, Dan Jurafsky, Paul Bloom, Dzmitry Bahdanau, and the Computational Linguistics Group at the University of Toronto for helpful discussion. We would also like to thank Ben Prystawski for his feedback on the manuscript. JX is supported by an NSERC USRA Fellowship and YX is funded through a SSHRC Insight Grant, an NSERC Discovery Grant, and a Connaught New Researcher Award." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0472/instruction.md b/qasper-0472/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..e0db5e99a1a7e0d6419ce10136b62172084f9f5c --- /dev/null +++ b/qasper-0472/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Bringing Stories Alive: Generating Interactive Fiction Worlds + +Question: How well did the system do? \ No newline at end of file diff --git a/qasper-0473/instruction.md b/qasper-0473/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..12aa85a6f72a93f5697c64aef17ff20594d80966 --- /dev/null +++ b/qasper-0473/instruction.md @@ -0,0 +1,92 @@ +Name of Paper: Bringing Stories Alive: Generating Interactive Fiction Worlds + +Question: How is the information extracted? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "World Generation", + "World Generation ::: Knowledge Graph Construction", + "World Generation ::: Knowledge Graph Construction ::: Neural Graph Construction", + "World Generation ::: Knowledge Graph Construction ::: Rule-Based Graph Construction", + "World Generation ::: Description Generation", + "World Generation ::: Description Generation ::: Neural Description Generation", + "World Generation ::: Description Generation ::: Rules-Based Description Generation", + "Evaluation", + "Evaluation ::: Knowledge Graph Construction Evaluation", + "Evaluation ::: Full Game Evaluation", + "Conclusion" + ], + "paragraphs": [ + [ + "Interactive fictions\u2014also called text-adventure games or text-based games\u2014are games in which a player interacts with a virtual world purely through textual natural language\u2014receiving descriptions of what they \u201csee\u201d and writing out how they want to act, an example can be seen in Figure FIGREF2. Interactive fiction games are often structured as puzzles, or quests, set within the confines of given game world. Interactive fictions have been adopted as a test-bed for real-time game playing agents BIBREF0, BIBREF1, BIBREF2. Unlike other, graphical games, interactive fictions test agents' abilities to infer the state of the world through communication and to indirectly affect change in the world through language. Interactive fictions are typically modeled after real or fantasy worlds; commonsense knowledge is an important factor in successfully playing interactive fictions BIBREF3, BIBREF4.", + "In this paper we explore a different challenge for artificial intelligence: automatically generating text-based virtual worlds for interactive fictions. A core component of many narrative-based tasks\u2014everything from storytelling to game generation\u2014is world building. The world of a story or game defines the boundaries of where the narrative is allowed and what the player is allowed to do. There are four core challenges to world generation: (1) commonsense knowledge: the world must reference priors that the player possesses so that players can make sense of the world and build expectations on how to interact with it. This is especially true in interactive fictions where the world is presented textually because many details of the world necessarily be left out (e.g., the pot is on a stove; kitchens are found in houses) that might otherwise be literal in a graphical virtual world. (2) Thematic knowledge: interactive fictions usually involve a theme or genre that comes with its own expectations. For example, light speed travel is plausible in sci-fi worlds but not realistic in the real world. (3) Coherence: the world must not appear to be an random assortment of locations. (3) Natural language: The descriptions of the rooms as well as the permissible actions must text, implying that the system has natural language generation capability.", + "Because worlds are conveyed entirely through natural language, the potential output space for possible generated worlds is combinatorially large. To constrain this space and to make it possible to evaluate generated world, we present an approach which makes use of existing stories, building on the worlds presented in them but leaving enough room for the worlds to be unique. Specifically, we take a story such as Sherlock Holmes or Rapunzel\u2014a linear reading experience\u2014and extract the description of the world the story is set in to make an interactive world the player can explore.", + "Our method first extracts a partial, potentially disconnected knowledge graph from the story, encoding information regarding locations, characters, and objects in the form of $\\langle entity,relation,entity\\rangle $ triples. Relations between these types of entities as well as their properties are captured in this knowledge graph. However, stories often do not explicitly contain all the information required to fully fill out such a graph. A story may mention that there is a sword stuck in a stone but not what you can do with the sword or where it is in relation to everything else. Our method fills in missing relation and affordance information using thematic knowledge gained from training on stories in a similar genre. This knowledge graph is then used to guide the text description generation process for the various locations, characters, and objects. The game is then assembled on the basis of the knowledge graph and the corresponding generated descriptions.", + "We have two major contributions. (1) A neural model and a rules-based baseline for each of the tasks described above. The phases are that of graph extraction and completion followed by description generation and game formulation. Each of these phases are relatively distinct and utilize their own models. (2) A human subject study for comparing the neural model and variations on it to the rules-based and human-made approaches. We perform two separate human subject studies\u2014one for the first phase of knowledge graph construction and another for the overall game creation process\u2014testing specifically for coherence, interestingness, and the ability to maintain a theme or genre." + ], + [ + "There has been a slew of recent work in developing agents that can play text games BIBREF0, BIBREF5, BIBREF1, BIBREF6. BIBREF7 ammanabrolutransfer,ammanabrolu,ammanabrolu2020graph in particular use knowledge graphs as state representations for game-playing agents. BIBREF8 propose QAit, a set of question answering tasks framed as text-based or interactive fiction games. QAit focuses on helping agents learn procedural knowledge through interaction with a dynamic environment. These works all focus on agents that learn to play a given set of interactive fiction games as opposed to generating them.", + "Scheherazade BIBREF9 is a system that learns a plot graph based on stories written by crowd sourcing the task of writing short stories. The learned plot graph contains details relevant to ensure story coherence. It includes: plot events, temporal precedence, and mutual exclusion relations. Scheherazade-IF BIBREF10 extends the system to generate choose-your-own-adventure style interactive fictions in which the player chooses from prescribed options. BIBREF11 explore a method of creating interactive narratives revolving around locations, wherein sentences are mapped to a real-world GPS location from a corpus of sentences belonging to a certain genre. Narratives are made by chaining together sentences selected based on the player's current real-world location. In contrast to these models, our method generates a parser-based interactive fiction in which the player types in a textual command, allowing for greater expressiveness.", + "BIBREF12 define the problem of procedural content generation in interactive fiction games in terms of the twin considerations of world and quest generation and focus on the latter. They present a system in which quest content is first generated by learning from a corpus and then grounded into a given interactive fiction world. The work is this paper focuses on the world generation problem glossed in the prior work. Thus these two systems can be seen as complimentary.", + "Light BIBREF13 is a crowdsourced dataset of grounded text-adventure game dialogues. It contains information regarding locations, characters, and objects set in a fantasy world. The authors demonstrate that the supervised training of transformer-based models lets us contextually relevant dialog, actions, and emotes. Most in line with the spirit of this paper, BIBREF14 leverage Light to generate worlds for text-based games. They train a neural network based model using Light to compositionally arrange locations, characters, and objects into an interactive world. Their model is tested using a human subject study against other machine learning based algorithms with respect to the cohesiveness and diversity of generated worlds. Our work, in contrast, focuses on extracting the information necessary for building interactive worlds from existing story plots." + ], + [ + "World generation happens in two phases. In the first phase, a partial knowledge graph is extracted from a story plot and then filled in using thematic commonsense knowledge. In the second phase, the graph is used as the skeleton to generate a full interactive fiction game\u2014generating textual descriptions or \u201cflavortext\u201d for rooms and embedded objects. We present a novel neural approach in addition to a rule guided baseline for each of these phases in this section." + ], + [ + "The first phase is to extract a knowledge graph from the story that depicts locations, characters, objects, and the relations between these entities. We present two techniques. The first uses neural question-answering technique to extract relations from a story text. The second, provided as a baseline, uses OpenIE5, a commonly used rule-based information extraction technique. For the sake of simplicity, we considered primarily the location-location and location-character/object relations, represented by the \u201cnext to\u201d and \u201chas\u201d edges respectively in Figure FIGREF4." + ], + [ + "While many neural models already exist that perform similar tasks such as named entity extraction and part of speech tagging, they often come at the cost of large amounts of specialized labeled data suited for that task. We instead propose a new method that leverages models trained for context-grounded question-answering tasks to do entity extraction with no task dependent data or fine-tuning necessary. Our method, dubbed AskBERT, leverages the Question-Answering (QA) model ALBERT BIBREF15. AskBERT consists of two main steps as shown in Figure FIGREF7: vertex extraction and graph construction.", + "The first step is to extract the set of entities\u2014graph vertices\u2014from the story. We are looking to extract information specifically regarding characters, locations, and objects. This is done by using asking the QA model questions such as \u201cWho is a character in the story?\u201d. BIBREF16 have shown that the phrasing of questions given to a QA model is important and this forms the basis of how we formulate our questions\u2014questions are asked so that they are more likely to return a single answer, e.g. asking \u201cWhere is a location in the story?\u201d as opposed to \u201cWhere are the locations in the story?\u201d. In particular, we notice that pronoun choice can be crucial; \u201cWhere is a location in the story?\u201d yielded more consistent extraction than \u201cWhat is a location in the story?\u201d. ALBERT QA is trained to also output a special <$no$-$answer$> token when it cannot find an answer to the question within the story. Our method makes use of this by iteratively asking QA model a question and masking out the most likely answer outputted on the previous step. This process continues until the <$no$-$answer$> token becomes the most likely answer.", + "The next step is graph construction. Typical interactive fiction worlds are usually structured as trees, i.e. no cycles except between locations. Using this fact, we use an approach that builds a graph from the vertex set by one relation\u2014or edge\u2014at a time. Once again using the entire story plot as context, we query the ALBERT-QA model picking a random starting location $x$ from the set of vertices previously extracted.and asking the questions \u201cWhat location can I visit from $x$?\u201d and \u201cWho/What is in $x$?\u201d. The methodology for phrasing these questions follows that described for the vertex extraction. The answer given by the QA model is matched to the vertex set by picking the vertex $u$ that contains the best word-token overlap with the answer. Relations between vertices are added by computing a relation probability on the basis of the output probabilities of the answer given by the QA model. The probability that vertices $x,u$ are related:", + "where", + "is the sum of the individual token probabilities of all the overlapping tokens in the answer from the QA model and $u$." + ], + [ + "We compared our proposed AskBERT method with a non-neural, rule-based approach. This approach is based on the information extracted by OpenIE5, followed by some post-processing such as named-entity recognition and part-of-speech tagging. OpenIE5 combines several cutting-edge ideas from several existing papers BIBREF17, BIBREF18, BIBREF19 to create a powerful information extraction tools. For a given sentence, OpenIE5 generates multiple triples in the format of $\\langle entity, relation, entity\\rangle $ as concise representations of the sentence, each with a confidence score. These triples are also occasionally annotated with location information indicating that a triple happened in a location.", + "As in the neural AskBERT model, we attempt to extract information regarding locations, characters, and objects. The entire story plot is passed into the OpenIE5 and we receive a set of triples. The location annotations on the triples are used to create a set of locations. We mark which sentences in the story contain these locations. POS tagging based on marking noun-phrases is then used in conjunction with NER to further filter the set of triples\u2014identifying the set of characters and objects in the story.", + "The graph is constructed by linking the set of triples on the basis of the location they belong to. While some sentences contain very explicit location information for OpenIE5 to mark it out in the triples, most of them do not. We therefore make the assumption that the location remains the same for all triples extracted in between sentences where locations are explicitly mentioned. For example, if there exists $location A$ in the 1st sentence and $location B$ in the 5th sentence of the story, all the events described in sentences 1-4 are considered to take place in $location A$. The entities mentioned in these events are connected to $location A$ in the graph." + ], + [ + "The second phase involves using the constructed knowledge graph to generate textual descriptions of the entities we have extracted, also known as flavortext. This involves generating descriptions of what a player \u201csees\u201d when they enter a location and short blurbs for each object and character. These descriptions need to not only be faithful to the information present in the knowledge graph and the overall story plot but to also contain flavor and be interesting for the player." + ], + [ + "Here, we approach the problem of description generation by taking inspiration from conditional transformer-based generation methods BIBREF20. Our approach is outlined in Figure FIGREF11 and an example description shown in Figure FIGREF2. For any given entity in the story, we first locate it in the story plot and then construct a prompt which consists of the entire story up to and including the sentence when the entity is first mentioned in the story followed by a question asking to describe that entity. With respect to prompts, we found that more direct methods such as question-answering were more consistent than open-ended sentence completion. For example, \u201cQ: Who is the prince? A:\u201d often produced descriptions that were more faithful to the information already present about the prince in the story than \u201cYou see the prince. He is/looks\u201d. For our transformer-based generation, we use a pre-trained 355M GPT-2 model BIBREF21 finetuned on a corpus of plot summaries collected from Wikipedia. The plots used for finetuning are tailored specific to the genre of the story in order to provide more relevant generation for the target genre. Additional details regarding the datasets used are provided in Section SECREF4. This method strikes a balance between knowledge graph verbalization techniques which often lack \u201cflavor\u201d and open ended generation which struggles to maintain semantic coherence." + ], + [ + "In the rule-based approach, we utilized the templates from the built-in text game generator of TextWorld BIBREF1 to generate the description for our graphs. TextWorld is an open-source library that provides a way to generate text-game learning environments for training reinforcement learning agents using pre-built grammars.", + "Two major templates involved here are the Room Intro Templates and Container Description Templates from TextWorld, responsible for generating descriptions of locations and blurbs for objects/characters respectively. The location and object/character information are taken from the knowledge graph constructed previously.", + "Example of Room Intro Templates: \u201cThis might come as a shock to you, but you've just $\\#entered\\#$ a <$location$-$name$>\u201d", + "Example of Container Description Templates: \u201cThe <$location$-$name$> $\\#contains\\#$ <$object/person$-$name$>\u201d", + "Each token surrounded by $\\#$ sign can be expanded using a select set of terminal tokens. For instance, $\\#entered\\#$ could be filled with any of the following phrases here: entered; walked into; fallen into; moved into; stumbled into; come into. Additional prefixes, suffixes and adjectives were added to increase the relative variety of descriptions. Unlike the neural methods, the rule-based approach is not able to generate detailed and flavorful descriptions of the properties of the locations/objects/characters. By virtue of the templates, however, it is much better at maintaining consistency with the information contained in the knowledge graph." + ], + [ + "We conducted two sets of human participant evaluations by recruiting participants over Amazon Mechanical Turk. The first evaluation tests the knowledge graph construction phase, in which we measure perceived coherence and genre or theme resemblance of graphs extracted by different models. The second study compares full games\u2014including description generation and game assembly, which can't easily be isolated from graph construction\u2014generated by different methods. This study looks at how interesting the games were to the players in addition to overall coherence and genre resemblance. Both studies are performed across two genres: mystery and fairy-tales. This is done in part to test the relative effectiveness of our approach across different genres with varying thematic commonsense knowledge. The dataset used was compiled via story summaries that were scraped from Wikipedia via a recursive crawling bot. The bot searched pages for both for plot sections as well as links to other potential stories. From the process, 695 fairy-tales and 536 mystery stories were compiled from two categories: novels and short stories. We note that the mysteries did not often contain many fantasy elements, i.e. they consisted of mysteries set in our world such as Sherlock Holmes, while the fairy-tales were much more removed from reality. Details regarding how each of the studies were conducted and the corresponding setup are presented below." + ], + [ + "We first select a subset of 10 stories randomly from each genre and then extract a knowledge graph using three different models. Each participant is presented with the three graphs extracted from a single story in each genre and then asked to rank them on the basis of how coherent they were and how well the graphs match the genre. The graphs resembles the one shown in in Figure FIGREF4 and are presented to the participant sequentially. The exact order of the graphs and genres was also randomized to mitigate any potential latent correlations. Overall, this study had a total of 130 participants.This ensures that, on average, graphs from every story were seen by 13 participants.", + "In addition to the neural AskBERT and rules-based methods, we also test a variation of the neural model which we dub to be the \u201crandom\u201d approach. The method of vertex extraction remains identical to the neural method, but we instead connect the vertices randomly instead of selecting the most confident according to the QA model. We initialize the graph with a starting location entity. Then, we randomly sample from the vertex set and connect it to a randomly sampled location in the graph until every vertex has been connected. This ablation in particular is designed to test the ability of our neural model to predict relations between entities. It lets us observe how accurately linking related vertices effects each of the metrics that we test for. For a fair comparison between the graphs produced by different approaches, we randomly removed some of the nodes and edges from the initial graphs so that the maximum number of locations per graph and the maximum number of objects/people per location in each story genre are the same.", + "The results are shown in Table TABREF20. We show the median rank of each of the models for both questions across the genres. Ranked data is generally closely interrelated and so we perform Friedman's test between the three models to validate that the results are statistically significant. This is presented as the $p$-value in table (asterisks indicate significance at $p<0.05$). In cases where we make comparisons between specific pairs of models, when necessary, we additionally perform the Mann-Whitney U test to ensure that the rankings differed significantly.", + "In the mystery genre, the rules-based method was often ranked first in terms of genre resemblance, followed by the neural and random models. This particular result was not statistically significant however, likely indicating that all the models performed approximately equally in this category. The neural approach was deemed to be the most coherent followed by the rules and random. For the fairy-tales, the neural model ranked higher on both of the questions asked of the participants. In this genre, the random neural model also performed better than the rules based approach.", + "Tables TABREF18 and TABREF19 show the statistics of the constructed knowledge graphs in terms of vertices and edges. We see that the rules-based graph construction has a lower number of locations, characters, and relations between entities but far more objects in general. The greater number of objects is likely due to the rules-based approach being unable to correctly identify locations and characters. The gap between the methods is less pronounced in the mystery genre as opposed to the fairy-tales, in fact the rules-based graphs have more relations than the neural ones. The random and neural models have the same number of entities in all categories by construction but random in general has lower variance on the number of relations found. In this case as well, the variance is lower for mystery as opposed to fairy-tales. When taken in the context of the results in Table TABREF20, it appears to indicate that leveraging thematic commonsense in the form of AskBERT for graph construction directly results in graphs that are more coherent and maintain genre more easily. This is especially true in the case of the fairy-tales where the thematic and everyday commonsense diverge more than than in the case of the mysteries." + ], + [ + "This participant study was designed to test the overall game formulation process encompassing both phases described in Section SECREF3. A single story from each genre was chosen by hand from the 10 stories used for the graph evaluation process. From the knowledge graphs for this story, we generate descriptions using the neural, rules, and random approaches described previously. Additionally, we introduce a human-authored game for each story here to provide an additional benchmark. This author selected was familiar with text-adventure games in general as well as the genres of detective mystery and fairy tale. To ensure a fair comparison, we ensure that the maximum number of locations and maximum number of characters/objects per location matched the other methods. After setting general format expectations, the author read the selected stories and constructed knowledge graphs in a corresponding three step process of: identifying the $n$ most important entities in the story, mapping positional relationships between entities, and then synthesizing flavor text for the entities based off of said location, the overall story plot, and background topic knowledge.", + "Once the knowledge graph and associated descriptions are generated for a particular story, they are then automatically turned into a fully playable text-game using the text game engine Evennia. Evennia was chosen for its flexibility and customization, as well as a convenient web client for end user testing. The data structures were translated into builder commands within Evennia that constructed the various layouts, flavor text, and rules of the game world. Users were placed in one \u201croom\u201d out of the different world locations within the game they were playing, and asked to explore the game world that was available to them. Users achieved this by moving between rooms and investigating objects. Each time a new room was entered or object investigated, the player's total number of explored entities would be displayed as their score.", + "Each participant was was asked to play the neural game and then another one from one of the three additional models within a genre. The completion criteria for each game is collect half the total score possible in the game, i.e. explore half of all possible rooms and examine half of all possible entities. This provided the participant with multiple possible methods of finishing a particular game. On completion, the participant was asked to rank the two games according to overall perceived coherence, interestingness, and adherence to the genre. We additionally provided a required initial tutorial game which demonstrated all of these mechanics. The order in which participants played the games was also randomized as in the graph evaluation to remove potential correlations. We had 75 participants in total, 39 for mystery and 36 for fairy-tales. As each player played the neural model created game and one from each of the other approaches\u2014this gave us 13 on average for the other approaches in the mystery genre and 12 for fairy-tales.", + "The summary of the results of the full game study is shown in Table TABREF23. As the comparisons made in this study are all made pairwise between our neural model and one of the baselines\u2014they are presented in terms of what percentage of participants prefer the baseline game over the neural game. Once again, as this is highly interrelated ranked data, we perform the Mann-Whitney U test between each of the pairs to ensure that the rankings differed significantly. This is also indicated on the table.", + "In the mystery genre, the neural approach is generally preferred by a greater percentage of participants than the rules or random. The human-made game outperforms them all. A significant exception to is that participants thought that the rules-based game was more interesting than the neural game. The trends in the fairy-tale genre are in general similar with a few notable deviations. The first deviation is that the rules-based and random approaches perform significantly worse than neural in this genre. We see also that the neural game is as coherent as the human-made game.", + "As in the previous study, we hypothesize that this is likely due to the rules-based approach being more suited to the mystery genre, which is often more mundane and contains less fantastical elements. By extension, we can say that thematic commonsense in fairy-tales has less overlap with everyday commonsense than for mundane mysteries. This has a few implications, one of which is that this theme specific information is unlikely to have been seen by OpenIE5 before. This is indicated in the relatively improved performance of the rules-based model in this genre across in terms of both interestingness and coherence.The genre difference can also be observed in terms of the performance of the random model. This model also lacking when compared to our neural model across all the questions asked especially in the fairy-tale setting. This appears to imply that filling in gaps in the knowledge graph using thematically relevant information such as with AskBERT results in more interesting and coherent descriptions and games especially in settings where the thematic commonsense diverges from everyday commonsense." + ], + [ + "Procedural world generation systems are required to be semantically consistent, comply with thematic and everyday commonsense understanding, and maintain overall interestingness. We describe an approach that transform a linear reading experience in the form of a story plot into a interactive narrative experience. Our method, AskBERT, extracts and fills in a knowledge graph using thematic commonsense and then uses it as a skeleton to flesh out the rest of the world. A key insight from our human participant study reveals that the ability to construct a thematically consistent knowledge graph is critical to overall perceptions of coherence and interestingness particularly when the theme diverges from everyday commonsense understanding." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0474/instruction.md b/qasper-0474/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..fc9cbe9b94133976c8479b558c68ea9eb55d7448 --- /dev/null +++ b/qasper-0474/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Generating Classical Chinese Poems from Vernacular Chinese + +Question: What are some guidelines in writing input vernacular so model can generate \ No newline at end of file diff --git a/qasper-0475/instruction.md b/qasper-0475/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..580cc9225a327408e74e4f33d4e275adeb90a6be --- /dev/null +++ b/qasper-0475/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Generating Classical Chinese Poems from Vernacular Chinese + +Question: How much is proposed model better in perplexity and BLEU score than typical UMT models? \ No newline at end of file diff --git a/qasper-0480/instruction.md b/qasper-0480/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..3f91cad0bfeee36266415471c1c73a132b9ece20 --- /dev/null +++ b/qasper-0480/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever + +Question: What KB is used? \ No newline at end of file diff --git a/qasper-0481/instruction.md b/qasper-0481/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..288c55754edf89fa774aec74000fdfc79ec0a5cf --- /dev/null +++ b/qasper-0481/instruction.md @@ -0,0 +1,83 @@ +Name of Paper: From FiLM to Video: Multi-turn Question Answering with Multi-modal Context + +Question: At which interval do they extract video and audio frames? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "The avsd dataset and challenge", + "Models", + "Utterance-level Encoder", + "Description Encoder", + "Video Encoder with Time-Extended FiLM", + "Audio Encoder", + "Fusing Modalities for Dialogue Context", + "Decoders", + "Loss Function", + "Experiments", + "Conclusions" + ], + "paragraphs": [ + [ + "Deep neural networks have been successfully applied to several computer vision tasks such as image classification BIBREF0 , object detection BIBREF1 , video action classification BIBREF2 , etc. They have also been successfully applied to natural language processing tasks such as machine translation BIBREF3 , machine reading comprehension BIBREF4 , etc. There has also been an explosion of interest in tasks which combine multiple modalities such as audio, vision, and language together. Some popular multi-modal tasks combining these three modalities, and their differences are highlighted in Table TABREF1 .", + "Given an image and a question related to the image, the vqa challenge BIBREF5 tasked users with selecting an answer to the question. BIBREF6 identified several sources of bias in the vqa dataset, which led to deep neural models answering several questions superficially. They found that in several instances, deep architectures exploited the statistics of the dataset to select answers ignoring the provided image. This prompted the release of vqa 2.0 BIBREF7 which attempts to balance the original dataset. In it, each question is paired to two similar images which have different answers. Due to the complexity of vqa, understanding the failures of deep neural architectures for this task has been a challenge. It is not easy to interpret whether the system failed in understanding the question or in understanding the image or in reasoning over it. The CLEVR dataset BIBREF8 was hence proposed as a useful benchmark to evaluate such systems on the task of visual reasoning. Extending question answering over images to videos, BIBREF9 have proposed MovieQA, where the task is to select the correct answer to a provided question given the movie clip on which it is based.", + "Intelligent systems that can interact with human users for a useful purpose are highly valuable. To this end, there has been a recent push towards moving from single-turn qa to multi-turn dialogue, which is a natural and intuitive setting for humans. Among multi-modal dialogue tasks, visdial BIBREF10 provides an image and dialogue where each turn is a qa pair. The task is to train a model to answer these questions within the dialogue. The avsd challenge extends the visdial task from images to the audio-visual domain.", + "We present our modelname model for the avsd task. modelname combines a hred for encoding and generating qa-dialogue with a novel FiLM-based audio-visual feature extractor for videos and an auxiliary multi-task learning-based decoder for decoding a summary of the video. It outperforms the baseline results for the avsd dataset BIBREF11 and was ranked 2nd overall among the dstc7 avsd challenge participants.", + "In Section SECREF2 , we discuss existing literature on end-to-end dialogue systems with a special focus on multi-modal dialogue systems. Section SECREF3 describes the avsd dataset. In Section SECREF4 , we present the architecture of our modelname model. We describe our evaluation and experimental setup in Section SECREF5 and then conclude in Section SECREF6 ." + ], + [ + "With the availability of large conversational corpora from sources like Reddit and Twitter, there has been a lot of recent work on end-to-end modelling of dialogue for open domains. BIBREF12 treated dialogue as a machine translation problem where they translate from the stimulus to the response. They observed this to be more challenging than machine translation tasks due the larger diversity of possible responses. Among approaches that just use the previous utterance to generate the current response, BIBREF13 proposed a response generation model based on the encoder decoder framework. BIBREF14 also proposed an encoder-decoder based neural network architecture that uses the previous two utterances to generate the current response. Among discriminative methods (i.e. methods that produce a score for utterances from a set and then rank them), BIBREF15 proposed a neural architecture to select the best next response from a list of responses by measuring their similarity to the dialogue context. BIBREF16 extended prior work on encoder-decoder-based models to multi-turn conversations. They trained a hierarchical model called hred for generating dialogue utterances where a recurrent neural network encoder encodes each utterance. A higher-level recurrent neural network maintains the dialogue state by further encoding the individual utterance encodings. This dialogue state is then decoded by another recurrent decoder to generate the response at that point in time. In followup work, BIBREF17 used a latent stochastic variable to condition the generation process which aided their model in producing longer coherent outputs that better retain the context.", + "Datasets and tasks BIBREF10 , BIBREF18 , BIBREF19 have also been released recently to study visual-input based conversations. BIBREF10 train several generative and discriminative deep neural models for the visdial task. They observe that on this task, discriminative models outperform generative models and that models making better use of the dialogue history do better than models that do not use dialogue history at all. Unexpectedly, the performance between models that use the image features and models that do no use these features is not significantly different. As we discussed in Section SECREF1 , this is similar to the issues vqa models faced initially due to the imbalanced nature of the dataset, which leads us to believe that language is a strong prior on the visdial dataset too. BIBREF20 train two separate agents to play a cooperative game where one agent has to answer the other agent's questions, which in turn has to predict the fc7 features of the Image obtained from VGGNet. Both agents are based on hred models and they show that agents fine-tuned with rl outperform agents trained solely with supervised learning. BIBREF18 train both generative and discriminative deep neural models on the igc dataset, where the task is to generate questions and answers to carry on a meaningful conversation. BIBREF19 train hred-based models on GuessWhat?! dataset in which agents have to play a guessing game where one player has to find an object in the picture which the other player knows about and can answer questions about them.", + "Moving from image-based dialogue to video-based dialogue adds further complexity and challenges. Limited availability of such data is one of the challenges. Apart from the avsd dataset, there does not exist a video dialogue dataset to the best of our knowledge and the avsd data itself is fairly limited in size. Extracting relevant features from videos also contains the inherent complexity of extracting features from individual frames and additionally requires understanding their temporal interaction. The temporal nature of videos also makes it important to be able to focus on a varying-length subset of video frames as the action which is being asked about might be happening within them. There is also the need to encode the additional modality of audio which would be required for answering questions that rely on the audio track. With limited size of publicly available datasets based on the visual modality, learning useful features from high dimensional visual data has been a challenge even for the visdial dataset, and we anticipate this to be an even more significant challenge on the avsd dataset as it involves videos.", + "On the avsd task, BIBREF11 train an attention-based audio-visual scene-aware dialogue model which we use as the baseline model for this paper. They divide each video into multiple equal-duration segments and, from each of them, extract video features using an I3D BIBREF21 model, and audio features using a VGGish BIBREF22 model. The I3D model was pre-trained on Kinetics BIBREF23 dataset and the VGGish model was pre-trained on Audio Set BIBREF24 . The baseline encodes the current utterance's question with a lstm BIBREF25 and uses the encoding to attend to the audio and video features from all the video segments and to fuse them together. The dialogue history is modelled with a hierarchical recurrent lstm encoder where the input to the lower level encoder is a concatenation of question-answer pairs. The fused feature representation is concatenated with the question encoding and the dialogue history encoding and the resulting vector is used to decode the current answer using an lstm decoder. Similar to the visdial models, the performance difference between the best model that uses text and the best model that uses both text and video features is small. This indicates that the language is a stronger prior here and the baseline model is unable to make good use of the highly relevant video.", + "Automated evaluation of both task-oriented and non-task-oriented dialogue systems has been a challenge BIBREF26 , BIBREF27 too. Most such dialogue systems are evaluated using per-turn evaluation metrics since there is no suitable per-dialogue metric as conversations do not need to happen in a deterministic ordering of turns. These per-turn evaluation metrics are mostly word-overlap-based metrics such as BLEU, METEOR, ROUGE, and CIDEr, borrowed from the machine translation literature. Due to the diverse nature of possible responses, world-overlap metrics are not highly suitable for evaluating these tasks. Human evaluation of generated responses is considered the most reliable metric for such tasks but it is cost prohibitive and hence the dialogue system literature continues to rely widely on word-overlap-based metrics." + ], + [ + "The avsd dataset BIBREF28 consists of dialogues collected via amt. Each dialogue is associated with a video from the Charades BIBREF29 dataset and has conversations between two amt workers related to the video. The Charades dataset has multi-action short videos and it provides text descriptions for these videos, which the avsd challenge also distributes as the caption. The avsd dataset has been collected using similar methodology as the visdial dataset. In avsd, each dialogue turn consists of a question and answer pair. One of the amt workers assumes the role of questioner while the other amt worker assumes the role of answerer. The questioner sees three static frames from the video and has to ask questions. The answerer sees the video and answers the questions asked by the questioner. After 10 such qa turns, the questioner wraps up by writing a summary of the video based on the conversation.", + "Dataset statistics such as the number of dialogues, turns, and words for the avsd dataset are presented in Table TABREF5 . For the initially released prototype dataset, the training set of the avsd dataset corresponds to videos taken from the training set of the Charades dataset while the validation and test sets of the avsd dataset correspond to videos taken from the validation set of the Charades dataset. For the official dataset, training, validation and test sets are drawn from the corresponding Charades sets.", + "The Charades dataset also provides additional annotations for the videos such as action, scene, and object annotations, which are considered to be external data sources by the avsd challenge, for which there is a special sub-task in the challenge. The action annotations also include the start and end time of the action in the video." + ], + [ + "Our modelname model is based on the hred framework for modelling dialogue systems. In our model, an utterance-level recurrent lstm encoder encodes utterances and a dialogue-level recurrent lstm encoder encodes the final hidden states of the utterance-level encoders, thus maintaining the dialogue state and dialogue coherence. We use the final hidden states of the utterance-level encoders in the attention mechanism that is applied to the outputs of the description, video, and audio encoders. The attended features from these encoders are fused with the dialogue-level encoder's hidden states. An utterance-level decoder decodes the response for each such dialogue state following a question. We also add an auxiliary decoding module which is similar to the response decoder except that it tries to generate the caption and/or the summary of the video. We present our model in Figure FIGREF2 and describe the individual components in detail below." + ], + [ + "The utterance-level encoder is a recurrent neural network consisting of a single layer of lstm cells. The input to the lstm are word embeddings for each word in the utterance. The utterance is concatenated with a special symbol marking the end of the sequence. We initialize our word embeddings using 300-dimensional GloVe BIBREF30 and then fine-tune them during training. For words not present in the GloVe vocabulary, we initialize their word embeddings from a random uniform distribution." + ], + [ + "Similar to the utterance-level encoder, the description encoder is also a single-layer lstm recurrent neural network. Its word embeddings are also initialized with GloVe and then fine-tuned during training. For the description, we use the caption and/or the summary for the video provided with the dataset. The description encoder also has access to the last hidden state of the utterance-level encoder, which it uses to generate an attention map over the hidden states of its lstm. The final output of this module is the attention-weighted sum of the lstm hidden states." + ], + [ + "For the video encoder, we use an I3D model pre-trained on the Kinetics dataset BIBREF23 and extract the output of its Mixed_7c layer for INLINEFORM0 (30 for our models) equi-distant segments of the video. Over these features, we add INLINEFORM1 (2 for our models) FiLM BIBREF31 blocks which have been highly successful in visual reasoning problems. Each FiLM block applies a conditional (on the utterance encoding) feature-wise affine transformation on the features input to it, ultimately leading to the extraction of more relevant features. The FiLM blocks are followed by fully connected layers which are further encoded by a single layer recurrent lstm network. The last hidden state of the utterance-level encoder then generates an attention map over the hidden states of its lstm, which is multiplied by the hidden states to provide the output of this module. We also experimented with using convolutional Mixed_5c features to capture spatial information but on the limited avsd dataset they did not yield any improvement. When not using the FiLM blocks, we use the final layer I3D features (provided by the avsd organizers) and encode them with the lstm directly, followed by the attention step. We present the video encoder in Figure FIGREF3 ." + ], + [ + "The audio encoder is structurally similar to the video encoder. We use the VGGish features provided by the avsd challenge organizers. Also similar to the video encoder, when not using the FiLM blocks, we use the VGGish features and encode them with the lstm directly, followed by the attention step. The audio encoder is depicted in Figure FIGREF4 ." + ], + [ + "The outputs of the encoders for past utterances, descriptions, video, and audio together form the dialogue context INLINEFORM0 which is the input of the decoder. We first combine past utterances using a dialogue-level encoder which is a single-layer lstm recurrent neural network. The input to this encoder are the final hidden states of the utterance-level lstm. To combine the hidden states of these diverse modalities, we found concatenation to perform better on the validation set than averaging or the Hadamard product." + ], + [ + "The answer decoder consists of a single-layer recurrent lstm network and generates the answer to the last question utterance. At each time-step, it is provided with the dialogue-level state and produces a softmax over a vector corresponding to vocabulary words and stops when 30 words were produced or an end of sentence token is encountered.", + "The auxiliary decoder is functionally similar to the answer decoder. The decoded sentence is the caption and/or description of the video. We use the Video Encoder state instead of the Dialogue-level Encoder state as input since with this module we want to learn a better video representation capable of decoding the description." + ], + [ + "For a given context embedding INLINEFORM0 at dialogue turn INLINEFORM1 , we minimize the negative log-likelihood of the answer word INLINEFORM2 (vocabulary size), normalized by the number of words INLINEFORM3 in the ground truth response INLINEFORM4 , L(Ct, r) = -1Mm=1MiV( [rt,m=i] INLINEFORM5 ) , where the probabilities INLINEFORM6 are given by the decoder LSTM output, r*t,m-1 ={ll rt,m-1 ; s>0.2, sU(0, 1)", + "v INLINEFORM0 ; else . is given by scheduled sampling BIBREF32 , and INLINEFORM1 is a symbol denoting the start of a sequence. We optimize the model using the AMSGrad algorithm BIBREF33 and use a per-condition random search to determine hyperparameters. We train the model using the BLEU-4 score on the validation set as our stopping citerion." + ], + [ + "The avsd challenge tasks we address here are:", + "We train our modelname model for Task 1.a and Task 2.a of the challenge and we present the results in Table TABREF9 . Our model outperforms the baseline model released by BIBREF11 on all of these tasks. The scores for the winning team have been released to challenge participants and are also included. Their approach, however, is not public as of yet. We observe the following for our models:", + "Since the official test set has not been released publicly, results reported on the official test set have been provided by the challenge organizers. For the prototype test set and for the ablation study presented in Table TABREF24 , we use the same code for evaluation metrics as used by BIBREF11 for fairness and comparability. We attribute the significant performance gain of our model over the baseline to a combination of several factors as described below:", + "Our primary architectural differences over the baseline model are: not concatenating the question, answer pairs before encoding them, the auxiliary decoder module, and using the Time-Extended FiLM module for feature extraction. These, combined with using scheduled sampling and running hyperparameter optimization over the validation set to select hyperparameters, give us the observed performance boost.", + "We observe that our models generate fairly relevant responses to questions in the dialogues, and models with audio-visual inputs respond to audio-visual questions (e.g. \u201cis there any voices or music ?\u201d) correctly more often.", + "We conduct an ablation study on the effectiveness of different components (eg., text, video and audio) and present it in Table TABREF24 . Our experiments show that:" + ], + [ + "We presented modelname, a state-of-the-art dialogue model for conversations about videos. We evaluated the model on the official AVSD test set, where it achieves a relative improvement of more than 16% over the baseline model on BLEU-4 and more than 33% on CIDEr. The challenging aspect of multi-modal dialogue is fusing modalities with varying information density. On AVSD, it is easiest to learn from the input text, while video features remain largely opaque to the decoder. modelname uses a generalization of FiLM to video that conditions video feature extraction on a question. However, similar to related work, absolute improvements of incorporating video features into dialogue are consistent but small. Thus, while our results indicate the suitability of our FiLM generalization, they also highlight that applications at the intersection between language and video are currently constrained by the quality of video features, and emphasizes the need for larger datasets." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0486/instruction.md b/qasper-0486/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..01d24de61dde68bfafdd8af0a480ec40dfa9f86e --- /dev/null +++ b/qasper-0486/instruction.md @@ -0,0 +1,93 @@ +Name of Paper: Civique: Using Social Media to Detect Urban Emergencies + +Question: What classifier is used for emergency categorization? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Motivation and Challenges", + "Our Approach", + "Pre-Processing Modules", + "Emergency Classification", + "Type Classification", + "Location Visualizer", + "Evaluation", + "Dataset Creation", + "Classifier Evaluation", + "Demostration Description", + "Conclusions" + ], + "paragraphs": [ + [ + "With the surge in the use of social media, micro-blogging sites like Twitter, Facebook, and Foursquare have become household words. Growing ubiquity of mobile phones in highly populated developing nations has spurred an exponential rise in social media usage. The heavy volume of social media posts tagged with users' location information on micro-blogging website Twitter presents a unique opportunity to scan these posts. These Short texts (e.g. \"tweets\") on social media contain information about various events happening around the globe, as people post about events and incidents alike. Conventional web outlets provide emergency phone numbers (i.e. 100, 911), etc., and are fast and accurate. Our system, on the other hand, connects its users through a relatively newer platform i.e. social media, and provides an alternative to these conventional methods. In case of their failure or when such means are busy/occupied, an alternative could prove to be life saving.", + "These real life events are reported on Twitter with different perspectives, opinions, and sentiment. Every day, people discuss events thousands of times across social media sites. We would like to detect such events in case of an emergency. Some previous studies BIBREF0 investigate the use of features such as keywords in the tweet, number of words, and context to devise a classifier for event detection. BIBREF1 discusses various techniques researchers have used previously to detect events from Twitter. BIBREF2 describe a system to automatically detect events about known entities from Twitter. This work is highly specific to detection of events only related to known entities. BIBREF3 discuss a system that returns a ranked list of relevant events given a user query.", + "Several research efforts have focused on identifying events in real time( BIBREF4 BIBREF5 BIBREF6 BIBREF0 ). These include systems to detect emergent topics from Twitter in real time ( BIBREF4 BIBREF7 ), an online clustering technique for identifying tweets in real time BIBREF5 , a system to detect localized events and also track evolution of such events over a period of time BIBREF6 . Our focus is on detecting urban emergencies as events from Twitter messages. We classify events ranging from natural disasters to fire break outs, and accidents. Our system detects whether a tweet, which contains a keyword from a pre-decided list, is related to an actual emergency or not. It also classifies the event into its appropriate category, and visualizes the possible location of the emergency event on the map. We also support notifications to our users, containing the contacts of specifically concerned authorities, as per the category of their tweet.", + "The rest of the paper is as follows: Section SECREF2 provides the motivation for our work, and the challenges in building such a system. Section SECREF3 describes the step by step details of our work, and its results. We evaluate our system and present the results in Section SECREF4 . Section SECREF5 showcases our demonstrations in detail, and Section SECREF6 concludes the paper by briefly describing the overall contribution, implementation and demonstration." + ], + [ + "In 2015, INLINEFORM0 of all unnatural deaths in India were caused by accidents, and INLINEFORM1 by accidental fires. Moreover, the Indian subcontinent suffered seven earthquakes in 2015, with the recent Nepal earthquake alone killing more than 9000 people and injuring INLINEFORM2 . We believe we can harness the current social media activity on the web to minimize losses by quickly connecting affected people and the concerned authorities. Our work is motivated by the following factors, (a) Social media is very accessible in the current scenario. (The \u201cDigital India\u201d initiative by the Government of India promotes internet activity, and thus a pro-active social media.) (b) As per the Internet trends reported in 2014, about 117 million Indians are connected to the Internet through mobile devices. (c) A system such as ours can point out or visualize the affected areas precisely and help inform the authorities in a timely fashion. (d) Such a system can be used on a global scale to reduce the effect of natural calamities and prevent loss of life.", + "There are several challenges in building such an application: (a) Such a system expects a tweet to be location tagged. Otherwise, event detection techniques to extract the spatio-temporal data from the tweet can be vague, and lead to false alarms. (b) Such a system should also be able to verify the user's credibility as pranksters may raise false alarms. (c) Tweets are usually written in a very informal language, which requires a sophisticated language processing component to sanitize the tweet input before event detection. (d) A channel with the concerned authorities should be established for them to take serious action, on alarms raised by such a system. (e) An urban emergency such as a natural disaster could affect communications severely, in case of an earthquake or a cyclone, communications channels like Internet connectivity may get disrupted easily. In such cases, our system may not be of help, as it requires the user to be connected to the internet. We address the above challenges and present our approach in the next section." + ], + [ + "We propose a software architecture for Emergency detection and visualization as shown in figure FIGREF9 . We collect data using Twitter API, and perform language pre-processing before applying a classification model. Tweets are labelled manually with and labels, and later classified manually to provide labels according to the type of emergency they indicate. We use the manually labeled data for training our classifiers.", + "We use traditional classification techniques such as Support Vector Machines(SVM), and Naive Bayes(NB) for training, and perform 10-fold cross validation to obtain f-scores. Later, in real time, our system uses the Twitter streaming APIs to get data, pre-processes it using the same modules, and detects emergencies using the classifiers built above. The tweets related to emergencies are displayed on the web interface along with the location and information for the concerned authorities. The pre-processing of Twitter data obtained is needed as it usually contains ad-hoc abbreviations, phonetic substitutions, URLs, hashtags, and a lot of misspelled words. We use the following language processing modules for such corrections." + ], + [ + "We implement a cleaning module to automate the cleaning of tweets obtained from the Twitter API. We remove URLs, special symbols like @ along with the user mentions, Hashtags and any associated text. We also replace special symbols by blank spaces, and inculcate the module as shown in figure FIGREF9 .", + "An example of such a sample tweet cleaning is shown in table TABREF10 .", + "While tweeting, users often express their emotions by stressing over a few characters in the word. For example, usage of words like hellpppp, fiiiiiireeee, ruuuuunnnnn, druuuuuunnnkkk, soooooooo actually corresponds to help, fire, run, drunk, so etc. We use the compression module implemented by BIBREF8 for converting terms like \u201cpleeeeeeeaaaaaassseeee\u201d to \u201cplease\u201d.", + "It is unlikely for an English word to contain the same character consecutively for three or more times. We, hence, compress all the repeated windows of character length greater than two, to two characters. For example \u201cpleeeeeaaaassee\u201d is converted to \u201cpleeaassee\u201d. Each window now contains two characters of the same alphabet in cases of repetition. Let n be the number of windows, obtained from the previous step. We, then, apply brute force search over INLINEFORM0 possibilities to select a valid dictionary word.", + "Table TABREF13 contains sanitized sample output from our compression module for further processing.", + "Text Normalization is the process of translating ad-hoc abbreviations, typographical errors, phonetic substitution and ungrammatical structures used in text messaging (Tweets and SMS) to plain English. Use of such language (often referred as Chatting Language) induces noise which poses additional processing challenges.", + "We use the normalization module implemented by BIBREF8 for text normalization. Training process requires a Language Model of the target language and a parallel corpora containing aligned un-normalized and normalized word pairs. Our language model consists of 15000 English words taken from various sources on the web.", + "Parallel corpora was collected from the following sources:", + "Stanford Normalization Corpora which consists of 9122 pairs of un-normalized and normalized words / phrases.", + "The above corpora, however, lacked acronyms and short hand texts like 2mrw, l8r, b4, hlp, flor which are frequently used in chatting. We collected 215 pairs un-normalized to normalized word/phrase mappings via crowd-sourcing.", + "Table TABREF16 contains input and normalized output from our module.", + "Users often make spelling mistakes while tweeting. A spell checker makes sure that a valid English word is sent to the classification system. We take this problem into account by introducing a spell checker as a pre-processing module by using the JAVA API of Jazzy spell checker for handling spelling mistakes.", + "An example of correction provided by the Spell Checker module is given below:-", + "Input: building INLINEFORM0 flor, help", + "Output: building INLINEFORM0 floor, help", + "Please note that, our current system performs compression, normalization and spell-checking if the language used is English. The classifier training and detection process are described below." + ], + [ + "The first classifier model acts as a filter for the second stage of classification. We use both SVM and NB to compare the results and choose SVM later for stage one classification model, owing to a better F-score. The training is performed on tweets labeled with classes , and based on unigrams as features. We create word vectors of strings in the tweet using a filter available in the WEKA API BIBREF9 , and perform cross validation using standard classification techniques." + ], + [ + "We employ a multi-class Naive Bayes classifier as the second stage classification mechanism, for categorizing tweets appropriately, depending on the type of emergencies they indicate. This multi-class classifier is trained on data manually labeled with classes. We tokenize the training data using \u201cNgramTokenizer\u201d and then, apply a filter to create word vectors of strings before training. We use \u201ctrigrams\u201d as features to build a model which, later, classifies tweets into appropriate categories, in real time. We then perform cross validation using standard techniques to calculate the results, which are shown under the label \u201cStage 2\u201d, in table TABREF20 ." + ], + [ + "We use Google Maps Geocoding API to display the possible location of the tweet origin based on longitude and latitude. Our visualizer presents the user with a map and pinpoints the location with custom icons for earthquake, cyclone, fire accident etc. Since we currently collect tweets with a location filter for the city of \"Mumbai\", we display its map location on the interface. The possible occurrences of such incidents are displayed on the map as soon as our system is able to detect it.", + "We also display the same on an Android device using the WebView functionality available to developers, thus solving the issue of portability. Our system displays visualization of the various emergencies detected on both web browsers and mobile devices." + ], + [ + "We evaluate our system using automated, and manual evaluation techniques. We perform 10-fold cross validation to obtain the F-scores for our classification systems. We use the following technique for dataset creation. We test the system in realtime environments, and tweet about fires at random locations in our city, using test accounts. Our system was able to detect such tweets and detect them with locations shown on the map." + ], + [ + "We collect data by using the Twitter API for saved data, available for public use. For our experiments we collect 3200 tweets filtered by keywords like \u201cfire\u201d, \u201cearthquake\u201d, \u201ctheft\u201d, \u201crobbery\u201d, \u201cdrunk driving\u201d, \u201cdrunk driving accident\u201d etc. Later, we manually label tweets with and labels for classification as stage one. Our dataset contains 1313 tweet with positive label and 1887 tweets with a negative label . We create another dataset with the positively labeled tweets and provide them with category labels like \u201cfire\u201d, \u201caccident\u201d, \u201cearthquake\u201d etc.", + "" + ], + [ + "The results of 10-fold cross-validation performed for stage one are shown in table TABREF20 , under the label \u201cStage 1\u201d. In table TABREF20 , For \u201cStage 1\u201d of classification, F-score obtained using SVM classifier is INLINEFORM0 as shown in row 2, column 2. We also provide the system with sample tweets in real time and assess its ability to detect the emergency, and classify it accordingly. The classification training for Stage 1 was performed using two traditional classification techniques SVM and NB. SVM outperformed NB by around INLINEFORM1 and became the choice of classification technique for stage one.", + "Some false positives obtained during manual evaluation are, \u201cI am sooooo so drunk right nowwwwwwww\u201d and \u201cfire in my office , the boss is angry\u201d. These occurrences show the need of more labeled gold data for our classifiers, and some other features, like Part-of-Speech tags, Named Entity recognition, Bigrams, Trigrams etc. to perform better.", + "The results of 10-fold cross-validation performed for stage two classfication model are also shown in table TABREF20 , under the label \u201cStage 2\u201d. The training for stage two was also performed using both SVM and NB, but NB outperformed SVM by around INLINEFORM0 to become a choice for stage two classification model.", + "We also perform attribute evaluation for the classification model, and create a word cloud based on the output values, shown in figure FIGREF24 . It shows that our classifier model is trained on appropriate words, which are very close to the emergency situations viz. \u201cfire\u201d, \u201cearthquake\u201d, \u201caccident\u201d, \u201cbreak\u201d (Unigram representation here, but possibly occurs in a bigram phrase with \u201cfire\u201d) etc. In figure FIGREF24 , the word cloud represents the word \u201crespond\u201d as the most frequently occurring word as people need urgent help, and quick response from the assistance teams." + ], + [ + "Users interact with Civique through its Web-based user interface and Android based application interface. The features underlying Civique are demonstrated through the following two show cases:", + "Show case 1: Tweet Detection and Classification", + "This showcase aims at detecting related tweets, and classifying them into appropriate categories. For this, we have created a list of filter words, which are used to filter tweets from the Twitter streaming API. These set of words help us filter the tweets related to any incident. We will tweet, and users are able to see how our system captures such tweets and classifies them. Users should be able to see the tweet emerge as an incident on the web-interface, as shown in figure FIGREF26 and the on the android application, as shown in figure FIGREF27 . Figure FIGREF27 demonstrates how a notification is generated when our system detects an emergency tweet. When a user clicks the emerged spot, the system should be able to display the sanitized version / extracted spatio-temporal data from the tweet. We test the system in a realtime environment, and validate our experiments. We also report the false positives generated during the process in section SECREF25 above.", + "Show case 2: User Notification and Contact Info.", + "Civique includes a set of local contacts for civic authorities who are to be / who can be contacted in case of various emergencies. Users can see how Civique detects an emergency and classifies it. They can also watch how the system generates a notification on the web interface and the Android interface, requesting them to contact the authorities for emergencies. Users can change their preferences on the mobile device anytime and can also opt not to receive notifications. Users should be able to contact the authorities online using the application, but in case the online contact is not responsive, or in case of a sudden loss of connectivity, we provide the user with the offline contact information of the concerned civic authorities along with the notifications." + ], + [ + "Civique is a system which detects urban emergencies like earthquakes, cyclones, fire break out, accidents etc. and visualizes them on both on a browsable web interface and an Android application. We collect data from the popular micro-blogging site Twitter and use language processing modules to sanitize the input. We use this data as input to train a two step classification system, which indicates whether a tweet is related to an emergency or not, and if it is, then what category of emergency it belongs to. We display such positively classified tweets along with their type and location on a Google map, and notify our users to inform the concerned authorities, and possibly evacuate the area, if his location matches the affected area. We believe such a system can help the disaster management machinery, and government bodies like Fire department, Police department, etc., to act swiftly, thus minimizing the loss of life.", + "Twitter users use slang, profanity, misspellings and neologisms. We, use standard cleaning methods, and combine NLP with Machine Learning (ML) to further our cause of tweet classification. At the current stage, we also have an Android application ready for our system, which shows the improvised, mobile-viewable web interface.", + "In the future, we aim to develop detection of emergency categories on the fly, obscure emergencies like \u201cairplane hijacking\u201d should also be detected by our system. We plan to analyze the temporal sequence of the tweet set from a single location to determine whether multiple problems on the same location are the result of a single event, or relate to multiple events." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0487/instruction.md b/qasper-0487/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..1c5bbc84d22b711f1aaacec47860cf631a3577b5 --- /dev/null +++ b/qasper-0487/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Civique: Using Social Media to Detect Urban Emergencies + +Question: What classifier is used for emergency detection? \ No newline at end of file diff --git a/qasper-0488/instruction.md b/qasper-0488/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..cc13d00d07e83a6d85cdeab7c651b8be3eb9d2f0 --- /dev/null +++ b/qasper-0488/instruction.md @@ -0,0 +1,93 @@ +Name of Paper: Civique: Using Social Media to Detect Urban Emergencies + +Question: Do the tweets come from any individual? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Motivation and Challenges", + "Our Approach", + "Pre-Processing Modules", + "Emergency Classification", + "Type Classification", + "Location Visualizer", + "Evaluation", + "Dataset Creation", + "Classifier Evaluation", + "Demostration Description", + "Conclusions" + ], + "paragraphs": [ + [ + "With the surge in the use of social media, micro-blogging sites like Twitter, Facebook, and Foursquare have become household words. Growing ubiquity of mobile phones in highly populated developing nations has spurred an exponential rise in social media usage. The heavy volume of social media posts tagged with users' location information on micro-blogging website Twitter presents a unique opportunity to scan these posts. These Short texts (e.g. \"tweets\") on social media contain information about various events happening around the globe, as people post about events and incidents alike. Conventional web outlets provide emergency phone numbers (i.e. 100, 911), etc., and are fast and accurate. Our system, on the other hand, connects its users through a relatively newer platform i.e. social media, and provides an alternative to these conventional methods. In case of their failure or when such means are busy/occupied, an alternative could prove to be life saving.", + "These real life events are reported on Twitter with different perspectives, opinions, and sentiment. Every day, people discuss events thousands of times across social media sites. We would like to detect such events in case of an emergency. Some previous studies BIBREF0 investigate the use of features such as keywords in the tweet, number of words, and context to devise a classifier for event detection. BIBREF1 discusses various techniques researchers have used previously to detect events from Twitter. BIBREF2 describe a system to automatically detect events about known entities from Twitter. This work is highly specific to detection of events only related to known entities. BIBREF3 discuss a system that returns a ranked list of relevant events given a user query.", + "Several research efforts have focused on identifying events in real time( BIBREF4 BIBREF5 BIBREF6 BIBREF0 ). These include systems to detect emergent topics from Twitter in real time ( BIBREF4 BIBREF7 ), an online clustering technique for identifying tweets in real time BIBREF5 , a system to detect localized events and also track evolution of such events over a period of time BIBREF6 . Our focus is on detecting urban emergencies as events from Twitter messages. We classify events ranging from natural disasters to fire break outs, and accidents. Our system detects whether a tweet, which contains a keyword from a pre-decided list, is related to an actual emergency or not. It also classifies the event into its appropriate category, and visualizes the possible location of the emergency event on the map. We also support notifications to our users, containing the contacts of specifically concerned authorities, as per the category of their tweet.", + "The rest of the paper is as follows: Section SECREF2 provides the motivation for our work, and the challenges in building such a system. Section SECREF3 describes the step by step details of our work, and its results. We evaluate our system and present the results in Section SECREF4 . Section SECREF5 showcases our demonstrations in detail, and Section SECREF6 concludes the paper by briefly describing the overall contribution, implementation and demonstration." + ], + [ + "In 2015, INLINEFORM0 of all unnatural deaths in India were caused by accidents, and INLINEFORM1 by accidental fires. Moreover, the Indian subcontinent suffered seven earthquakes in 2015, with the recent Nepal earthquake alone killing more than 9000 people and injuring INLINEFORM2 . We believe we can harness the current social media activity on the web to minimize losses by quickly connecting affected people and the concerned authorities. Our work is motivated by the following factors, (a) Social media is very accessible in the current scenario. (The \u201cDigital India\u201d initiative by the Government of India promotes internet activity, and thus a pro-active social media.) (b) As per the Internet trends reported in 2014, about 117 million Indians are connected to the Internet through mobile devices. (c) A system such as ours can point out or visualize the affected areas precisely and help inform the authorities in a timely fashion. (d) Such a system can be used on a global scale to reduce the effect of natural calamities and prevent loss of life.", + "There are several challenges in building such an application: (a) Such a system expects a tweet to be location tagged. Otherwise, event detection techniques to extract the spatio-temporal data from the tweet can be vague, and lead to false alarms. (b) Such a system should also be able to verify the user's credibility as pranksters may raise false alarms. (c) Tweets are usually written in a very informal language, which requires a sophisticated language processing component to sanitize the tweet input before event detection. (d) A channel with the concerned authorities should be established for them to take serious action, on alarms raised by such a system. (e) An urban emergency such as a natural disaster could affect communications severely, in case of an earthquake or a cyclone, communications channels like Internet connectivity may get disrupted easily. In such cases, our system may not be of help, as it requires the user to be connected to the internet. We address the above challenges and present our approach in the next section." + ], + [ + "We propose a software architecture for Emergency detection and visualization as shown in figure FIGREF9 . We collect data using Twitter API, and perform language pre-processing before applying a classification model. Tweets are labelled manually with and labels, and later classified manually to provide labels according to the type of emergency they indicate. We use the manually labeled data for training our classifiers.", + "We use traditional classification techniques such as Support Vector Machines(SVM), and Naive Bayes(NB) for training, and perform 10-fold cross validation to obtain f-scores. Later, in real time, our system uses the Twitter streaming APIs to get data, pre-processes it using the same modules, and detects emergencies using the classifiers built above. The tweets related to emergencies are displayed on the web interface along with the location and information for the concerned authorities. The pre-processing of Twitter data obtained is needed as it usually contains ad-hoc abbreviations, phonetic substitutions, URLs, hashtags, and a lot of misspelled words. We use the following language processing modules for such corrections." + ], + [ + "We implement a cleaning module to automate the cleaning of tweets obtained from the Twitter API. We remove URLs, special symbols like @ along with the user mentions, Hashtags and any associated text. We also replace special symbols by blank spaces, and inculcate the module as shown in figure FIGREF9 .", + "An example of such a sample tweet cleaning is shown in table TABREF10 .", + "While tweeting, users often express their emotions by stressing over a few characters in the word. For example, usage of words like hellpppp, fiiiiiireeee, ruuuuunnnnn, druuuuuunnnkkk, soooooooo actually corresponds to help, fire, run, drunk, so etc. We use the compression module implemented by BIBREF8 for converting terms like \u201cpleeeeeeeaaaaaassseeee\u201d to \u201cplease\u201d.", + "It is unlikely for an English word to contain the same character consecutively for three or more times. We, hence, compress all the repeated windows of character length greater than two, to two characters. For example \u201cpleeeeeaaaassee\u201d is converted to \u201cpleeaassee\u201d. Each window now contains two characters of the same alphabet in cases of repetition. Let n be the number of windows, obtained from the previous step. We, then, apply brute force search over INLINEFORM0 possibilities to select a valid dictionary word.", + "Table TABREF13 contains sanitized sample output from our compression module for further processing.", + "Text Normalization is the process of translating ad-hoc abbreviations, typographical errors, phonetic substitution and ungrammatical structures used in text messaging (Tweets and SMS) to plain English. Use of such language (often referred as Chatting Language) induces noise which poses additional processing challenges.", + "We use the normalization module implemented by BIBREF8 for text normalization. Training process requires a Language Model of the target language and a parallel corpora containing aligned un-normalized and normalized word pairs. Our language model consists of 15000 English words taken from various sources on the web.", + "Parallel corpora was collected from the following sources:", + "Stanford Normalization Corpora which consists of 9122 pairs of un-normalized and normalized words / phrases.", + "The above corpora, however, lacked acronyms and short hand texts like 2mrw, l8r, b4, hlp, flor which are frequently used in chatting. We collected 215 pairs un-normalized to normalized word/phrase mappings via crowd-sourcing.", + "Table TABREF16 contains input and normalized output from our module.", + "Users often make spelling mistakes while tweeting. A spell checker makes sure that a valid English word is sent to the classification system. We take this problem into account by introducing a spell checker as a pre-processing module by using the JAVA API of Jazzy spell checker for handling spelling mistakes.", + "An example of correction provided by the Spell Checker module is given below:-", + "Input: building INLINEFORM0 flor, help", + "Output: building INLINEFORM0 floor, help", + "Please note that, our current system performs compression, normalization and spell-checking if the language used is English. The classifier training and detection process are described below." + ], + [ + "The first classifier model acts as a filter for the second stage of classification. We use both SVM and NB to compare the results and choose SVM later for stage one classification model, owing to a better F-score. The training is performed on tweets labeled with classes , and based on unigrams as features. We create word vectors of strings in the tweet using a filter available in the WEKA API BIBREF9 , and perform cross validation using standard classification techniques." + ], + [ + "We employ a multi-class Naive Bayes classifier as the second stage classification mechanism, for categorizing tweets appropriately, depending on the type of emergencies they indicate. This multi-class classifier is trained on data manually labeled with classes. We tokenize the training data using \u201cNgramTokenizer\u201d and then, apply a filter to create word vectors of strings before training. We use \u201ctrigrams\u201d as features to build a model which, later, classifies tweets into appropriate categories, in real time. We then perform cross validation using standard techniques to calculate the results, which are shown under the label \u201cStage 2\u201d, in table TABREF20 ." + ], + [ + "We use Google Maps Geocoding API to display the possible location of the tweet origin based on longitude and latitude. Our visualizer presents the user with a map and pinpoints the location with custom icons for earthquake, cyclone, fire accident etc. Since we currently collect tweets with a location filter for the city of \"Mumbai\", we display its map location on the interface. The possible occurrences of such incidents are displayed on the map as soon as our system is able to detect it.", + "We also display the same on an Android device using the WebView functionality available to developers, thus solving the issue of portability. Our system displays visualization of the various emergencies detected on both web browsers and mobile devices." + ], + [ + "We evaluate our system using automated, and manual evaluation techniques. We perform 10-fold cross validation to obtain the F-scores for our classification systems. We use the following technique for dataset creation. We test the system in realtime environments, and tweet about fires at random locations in our city, using test accounts. Our system was able to detect such tweets and detect them with locations shown on the map." + ], + [ + "We collect data by using the Twitter API for saved data, available for public use. For our experiments we collect 3200 tweets filtered by keywords like \u201cfire\u201d, \u201cearthquake\u201d, \u201ctheft\u201d, \u201crobbery\u201d, \u201cdrunk driving\u201d, \u201cdrunk driving accident\u201d etc. Later, we manually label tweets with and labels for classification as stage one. Our dataset contains 1313 tweet with positive label and 1887 tweets with a negative label . We create another dataset with the positively labeled tweets and provide them with category labels like \u201cfire\u201d, \u201caccident\u201d, \u201cearthquake\u201d etc.", + "" + ], + [ + "The results of 10-fold cross-validation performed for stage one are shown in table TABREF20 , under the label \u201cStage 1\u201d. In table TABREF20 , For \u201cStage 1\u201d of classification, F-score obtained using SVM classifier is INLINEFORM0 as shown in row 2, column 2. We also provide the system with sample tweets in real time and assess its ability to detect the emergency, and classify it accordingly. The classification training for Stage 1 was performed using two traditional classification techniques SVM and NB. SVM outperformed NB by around INLINEFORM1 and became the choice of classification technique for stage one.", + "Some false positives obtained during manual evaluation are, \u201cI am sooooo so drunk right nowwwwwwww\u201d and \u201cfire in my office , the boss is angry\u201d. These occurrences show the need of more labeled gold data for our classifiers, and some other features, like Part-of-Speech tags, Named Entity recognition, Bigrams, Trigrams etc. to perform better.", + "The results of 10-fold cross-validation performed for stage two classfication model are also shown in table TABREF20 , under the label \u201cStage 2\u201d. The training for stage two was also performed using both SVM and NB, but NB outperformed SVM by around INLINEFORM0 to become a choice for stage two classification model.", + "We also perform attribute evaluation for the classification model, and create a word cloud based on the output values, shown in figure FIGREF24 . It shows that our classifier model is trained on appropriate words, which are very close to the emergency situations viz. \u201cfire\u201d, \u201cearthquake\u201d, \u201caccident\u201d, \u201cbreak\u201d (Unigram representation here, but possibly occurs in a bigram phrase with \u201cfire\u201d) etc. In figure FIGREF24 , the word cloud represents the word \u201crespond\u201d as the most frequently occurring word as people need urgent help, and quick response from the assistance teams." + ], + [ + "Users interact with Civique through its Web-based user interface and Android based application interface. The features underlying Civique are demonstrated through the following two show cases:", + "Show case 1: Tweet Detection and Classification", + "This showcase aims at detecting related tweets, and classifying them into appropriate categories. For this, we have created a list of filter words, which are used to filter tweets from the Twitter streaming API. These set of words help us filter the tweets related to any incident. We will tweet, and users are able to see how our system captures such tweets and classifies them. Users should be able to see the tweet emerge as an incident on the web-interface, as shown in figure FIGREF26 and the on the android application, as shown in figure FIGREF27 . Figure FIGREF27 demonstrates how a notification is generated when our system detects an emergency tweet. When a user clicks the emerged spot, the system should be able to display the sanitized version / extracted spatio-temporal data from the tweet. We test the system in a realtime environment, and validate our experiments. We also report the false positives generated during the process in section SECREF25 above.", + "Show case 2: User Notification and Contact Info.", + "Civique includes a set of local contacts for civic authorities who are to be / who can be contacted in case of various emergencies. Users can see how Civique detects an emergency and classifies it. They can also watch how the system generates a notification on the web interface and the Android interface, requesting them to contact the authorities for emergencies. Users can change their preferences on the mobile device anytime and can also opt not to receive notifications. Users should be able to contact the authorities online using the application, but in case the online contact is not responsive, or in case of a sudden loss of connectivity, we provide the user with the offline contact information of the concerned civic authorities along with the notifications." + ], + [ + "Civique is a system which detects urban emergencies like earthquakes, cyclones, fire break out, accidents etc. and visualizes them on both on a browsable web interface and an Android application. We collect data from the popular micro-blogging site Twitter and use language processing modules to sanitize the input. We use this data as input to train a two step classification system, which indicates whether a tweet is related to an emergency or not, and if it is, then what category of emergency it belongs to. We display such positively classified tweets along with their type and location on a Google map, and notify our users to inform the concerned authorities, and possibly evacuate the area, if his location matches the affected area. We believe such a system can help the disaster management machinery, and government bodies like Fire department, Police department, etc., to act swiftly, thus minimizing the loss of life.", + "Twitter users use slang, profanity, misspellings and neologisms. We, use standard cleaning methods, and combine NLP with Machine Learning (ML) to further our cause of tweet classification. At the current stage, we also have an Android application ready for our system, which shows the improvised, mobile-viewable web interface.", + "In the future, we aim to develop detection of emergency categories on the fly, obscure emergencies like \u201cairplane hijacking\u201d should also be detected by our system. We plan to analyze the temporal sequence of the tweet set from a single location to determine whether multiple problems on the same location are the result of a single event, or relate to multiple events." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0489/instruction.md b/qasper-0489/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..1abe90ee46b405da631f03c6c9b7a7ab4ab689aa --- /dev/null +++ b/qasper-0489/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Civique: Using Social Media to Detect Urban Emergencies + +Question: How many categories are there? \ No newline at end of file diff --git a/qasper-0492/instruction.md b/qasper-0492/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..98dd4133f6d6ee3cbaca285cc4a32451455073fa --- /dev/null +++ b/qasper-0492/instruction.md @@ -0,0 +1,114 @@ +Name of Paper: Can neural networks understand monotonicity reasoning? + +Question: Do they release MED? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Monotonicity", + "Human-oriented dataset", + "Linguistics-oriented dataset", + "Statistics", + "Baselines", + "Data augmentation for analysis", + "Discussion", + "Conclusion", + "Acknowledgement" + ], + "paragraphs": [ + [ + "Natural language inference (NLI), also known as recognizing textual entailment (RTE), has been proposed as a benchmark task for natural language understanding. Given a premise $P$ and a hypothesis $H$ , the task is to determine whether the premise semantically entails the hypothesis BIBREF0 . A number of recent works attempt to test and analyze what type of inferences an NLI model may be performing, focusing on various types of lexical inferences BIBREF1 , BIBREF2 , BIBREF3 and logical inferences BIBREF4 , BIBREF5 .", + "Concerning logical inferences, monotonicity reasoning BIBREF6 , BIBREF7 , which is a type of reasoning based on word replacement, requires the ability to capture the interaction between lexical and syntactic structures. Consider examples in ( \"Introduction\" ) and ( \"Introduction\" ).", + "All [ workers $\\leavevmode {\\color {blue!80!black}\\downarrow }$ ] [joined for a French dinner $\\leavevmode {\\color {red!80!black}\\uparrow }$ ] All workers joined for a dinner All new workers joined for a French dinner Not all [new workers $\\leavevmode {\\color {red!80!black}\\uparrow }$ ] joined for a dinner Not all workers joined for a dinner ", + "A context is upward entailing (shown by [... $\\leavevmode {\\color {red!80!black}\\uparrow }$ ]) that allows an inference from ( \"Introduction\" ) to ( \"Introduction\" ), where French dinner is replaced by a more general concept dinner. On the other hand, a downward entailing context (shown by [... $\\leavevmode {\\color {blue!80!black}\\downarrow }$ ]) allows an inference from ( \"Introduction\" ) to ( \"Introduction\" ), where workers is replaced by a more specific concept new workers. Interestingly, the direction of monotonicity can be reversed again by embedding yet another downward entailing context (e.g., not in ( \"Introduction\" )), as witness the fact that ( \"Introduction\" ) entails ( \"Introduction\" ). To properly handle both directions of monotonicity, NLI models must detect monotonicity operators (e.g., all, not) and their arguments from the syntactic structure.", + "For previous datasets containing monotonicity inference problems, FraCaS BIBREF8 and the GLUE diagnostic dataset BIBREF9 are manually-curated datasets for testing a wide range of linguistic phenomena. However, monotonicity problems are limited to very small sizes (FraCaS: 37/346 examples and GLUE: 93/1650 examples). The limited syntactic patterns and vocabularies in previous test sets are obstacles in accurately evaluating NLI models on monotonicity reasoning.", + "To tackle this issue, we present a new evaluation dataset that covers a wide range of monotonicity reasoning that was created by crowdsourcing and collected from linguistics publications (Section \"Dataset\" ). Compared with manual or automatic construction, we can collect naturally-occurring examples by crowdsourcing and well-designed ones from linguistics publications. To enable the evaluation of skills required for monotonicity reasoning, we annotate each example in our dataset with linguistic tags associated with monotonicity reasoning.", + "We measure the performance of state-of-the-art NLI models on monotonicity reasoning and investigate their generalization ability in upward and downward reasoning (Section \"Results and Discussion\" ). The results show that all models trained with SNLI BIBREF4 and MultiNLI BIBREF10 perform worse on downward inferences than on upward inferences.", + "In addition, we analyzed the performance of models trained with an automatically created monotonicity dataset, HELP BIBREF11 . The analysis with monotonicity data augmentation shows that models tend to perform better in the same direction of monotonicity with the training set, while they perform worse in the opposite direction. This indicates that the accuracy on monotonicity reasoning depends solely on the majority direction in the training set, and models might lack the ability to capture the structural relations between monotonicity operators and their arguments." + ], + [ + "As an example of a monotonicity inference, consider the example with the determiner every in ( \"Monotonicity\" ); here the premise $P$ entails the hypothesis $H$ .", + " $P$ : Every [ $_{\\scriptsize \\mathsf {NP}}$ person $\\leavevmode {\\color {blue!80!black}\\downarrow }$ ] [ $_{\\scriptsize \\mathsf {VP}}$ bought a movie ticket $\\leavevmode {\\color {red!80!black}\\uparrow }$ ] $H$ : Every young person bought a ticket ", + "Every is downward entailing in the first argument ( $\\mathsf {NP}$ ) and upward entailing in the second argument ( $\\mathsf {VP}$ ), and thus the term person can be more specific by adding modifiers (person $\\sqsupseteq $ young person), replacing it with its hyponym (person $\\sqsupseteq $ spectator), or adding conjunction (person $\\sqsupseteq $ person and alien). On the other hand, the term buy a ticket can be more general by removing modifiers (bought a movie ticket $\\sqsubseteq $ bought a ticket), replacing it with its hypernym (bought a movie ticket $\\sqsubseteq $ bought a show ticket), or adding disjunction (bought a movie ticket $\\sqsubseteq $ bought or sold a movie ticket). Table 1 shows determiners modeled as binary operators and their polarities with respect to the first and second arguments.", + "There are various types of downward operators, not limited to determiners (see Table 2 ). As shown in ( \"Monotonicity\" ), if a propositional object is embedded in a downward monotonic context (e.g., when), the polarity of words over its scope can be reversed.", + " $P$ : When [every [ $_{\\scriptsize \\mathsf {NP}}$ young person $\\leavevmode {\\color {red!80!black}\\uparrow }$ ] [ $_{\\scriptsize \\mathsf {VP}}$ bought a ticket $\\leavevmode {\\color {blue!80!black}\\downarrow }$ ]], [that shop was open] $H$ : When [every [ $_{\\scriptsize \\mathsf {NP}}$ person] [ $_{\\scriptsize \\mathsf {VP}}$ bought a movie ticket]], [that shop was open] ", + "Thus, the polarity ( $\\leavevmode {\\color {red!80!black}\\uparrow }$ and $\\leavevmode {\\color {blue!80!black}\\downarrow }$ ), where the replacement with more general (specific) phrases licenses entailment, needs to be determined by the interaction of monotonicity properties and syntactic structures; polarity of each constituent is calculated based on a monotonicity operator of functional expressions (e.g., every, when) and their function-term relations." + ], + [ + "To create monotonicity inference problems, we should satisfy three requirements: (a) detect the monotonicity operators and their arguments; (b) based on the syntactic structure, induce the polarity of the argument positions; and (c) replace the phrase in the argument position with a more general or specific phrase in natural and various ways (e.g., by using lexical knowledge or logical connectives). For (a) and (b), we first conduct polarity computation on a syntactic structure for each sentence, and then select premises involving upward/downward expressions.", + "For (c), we use crowdsourcing to narrow or broaden the arguments. The motivation for using crowdsourcing is to collect naturally alike monotonicity inference problems that include various expressions. One problem here is that it is unclear how to instruct workers to create monotonicity inference problems without knowledge of natural language syntax and semantics. We must make tasks simple for workers to comprehend and provide sound judgements. Moreover, recent studies BIBREF12 , BIBREF3 , BIBREF13 point out that previous crowdsourced datasets, such as SNLI BIBREF14 and MultiNLI BIBREF10 , include hidden biases. As these previous datasets are motivated by approximated entailments, workers are asked to freely write hypotheses given a premise, which does not strictly restrict them to creating logically complex inferences.", + "Taking these concerns into consideration, we designed two-step tasks to be performed via crowdsourcing for creating a monotonicity test set; (i) a hypothesis creation task and (ii) a validation task. The task (i) is to create a hypothesis by making some polarized part of an original sentence more specific. Instead of writing a complete sentence from scratch, workers are asked to rewrite only a relatively short sentence. By restricting workers to rewrite only a polarized part, we can effectively collect monotonicity inference examples. The task (ii) is to annotate an entailment label for the premise-hypothesis pair generated in (i). Figure 1 summarizes the overview of our human-oriented dataset creation. We used the crowdsourcing platform Figure Eight for both tasks.", + "As a resource, we use declarative sentences with more than five tokens from the Parallel Meaning Bank (PMB) BIBREF15 . The PMB contains syntactically correct sentences annotated with its syntactic category in Combinatory Categorial Grammar (CCG; BIBREF16 , BIBREF16 ) format, which is suitable for our purpose. To get a whole CCG derivation tree, we parse each sentence by the state-of-the-art CCG parser, depccg BIBREF17 . Then, we add a polarity to every constituent of the CCG tree by the polarity computation system ccg2mono BIBREF18 and make the polarized part a blank field.", + "We ran a trial rephrasing task on 500 examples and detected 17 expressions that were too general and thus difficult to rephrase them in a natural way (e.g., every one, no time). We removed examples involving such expressions. To collect more downward inference examples, we select examples involving determiners in Table 1 and downward operators in Table 2 . As a result, we selected 1,485 examples involving expressions having arguments with upward monotonicity and 1,982 examples involving expressions having arguments with downward monotonicity.", + "We present crowdworkers with a sentence whose polarized part is underlined, and ask them to replace the underlined part with more specific phrases in three different ways. In the instructions, we showed examples rephrased in various ways: by adding modifiers, by adding conjunction phrases, and by replacing a word with its hyponyms.", + "Workers were paid US$0.05 for each set of substitutions, and each set was assigned to three workers. To remove low-quality examples, we set the minimum time it should take to complete each set to 200 seconds. The entry in our task was restricted to workers from native speaking English countries. 128 workers contributed to the task, and we created 15,339 hypotheses (7,179 upward examples and 8,160 downward examples).", + "The gold label of each premise-hypothesis pair created in the previous task is automatically determined by monotonicity calculus. That is, a downward inference pair is labeled as entailment, while an upward inference pair is labeled as non-entailment.", + "However, workers sometimes provided some ungrammatical or unnatural sentences such as the case where a rephrased phrase does not satisfy the selectional restrictions (e.g., original: Tom doesn't live in Boston, rephrased: Tom doesn't live in yes), making it difficult to judge their entailment relations. Thus, we performed an annotation task to ensure accurate labeling of gold labels. We asked workers about the entailment relation of each premise-hypothesis pair as well as how natural it is.", + "Worker comprehension of an entailment relation directly affects the quality of inference problems. To avoid worker misunderstandings, we showed workers the following definitions of labels and five examples for each label:", + "entailment: the case where the hypothesis is true under any situation that the premise describes.", + "non-entailment: the case where the hypothesis is not always true under a situation that the premise describes.", + "unnatural: the case where either the premise and/or the hypothesis is ungrammatical or does not make sense.", + "Workers were paid US$0.04 for each question, and each question was assigned to three workers. To collect high-quality annotation results, we imposed ten test questions on each worker, and removed workers who gave more than three wrong answers. We also set the minimum time it should take to complete each question to 200 seconds. 1,237 workers contributed to this task, and we annotated gold labels of 15,339 premise-hypothesis pairs.", + "Table 3 shows the numbers of cases where answers matched gold labels automatically determined by monotonicity calculus. This table shows that there exist inference pairs whose labels are difficult even for humans to determine; there are 3,354 premise-hypothesis pairs whose gold labels as annotated by polarity computations match with those answered by all workers. We selected these naturalistic monotonicity inference pairs for the candidates of the final test set.", + "To make the distribution of gold labels symmetric, we checked these pairs to determine if we can swap the premise and the hypothesis, reverse their gold labels, and create another monotonicity inference pair. In some cases, shown below, the gold label cannot be reversed if we swap the premise and the hypothesis.", + "In ( UID15 ), child and kid are not hyponyms but synonyms, and the premise $P$ and the hypothesis $H$ are paraphrases.", + " $P$ : Tom is no longer a child $H$ : Tom is no longer a kid ", + "These cases are not strict downward inference problems, in the sense that a phrase is not replaced by its hyponym/hypernym.", + "Consider the example ( UID16 ).", + " $P$ : The moon has no atmosphere $H$ : The moon has no atmosphere, and the gravity force is too low ", + "The hypothesis $H$ was created by asking workers to make atmosphere in the premise $P$ more specific. However, the additional phrase and the gravity force is too low does not form constituents with atmosphere. Thus, such examples are not strict downward monotone inferences.", + "In such cases as (a) and (b), we do not swap the premise and the hypothesis. In the end, we collected 4,068 examples from crowdsourced datasets." + ], + [ + "We also collect monotonicity inference problems from previous manually curated datasets and linguistics publications. The motivation is that previous linguistics publications related to monotonicity reasoning are expected to contain well-designed inference problems, which might be challenging problems for NLI models.", + "We collected 1,184 examples from 11 linguistics publications BIBREF19 , BIBREF20 , BIBREF21 , BIBREF22 , BIBREF23 , BIBREF24 , BIBREF25 , BIBREF26 , BIBREF27 , BIBREF28 , BIBREF29 . Regarding previous manually-curated datasets, we collected 93 examples for monotonicity reasoning from the GLUE diagnostic dataset, and 37 single-premise problems from FraCaS.", + "Both the GLUE diagnostic dataset and FraCaS categorize problems by their types of monotonicity reasoning, but we found that each dataset has different classification criteria. Thus, following GLUE, we reclassified problems into three types of monotone reasoning (upward, downward, and non-monotone) by checking if they include (i) the target monotonicity operator in both the premise and the hypothesis and (ii) the phrase replacement in its argument position. In the GLUE diagnostic dataset, there are several problems whose gold labels are contradiction. We regard them as non-entailment in that the premise does not semantically entail the hypothesis." + ], + [ + "We merged the human-oriented dataset created via crowdsourcing and the linguistics-oriented dataset created from linguistics publications to create the current version of the monotonicity entailment dataset (MED). Table 4 shows some examples from the MED dataset. We can see that our dataset contains various phrase replacements (e.g., conjunction, relative clauses, and comparatives). Table 5 reports the statistics of the MED dataset, including 5,382 premise-hypothesis pairs (1,820 upward examples, 3,270 downward examples, and 292 non-monotone examples). Regarding non-monotone problems, gold labels are always non-entailment, whether a hypothesis is more specific or general than its premise, and thus almost all non-monotone problems are labeled as non-entailment. The size of the word vocabulary in the MED dataset is 4,023, and overlap ratios of vocabulary with previous standard NLI datasets is 95% with MultiNLI and 90% with SNLI.", + "We assigned a set of annotation tags for linguistic phenomena to each example in the test set. These tags allow us to analyze how well models perform on each linguistic phenomenon related to monotonicity reasoning. We defined 6 tags (see Table 4 for examples):", + "lexical knowledge (2,073 examples): inference problems that require lexical relations (i.e., hypernyms, hyponyms, or synonyms)", + "reverse (240 examples): inference problems where a propositional object is embedded in a downward environment more than once", + "conjunction (283 examples): inference problems that include the phrase replacement by adding conjunction (and) to the hypothesis", + "disjunction (254 examples): inference problems that include the phrase replacement by adding disjunction (or) to the hypothesis", + "conditionals (149 examples): inference problems that include conditionals (e.g., if, when, unless) in the hypothesis ", + "negative polarity items (NPIs) (338 examples): inference problems that include NPIs (e.g., any, ever, at all, anything, anyone, anymore, anyhow, anywhere) in the hypothesis" + ], + [ + "To test the difficulty of our dataset, we checked the majority class label and the accuracies of five state-of-the-art NLI models adopting different approaches: BiMPM (Bilateral Multi-Perspective Matching Model; BIBREF31 , BIBREF31 ), ESIM (Enhanced Sequential Inference Model; BIBREF32 , BIBREF32 ), Decomposable Attention Model BIBREF33 , KIM (Knowledge-based Inference Model; BIBREF34 , BIBREF34 ), and BERT (Bidirectional Encoder Representations from Transformers model; BIBREF35 , BIBREF35 ). Regarding BERT, we checked the performance of a model pretrained on Wikipedia and BookCorpus for language modeling and trained with SNLI and MultiNLI. For other models, we checked the performance trained with SNLI. In agreement with our dataset, we regarded the prediction label contradiction as non-entailment.", + "Table 6 shows that the accuracies of all models were better on upward inferences, in accordance with the reported results of the GLUE leaderboard. The overall accuracy of each model was low. In particular, all models underperformed the majority baseline on downward inferences, despite some models having rich lexical knowledge from a knowledge base (KIM) or pretraining (BERT). This indicates that downward inferences are difficult to perform even with the expansion of lexical knowledge. In addition, it is interesting to see that if a model performed better on upward inferences, it performed worse on downward inferences. We will investigate these results in detail below." + ], + [ + "To explore whether the performance of models on monotonicity reasoning depends on the training set or the model themselves, we conducted further analysis performed by data augmentation with the automatically generated monotonicity dataset HELP BIBREF11 . HELP contains 36K monotonicity inference examples (7,784 upward examples, 21,192 downward examples, and 1,105 non-monotone examples). The size of the HELP word vocabulary is 15K, and the overlap ratio of vocabulary between HELP and MED is 15.2%.", + "We trained BERT on MultiNLI only and on MultiNLI augmented with HELP, and compared their performance. Following BIBREF3 , we also checked the performance of a hypothesis-only model trained with each training set to test whether our test set contains undesired biases.", + "Table 7 shows that the performance of BERT with the hypothesis-only training set dropped around 10-40% as compared with the one with the premise-hypothesis training set, even if we use the data augmentation technique. This indicates that the MED test set does not allow models to predict from hypotheses alone. Data augmentation by HELP improved the overall accuracy to 71.6%, but there is still room for improvement. In addition, while adding HELP increased the accuracy on downward inferences, it slightly decreased accuracy on upward inferences. The size of downward examples in HELP is much larger than that of upward examples. This might improve accuracy on downward inferences, but might decrease accuracy on upward inferences.", + "To investigate the relationship between accuracy on upward inferences and downward inferences, we checked the performance throughout training BERT with only upward and downward inference examples in HELP (Figure 2 (i), (ii)). These two figures show that, as the size of the upward training set increased, BERT performed better on upward inferences but worse on downward inferences, and vice versa.", + "Figure 2 (iii) shows performance on a different ratio of upward and downward inference training sets. When downward inference examples constitute more than half of the training set, accuracies on upward and downward inferences were reversed. As the ratio of downward inferences increased, BERT performed much worse on upward inferences. This indicates that a training set in one direction (upward or downward entailing) of monotonicity might be harmful to models when learning the opposite direction of monotonicity.", + "Previous work using HELP BIBREF11 reported that the BERT trained with MultiNLI and HELP containing both upward and downward inferences improved accuracy on both directions of monotonicity. MultiNLI rarely comes from downward inferences (see Section \"Discussion\" ), and its size is large enough to be immune to the side-effects of downward inference examples in HELP. This indicates that MultiNLI might act as a buffer against side-effects of the monotonicity-driven data augmentation technique.", + "Table 8 shows the evaluation results by genre. This result shows that inference problems collected from linguistics publications are more challenging than crowdsourced inference problems, even if we add HELP to training sets. As shown in Figure 2 , the change in performance on problems from linguistics publications is milder than that on problems from crowdsourcing. This result also indicates the difficulty of problems from linguistics publications. Regarding non-monotone problems collected via crowdsourcing, there are very few non-monotone problems, so accuracy is 100%. Adding non-monotone problems to our test set is left for future work.", + "Table 9 shows the evaluation results by type of linguistic phenomenon. While accuracy on problems involving NPIs and conditionals was improved on both upward and downward inferences, accuracy on problems involving conjunction and disjunction was improved on only one direction. In addition, it is interesting to see that the change in accuracy on conjunction was opposite to that on disjunction. Downward inference examples involving disjunction are similar to upward inference ones; that is, inferences from a sentence to a shorter sentence are valid (e.g., Not many campers have had a sunburn or caught a cold $\\Rightarrow $ Not many campers have caught a cold). Thus, these results were also caused by addition of downward inference examples. Also, accuracy on problems annotated with reverse tags was apparently better without HELP because all examples are upward inferences embedded in a downward environment twice.", + "Table 9 also shows that accuracy on conditionals was better on upward inferences than that on downward inferences. This indicates that BERT might fail to capture the monotonicity property that conditionals create a downward entailing context in their scope while they create an upward entailing context out of their scope.", + "Regarding lexical knowledge, the data augmentation technique improved the performance much better on downward inferences which do not require lexical knowledge. However, among the 394 problems for which all models provided wrong answers, 244 problems are non-lexical inference problems. This indicates that some non-lexical inference problems are more difficult than lexical inference problems, though accuracy on non-lexical inference problems was better than that on lexical inference problems." + ], + [ + "One of our findings is that there is a type of downward inferences to which every model fails to provide correct answers. One such example is concerned with the contrast between few and a few. Among 394 problems for which all models provided wrong answers, 148 downward inference problems were problems involving the downward monotonicity operator few such as in the following example:", + " $P$ : Few of the books had typical or marginal readers $H$ : Few of the books had some typical readers We transformed these downward inference problems to upward inference problems in two ways: (i) by replacing the downward operator few with the upward operator a few, and (ii) by removing the downward operator few. We tested BERT using these transformed test sets. The results showed that BERT predicted the same answers for the transformed test sets. This suggests that BERT does not understand the difference between the downward operator few and the upward operator a few.", + "The results of crowdsourcing tasks in Section 3.1.3 showed that some downward inferences can naturally be performed in human reasoning. However, we also found that the MultiNLI training set BIBREF10 , which is one of the dataset created from naturally-occurring texts, contains only 77 downward inference problems, including the following one.", + " $P$ : No racin' on the Range $H$ : No horse racing is allowed on the Range ", + "One possible reason why there are few downward inferences is that certain pragmatic factors can block people to draw a downward inference. For instance, in the case of the inference problem in ( \"Discussion\" ), unless the added disjunct in $H$ , i.e., a small cat with green eyes, is salient in the context, it would be difficult to draw the conclusion $H$ from the premise $P$ .", + " $P$ : I saw a dog $H$ : I saw a dog or a small cat with green eyes ", + "Such pragmatic factors would be one of the reasons why it is difficult to obtain downward inferences in naturally occurring texts." + ], + [ + "We introduced a large monotonicity entailment dataset, called MED. To illustrate the usefulness of MED, we tested state-of-the-art NLI models, and found that performance on the new test set was substantially worse for all state-of-the-art NLI models. In addition, the accuracy on downward inferences was inversely proportional to the one on upward inferences.", + "An experiment with the data augmentation technique showed that accuracy on upward and downward inferences depends on the proportion of upward and downward inferences in the training set. This indicates that current neural models might have limitations on their generalization ability in monotonicity reasoning. We hope that the MED will be valuable for future research on more advanced models that are capable of monotonicity reasoning in a proper way." + ], + [ + "This work was partially supported by JST AIP- PRISM Grant Number JPMJCR18Y1, Japan, and JSPS KAKENHI Grant Number JP18H03284, Japan. We thank our three anonymous reviewers for helpful suggestions. We are also grateful to Koki Washio, Masashi Yoshikawa, and Thomas McLachlan for helpful discussion." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0493/instruction.md b/qasper-0493/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..c6eab988156bdff6d1555a0405fda5979aba2f5d --- /dev/null +++ b/qasper-0493/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Can neural networks understand monotonicity reasoning? + +Question: What NLI models do they analyze? \ No newline at end of file diff --git a/qasper-0494/instruction.md b/qasper-0494/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..e82d8692580c5106792d05af8deb3e7761fdf3cd --- /dev/null +++ b/qasper-0494/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Can neural networks understand monotonicity reasoning? + +Question: How do they define upward and downward reasoning? \ No newline at end of file diff --git a/qasper-0495/instruction.md b/qasper-0495/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..8500768ab2ac9ce0db7a8183b5ffded373187ebd --- /dev/null +++ b/qasper-0495/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Can neural networks understand monotonicity reasoning? + +Question: What is monotonicity reasoning? \ No newline at end of file diff --git a/qasper-0500/environment/Dockerfile b/qasper-0500/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0500/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/* diff --git a/qasper-0500/instruction.md b/qasper-0500/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..1f1610249f61446a3044151246a639eb45489a02 --- /dev/null +++ b/qasper-0500/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Synchronising audio and ultrasound by learning cross-modal embeddings + +Question: Do they compare their neural network against any other model? \ No newline at end of file diff --git a/qasper-0501/environment/Dockerfile b/qasper-0501/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0501/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/* diff --git a/qasper-0501/instruction.md b/qasper-0501/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..2c5f1e57f71989b958f7d92d6cd5ff35bf39b784 --- /dev/null +++ b/qasper-0501/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Synchronising audio and ultrasound by learning cross-modal embeddings + +Question: Do they annotate their own dataset or use an existing one? \ No newline at end of file diff --git a/qasper-0506/environment/Dockerfile b/qasper-0506/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0506/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/* diff --git a/qasper-0506/instruction.md b/qasper-0506/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..83e084cf014a7ce4ac660fdda49cc92a25757efb --- /dev/null +++ b/qasper-0506/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: wav2vec: Unsupervised Pre-training for Speech Recognition + +Question: Which unlabeled data do they pretrain with? \ No newline at end of file diff --git a/qasper-0507/environment/Dockerfile b/qasper-0507/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0507/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/* diff --git a/qasper-0507/instruction.md b/qasper-0507/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..b432e0710481b192120571e17ff4c96886c0c185 --- /dev/null +++ b/qasper-0507/instruction.md @@ -0,0 +1,88 @@ +Name of Paper: wav2vec: Unsupervised Pre-training for Speech Recognition + +Question: How many convolutional layers does their model have? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Pre-training Approach", + "Model", + "Objective", + "Data", + "Acoustic Models", + "Decoding", + "Pre-training Models", + "Results", + "Pre-training for the WSJ benchmark", + "Pre-training for TIMIT", + "Ablations", + "Conclusions", + "Acknowledgements" + ], + "paragraphs": [ + [ + "Current state of the art models for speech recognition require large amounts of transcribed audio data to attain good performance BIBREF1 . Recently, pre-training of neural networks has emerged as an effective technique for settings where labeled data is scarce. The key idea is to learn general representations in a setup where substantial amounts of labeled or unlabeled data is available and to leverage the learned representations to improve performance on a downstream task for which the amount of data is limited. This is particularly interesting for tasks where substantial effort is required to obtain labeled data, such as speech recognition.", + "In computer vision, representations for ImageNet BIBREF2 and COCO BIBREF3 have proven to be useful to initialize models for tasks such as image captioning BIBREF4 or pose estimation BIBREF5 . Unsupervised pre-training for computer vision has also shown promise BIBREF6 . In natural language processing (NLP), unsupervised pre-training of language models BIBREF7 , BIBREF8 , BIBREF9 improved many tasks such as text classification, phrase structure parsing and machine translation BIBREF10 , BIBREF11 . In speech processing, pre-training has focused on emotion recogniton BIBREF12 , speaker identification BIBREF13 , phoneme discrimination BIBREF14 , BIBREF15 as well as transferring ASR representations from one language to another BIBREF16 . There has been work on unsupervised learning for speech but the resulting representations have not been applied to improve supervised speech recognition BIBREF17 , BIBREF18 , BIBREF19 , BIBREF20 , BIBREF21 .", + "In this paper, we apply unsupervised pre-training to improve supervised speech recognition. This enables exploiting unlabeled audio data which is much easier to collect than labeled data. Our model, , is a convolutional neural network that takes raw audio as input and computes a general representation that can be input to a speech recognition system. The objective is a contrastive loss that requires distinguishing a true future audio sample from negatives BIBREF22 , BIBREF23 , BIBREF15 . Different to previous work BIBREF15 , we move beyond frame-wise phoneme classification and apply the learned representations to improve strong supervised ASR systems. relies on a fully convolutional architecture which can be easily parallelized over time on modern hardware compared to recurrent autoregressive models used in previous work (\u00a7 SECREF2 ).", + "Our experimental results on the WSJ benchmark demonstrate that pre-trained representations estimated on about 1,000 hours of unlabeled speech can substantially improve a character-based ASR system and outperform the best character-based result in the literature, Deep Speech 2. On the TIMIT task, pre-training enables us to match the best reported result in the literature. In a simulated low-resource setup with only eight hours of transcriped audio data, reduces WER by up to 32% compared to a baseline model that relies on labeled data only (\u00a7 SECREF3 & \u00a7 SECREF4 )." + ], + [ + "Given an audio signal as input, we optimize our model (\u00a7 SECREF3 ) to predict future samples from a given signal context. A common problem with these approaches is the requirement to accurately model the data distribution INLINEFORM0 , which is challenging. We avoid this problem by first encoding raw speech samples INLINEFORM1 into a feature representation INLINEFORM2 at a lower temporal frequency and then implicitly model a density function INLINEFORM3 similar to BIBREF15 ." + ], + [ + "Our model takes raw audio signal as input and then applies two networks. The encoder network embeds the audio signal in latent space and the context network combines multiple time-steps of the encoder to obtain contextualized representations (Figure FIGREF2 ). Both networks are then used to compute the objective function (\u00a7 SECREF4 ).", + "Given raw audio samples INLINEFORM0 , we apply the encoder network INLINEFORM1 which we parameterize as a five-layer convolutional network similar to BIBREF15 . Alternatively, one could use other architectures such as the trainable frontend of BIBREF24 amongst others. The encoder layers have kernel sizes INLINEFORM2 and strides INLINEFORM3 . The output of the encoder is a low frequency feature representation INLINEFORM4 which encodes about 30ms of 16KHz of audio and the striding results in representation INLINEFORM5 every 10ms.", + "Next, we apply the context network INLINEFORM0 to the output of the encoder network to mix multiple latent representations INLINEFORM1 into a single contextualized tensor INLINEFORM2 for a receptive field size INLINEFORM3 . The context network has seven layers and each layer has kernel size three and stride one. The total receptive field of the context network is about 180ms.", + "The layers of both networks consist of a causal convolution with 512 channels, a group normalization layer and a ReLU nonlinearity. We normalize both across the feature and temporal dimension for each sample which is equivalent to group normalization with a single normalization group BIBREF25 . We found it important to choose a normalization scheme that is invariant to the scaling and the offset of the input data. This choice resulted in representations that generalize well across datasets." + ], + [ + "We train the model to distinguish a sample INLINEFORM0 that is k steps in the future from distractor samples INLINEFORM1 drawn from a proposal distribution INLINEFORM2 , by minimizing the contrastive loss for each step INLINEFORM3 : DISPLAYFORM0 ", + "where we denote the sigmoid INLINEFORM0 , and where INLINEFORM1 is the probability of INLINEFORM2 being the true sample. We consider a step-specific affine transformation INLINEFORM3 for each step INLINEFORM4 , that is applied to INLINEFORM5 BIBREF15 . We optimize the loss INLINEFORM6 , summing ( EQREF5 ) over different step sizes. In practice, we approximate the expectation by sampling ten negatives examples by uniformly choosing distractors from each audio sequence, i.e., INLINEFORM7 , where INLINEFORM8 is the sequence length and we set INLINEFORM9 to the number of negatives.", + "After training, we input the representations produced by the context network INLINEFORM0 to the acoustic model instead of log-mel filterbank features." + ], + [ + "We consider the following corpora: For phoneme recognition on TIMIT BIBREF26 we use the standard train, dev and test split where the training data contains just over three hours of audio data. Wall Street Journal (WSJ; Woodland et al., 1994) comprises about 81 hours of transcribed audio data. We train on si284, validate on nov93dev and test on nov92. Librispeech BIBREF27 contains a total of 960 hours of clean and noisy speech for training. For pre-training, we use either the full 81 hours of the WSJ corpus, an 80 hour subset of clean Librispeech, the full 960 hour Librispeech training set, or a combination of all of them.", + "To train the baseline acoustic model we compute 80 log-mel filterbank coefficients for a 25ms sliding window with stride 10ms. Final models are evaluated in terms of both word error rate (WER) and letter error rate (LER)." + ], + [ + "We use the wav2letter++ toolkit for training and evaluation of acoustic models BIBREF28 . For the TIMIT task, we follow the character-based wav2letter++ setup of BIBREF24 which uses seven consecutive blocks of convolutions (kernel size 5 with 1,000 channels), followed by a PReLU nonlinearity and a dropout rate of 0.7. The final representation is projected to a 39-dimensional phoneme probability. The model is trained using the Auto Segmentation Criterion (ASG; Collobert et al., 2016)) using SGD with momentum.", + "Our baseline for the WSJ benchmark is the wav2letter++ setup described in BIBREF29 which is a 17 layer model with gated convolutions BIBREF30 . The model predicts probabilities for 31 graphemes, including the standard English alphabet, the apostrophe and period, two repetition characters (e.g. the word ann is transcribed as an1), and a silence token (|) used as word boundary.", + "All acoustic models are trained on 8 Nvidia V100 GPUs using the distributed training implementations of fairseq and wav2letter++. When training acoustic models on WSJ, we use plain SGD with learning rate 5.6 as well as gradient clipping BIBREF29 and train for 1,000 epochs with a total batch size of 64 audio sequences. We use early stopping and choose models based on validation WER after evaluating checkpoints with a 4-gram language model. For TIMIT we use learning rate 0.12, momentum of 0.9 and train for 1,000 epochs on 8 GPUs with a batch size of 16 audio sequences." + ], + [ + "For decoding the emissions from the acoustic model we use a lexicon as well as a separate language model trained on the WSJ language modeling data only. We consider a 4-gram KenLM language model BIBREF31 , a word-based convolutional language model BIBREF29 , and a character based convolutional language model BIBREF32 . We decode the word sequence INLINEFORM0 from the output of the context network INLINEFORM1 or log-mel filterbanks using the beam search decoder of BIBREF29 by maximizing DISPLAYFORM0 ", + "where INLINEFORM0 is the acoustic model, INLINEFORM1 is the language model, INLINEFORM2 are the characters of INLINEFORM3 . Hyper-parameters INLINEFORM4 , INLINEFORM5 and INLINEFORM6 are weights for the language model, the word penalty, and the silence penalty.", + "For decoding WSJ, we tune the hyperparameters INLINEFORM0 , INLINEFORM1 and INLINEFORM2 using a random search. Finally, we decode the emissions from the acoustic model with the best parameter setting for INLINEFORM3 , INLINEFORM4 and INLINEFORM5 , and a beam size of 4000 and beam score threshold of 250." + ], + [ + "The pre-training models are implemented in PyTorch in the fairseq toolkit BIBREF0 . We optimize them with Adam BIBREF33 and a cosine learning rate schedule BIBREF34 annealed over 40K update steps for both WSJ and the clean Librispeech training datasets. We start with a learning rate of 1e-7, and the gradually warm it up for 500 updates up to 0.005 and then decay it following the cosine curve up to 1e-6. We train for 400K steps for full Librispeech. To compute the objective, we sample ten negatives and we use INLINEFORM0 tasks.", + "We train on 8 GPUs and put a variable number of audio sequences on each GPU, up to a pre-defined limit of 1.5M frames per GPU. Sequences are grouped by length and we crop them to a maximum size of 150K frames each, or the length of the shortest sequence in the batch, whichever is smaller. Cropping removes speech signal from either the beginning or end of the sequence and we randomly decide the cropping offsets for each sample; we re-sample every epoch. This is a form of data augmentation but also ensures equal length of all sequences on a GPU and removes on average 25% of the training data. After cropping the total effective batch size across GPUs is about 556 seconds of speech signal (for a variable number of audio sequences)." + ], + [ + "Different to BIBREF15 , we evaluate the pre-trained representations directly on downstream speech recognition tasks. We measure speech recognition performance on the WSJ benchmark and simulate various low resource setups (\u00a7 SECREF12 ). We also evaluate on the TIMIT phoneme recognition task (\u00a7 SECREF13 ) and ablate various modeling choices (\u00a7 SECREF14 )." + ], + [ + "We consider pre-training on the audio data (without labels) of WSJ, part of clean Librispeech (about 80h) and full Librispeech as well as a combination of all datasets (\u00a7 SECREF7 ). For the pre-training experiments we feed the output of the context network to the acoustic model, instead of log-mel filterbank features.", + "Table shows that pre-training on more data leads to better accuracy on the WSJ benchmark. Pre-trained representations can substantially improve performance over our character-based baseline which is trained on log-mel filterbank features. This shows that pre-training on unlabeled audio data can improve over the best character-based approach, Deep Speech 2 BIBREF1 , by 0.3 WER on nov92. Our best pre-training model performs as well as the phoneme-based model of BIBREF35 . BIBREF36 is a phoneme-based approach that pre-trains on the transcribed Libirspeech data and then fine-tunes on WSJ. In comparison, our method requires only unlabeled audio data and BIBREF36 also rely on a stronger baseline model than our setup.", + "What is the impact of pre-trained representations with less transcribed data? In order to get a better understanding of this, we train acoustic models with different amounts of labeled training data and measure accuracy with and without pre-trained representations (log-mel filterbanks). The pre-trained representations are trained on the full Librispeech corpus and we measure accuracy in terms of WER when decoding with a 4-gram language model. Figure shows that pre-training reduces WER by 32% on nov93dev when only about eight hours of transcribed data is available. Pre-training only on the audio data of WSJ ( WSJ) performs worse compared to the much larger Librispeech ( Libri). This further confirms that pre-training on more data is crucial to good performance." + ], + [ + "On the TIMIT task we use a 7-layer wav2letter++ model with high dropout (\u00a7 SECREF3 ; Synnaeve et al., 2016). Table shows that we can match the state of the art when we pre-train on Librispeech and WSJ audio data. Accuracy steadily increases with more data for pre-training and the best accuracy is achieved when we use the largest amount of data for pre-training." + ], + [ + "In this section we analyze some of the design choices we made for . We pre-train on the 80 hour subset of clean Librispeech and evaluate on TIMIT. Table shows that increasing the number of negative samples only helps up to ten samples. Thereafter, performance plateaus while training time increases. We suspect that this is because the training signal from the positive samples decreases as the number of negative samples increases. In this experiment, everything is kept equal except for the number of negative samples.", + "Next, we analyze the effect of data augmentation through cropping audio sequences (\u00a7 SECREF11 ). When creating batches we crop sequences to a pre-defined maximum length. Table shows that a crop size of 150K frames results in the best performance. Not restricting the maximum length (None) gives an average sequence length of about 207K frames and results in the worst accuracy. This is most likely because the setting provides the least amount of data augmentation.", + "Table shows that predicting more than 12 steps ahead in the future does not result in better performance and increasing the number of steps increases training time." + ], + [ + "We introduce , the first application of unsupervised pre-training to speech recognition with a fully convolutional model. Our approach achieves 2.78 WER on the test set of WSJ, a result that outperforms the next best known character-based speech recognition model in the literature BIBREF1 while using three orders of magnitude less transcribed training data. We show that more data for pre-training improves performance and that this approach not only improves resource-poor setups, but also settings where all WSJ training data is used. In future work, we will investigate different architectures and fine-tuning which is likely to further improve performance." + ], + [ + "We thank the Speech team at FAIR, especially Jacob Kahn, Vineel Pratap and Qiantong Xu for help with wav2letter++ experiments, and Tatiana Likhomanenko for providing convolutional language models for our experiments." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0508/instruction.md b/qasper-0508/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..971704218f3bf46821193125693c60761dc122cf --- /dev/null +++ b/qasper-0508/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: wav2vec: Unsupervised Pre-training for Speech Recognition + +Question: Do they explore how much traning data is needed for which magnitude of improvement for WER? \ No newline at end of file diff --git a/qasper-0509/environment/Dockerfile b/qasper-0509/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0509/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/* diff --git a/qasper-0509/instruction.md b/qasper-0509/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..f761100e073464a3418bcca740c5959ac476afd6 --- /dev/null +++ b/qasper-0509/instruction.md @@ -0,0 +1,119 @@ +Name of Paper: Cross-lingual, Character-Level Neural Morphological Tagging + +Question: How are character representations from various languages joint? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Morphological Tagging", + "Character-Level Neural Transfer", + "Character-Level Neural Networks", + "Cross-Lingual Morphological Transfer as Multi-Task Learning", + "Experiments", + "Experimental Languages", + "Datasets", + "Baselines", + "Experimental Details", + "Results and Discussion", + "Related Work", + "Alignment-Based Distant Supervision.", + "Character-level NLP.", + "Neural Cross-lingual Transfer in NLP.", + "Conclusion", + "Acknowledgements" + ], + "paragraphs": [ + [ + "State-of-the-art morphological taggers require thousands of annotated sentences to train. For the majority of the world's languages, however, sufficient, large-scale annotation is not available and obtaining it would often be infeasible. Accordingly, an important road forward in low-resource NLP is the development of methods that allow for the training of high-quality tools from smaller amounts of data. In this work, we focus on transfer learning\u2014we train a recurrent neural tagger for a low-resource language jointly with a tagger for a related high-resource language. Forcing the models to share character-level features among the languages allows large gains in accuracy when tagging the low-resource languages, while maintaining (or even improving) accuracy on the high-resource language.", + "Recurrent neural networks constitute the state of the art for a myriad of tasks in NLP, e.g., multi-lingual part-of-speech tagging BIBREF0 , syntactic parsing BIBREF1 , BIBREF2 , morphological paradigm completion BIBREF3 , BIBREF4 and language modeling BIBREF5 , BIBREF6 ; recently, such models have also improved morphological tagging BIBREF7 , BIBREF8 . In addition to increased performance over classical approaches, neural networks also offer a second advantage: they admit a clean paradigm for multi-task learning. If the learned representations for all of the tasks are embedded jointly into a shared vector space, the various tasks reap benefits from each other and often performance improves for all BIBREF9 . We exploit this idea for language-to-language transfer to develop an approach for cross-lingual morphological tagging.", + "We experiment on 18 languages taken from four different language families. Using the Universal Dependencies treebanks, we emulate a low-resource setting for our experiments, e.g., we attempt to train a morphological tagger for Catalan using primarily data from a related language like Spanish. Our results demonstrate the successful transfer of morphological knowledge from the high-resource languages to the low-resource languages without relying on an externally acquired bilingual lexicon or bitext. We consider both the single- and multi-source transfer case and explore how similar two languages must be in order to enable high-quality transfer of morphological taggers." + ], + [ + "Many languages in the world exhibit rich inflectional morphology: the form of individual words mutates to reflect the syntactic function. For example, the Spanish verb so\u00f1ar will appear as sue\u00f1o in the first person present singular, but so\u00f1\u00e1is in the second person present plural, depending on the bundle of syntaco-semantic attributes associated with the given form (in a sentential context). For concreteness, we list a more complete table of Spanish verbal inflections in tab:paradigm. [author=Ryan,color=purple!40,size=,fancyline,caption=,]Notation in table is different. Note that some languages, e.g. the Northeastern Caucasian language Archi, display a veritable cornucopia of potential forms with the size of the verbal paradigm exceeding 10,000 BIBREF10 .", + "Standard NLP annotation, e.g., the scheme in sylakglassman-EtAl:2015:ACL-IJCNLP, marks forms in terms of universal key\u2013attribute pairs, e.g., the first person present singular is represented as $\\left[\\right.$ pos=V, per=1, num=sg, tns=pres $\\left.\\right]$ . This bundle of key\u2013attributes pairs is typically termed a morphological tag and we may view the goal of morphological tagging to label each word in its sentential context with the appropriate tag BIBREF11 , BIBREF12 . As the part-of-speech (POS) is a component of the tag, we may view morphological tagging as a strict generalization of POS tagging, where we have significantly refined the set of available tags. All of the experiments in this paper make use of the universal morphological tag set available in the Universal Dependencies (UD) BIBREF13 . As an example, we have provided a Russian sentence with its UD tagging in fig:russian-sentence." + ], + [ + "Our formulation of transfer learning builds on work in multi-task learning BIBREF15 , BIBREF9 . We treat each individual language as a task and train a joint model for all the tasks. We first discuss the current state of the art in morphological tagging: a character-level recurrent neural network. After that, we explore three augmentations to the architecture that allow for the transfer learning scenario. All of our proposals force the embedding of the characters for both the source and the target language to share the same vector space, but involve different mechanisms, by which the model may learn language-specific features." + ], + [ + "Character-level neural networks currently constitute the state of the art in morphological tagging BIBREF8 . We draw on previous work in defining a conditional distribution over taggings ${t}$ for a sentence ${w}$ of length $|{w}| = N$ as ", + "$$p_{{\\theta }}({{t}} \\mid {{w}}) = \\prod _{i=1}^N p_{{\\theta }}(t_i \\mid {{w}}), $$ (Eq. 12) ", + "which may be seen as a $0^\\text{th}$ order conditional random field (CRF) BIBREF16 with parameter vector ${{\\theta }}$ . Importantly, this factorization of the distribution $p_{{\\theta }}({{t}} \\mid {{w}})$ also allows for efficient exact decoding and marginal inference in ${\\cal O}(N)$ -time, but at the cost of not admitting any explicit interactions in the output structure, i.e., between adjacent tags. We parameterize the distribution over tags at each time step as ", + "$$p_{{\\theta }}(t_i \\mid {{w}}) = \\text{softmax}\\left(W {e}_i + {b}\\right), $$ (Eq. 15) ", + "where $W \\in \\mathbb {R}^{|{\\cal T}| \\times n}$ is an embedding matrix, ${b}\\in \\mathbb {R}^{|{\\cal T}|}$ is a bias vector and positional embeddings ${e}_i$ are taken from a concatenation of the output of two long short-term memory recurrent neural networks (LSTMs) BIBREF18 , folded forward and backward, respectively, over a sequence of input vectors. This constitutes a bidirectional LSTM BIBREF19 . We define the positional embedding vector as follows ", + "$${e}_i = \\left[{\\text{LSTM}}({v}_{1:i});\n{\\text{LSTM}}({v}_{i+1:N})\\right], $$ (Eq. 17) ", + "where each ${v}_i \\in \\mathbb {R}^n$ is, itself, a word embedding. Note that the function $\\text{LSTM}$ returns the last final hidden state vector of the network. This architecture is the context bidirectional recurrent neural network of plank-sogaard-goldberg:2016:P16-2. Finally, we derive each word embedding vector ${v}_i$ from a character-level bidirectional LSTM embedder. Namely, we define each word embedding as the concatenation ", + "$${v}_i = &\\left[ {\\text{LSTM}}\\left(\\langle c_{i_1}, \\ldots ,\nc_{i_{M_i}}\\rangle \\right); \\right. \\\\\n&\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\, \\left. {\\text{LSTM}} \\left(\\langle c_{i_{M_i}}, \\ldots , c_{i_1}\\rangle \\right) \\right]. \\nonumber $$ (Eq. 18) ", + " In other words, we run a bidirectional LSTM over the character stream. This bidirectional LSTM is the sequence bidirectional recurrent neural network of plank-sogaard-goldberg:2016:P16-2. Note a concatenation of the sequence of character symbols $\\langle c_{i_1}, \\ldots , c_{i_{M_i}} \\rangle $ results in the word string $w_i$ . Each of the $M_i$ characters $c_{i_k}$ is a member of the set $\\Sigma $ . We take $\\Sigma $ to be the union of sets of characters in the languages considered.", + "We direct the reader to heigold2017 for a more in-depth discussion of this and various additional architectures for the computation of ${v}_i$ ; the architecture we have presented in eq:embedder-v is competitive with the best performing setting in Heigold et al.'s study." + ], + [ + "Cross-lingual morphological tagging may be formulated as a multi-task learning problem. We seek to learn a set of shared character embeddings for taggers in both languages together through optimization of a joint loss function that combines the high-resource tagger and the low-resource one. The first loss function we consider is the following: ", + "$${\\cal L}_{\\textit {multi}}({\\theta }) = -\\!\\!\\!\\sum _{({t}, {w}) \\in {\\cal D}_s} \\!\\!\\!\\! \\log &\\, p_{{\\theta }} ({t}\\mid {w}, \\ell _s ) \\\\[-5]\n\\nonumber & -\\!\\!\\!\\!\\sum _{({t}, {w}) \\in {\\cal D}_t} \\!\\!\n\\log p_{{\\theta }}\\left({t}\\mid {w}, \\ell _t \\right).$$ (Eq. 20) ", + " Crucially, our cross-lingual objective forces both taggers to share part of the parameter vector ${\\theta }$ , which allows it to represent morphological regularities between the two languages in a common embedding space and, thus, enables transfer of knowledge. This is no different from monolingual multi-task settings, e.g., jointly training a chunker and a tagger for the transfer of syntactic information BIBREF9 . We point out that, in contrast to our approach, almost all multi-task transfer learning, e.g., for dependency parsing BIBREF20 , has shared word-level embeddings rather than character-level embeddings. See sec:related-work for a more complete discussion.", + "We consider two parameterizations of this distribution $p_{{\\theta }}(t_i\n\\mid {w}, \\ell )$ . First, we modify the initial character-level LSTM embedding such that it also encodes the identity of the language. Second, we modify the softmax layer, creating a language-specific softmax.", + "Our first architecture has one softmax, as in eq:tagger, over all morphological tags in ${\\cal T}$ (shared among all the languages). To allow the architecture to encode morphological features specific to one language, e.g., the third person present plural ending in Spanish is -an, but -\u00e3o in Portuguese, we modify the creation of the character-level embeddings. Specifically, we augment the character alphabet $\\Sigma $ with a distinguished symbol that indicates the language: $\\text{{\\tt id}}_\\ell $ . We then pre- and postpend this symbol to the character stream for every word before feeding the characters into the bidirectional LSTM Thus, we arrive at the new language-specific word embeddings, ", + "$${v}^{\\ell }_i = &\\left[ {\\text{LSTM}}\\left(\\langle \\text{{\\tt id}}_\\ell , c_{i_1}, \\ldots ,\nc_{i_{M_i}}, \\text{{\\tt id}}_\\ell \\rangle \\right); \\right. \\\\\n&\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\,\\, \\left. {\\text{LSTM}} \\left(\\langle \\text{{\\tt id}}_\\ell , c_{i_{M_i}}, \\ldots , c_{i_1}, \\text{{\\tt id}}_\\ell \\rangle \\right) \\right]. \\nonumber $$ (Eq. 22) ", + " This model creates a language-specific embedding vector ${v}_i$ , but the individual embeddings for a given character are shared among the languages jointly trained on. The remainder of the architecture is held constant.", + "Next, inspired by the architecture of heigold2013multilingual, we consider a language-specific softmax layer, i.e., we define a new output layer for every language: ", + "$$p_{{\\theta }}\\left(t_i \\mid {w}, \\ell \\right) = \\text{softmax}\\left(W_{\\ell } {e}_i + {b}_{\\ell }\\right),$$ (Eq. 24) ", + "where $W_{\\ell } \\in \\mathbb {R}^{|{\\cal T}| \\times n}$ and ${b}_{\\ell } \\in \\mathbb {R}^{|{\\cal T}|}$ are now language-specific. In this architecture, the embeddings ${e}_i$ are the same for all languages\u2014the model has to learn language-specific behavior exclusively through the output softmax of the tagging LSTM.", + "The third model we exhibit is a joint architecture for tagging and language identification. We consider the following loss function: ", + "$${\\cal L}_{\\textit {joint}} ({\\theta }) = -\\!\\!\\!\\sum _{({t}, {w}) \\in {\\cal D}_s} \\!\\!\\! \\log \\, & p_{{\\theta }}(\\ell _s, {t}\\mid {w}) \\\\[-5] \\nonumber &-\\!\\sum _{({t}, {w}) \\in {\\cal D}_t} \\!\\!\\!\\!\\! \\log p_{{\\theta }}\\left(\\ell _t, {t}\\mid {w}\\right),$$ (Eq. 26) ", + " where we factor the joint distribution as ", + "$$p_{{\\theta }}\\left(\\ell , {t}\\mid {w}\\right) &= p_{{\\theta }}\\left(\\ell \\mid {w}\\right) \\cdot p_{{\\theta }}\\left({t}\\mid {w}, \\ell \\right).$$ (Eq. 27) ", + " Just as before, we define $p_{{\\theta }}\\left({t}\\mid {w}, \\ell \\right)$ above as in eq:lang-specific and we define ", + "$$p_{{\\theta }}(\\ell \\mid {w}) = \\text{softmax}\\left(U\\tanh (V{e}_i)\\right),$$ (Eq. 28) ", + "which is a multi-layer perceptron with a binary softmax (over the two languages) as an output layer; we have added the additional parameters $V \\in \\mathbb {R}^{2 \\times n}$ and $U \\in \\mathbb {R}^{2 \\times 2}$ . In the case of multi-source transfer, this is a softmax over the set of languages.", + "The first two architectures discussed in par:arch1 represent two possibilities for a multi-task objective, where we condition on the language of the sentence. The first integrates this knowledge at a lower level and the second at a higher level. The third architecture discussed in sec:joint-arch takes a different tack\u2014rather than conditioning on the language, it predicts it. The joint model offers one interesting advantage over the two architectures proposed. Namely, it allows us to perform a morphological analysis on a sentence where the language is unknown. This effectively alleviates an early step in the NLP pipeline, where language id is performed and is useful in conditions where the language to be tagged may not be known a-priori, e.g., when tagging social media data.", + "While there are certainly more complex architectures one could engineer for the task, we believe we have found a relatively diverse sampling, enabling an interesting experimental comparison. Indeed, it is an important empirical question which architectures are most appropriate for transfer learning. Since transfer learning affords the opportunity to reduce the sample complexity of the \u201cdata-hungry\u201d neural networks that currently dominate NLP research, finding a good solution for cross-lingual transfer in state-of-the-art neural models will likely be a boon for low-resource NLP in general." + ], + [ + "Empirically, we ask three questions of our architectures. i) How well can we transfer morphological tagging models from high-resource languages to low-resource languages in each architecture? (Does one of the three outperform the others?) ii) How many annotated data in the low-resource language do we need? iii) How closely related do the languages need to be to get good transfer?" + ], + [ + "We experiment with the language families: Romance (Indo-European), Northern Germanic (Indo-European), Slavic (Indo-European) and Uralic. In the Romance sub-grouping of the wider Indo-European family, we experiment on Catalan (ca), French (fr), Italian (it), Portuguese (pt), Romanian (ro) and Spanish (es). In the Northern Germanic family, we experiment on Danish (da), Norwegian (no) and Swedish (sv). In the Slavic family, we experiment on Bulgarian (bg), Czech (bg), Polish (pl), Russian (ru), Slovak (sk) and Ukrainian (uk). Finally, in the Uralic family we experiment on Estonian (et), Finnish (fi) and Hungarian (hu)." + ], + [ + "We use the morphological tagging datasets provided by the Universal Dependencies (UD) treebanks (the concatenation of the $4^\\text{th}$ and $6^\\text{th}$ columns of the file format) BIBREF13 . We list the size of the training, development and test splits of the UD treebanks we used in tab:lang-size. Also, we list the number of unique morphological tags in each language in tab:num-tags, which serves as an approximate measure of the morphological complexity each language exhibits. Crucially, the data are annotated in a cross-linguistically consistent manner, such that words in the different languages that have the same syntacto-semantic function have the same bundle of tags (see sec:morpho-tagging for a discussion). Potentially, further gains would be possible by using a more universal scheme, e.g., the UniMorph scheme." + ], + [ + "We consider two baselines in our work. First, we consider the MarMoT tagger BIBREF17 , which is currently the best performing non-neural model. The source code for MarMoT is freely available online, which allows us to perform fully controlled experiments with this model. Second, we consider the alignment-based projection approach of buys-botha:2016:P16-1. We discuss each of the two baselines in turn.", + "The MarMoT tagger is the leading non-neural approach to morphological tagging. This baseline is important since non-neural, feature-based approaches have been found empirically to be more efficient, in the sense that their learning curves tend to be steeper. Thus, in the low-resource setting we would be remiss to not consider a feature-based approach. Note that this is not a transfer approach, but rather only uses the low-resource data.", + "The projection approach of buys-botha:2016:P16-1 provides an alternative method for transfer learning. The idea is to construct pseudo-annotations for bitext given an alignments BIBREF21 . Then, one trains a standard tagger using the projected annotations. The specific tagger employed is the wsabie model of DBLP:conf/ijcai/WestonBU11, which\u2014like our approach\u2014 is a $0^\\text{th}$ -order discriminative neural model. In contrast to ours, however, their network is shallow. We compare the two methods in more detail in sec:related-work.", + "Additionally, we perform a thorough study of the neural transfer learner, considering all three architectures. A primary goal of our experiments is to determine which of our three proposed neural transfer techniques is superior. Even though our experiments focus on morphological tagging, these architectures are more general in that they may be easily applied to other tasks, e.g., parsing or machine translation. We additionally explore the viability of multi-source transfer, i.e., the case where we have multiple source languages. All of our architectures generalize to the multi-source case without any complications." + ], + [ + "We train our models with the following conditions.", + "We evaluate using average per token accuracy, as is standard for both POS tagging and morphological tagging, and per feature $F_1$ as employed in buys-botha:2016:P16-1. The per feature $F_1$ calculates a key $F^k_1$ for each key in the target language's tags by asking if the key-attribute pair $k_i$ $=$ $v_i$ is in the predicted tag. Then, the key-specific $F^k_1$ values are averaged equally. Note that $F_1$ is a more flexible metric as it gives partial credit for getting some of the attributes in the bundle correct, where accuracy does not.", + "[author=Ryan,color=purple!40,size=,fancyline,caption=,]Georg needs to check. Taken from: http://www.dfki.de/ neumann/publications/new-ps/BigNLP2016.pdf Our networks are four layers deep (two LSTM layers for the character embedder, i.e., to compute ${v_i}$ and two LSTM layers for the tagger, i.e., to compute ${e_i}$ ) and we use an embedding size of 128 for the character input vector size and hidden layers of 256 nodes in all other cases. All networks are trained with the stochastic gradient method RMSProp BIBREF22 , with a fixed initial learning rate and a learning rate decay that is adjusted for the other languages according to the amount of training data. The batch size is always 16. Furthermore, we use dropout BIBREF23 . The dropout probability is set to 0.2. We used Torch 7 BIBREF24 to configure the computation graphs implementing the network architectures." + ], + [ + "[author=Ryan,color=purple!40,size=,fancyline,caption=,]Needs to be updated! We report our results in two tables. First, we report a detailed cross-lingual evaluation in tab:results. Secondly, we report a comparison against two baselines in tab:baseline-table1 (accuracy) and tab:baseline-table2 ( $F_1$ ). We see two general trends of the data. First, we find that genetically closer languages yield better source languages. Second, we find that the multi-softmax architecture is the best in terms of transfer ability, as evinced by the results in tab:results. We find a wider gap between our model and the baselines under the accuracy than under $F_1$ . We attribute this to the fact that $F_1$ is a softer metric in that it assigns credit to partially correct guesses." + ], + [ + "We divide the discussion of related work topically into three parts for ease of intellectual digestion." + ], + [ + "Most cross-lingual work in NLP\u2014focusing on morphology or otherwise\u2014has concentrated on indirect supervision, rather than transfer learning. The goal in such a regime is to provide noisy labels for training the tagger in the low-resource language through annotations projected over aligned bitext with a high-resource language. This method of projection was first introduced by DBLP:conf/naacl/YarowskyN01 for the projection of POS annotation. While follow-up work BIBREF26 , BIBREF27 , BIBREF28 has continually demonstrated the efficacy of projecting simple part-of-speech annotations, buys-botha:2016:P16-1 were the first to show the use of bitext-based projection for the training of a morphological tagger for low-resource languages.", + "As we also discuss the training of a morphological tagger, our work is most closely related to buys-botha:2016:P16-1 in terms of the task itself. We contrast the approaches. The main difference lies therein, that our approach is not projection-based and, thus, does not require the construction of a bilingual lexicon for projection based on bitext. Rather, our method jointly learns multiple taggers and forces them to share features\u2014a true transfer learning scenario. In contrast to projection-based methods, our procedure always requires a minimal amount of annotated data in the low-resource target language\u2014in practice, however, this distinction is non-critical as projection-based methods without a small mount of seed target language data perform poorly BIBREF29 ." + ], + [ + "Our work also follows a recent trend in NLP, whereby traditional word-level neural representations are being replaced by character-level representations for a myriad tasks, e.g., POS tagging DBLP:conf/icml/SantosZ14, parsing BIBREF30 , language modeling BIBREF31 , sentiment analysis BIBREF32 as well as the tagger of heigold2017, whose work we build upon. Our work is also related to recent work on character-level morphological generation using neural architectures BIBREF33 , BIBREF34 ." + ], + [ + "In terms of methodology, however, our proposal bears similarity to recent work in speech and machine translation\u2013we discuss each in turn. In speech recognition, heigold2013multilingual train a cross-lingual neural acoustic model on five Romance languages. The architecture bears similarity to our multi-language softmax approach. Dependency parsing benefits from cross-lingual learning in a similar fashion BIBREF35 , BIBREF20 .", + "In neural machine translation BIBREF36 , BIBREF37 , recent work BIBREF38 , BIBREF39 , BIBREF40 has explored the possibility of jointly train translation models for a wide variety of languages. Our work addresses a different task, but the undergirding philosophical motivation is similar, i.e., attack low-resource NLP through multi-task transfer learning. kann-cotterell-schutze:2017:ACL2017 offer a similar method for cross-lingual transfer in morphological inflection generation." + ], + [ + "We have presented three character-level recurrent neural network architectures for multi-task cross-lingual transfer of morphological taggers. We provided an empirical evaluation of the technique on 18 languages from four different language families, showing wide-spread applicability of the method. We found that the transfer of morphological taggers is an eminently viable endeavor among related language and, in general, the closer the languages, the easier the transfer of morphology becomes. Our technique outperforms two strong baselines proposed in previous work. Moreover, we define standard low-resource training splits in UD for future research in low-resource morphological tagging. Future work should focus on extending the neural morphological tagger to a joint lemmatizer BIBREF41 and evaluate its functionality in the low-resource setting." + ], + [ + "RC acknowledges the support of an NDSEG fellowship. Also, we would like to thank Jan Buys and Jan Botha who helped us compare to the numbers reported in their paper. We would also like to thank Hinrich Sch\u00fctze for reading an early draft and Tim Vieira and Jason Naradowsky for helpful initial discussions." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0530/instruction.md b/qasper-0530/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..bcdc1f2b81feaa0bdaf33e7b50dab9e564743675 --- /dev/null +++ b/qasper-0530/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Morphological Word Segmentation on Agglutinative Languages for Neural Machine Translation + +Question: How is morphology knowledge implemented in the method? \ No newline at end of file diff --git a/qasper-0531/environment/Dockerfile b/qasper-0531/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0531/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/* diff --git a/qasper-0531/instruction.md b/qasper-0531/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..226e7564cde2bc213f665bfa1c885214643c5a35 --- /dev/null +++ b/qasper-0531/instruction.md @@ -0,0 +1,104 @@ +Name of Paper: Morphological Word Segmentation on Agglutinative Languages for Neural Machine Translation + +Question: How does the word segmentation method work? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Approach", + "Approach ::: Morpheme Segmentation", + "Approach ::: Morpheme Segmentation ::: Stem with Combined Suffix", + "Approach ::: Morpheme Segmentation ::: Stem with Singular Suffix", + "Approach ::: Byte Pair Encoding (BPE)", + "Approach ::: Morphologically Motivated Segmentation", + "Approach ::: Morphologically Motivated Segmentation ::: BPE on Stem with Combined Suffix", + "Approach ::: Morphologically Motivated Segmentation ::: BPE on Stem with Singular Suffix", + "Experiments ::: Experimental Setup ::: Turkish-English Data :", + "Experiments ::: Experimental Setup ::: Uyghur-Chinese Data :", + "Experiments ::: Experimental Setup ::: Data Preprocessing :", + "Experiments ::: Experimental Setup ::: Number of Merge Operations :", + "Experiments ::: NMT Configuration", + "Results", + "Discussion", + "Related Work", + "Conclusion", + "Acknowledgments" + ], + "paragraphs": [ + [ + "Neural machine translation (NMT) has achieved impressive performance on machine translation task in recent years for many language pairs BIBREF0, BIBREF1, BIBREF2. However, in consideration of time cost and space capacity, the NMT model generally employs a limited-size vocabulary that only contains the top-N highest frequency words (commonly in the range of 30K to 80K) BIBREF3, which leads to the Out-of-Vocabulary (OOV) problem following with inaccurate and terrible translation results. Research indicated that sentences with too many unknown words tend to be translated much more poorly than sentences with mainly frequent words. For the low-resource and source-side morphologically-rich machine translation tasks, such as Turkish-English and Uyghur-Chinese, all the above issues are more serious due to the fact that the NMT model cannot effectively identify the complex morpheme structure or capture the linguistic and semantic information with too many rare and unknown words in the training corpus.", + "Both the Turkish and Uyghur are agglutinative and highly-inflected languages in which the word is formed by suffixes attaching to a stem BIBREF4. The word consists of smaller morpheme units without any splitter between them and its structure can be denoted as \u201cstem + suffix1 + suffix2 + ... + suffixN\u201d. A stem is attached in the rear by zero to many suffixes that have many inflected and morphological variants depending on case, number, gender, and so on. The complex morpheme structure and relatively free constituent order can produce very large vocabulary because of the derivational morphology, so when translating from the agglutinative languages, many words are unseen at training time. Moreover, due to the semantic context, the same word generally has different segmentation forms in the training corpus.", + "For the purpose of incorporating morphology knowledge of agglutinative languages into word segmentation for NMT, we propose a morphological word segmentation method on the source-side of Turkish-English and Uyghur-Chinese machine translation tasks, which segments the complex words into simple and effective morpheme units while reducing the vocabulary size for model training. In this paper, we investigate and compare the following segmentation strategies:", + "Stem with combined suffix", + "Stem with singular suffix", + "Byte Pair Encoding (BPE)", + "BPE on stem with combined suffix", + "BPE on stem with singular suffix", + "The latter two segmentation strategies are our newly proposed methods. Experimental results show that our morphologically motivated word segmentation method can achieve significant improvement of up to 1.2 and 2.5 BLEU points on Turkish-English and Uyghur-Chinese machine translation tasks over the strong baseline of pure BPE method respectively, indicating that it can provide better translation performance for the NMT model." + ], + [ + "We will elaborate two popular word segmentation methods and our newly proposed segmentation strategies in this section. The two popular segmentation methods are morpheme segmentation BIBREF4 and Byte Pair Encoding (BPE) BIBREF5. After word segmentation, we additionally add an specific symbol behind each separated subword unit, which aims to assist the NMT model to identify the morpheme boundaries and capture the semantic information effectively. The sentence examples with different segmentation strategies for Turkish-English machine translation task are shown in Table 1." + ], + [ + "The words of Turkish and Uyghur are formed by a stem followed with unlimited number of suffixes. Both of the stem and suffix are called morphemes, and they are the smallest functional unit in agglutinative languages. Study indicated that modeling language based on the morpheme units can provide better performance BIBREF6. Morpheme segmentation can segment the complex word into morpheme units of stem and suffix. This representation maintains a full description of the morphological properties of subwords while minimizing the data sparseness caused by inflection and allomorphy phenomenon in highly-inflected languages." + ], + [ + "In this segmentation strategy, each word is segmented into a stem unit and a combined suffix unit. We add \u201c##\u201d behind the stem unit and add \u201c$$\u201d behind the combined suffix unit. We denote this method as SCS. The segmented word can be denoted as two parts of \u201cstem##\u201d and \u201csuffix1suffix2...suffixN$$\u201d. If the original word has no suffix unit, the word is treated as its stem unit. All the following segmentation strategies will follow this rule." + ], + [ + "In this segmentation strategy, each word is segmented into a stem unit and a sequence of suffix units. We add \u201c##\u201d behind the stem unit and add \u201c$$\u201d behind each singular suffix unit. We denote this method as SSS. The segmented word can be denoted as a sequence of \u201cstem##\u201d, \u201csuffix1$$\u201d, \u201csuffix2$$\u201d until \u201csuffixN$$\u201d." + ], + [ + "BPE BIBREF7 is originally a data compression technique and it is adapted by BIBREF5 for word segmentation and vocabulary reduction by encoding the rare and unknown words as a sequence of subword units, in which the most frequent character sequences are merged iteratively. Frequent character n-grams are eventually merged into a single symbol. This is based on the intuition that various word classes are translatable via smaller units than words. This method making the NMT model capable of open-vocabulary translation, which can generalize to translate and produce new words on the basis of these subword units. The BPE algorithm can be run on the dictionary extracted from a training text, with each word being weighted by its frequency. In this segmentation strategy, we add \u201c@@\u201d behind each no-final subword unit of the segmented word." + ], + [ + "The problem with morpheme segmentation is that the vocabulary of stem units is still very large, which leads to many rare and unknown words at the training time. The problem with BPE is that it do not consider the morpheme boundaries inside words, which might cause a loss of morphological properties and semantic information. Hence, on the analyses of the above popular word segmentation methods, we propose the morphologically motivated segmentation strategy that combines the morpheme segmentation and BPE for further improving the translation performance of NMT.", + "Compared with the sentence of word surface forms, the corresponding sentence of stem units only contains the structure information without considering morphological information, which can make better generalization over inflectional variants of the same word and reduce data sparseness BIBREF8. Therefore, we learn a BPE model on the stem units in the training corpus rather than the words, and then apply it on the stem unit of each word after morpheme segmentation." + ], + [ + "In this segmentation strategy, firstly we segment each word into a stem unit and a combined suffix unit as SCS. Secondly, we apply BPE on the stem unit. Thirdly, we add \u201c$$\u201d behind the combined suffix unit. If the stem unit is not segmented, we add \u201c##\u201d behind itself. Otherwise, we add \u201c@@\u201d behind each no-final subword of the segmented stem unit. We denote this method as BPE-SCS." + ], + [ + "In this segmentation strategy, firstly we segment each word into a stem unit and a sequence of suffix units as SSS. Secondly, we apply BPE on the stem unit. Thirdly, we add \u201c$$\u201d behind each singular suffix unit. If the stem unit is not segmented, we add \u201c##\u201d behind itself. Otherwise, we add \u201c@@\u201d behind each no-final subword of the segmented stem unit. We denote this method as BPE-SSS." + ], + [ + "Following BIBREF9, we use the WIT corpus BIBREF10 and SETimes corpus BIBREF11 for model training, and use the newsdev2016 from Workshop on Machine Translation in 2016 (WMT2016) for validation. The test data are newstest2016 and newstest2017." + ], + [ + "We use the news data from China Workshop on Machine Translation in 2017 (CWMT2017) for model training, validation and test." + ], + [ + "We utilize the Zemberek with a morphological disambiguation tool to segment the Turkish words into morpheme units, and utilize the morphology analysis tool BIBREF12 to segment the Uyghur words into morpheme units. We employ the python toolkits of jieba for Chinese word segmentation. We apply BPE on the target-side words and we set the number of merge operations to 35K for Chinese and 30K for English and we set the maximum sentence length to 150 tokens. The training corpus statistics of Turkish-English and Uyghur-Chinese machine translation tasks are shown in Table 2 and Table 3 respectively." + ], + [ + "We set the number of merge operations on the stem units in the consideration of keeping the vocabulary size of BPE, BPE-SCS and BPE-SSS segmentation strategies on the same scale. We will elaborate the number settings for our proposed word segmentation strategies in this section.", + "In the Turkish-English machine translation task, for the pure BPE strategy, we set the number of merge operations on the words to 35K, set the number of merge operations on the stem units for BPE-SCS strategy to 15K, and set the number of merge operations on the stem units for BPE-SSS strategy to 25K. In the Uyghur-Chinese machine translation task, for the pure BPE strategy, we set the number of merge operations on the words to 38K, set the number of merge operations on the stem units for BPE-SCS strategy to 10K, and set the number of merge operations on the stem units for BPE-SSS strategy to 35K. The detailed training corpus statistics with different segmentation strategies of Turkish and Uyghur are shown in Table 4 and Table 5 respectively.", + "According to Table 4 and Table 5, we can find that both the Turkish and Uyghur have a very large vocabulary even in the low-resource training corpus. So we propose the morphological word segmentation strategies of BPE-SCS and BPE-SSS that additionally applying BPE on the stem units after morpheme segmentation, which not only consider the morphological properties but also eliminate the rare and unknown words." + ], + [ + "We employ the Transformer model BIBREF13 with self-attention mechanism architecture implemented in Sockeye toolkit BIBREF14. Both the encoder and decoder have 6 layers. We set the number of hidden units to 512, the number of heads for self-attention to 8, the source and target word embedding size to 512, and the number of hidden units in feed-forward layers to 2048. We train the NMT model by using the Adam optimizer BIBREF15 with a batch size of 128 sentences, and we shuffle all the training data at each epoch. The label smoothing is set to 0.1. We report the result of averaging the parameters of the 4 best checkpoints on the validation perplexity. Decoding is performed by beam search with beam size of 5. To effectively evaluate the machine translation quality, we report case-sensitive BLEU score with standard tokenization and character n-gram ChrF3 score ." + ], + [ + "In this paper, we investigate and compare morpheme segmentation, BPE and our proposed morphological segmentation strategies on the low resource and morphologically-rich agglutinative languages. Experimental results of Turkish-English and Uyghur-Chinese machine translation tasks are shown in Table 6 and Table 7 respectively." + ], + [ + "According to Table 6 and Table 7, we can find that both the BPE-SCS and BPE-SSS strategies outperform morpheme segmentation and the strong baseline of pure BPE method. Especially, the BPE-SSS strategy is better and it achieves significant improvement of up to 1.2 BLEU points on Turkish-English machine translation task and 2.5 BLEU points on Uyghur-Chinese machine translation task. Furthermore, we also find that the translation performance of our proposed segmentation strategy on Turkish-English machine translation task is not obvious than Uyghur-Chinese machine translation task, the probable reasons are: the training corpus of Turkish-English consists of talk and news data while most of the talk data are short informal sentences compared with the news data, which cannot provide more language information for the NMT model. Moreover, the test corpus consists of news data, so due to the data domain is different, the improvement of machine translation quality is limited.", + "In addition, we estimate how the number of merge operations on the stem units for BPE-SSS strategy effects the machine translation quality. Experimental results are shown in Table 8 and Table 9. We find that the number of 25K for Turkish, 30K and 35K for Uyghur maximizes the translation performance. The probable reason is that these numbers of merge operations are able to generate a more appropriate vocabulary that containing effective morpheme units and moderate subword units, which makes better generalization over the morphologically-rich words." + ], + [ + "The NMT system is typically trained with a limited vocabulary, which creates bottleneck on translation accuracy and generalization capability. Many word segmentation methods have been proposed to cope with the above problems, which consider the morphological properties of different languages.", + "Bradbury and Socher BIBREF16 employed the modified Morfessor to provide morphology knowledge into word segmentation, but they neglected the morphological varieties between subword units, which might result in ambiguous translation results. Sanchez-Cartagena and Toral BIBREF17 proposed a rule-based morphological word segmentation for Finnish, which applies BPE on all the morpheme units uniformly without distinguishing their inner morphological roles. Huck BIBREF18 explored target-side segmentation method for German, which shows that the cascading of suffix splitting and compound splitting with BPE can achieve better translation results. Ataman et al. BIBREF19 presented a linguistically motivated vocabulary reduction approach for Turkish, which optimizes the segmentation complexity with constraint on the vocabulary based on a category-based hidden markov model (HMM). Our work is closely related to their idea while ours are more simple and realizable. Tawfik et al. BIBREF20 confirmed that there is some advantage from using a high accuracy dialectal segmenter jointly with a language independent word segmentation method like BPE. The main difference is that their approach needs sufficient monolingual data additionally to train a segmentation model while ours do not need any external resources, which is very convenient for word segmentation on the low-resource and morphologically-rich agglutinative languages." + ], + [ + "In this paper, we investigate morphological segmentation strategies on the low-resource and morphologically-rich languages of Turkish and Uyghur. Experimental results show that our proposed morphologically motivated word segmentation method is better suitable for NMT. And the BPE-SSS strategy achieves the best machine translation performance, as it can better preserve the syntactic and semantic information of the words with complex morphology as well as reduce the vocabulary size for model training. Moreover, we also estimate how the number of merge operations on the stem units for BPE-SSS strategy effects the translation quality, and we find that an appropriate vocabulary size is more useful for the NMT model.", + "In future work, we are planning to incorporate more linguistic and morphology knowledge into the training process of NMT to enhance its capacity of capturing syntactic structure and semantic information on the low-resource and morphologically-rich languages." + ], + [ + "This work is supported by the National Natural Science Foundation of China, the Open Project of Key Laboratory of Xinjiang Uygur Autonomous Region, the Youth Innovation Promotion Association of the Chinese Academy of Sciences, and the High-level Talents Introduction Project of Xinjiang Uyghur Autonomous Region." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0536/environment/Dockerfile b/qasper-0536/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0536/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/* diff --git a/qasper-0536/instruction.md b/qasper-0536/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..b8e17973299b68c2e2cca4b5456035c9fceca843 --- /dev/null +++ b/qasper-0536/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Deja-vu: Double Feature Presentation and Iterated Loss in Deep Transformer Networks + +Question: Does their model take more time to train than regular transformer models? \ No newline at end of file diff --git a/qasper-0537/instruction.md b/qasper-0537/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..e323fce2f109f2e5fa63aabcf8908a2405b2d84e --- /dev/null +++ b/qasper-0537/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge + +Question: Are agglutinative languages used in the prediction of both prefixing and suffixing languages? \ No newline at end of file diff --git a/qasper-0538/environment/Dockerfile b/qasper-0538/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0538/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/* diff --git a/qasper-0538/instruction.md b/qasper-0538/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..acc8f40267bd910a05db6b89cd7eb0220b6ded5c --- /dev/null +++ b/qasper-0538/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge + +Question: What is an example of a prefixing language? \ No newline at end of file diff --git a/qasper-0539/environment/Dockerfile b/qasper-0539/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0539/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/* diff --git a/qasper-0539/instruction.md b/qasper-0539/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..af9062d775fca1e1f9688c1c94f79e52c07e0da3 --- /dev/null +++ b/qasper-0539/instruction.md @@ -0,0 +1,177 @@ +Name of Paper: Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge + +Question: How is the performance on the task evaluated? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Task", + "Task ::: Formal definition.", + "Model ::: Pointer\u2013Generator Network", + "Model ::: Pointer\u2013Generator Network ::: Encoders.", + "Model ::: Pointer\u2013Generator Network ::: Attention.", + "Model ::: Pointer\u2013Generator Network ::: Decoder.", + "Model ::: Pretraining and Finetuning", + "Experimental Design ::: Target Languages", + "Experimental Design ::: Source Languages", + "Experimental Design ::: Hyperparameters and Data", + "Quantitative Results", + "Qualitative Results", + "Qualitative Results ::: Stem Errors", + "Qualitative Results ::: Affix Errors", + "Qualitative Results ::: Miscellaneous Errors", + "Qualitative Results ::: Error Analysis: English", + "Qualitative Results ::: Error Analysis: Spanish", + "Qualitative Results ::: Error Analysis: Zulu", + "Qualitative Results ::: Limitations", + "Related Work ::: Neural network models for inflection.", + "Related Work ::: Cross-lingual transfer in NLP.", + "Related Work ::: Acquisition of morphological inflection.", + "Conclusion and Future Work", + "Acknowledgments" + ], + "paragraphs": [ + [ + "A widely agreed-on fact in language acquisition research is that learning of a second language (L2) is influenced by a learner's native language (L1) BIBREF0, BIBREF1. A language's morphosyntax seems to be no exception to this rule BIBREF2, but the exact nature of this influence remains unknown. For instance, it is unclear whether it is constraints imposed by the phonological or by the morphosyntactic attributes of the L1 that are more important during the process of learning an L2's morphosyntax.", + "Within the area of natural language processing (NLP) research, experimenting on neural network models just as if they were human subjects has recently been gaining popularity BIBREF3, BIBREF4, BIBREF5. Often, so-called probing tasks are used, which require a specific subset of linguistic knowledge and can, thus, be leveraged for qualitative evaluation. The goal is to answer the question: What do neural networks learn that helps them to succeed in a given task?", + "Neural network models, and specifically sequence-to-sequence models, have pushed the state of the art for morphological inflection \u2013 the task of learning a mapping from lemmata to their inflected forms \u2013 in the last years BIBREF6. Thus, in this work, we experiment on such models, asking not what they learn, but, motivated by the respective research on human subjects, the related question of how what they learn depends on their prior knowledge. We manually investigate the errors made by artificial neural networks for morphological inflection in a target language after pretraining on different source languages. We aim at finding answers to two main questions: (i) Do errors systematically differ between source languages? (ii) Do these differences seem explainable, given the properties of the source and target languages? In other words, we are interested in exploring if and how L2 acquisition of morphological inflection depends on the L1, i.e., the \"native language\", in neural network models.", + "To this goal, we select a diverse set of eight source languages from different language families \u2013 Basque, French, German, Hungarian, Italian, Navajo, Turkish, and Quechua \u2013 and three target languages \u2013 English, Spanish and Zulu. We pretrain a neural sequence-to-sequence architecture on each of the source languages and then fine-tune the resulting models on small datasets in each of the target languages. Analyzing the errors made by the systems, we find that (i) source and target language being closely related simplifies the successful learning of inflection in the target language, (ii) the task is harder to learn in a prefixing language if the source language is suffixing \u2013 as well as the other way around, and (iii) a source language which exhibits an agglutinative morphology simplifies learning of a second language's inflectional morphology." + ], + [ + "Many of the world's languages exhibit rich inflectional morphology: the surface form of an individual lexical entry changes in order to express properties such as person, grammatical gender, or case. The citation form of a lexical entry is referred to as the lemma. The set of all possible surface forms or inflections of a lemma is called its paradigm. Each inflection within a paradigm can be associated with a tag, i.e., 3rdSgPres is the morphological tag associated with the inflection dances of the English lemma dance. We display the paradigms of dance and eat in Table TABREF1.", + "The presence of rich inflectional morphology is problematic for NLP systems as it increases word form sparsity. For instance, while English verbs can have up to 5 inflected forms, Archi verbs have thousands BIBREF7, even by a conservative count. Thus, an important task in the area of morphology is morphological inflection BIBREF8, BIBREF9, which consists of mapping a lemma to an indicated inflected form. An (irregular) English example would be", + "with PAST being the target tag, denoting the past tense form. Additionally, a rich inflectional morphology is also challenging for L2 language learners, since both rules and their exceptions need to be memorized.", + "In NLP, morphological inflection has recently frequently been cast as a sequence-to-sequence problem, where the sequence of target (sub-)tags together with the sequence of input characters constitute the input sequence, and the characters of the inflected word form the output. Neural models define the state of the art for the task and obtain high accuracy if an abundance of training data is available. Here, we focus on learning of inflection from limited data if information about another language's morphology is already known. We, thus, loosely simulate an L2 learning setting." + ], + [ + "Let ${\\cal M}$ be the paradigm slots which are being expressed in a language, and $w$ a lemma in that language. We then define the paradigm $\\pi $ of $w$ as:", + "$f_k[w]$ denotes an inflected form corresponding to tag $t_{k}$, and $w$ and $f_k[w]$ are strings consisting of letters from an alphabet $\\Sigma $.", + "The task of morphological inflection consists of predicting a missing form $f_i[w]$ from a paradigm, given the lemma $w$ together with the tag $t_i$." + ], + [ + "The models we experiment with are based on a pointer\u2013generator network architecture BIBREF10, BIBREF11, i.e., a recurrent neural network (RNN)-based sequence-to-sequence network with attention and a copy mechanism. A standard sequence-to-sequence model BIBREF12 has been shown to perform well for morphological inflection BIBREF13 and has, thus, been subject to cognitively motivated experiments BIBREF14 before. Here, however, we choose the pointer\u2013generator variant of sharma-katrapati-sharma:2018:K18-30, since it performs better in low-resource settings, which we will assume for our target languages. We will explain the model shortly in the following and refer the reader to the original paper for more details." + ], + [ + "Our architecture employs two separate encoders, which are both bi-directional long short-term memory (LSTM) networks BIBREF15: The first processes the morphological tags which describe the desired target form one by one. The second encodes the sequence of characters of the input word." + ], + [ + "Two separate attention mechanisms are used: one per encoder LSTM. Taking all respective encoder hidden states as well as the current decoder hidden state as input, each of them outputs a so-called context vector, which is a weighted sum of all encoder hidden states. The concatenation of the two individual context vectors results in the final context vector $c_t$, which is the input to the decoder at time step $t$." + ], + [ + "Our decoder consists of a uni-directional LSTM. Unlike a standard sequence-to-sequence model, a pointer\u2013generator network is not limited to generating characters from the vocabulary to produce the output. Instead, the model gives certain probability to copying elements from the input over to the output. The probability of a character $y_t$ at time step $t$ is computed as a sum of the probability of $y_t$ given by the decoder and the probability of copying $y_t$, weighted by the probabilities of generating and copying:", + "$p_{\\textrm {dec}}(y_t)$ is calculated as an LSTM update and a projection of the decoder state to the vocabulary, followed by a softmax function. $p_{\\textrm {copy}}(y_t)$ corresponds to the attention weights for each input character. The model computes the probability $\\alpha $ with which it generates a new output character as", + "for context vector $c_t$, decoder state $s_t$, embedding of the last output $y_{t-1}$, weights $w_c$, $w_s$, $w_y$, and bias vector $b$. It has been shown empirically that the copy mechanism of the pointer\u2013generator network architecture is beneficial for morphological generation in the low-resource setting BIBREF16." + ], + [ + "Pretraining and successive fine-tuning of neural network models is a common approach for handling of low-resource settings in NLP. The idea is that certain properties of language can be learned either from raw text, related tasks, or related languages. Technically, pretraining consists of estimating some or all model parameters on examples which do not necessarily belong to the final target task. Fine-tuning refers to continuing training of such a model on a target task, whose data is often limited. While the sizes of the pretrained model parameters usually remain the same between the two phases, the learning rate or other details of the training regime, e.g., dropout, might differ. Pretraining can be seen as finding a suitable initialization of model parameters, before training on limited amounts of task- or language-specific examples.", + "In the context of morphological generation, pretraining in combination with fine-tuning has been used by kann-schutze-2018-neural, which proposes to pretrain a model on general inflection data and fine-tune on examples from a specific paradigm whose remaining forms should be automatically generated. Famous examples for pretraining in the wider area of NLP include BERT BIBREF17 or GPT-2 BIBREF18: there, general properties of language are learned using large unlabeled corpora.", + "Here, we are interested in pretraining as a simulation of familiarity with a native language. By investigating a fine-tuned model we ask the question: How does extensive knowledge of one language influence the acquisition of another?" + ], + [ + "We choose three target languages.", + "English (ENG) is a morphologically impoverished language, as far as inflectional morphology is concerned. Its verbal paradigm only consists of up to 5 different forms and its nominal paradigm of only up to 2. However, it is one of the most frequently spoken and taught languages in the world, making its acquisition a crucial research topic.", + "Spanish (SPA), in contrast, is morphologically rich, and disposes of much larger verbal paradigms than English. Like English, it is a suffixing language, and it additionally makes use of internal stem changes (e.g., o $\\rightarrow $ ue).", + "Since English and Spanish are both Indo-European languages, and, thus, relatively similar, we further add a third, unrelated target language. We choose Zulu (ZUL), a Bantoid language. In contrast to the first two, it is strongly prefixing." + ], + [ + "For pretraining, we choose languages with different degrees of relatedness and varying morphological similarity to English, Spanish, and Zulu. We limit our experiments to languages which are written in Latin script.", + "As an estimate for morphological similarity we look at the features from the Morphology category mentioned in The World Atlas of Language Structures (WALS). An overview of the available features as well as the respective values for our set of languages is shown in Table TABREF13.", + "We decide on Basque (EUS), French (FRA), German (DEU), Hungarian (HUN), Italian (ITA), Navajo (NAV), Turkish (TUR), and Quechua (QVH) as source languages.", + "Basque is a language isolate. Its inflectional morphology makes similarly frequent use of prefixes and suffixes, with suffixes mostly being attached to nouns, while prefixes and suffixes can both be employed for verbal inflection.", + "French and Italian are Romance languages, and thus belong to the same family as the target language Spanish. Both are suffixing and fusional languages.", + "German, like English, belongs to the Germanic language family. It is a fusional, predominantly suffixing language and, similarly to Spanish, makes use of stem changes.", + "Hungarian, a Finno-Ugric language, and Turkish, a Turkic language, both exhibit an agglutinative morphology, and are predominantly suffixing. They further have vowel harmony systems.", + "Navajo is an Athabaskan language and the only source language which is strongly prefixing. It further exhibits consonant harmony among its sibilants BIBREF19, BIBREF20.", + "Finally, Quechua, a Quechuan language spoken in South America, is again predominantly suffixing and unrelated to all of our target languages." + ], + [ + "We mostly use the default hyperparameters by sharma-katrapati-sharma:2018:K18-30. In particular, all RNNs have one hidden layer of size 100, and all input and output embeddings are 300-dimensional.", + "For optimization, we use ADAM BIBREF21. Pretraining on the source language is done for exactly 50 epochs. To obtain our final models, we then fine-tune different copies of each pretrained model for 300 additional epochs for each target language. We employ dropout BIBREF22 with a coefficient of 0.3 for pretraining and, since that dataset is smaller, with a coefficient of 0.5 for fine-tuning.", + "We make use of the datasets from the CoNLL\u2013SIGMORPHON 2018 shared task BIBREF9. The organizers provided a low, medium, and high setting for each language, with 100, 1000, and 10000 examples, respectively. For all L1 languages, we train our models on the high-resource datasets with 10000 examples. For fine-tuning, we use the low-resource datasets." + ], + [ + "In Table TABREF18, we show the final test accuracy for all models and languages. Pretraining on EUS and NAV results in the weakest target language inflection models for ENG, which might be explained by those two languages being unrelated to ENG and making at least partial use of prefixing, while ENG is a suffixing language (cf. Table TABREF13). In contrast, HUN and ITA yield the best final models for ENG. This is surprising, since DEU is the language in our experiments which is closest related to ENG.", + "For SPA, again HUN performs best, followed closely by ITA. While the good performance of HUN as a source language is still unexpected, ITA is closely related to SPA, which could explain the high accuracy of the final model. As for ENG, pretraining on EUS and NAV yields the worst final models \u2013 importantly, accuracy is over $15\\%$ lower than for QVH, which is also an unrelated language. This again suggests that the prefixing morphology of EUS and NAV might play a role.", + "Lastly, for ZUL, all models perform rather poorly, with a minimum accuracy of 10.7 and 10.8 for the source languages QVH and EUS, respectively, and a maximum accuracy of 24.9 for a model pretrained on Turkish. The latter result hints at the fact that a regular and agglutinative morphology might be beneficial in a source language \u2013 something which could also account for the performance of models pretrained on HUN." + ], + [ + "For our qualitative analysis, we make use of the validation set. Therefore, we show validation set accuracies in Table TABREF19 for comparison. As we can see, the results are similar to the test set results for all language combinations. We manually annotate the outputs for the first 75 development examples for each source\u2013target language combination. All found errors are categorized as belonging to one of the following categories." + ], + [ + "SUB(X): This error consists of a wrong substitution of one character with another. SUB(V) and SUB(C) denote this happening with a vowel or a consonant, respectively. Letters that differ from each other by an accent count as different vowels.", + "Example: decultared instead of decultured", + "DEL(X): This happens when the system ommits a letter from the output. DEL(V) and DEL(C) refer to a missing vowel or consonant, respectively.", + "Example: firte instead of firtle", + "NO_CHG(X): This error occurs when inflecting the lemma to the gold form requires a change of either a vowel (NO_CHG(V)) or a consonant (NO_CHG(C)), but this is missing in the predicted form.", + "Example: verto instead of vierto", + "MULT: This describes cases where two or more errors occur in the stem. Errors concerning the affix are counted for separately.", + "Example: aconcoonaste instead of acondicionaste", + "ADD(X): This error occurs when a letter is mistakenly added to the inflected form. ADD(V) refers to an unnecessary vowel, ADD(C) refers to an unnecessary consonant.", + "Example: compillan instead of compilan", + "CHG2E(X): This error occurs when inflecting the lemma to the gold form requires a change of either a vowel (CHG2E(V)) or a consonant (CHG2E(C)), and this is done, but the resulting vowel or consonant is incorrect.", + "Example: propace instead of propague" + ], + [ + "AFF: This error refers to a wrong affix. This can be either a prefix or a suffix, depending on the correct target form.", + "Example: ezoJulayi instead of esikaJulayi", + "CUT: This consists of cutting too much of the lemma's prefix or suffix before attaching the inflected form's prefix or suffix, respectively.", + "Example: irradiseis instead of irradiaseis" + ], + [ + "REFL: This happens when a reflective pronoun is missing in the generated form.", + "Example: doli\u00e9ramos instead of nos doli\u00e9ramos", + "REFL_LOC: This error occurs if the reflective pronouns appears at an unexpected position within the generated form.", + "Example: taparsebais instead of os tapabais", + "OVERREG: Overregularization errors occur when the model predicts a form which would be correct if the lemma's inflections were regular but they are not.", + "Example: underteach instead of undertaught" + ], + [ + "Table TABREF35 displays the errors found in the 75 first ENG development examples, for each source language. From Table TABREF19, we know that HUN $>$ ITA $>$ TUR $>$ DEU $>$ FRA $>$ QVH $>$ NAV $>$ EUS, and we get a similar picture when analyzing the first examples. Thus, especially keeping HUN and TUR in mind, we cautiously propose a first conclusion: familiarity with languages which exhibit an agglutinative morphology simplifies learning of a new language's morphology.", + "Looking at the types of errors, we find that EUS and NAV make the most stem errors. For QVH we find less, but still over 10 more than for the remaining languages. This makes it seem that models pretrained on prefixing or partly prefixing languages indeed have a harder time to learn ENG inflectional morphology, and, in particular, to copy the stem correctly. Thus, our second hypotheses is that familiarity with a prefixing language might lead to suspicion of needed changes to the part of the stem which should remain unaltered in a suffixing language. DEL(X) and ADD(X) errors are particularly frequent for EUS and NAV, which further suggests this conclusion.", + "Next, the relatively large amount of stem errors for QVH leads to our second hypothesis: language relatedness does play a role when trying to produce a correct stem of an inflected form. This is also implied by the number of MULT errors for EUS, NAV and QVH, as compared to the other languages.", + "Considering errors related to the affixes which have to be generated, we find that DEU, HUN and ITA make the fewest. This further suggests the conclusion that, especially since DEU is the language which is closest related to ENG, language relatedness plays a role for producing suffixes of inflected forms as well.", + "Our last observation is that many errors are not found at all in our data sample, e.g., CHG2E(X) or NO_CHG(C). This can be explained by ENG having a relatively poor inflectional morphology, which does not leave much room for mistakes." + ], + [ + "The errors committed for SPA are shown in Table TABREF37, again listed by source language. Together with Table TABREF19 it gets clear that SPA inflectional morphology is more complex than that of ENG: systems for all source languages perform worse.", + "Similarly to ENG, however, we find that most stem errors happen for the source languages EUS and NAV, which is further evidence for our previous hypothesis that familiarity with prefixing languages impedes acquisition of a suffixing one. Especially MULT errors are much more frequent for EUS and NAV than for all other languages. ADD(X) happens a lot for EUS, while ADD(C) is also frequent for NAV. Models pretrained on either language have difficulties with vowel changes, which reflects in NO_CHG(V). Thus, we conclude that this phenomenon is generally hard to learn.", + "Analyzing next the errors concerning affixes, we find that models pretrained on HUN, ITA, DEU, and FRA (in that order) commit the fewest errors. This supports two of our previous hypotheses: First, given that ITA and FRA are both from the same language family as SPA, relatedness seems to be benficial for learning of the second language. Second, the system pretrained on HUN performing well suggests again that a source language with an agglutinative, as opposed to a fusional, morphology seems to be beneficial as well." + ], + [ + "In Table TABREF39, the errors for Zulu are shown, and Table TABREF19 reveals the relative performance for different source languages: TUR $>$ HUN $>$ DEU $>$ ITA $>$ FRA $>$ NAV $>$ EUS $>$ QVH. Again, TUR and HUN obtain high accuracy, which is an additional indicator for our hypothesis that a source language with an agglutinative morphology facilitates learning of inflection in another language.", + "Besides that, results differ from those for ENG and SPA. First of all, more mistakes are made for all source languages. However, there are also several finer differences. For ZUL, the model pretrained on QVH makes the most stem errors, in particular 4 more than the EUS model, which comes second. Given that ZUL is a prefixing language and QVH is suffixing, this relative order seems important. QVH also committs the highest number of MULT errors.", + "The next big difference between the results for ZUL and those for ENG and SPA is that DEL(X) and ADD(X) errors, which previously have mostly been found for the prefixing or partially prefixing languages EUS and NAV, are now most present in the outputs of suffixing languages. Namely, DEL(C) occurs most for FRA and ITA, DEL(V) for FRA and QVH, and ADD(C) and ADD(V) for HUN. While some deletion and insertion errors are subsumed in MULT, this does not fully explain this difference. For instance, QVH has both the second most DEL(V) and the most MULT errors.", + "The overall number of errors related to the affix seems comparable between models with different source languages. This weakly supports the hypothesis that relatedness reduces affix-related errors, since none of the pretraining languages in our experiments is particularly close to ZUL. However, we do find more CUT errors for HUN and TUR: again, these are suffixing, while CUT for the target language SPA mostly happened for the prefixing languages EUS and NAV." + ], + [ + "A limitation of our work is that we only include languages that are written in Latin script. An interesting question for future work might, thus, regard the effect of disjoint L1 and L2 alphabets.", + "Furthermore, none of the languages included in our study exhibits a templatic morphology. We make this choice because data for templatic languages is currently mostly available in non-Latin alphabets. Future work could investigate languages with templatic morphology as source or target languages, if needed by mapping the language's alphabet to Latin characters.", + "Finally, while we intend to choose a diverse set of languages for this study, our overall number of languages is still rather small. This affects the generalizability of the results, and future work might want to look at larger samples of languages." + ], + [ + "Most research on inflectional morphology in NLP within the last years has been related to the SIGMORPHON and CoNLL\u2013SIGMORPHON shared tasks on morphological inflection, which have been organized yearly since 2016 BIBREF6. Traditionally being focused on individual languages, the 2019 edition BIBREF23 contained a task which asked for transfer learning from a high-resource to a low-resource language. However, source\u2013target pairs were predefined, and the question of how the source language influences learning besides the final accuracy score was not considered. Similarly to us, kyle performed a manual error analysis of morphological inflection systems for multiple languages. However, they did not investigate transfer learning, but focused on monolingual models.", + "Outside the scope of the shared tasks, kann-etal-2017-one investigated cross-lingual transfer for morphological inflection, but was limited to a quantitative analysis. Furthermore, that work experimented with a standard sequence-to-sequence model BIBREF12 in a multi-task training fashion BIBREF24, while we pretrain and fine-tune pointer\u2013generator networks. jin-kann-2017-exploring also investigated cross-lingual transfer in neural sequence-to-sequence models for morphological inflection. However, their experimental setup mimicked kann-etal-2017-one, and the main research questions were different: While jin-kann-2017-exploring asked how cross-lingual knowledge transfer works during multi-task training of neural sequence-to-sequence models on two languages, we investigate if neural inflection models demonstrate interesting differences in production errors depending on the pretraining language. Besides that, we differ in the artificial neural network architecture and language pairs we investigate." + ], + [ + "Cross-lingual transfer learning has been used for a large variety NLP of tasks, e.g., automatic speech recognition BIBREF25, entity recognition BIBREF26, language modeling BIBREF27, or parsing BIBREF28, BIBREF29, BIBREF30. Machine translation has been no exception BIBREF31, BIBREF32, BIBREF33. Recent research asked how to automatically select a suitable source language for a given target language BIBREF34. This is similar to our work in that our findings could potentially be leveraged to find good source languages." + ], + [ + "Finally, a lot of research has focused on human L1 and L2 acquisition of inflectional morphology BIBREF35, BIBREF36, BIBREF37, BIBREF38, BIBREF39, BIBREF40.", + "To name some specific examples, marques2011study investigated the effect of a stay abroad on Spanish L2 acquisition, including learning of its verbal morphology in English speakers. jia2003acquisition studied how Mandarin Chinese-speaking children learned the English plural morpheme. nicoladis2012young studied the English past tense acquisition in Chinese\u2013English and French\u2013English bilingual children. They found that, while both groups showed similar production accuracy, they differed slightly in the type of errors they made. Also considering the effect of the native language explicitly, yang2004impact investigated the acquisition of the tense-aspect system in an L2 for speakers of a native language which does not mark tense explicitly.", + "Finally, our work has been weakly motivated by bliss2006l2. There, the author asked a question for human subjects which is similar to the one we ask for neural models: How does the native language influence L2 acquisition of inflectional morphology?" + ], + [ + "Motivated by the fact that, in humans, learning of a second language is influenced by a learner's native language, we investigated a similar question in artificial neural network models for morphological inflection: How does pretraining on different languages influence a model's learning of inflection in a target language?", + "We performed experiments on eight different source languages and three different target languages. An extensive error analysis of all final models showed that (i) for closely related source and target languages, acquisition of target language inflection gets easier; (ii) knowledge of a prefixing language makes learning of inflection in a suffixing language more challenging, as well as the other way around; and (iii) languages which exhibit an agglutinative morphology facilitate learning of inflection in a second language.", + "Future work might leverage those findings to improve neural network models for morphological inflection in low-resource languages, by choosing suitable source languages for pretraining.", + "Another interesting next step would be to investigate how the errors made by our models compare to those by human L2 learners with different native languages. If the exhibited patterns resemble each other, computational models could be used to predict errors a person will make, which, in turn, could be leveraged for further research or the development of educational material." + ], + [ + "I would like to thank Samuel R. Bowman and Kyle Gorman for helpful discussions and suggestions. This work has benefited from the support of Samsung Research under the project Improving Deep Learning using Latent Structure and from the donation of a Titan V GPU by NVIDIA Corporation." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0552/instruction.md b/qasper-0552/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..f3ccc221a3744203338f5ccfedc45f6a80485fb8 --- /dev/null +++ b/qasper-0552/instruction.md @@ -0,0 +1,150 @@ +Name of Paper: Frozen Binomials on the Web: Word Ordering and Language Conventions in Online Text + +Question: Are there any new finding in analasys of trinomials that was not present binomials? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Introduction ::: Related Work", + "Data", + "Dimensions of Binomials", + "Dimensions of Binomials ::: Definitions", + "Dimensions of Binomials ::: Dimensions", + "Models And Predictions", + "Models And Predictions ::: Stability of Asymmetry", + "Models And Predictions ::: Prediction Results", + "Proper Nouns and the Proximity Principle", + "Proper Nouns and the Proximity Principle ::: NBA Names", + "Proper Nouns and the Proximity Principle ::: Subreddit and team names", + "Proper Nouns and the Proximity Principle ::: Political Names", + "Formal Text", + "Formal Text ::: Wine", + "Formal Text ::: News", + "Global Structure", + "Multinomials", + "Discussion", + "Acknowledgements" + ], + "paragraphs": [ + [ + "Lists are extremely common in text and speech, and the ordering of items in a list can often reveal information. For instance, orderings can denote relative importance, such as on a to-do list, or signal status, as is the case for author lists of scholarly publications. In other cases, orderings might come from cultural or historical conventions. For example, `red, white, and blue' is a specific ordering of colors that is recognizable to those familiar with American culture.", + "The orderings of lists in text and speech is a subject that has been repeatedly touched upon for more than a century. By far the most frequently studied aspect of list ordering is the binomial, a list of two words usually separated by a conjunction such as `and' or `or', which is the focus of our paper. The academic treatment of binomial orderings dates back more than a century to Jespersen BIBREF0, who proposed in 1905 that the ordering of many common English binomials could be predicted by the rhythm of the words. In the case of a binomial consisting of a monosyllable and a disyllable, the prediction was that the monosyllable would appear first followed by the conjunction `and'. The idea was that this would give a much more standard and familiar syllable stress to the overall phrase, e.g., the binomial `bread and butter' would have the preferable rhythm compared to `butter and bread.'", + "This type of analysis is meaningful when the two words in the binomial nearly always appear in the same ordering. Binomials like this that appear in strictly one order (perhaps within the confines of some text corpus), are commonly termed frozen binomials BIBREF1, BIBREF2. Examples of frozen binomials include `salt and pepper' and `pros and cons', and explanations for their ordering in English and other languages have become increasingly complex. Early work focused almost exclusively on common frozen binomials, often drawn from everyday speech. More recent work has expanded this view to include nearly frozen binomials, binomials from large data sets such as books, and binomials of particular types such as food, names, and descriptors BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8. Additionally, explanations have increasingly focused on meaning rather than just sound, implying value systems inherent to the speaker or the culture of the language's speakers (one such example is that men are usually listed before women in English BIBREF9). The fact that purely phonetic explanations have been insufficient suggests that list orderings rely at least partially on semantics, and it has previously been suggested that these semantics could be revealing about the culture in which the speech takes place BIBREF3. Thus, it is possible that understanding these orderings could reveal biases or values held by the speaker.", + "Overall, this prior research has largely been confined to pristine examples, often relying on small samples of lists to form conclusions. Many early studies simply drew a small sample of what the author(s) considered some of the more representative or prominent binomials in whatever language they were studying BIBREF10, BIBREF1, BIBREF11, BIBREF0, BIBREF12, BIBREF13, BIBREF14, BIBREF15, BIBREF3. Other researchers have used books or news articles BIBREF2, BIBREF4, or small samples from the Web (web search results and Google books) BIBREF5. Many of these have lacked a large-scale text corpus and have relied on a focused set of statistics about word orderings.", + "Thus, despite the long history of this line of inquiry, there is an opportunity to extend it significantly by examining a broad range of questions about binomials coming from a large corpus of online text data produced organically by many people. Such an analysis could produce at least two types of benefits. First, such a study could help us learn about cultural phenomena embedded in word orderings and how they vary across communities and over time. Second, such an analysis could become a case study for the extension of theories developed at small scales in this domain to a much larger context.", + "The present work: Binomials in large-scale online text. In this work, we use data from large-scale Internet text corpora to study binomials at a massive scale, drawing on text created by millions of users. Our approach is more wholesale than prior work - we focus on all binomials of sufficient frequency, without first restricting to small samples of binomials that might be frozen. We draw our data from news publications, wine reviews, and Reddit, which in addition to large volume, also let us characterize binomials in new ways, and analyze differences in binomial orderings across communities and over time. Furthermore, the subject matter on Reddit leads to many lists about people and organizations that lets us study orderings of proper names \u2014 a key setting for word ordering which has been difficult to study by other means.", + "We begin our analysis by introducing several new key measures for the study of binomials, including a quantity we call asymmetry that measures how frequently a given binomial appears in some ordering. By looking at the distribution of asymmetries across a wide range of binomials, we find that most binomials are not frozen, barring a few strong exceptions. At the same time, there may still be an ordering preference. For example, `10 and 20' is not a frozen binomial; instead, the binomial ordering `10 and 20' appears 60% of the time and `20 and 10' appears 40% of time.", + "We also address temporal and community structure in collections of binomials. While it has been recognized that the orderings of binomials may change over time or between communities BIBREF5, BIBREF10, BIBREF1, BIBREF13, BIBREF14, BIBREF15, there has been little analysis of this change. We develop new metrics for the agreement of binomial orderings across communities and the movement of binomial orderings over time. Using subreddits as communities, these metrics reveal variations in orderings, some of which suggest cultural change influencing language. For example, in one community, we find that over a period of 10 years, the binomial `son and daughter' went from nearly frozen to appearing in that order only 64% of the time.", + "While these changes do happen, they are generally quite rare. Most binomials \u2014 frozen or not \u2014 are ordered in one way about the same percentage of the time, regardless of community or the year. We develop a null model to determine how much variation in binomial orderings we might expect across communities and across time, if binomial orderings were randomly ordered according to global asymmetry values. We find that there is less variation across time and communities in the data compared to this model, implying that binomial orderings are indeed remarkably stable.", + "Given this stability, one might expect that the dominant ordinality of a given binomial is still predictable, even if the binomial is not frozen. For example, one might expect that the global frequency of a single word or the number of syllables in a word would predict ordering in many cases. However, we find that these simple predictors are quite poor at determining binomial ordering.", + "On the other hand, we find that a notion of `proximity' is robust at predicting ordering in some cases. Here, the idea is that the person producing the text will list the word that is conceptually \u201ccloser\u201d to them first \u2014 a phenomenon related to a \u201cMe First\u201d principle of binomial orderings suggested by Cooper and Ross BIBREF3. One way in which we study this notion of proximity is through sports team subreddits. For example, we find that when two NBA team names form a binomial on a specific team's subreddit, the team that is the subject of the subreddit tends to appear first.", + "The other source of improved predictions comes from using word embeddings BIBREF16: we find that a model based on the positions of words in a standard pre-trained word embedding can be a remarkably reliable predictor of binomial orderings. While not applicable to all words, such as names, this type of model is strongly predictive in most cases.", + "Since binomial orderings are in general difficult to predict individually, we explore a new way of representing the global binomial ordering structure, we form a directed graph where an edge from $i$ to $j$ means that $i$ tends to come before $j$ in binomials. These graphs show tendencies across the English language and also reveal peculiarities in the language of particular communities. For instance, in a graph formed from the binomials in a sports community, the names of sports teams and cities are closely clustered, showing that they are often used together in binomials. Similarly, we identify clusters of names, numbers, and years. The presence of cycles in these graphs are also informative. For example, cycles are rare in graphs formed from proper names in politics, suggesting a possible hierarchy of names, and at the same time very common for other binomials. This suggests that no such hierarchy exists for most of the English language, further complicating attempts to predict binomial order.", + "Finally, we expand our work to include multinomials, which are lists of more than two words. There already appears to be more structure in trinomials (lists of three) compared to binomials. Trinomials are likely to appear in exactly one order, and when they appear in more than one order the last word is almost always the same across all instances. For instance, in one section of our Reddit data, `Fraud, Waste, and Abuse' appears 34 times, and `Waste, Fraud, and Abuse' appears 20 times. This could point to, for example, recency principles being more important in lists of three than in lists of two. While multinomials were in principle part of the scope of past research in this area, they were difficult to study in smaller corpora, suggesting another benefit of working at our current scale." + ], + [ + "Interest in list orderings spans the last century BIBREF10, BIBREF1, with a focus almost exclusively on binomials. This research has primarily investigated frozen binomials, also called irreversible binomials, fixed coordinates, and fixed conjuncts BIBREF11, although some work has also looked at non-coordinate freezes where the individual words are nonsensical by themselves (e.g., `dribs and drabs') BIBREF11. One study has directly addressed mostly frozen binomials BIBREF5, and we expand the scope of this paper by exploring the general question of how frequently binomials appear in a particular order. Early research investigated languages other than English BIBREF1, BIBREF10, but most recent research has worked almost exclusively with English. Overall, this prior research can be separated into three basic categories \u2014 phonological rules, semantic rules, and metadata rules.", + "Phonology. The earliest research on binomial orderings proposed mostly phonological explanations, particularly rhythm BIBREF0, BIBREF12. Another highly supported proposal is Panini's Law, which claims that words with fewer syllables come first BIBREF17; we find only very mild preference for this type of ordering. Cooper and Ross's work expands these to a large list of rules, many overlapping, and suggests that they can compound BIBREF3; a number of subsequent papers have expanded on their work BIBREF11, BIBREF15, BIBREF9, BIBREF17.", + "Semantics. There have also been a number of semantic explanations, mostly in the form of categorical tendencies (such as `desirable before undesirable') that may have cultural differences BIBREF10, BIBREF1. The most influential of these may be the `Me First' principle codified by Cooper and Ross. This suggests that the first word of a binomial tends to follow a hierarchy that favors `here', `now', present generation, adult, male, and positive. Additional hierarchies also include a hierarchy of food, plants vs. animals, etc. BIBREF3.", + "Frequency. More recently, it has been proposed that the more cognitively accessible word might come first, which often means the word the author sees or uses most frequently BIBREF18. There has also been debate on whether frequency may encompass most phonological and semantic rules that have been previously proposed BIBREF13, BIBREF4. We find that frequency is in general a poor predictor of word ordering.", + "Combinations. Given the number of theories, there have also been attempts to give a hierarchy of rules and study their interactions BIBREF4, BIBREF5. This research has complemented the proposals of Cooper and Ross BIBREF3. These types of hierarchies are also presented as explanations for the likelihood of a binomial becoming frozen BIBREF5.", + "Names. Work on the orderings of names has been dominated by a single phenomenon: men's names usually come before women's names. Explanations range from a power differential, to men being more `agentic' within `Me First', to men's names being more common or even exhibiting more of the phonological features of words that usually come first BIBREF8, BIBREF5, BIBREF18, BIBREF3, BIBREF13, BIBREF9, BIBREF19, BIBREF6. However, it has also been demonstrated that this preference may be affected by the author's own gender and relationship with the people named BIBREF6, BIBREF19, as well as context more generally BIBREF20.", + "Orderings on the Web. List orderings have also been explored in other Web data, specifically on the ordering of tags applied to images BIBREF21. There is evidence that these tags are ordered intentionally by users, and that a bias to order tag A before tag B may be influenced by historical precedent in that environment but also by the relative importance of A and B BIBREF21. Further work also demonstrates that exploiting the order of tags on images can improve models that rank those images BIBREF22." + ], + [ + "We take our data mostly from Reddit, a large social media website divided into subcommunities called `subreddits' or `subs'. Each subreddit has a theme (usually clearly expressed in its name), and we have focused our study on subreddits primarily in sports and politics, in part because of the richness of proper names in these domains: r/nba, r/nfl, r/politics, r/Conservative, r/Libertarian, r/The_Donald, r/food, along with a variety of NBA team subreddits (e.g., r/rockets for the Houston Rockets). Apart from the team-specific and food subreddits, these are among the largest and most heavily used subreddits BIBREF23. We gather text data from comments made by users in discussion threads. In all cases, we have data from when the subreddit started until mid-2018. (Data was contributed by Cristian Danescu-Niculescu-Mizil.) Reddit in general, and the subreddits we examined in particular, are rapidly growing, both in terms of number of users and number of comments.", + "Some of the subreddits we looked at (particularly sports subreddits) exhibited very distinctive `seasons', where commenting spikes (Fig. FIGREF2). These align with, e.g., the season of the given sport. When studying data across time, our convention is to bin the data by year, but we adjust the starting point of a year based on these seasons. Specifically, a year starts in May for r/nfl, August for r/nba, and February for all politics subreddits.", + "We use two methods to identify lists from user comments: `All Words' and `Names Only', with the latter focusing on proper names. In both cases, we collect a number of lists and discard lists for any pair of words that appear fewer than 30 times within the time frame that we examined (see Table TABREF3 for summary statistics).", + "The All Words method simply searches for two words $A$ and $B$ separated by `and' or `or', where a word is merely a series of characters separated by a space or punctuation. This process only captures lists of length two, or binomials. We then filter out lists containing words from a collection of stop-words that, by their grammatical role or formatting structure, are almost exclusively involved in false positive lists. No metadata is captured for these lists beyond the month and year of posting.", + "The Names Only method uses a curated list of full names relevant to the subreddit, focusing on sports and politics. For sports, we collected names of all NBA and NFL player active during 1980\u20132019 from basketball-reference.com and pro-football-reference.com. For politics, we collected the names of congresspeople from the @unitedstates project BIBREF24. To form lists, we search for any combination of any part of these names such that at least two partial names are separated by `and', `or', `v.s.', `vs', or `/' and the rest are separated by `,'. While we included a variety of separators, about 83% of lists include only `and', about 17% include `or' and the rest of the separators are negligible. Most lists that we retrieve in this way are of length 2, but we also found lists up to length 40 (Fig. FIGREF5). Finally, we also captured full metadata for these lists, including a timestamp, the user, any flairs attributed to the user (short custom text that appears next to the username), and other information.", + "We additionally used wine reviews and a variety of news paper articles for additional analysis. The wine data gives reviews of wine from WineEnthusiast and is hosted on Kaggle BIBREF25. While not specifically dated, the reviews were scraped between June and November of 2017. There are 20 different reviewers included, but the amount of reviews each has ranges from tens to thousands. The news data consists of news articles pulled from a variety of sources, including (in random order) the New York Times, Breitbart, CNN, the Atlantic, Buzzfeed News, National Review, New York Post, NPR, Reuters, and the Washington Post. The articles are primarily from 2016 and early 2017 with a few from 2015. The articles are scraped from home-page headline and RSS feeds BIBREF26. Metadata was limited for both of these data sets." + ], + [ + "In this paper we introduce a new framework to interpret binomials, based on three properties: asymmetry (how frozen a binomial is), movement (how binomial orderings change over time), and agreement (how consistent binomial orderings are between communities), which we will visualize as a cube with three dimensions. Again, prior work has focused essentially entirely on asymmetry, and we argue that this can only really be understood in the context of the other two dimensions.", + "For this paper we will use the convention {A,B} to refer to an unordered pair of words, and [A,B] to refer to an ordered pair where A comes before B. We say that [A,B] and [B,A] are the two possible orientations of {A,B}." + ], + [ + "Previous work has one main measure of binomials \u2014 their `frozen-ness'. A binomial is `frozen' if it always appears with a particular order. For example, if the pair {`arrow', `bow'} always occurs as [`bow', `arrow'] and never as [`arrow', `bow'], then it is frozen. This leaves open the question of how describe the large number of binomials that are not frozen. To address this point, we instead consider the ordinality of a list, or how often the list is `in order' according to some arbitrary underlying reference order. Unless otherwise specified, the underlying order is assumed to be alphabetical. If the list [`cat', `dog'] appears 40 times and the list [`dog', `cat'] 10 times, then the list {`cat', `dog'} would have an ordinality of 0.8.", + "Let $n_{x,y}$ be the number of times the ordered list $[x,y]$ appears, and let $f_{x,y} = n_{x,y} / (n_{x,y} + n_{y,x})$ be the fraction of times that the unordered version of the list appears in that order. We formalize ordinality as follows. [Ordinality] Given an ordering $<$ on words (by default, we assume alphabetical ordering), the ordinality $o_{x,y}$ of the pair $\\lbrace x,y\\rbrace $ is equal to $f_{x,y}$ if $x < y$ and $f_{y,x}$ otherwise.", + "Similarly, we introduce the concept of asymmetry in the context of binomials, which is how often the word appears in its dominant order. In our framework, a `frozen' list is one with ordinality 0 or 1 and would be considered a high asymmetry list, with asymmetry of 1. A list that appears as [`A', `B'] half of the time and [`B', `A'] half of the time (or with ordinality 0.5) would be considered a low asymmetry list, with asymmetry of 0.", + "[Asymmetry] The asymmetry of an unordered list $\\lbrace x,y\\rbrace $ is $A_{x,y} = 2 \\cdot \\vert o_{x,y} - 0.5 \\vert $.", + "The Reddit data described above gives us access to new dimensions of binomials not previously addressed. We define movement as how the ordinality of a list changes over time [Movement] Let $o_{x,y,t}$ be the ordinality of an unordered list $\\lbrace x,y\\rbrace $ for data in year $t \\in T$. The movement of $\\lbrace x,y\\rbrace $ is $M_{x,y} = \\max _{t \\in T} o_{x,y,t} - \\min _{t \\in T} o_{x,y,t}$. And agreement describes how the ordinality of a list differs between different communities. [Agreement] Let $o_{x,y,c}$ be the ordinality of an unordered list ${x,y}$ for data in community (subreddit) $c \\in C$. The agreement of $\\lbrace x,y\\rbrace $ is $A_{x,y} = 1 - (\\max _{c \\in C} o_{x,y,c} - \\min _{c \\in C} o_{x,y,c})$." + ], + [ + "Let the point $(A,M,G)_{x,y}$ be a vector of the asymmetry, movement, and agreement for some unordered list $\\lbrace x,y\\rbrace $. These vectors then define a 3-dimensional space in which each list occupies a point. Since our measures for asymmetry, agreement, and movement are all defined from 0 to 1, their domains form a unit cube (Fig. FIGREF8). The corners of this cube correspond to points with coordinates are entirely made up of 0s or 1s. By examining points near the corners of this cube, we can get a better understanding of the range of binomials. Some corners are natural \u2014 it is easy to imagine a high asymmetry, low movement, high agreement binomial \u2014 such as {`arrow', `bow'} from earlier. On the other hand, we have found no good examples of a high asymmetry, low movement, low agreement binomial. There are a few unusual examples, such as {10, 20}, which has 0.4 asymmetry, 0.2 movement, and 0.1 agreement and is clearly visible as an isolated point in Fig. FIGREF8.", + "Asymmetry. While a majority of binomials have low asymmetry, almost all previous work has focused exclusively on high-asymmetry binomials. In fact, asymmetry is roughly normally distributed across binomials with an additional increase of highly asymmetric binomials (Fig. FIGREF9). This implies that previous work has overlooked the vast majority of binomials, and an investigation into whether rules proposed for highly asymmetric binomials also functions for other binomials is a core piece of our analysis.", + "Movement. The vast majority of binomials have low movement. However, the exceptions to this can be very informative. Within r/nba a few of these pairs show clear change in linguistics and/or culture. The binomial [`rpm', `vorp'] (a pair of basketball statistics) started at 0.74 ordinality and within three years dropped to 0.32 ordinality, showing a potential change in users' representation of how these statistics relate to each other. In r/politics, [`daughter', `son'] moved from 0.07 ordinality to 0.36 ordinality over ten years. This may represent a cultural shift in how users refer to children, or a shift in topics discussed relating to children. And in r/politics, ['dems', 'obama'] went from 0.75 ordinality to 0.43 ordinality from 2009\u20132018, potentially reflecting changes in Obama's role as a defining feature of the Democratic Party. Meanwhile the ratio of unigram frequency of `dems' to `obama' actually increased from 10% to 20% from 2010 to 2017. Similarly, [`fdr', `lincoln'] moved from 0.49 ordinality to 0.17 ordinality from 2015\u20132018. This is particularly interesting, since in 2016 `fdr' had a unigram frequency 20% higher than `lincoln', but in 2017 they are almost the same. This suggests that movement could be unrelated to unigram frequency changes. Note also that the covariance for movement across subreddits is quite low TABREF10, and movement in one subreddit is not necessarily reflected by movement in another.", + "Agreement. Most binomials have high agreement (Table TABREF11) but again the counterexamples are informative. For instance, [`score', `kick'] has ordinality of 0.921 in r/nba and 0.204 in r/nfl. This likely points to the fact that American football includes field goals. A less obvious example is the list [`ceiling', `floor']. In r/nba and r/nfl, it has ordinality 0.44, and in r/politics, it has ordinality 0.27.", + "There are also differences among proper nouns. One example is [`france', `israel'], which has ordinality 0.6 in r/politics, 0.16 in r/Libertarian, and 0.51 in r/The_Donald (and the list does not appear in r/Conservative). And the list [`romney', `trump'] has ordinality 0.48 in r/poltics, 0.55 in r/The_Donald, and 0.73 in r/Conservative." + ], + [ + "In this section, we establish a null model under which different communities or time slices have the same probability of ordering a binomial in a particular way. With this, we would expect to see variation in binomial asymmetry. We find that our data shows smaller variation than this null model predicts, suggesting that binomial orderings are extremely stable across communities and time. From this, we might also expect that orderings are predictable; but we find that standard predictors in fact have limited success." + ], + [ + "Recall that the asymmetry of binomials with respect to alphabetic order (excluding frozen binomials) is roughly normal centered around $0.5$ (Fig. FIGREF9). One way of seeing this type of distribution would be if binomials are ordered randomly, with $p=0.5$ for each order. In this case, if each instance $l$ of a binomial $\\lbrace x,y\\rbrace $ takes value 0 (non-alphabetical ordering) or 1 (alphabetical ordering), then $l \\sim \\text{Bernoulli}(0.5)$. If $\\lbrace x,y\\rbrace $ appears $n$ times, then the number of instances of value 1 is distributed by $W \\sim \\text{Bin}(n, 0.5)$, and $W / n$ is approximately normally distributed with mean 0.5.", + "One way to test this behavior is to first estimate $p$ for each list within each community. If the differences in these estimates are not normal, then the above model is incorrect. We first omit frozen binomials before any analysis. Let $L$ be a set of unordered lists and $C$ be a set of communities. We estimate $p$ for list $l \\in L$ in community $c \\in C$ by $\\hat{p}_{l,c} = o_{l,c}$, the ordinality of $l$ in $C$. Next, for all $l \\in L$ let $p^*_{l} = \\max _{c \\in C}(\\hat{p}_{l, c}) - \\min _{ c \\in C}(\\hat{p}_{l, c})$. The distribution of $p^*_{l}$ over $l \\in L$ has median 0, mean 0.0145, and standard deviation 0.0344. We can perform a similar analysis over time. Define $Y$ as our set of years, and $\\hat{p}_{l, y} = o_{l,y}$ for $y \\in Y$ our estimates. The distribution of $p^{\\prime }_{l} = \\max _{y \\in Y}(\\hat{p}_{l, y}) - \\min _{y \\in Y}(\\hat{p}_{l, y})$ over $l \\in L$ has median 0.0216, mean 0.0685, and standard deviation 0.0856. The fact that $p$ varies very little across both time and communities suggests that there is some $p_l$ for each $l \\in L$ that is consistent across time and communities, which is not the case in the null model, where these values would be normally distributed.", + "We also used a bootstrapping technique to understand the mean variance in ordinality for lists over communities and years. Specifically, let $o_{l, c, y}$ be the ordinality of list $l$ in community $c$ and year $y$, $O_l$ be the set of $o_{l,c,y}$ for a given list $l$, and $s_l$ be the standard deviation of $O_l$. Finally, let $\\bar{s}$ be the average of the $s_l$. We re-sample data by randomizing the order of each binomial instance, sampling its orderings by a binomial random variable with success probability equal to its ordinality across all seasons and communities ($p_l$). We repeated this process to get samples estimates $\\lbrace \\bar{s}_1, \\ldots , \\bar{s}_{k}\\rbrace $, where $k$ is the size of the set of seasons and communities. These averages range from 0.0277 to 0.0278 and are approximately normally distributed (each is a mean over an approximately normal scaled Binomial random variable). However, $\\bar{s} = 0.0253$ for our non-randomized data. This is significantly smaller than the randomized data and implies that the true variation in $p_l$ across time and communities is even smaller than a binomial distribution would predict. One possible explanation for this is that each instance of $l$ is not actually independent, but is in fact anti-correlated, violating one of the conditions of the binomial distribution. An explanation for that could be that users attempt to draw attention by intentionally going against the typical ordering BIBREF1, but it is an open question what the true model is and why the variation is so low. Regardless, it is clear that the orientation of binomials varies very little across years and communities (Fig. FIGREF13)." + ], + [ + "Given the stability of binomials within our data, we now try to predict their ordering. We consider deterministic or rule-based methods that predict the order for a given binomial. We use two classes of evaluation measures for success on this task: (i) by token \u2014 judging each instance of a binomial separately; and (ii) by type \u2014 judging all instances of a particular binomial together. We further characterize these into weighted and unweighted.", + "To formalize these notions, first consider any unordered list $\\lbrace x,y\\rbrace $ that appears $n_{x,y}$ times in the orientation $[x,y]$ and $n_{y,x}$ times in the orientation $[y,x]$. Since we can only guess one order, we will have either $n_{x,y}$ or $n_{y,x}$ successful guesses for $\\lbrace x,y\\rbrace $ when guessing by token. The unweighted token score (UO) and weighted token score (WO) are the macro and micro averages of this accuracy.", + "If predicting by type, let $S$ be the lists such that the by-token prediction is successful at least half of the time. Then the unweighted type score (UT) and weighted type score (WT) are the macro and micro averages of $S$.", + "Basic Features. We first use predictors based on rules that have previously been proposed in the literature: word length, number of phonemes, number of syllables, alphabetical order, and frequency. We collect all binomials but make predictions only on binomials appearing at least 30 times total, stratified by subreddit. However, none of these features appear to be particularly predictive across the board (Table TABREF15). A simple linear regression model predicts close to random, which bolsters the evidence that these classical rules for frozen binomials are not predictive for general binomials.", + "Perhaps the oldest suggestion to explain binomial orderings is that if there are two words A and B, and A is monosyllabic and B is disyllabic, then A comes before B BIBREF0. Within r/politics, we gathered an estimate of number of syllables for each word as given by a variation on the CMU Pronouncing Dictionary BIBREF27 (Tables TABREF16 and TABREF17). In a weak sense, Jespersen was correct that monosyllabic words come before disyllabic words more often than not; and more generally, shorter words come before longer words more often than not. However, as predictors, these principles are close to random guessing.", + "Paired Predictions. Another measure of predictive power is predicting which of two binomials has higher asymmetry. In this case, we take two binomials with very different asymmetry and try to predict which has higher asymmetry by our measures (we use the top-1000 and bottom-1000 binomials in terms of asymmetry for these tasks). For instance, we may predict that [`red', `turquoise'] is more asymmetric than [`red', `blue'] because the differences in lengths is more extreme. Overall, the basic predictors from the literature are not very successful (Table TABREF18).", + "Word Embeddings. If we turn to more modern approaches to text analysis, one of the most common is word embeddings BIBREF16. Word embeddings assign a vector $x_i$ to each word $i$ in the corpus, such that the relative position of these vectors in space encode information lingustically relevant relationships among the words. Using the Google News word embeddings, via a simple logistic model, we produce a vector $v^*$ and predict the ordering of a binomial on words $i$ and $j$ from $v^* \\cdot (x_i - x_j)$. In this sense, $v^*$ can be thought of as a \u201csweep-line\u201d direction through the space containing the word vectors, such that the ordering along this sweep-line is the predicted ordering of all binomials in the corpus. This yields surprisingly accurate results, with accuracy ranging from 70% to 85% across various subreddits (Table TABREF20), and 80-100% accuracy on frozen binomials. This is by far the best prediction method we tested. It is important to note that not all words in our binomials could be associated with an embedding, so it was necessary to remove binomials containing words such as names or slang. However, retesting our basic features on this data set did not show any improvement, implying that the drastic change in predictive power is not due to the changed data set." + ], + [ + "Proper nouns, and names in particular, have been a focus within the literature on frozen binomials BIBREF8, BIBREF5, BIBREF18, BIBREF3, BIBREF13, BIBREF9, BIBREF6, BIBREF19, BIBREF20, BIBREF28, but these studies have largely concentrated on the effect of gender in ordering BIBREF8, BIBREF5, BIBREF18, BIBREF3, BIBREF13, BIBREF9, BIBREF6, BIBREF19, BIBREF20. With Reddit data, however, we have many conversations about large numbers of celebrities, with significant background information on each. As such, we can investigate proper nouns in three subreddits: r/nba, r/nfl, and r/politics. The names we used are from NBA and NFL players (1970\u20132019) and congresspeople (pre-1800 and 2000\u20132019) respectively. We also investigated names of entities for which users might feel a strong sense of identification, such as a team or political group they support, or a subreddit to which they subscribe. We hypothesized that the group with which the user identifies the most would come first in binomial orderings. Inspired by the `Me First Principle', we call this the Proximity Principle." + ], + [ + "First, we examined names in r/nba. One advantage of using NBA players is that we have detailed statistics for ever player in every year. We tested a number of these statistics, and while all of them predicted statistically significant numbers ($p <$ 1e-6) of binomials, they were still not very predictive in a practical sense (Table TABREF23). The best predictor was actually how often the player's team was mentioned. Interestingly, the unigram frequency (number of times the player's name was mentioned overall) was not a good predictor. It is relevant to these observations that some team subreddits (and thus, presumably, fanbases) are significantly larger than others." + ], + [ + "Additionally, we also investigated lists of names of sports teams and subreddits as proper nouns. In this case we exploit an interesting structure of the r/nba subreddit which is not evident at scale in other subreddits we examined. In addition to r/nba, there exists a number of subreddits that are affiliated with a particular NBA team, with the purpose of allowing discussion between fans of that team. This implies that most users in a team subreddit are fans of that team. We are then able to look for lists of NBA teams by name, city, and abbreviation. We found 2520 instances of the subreddit team coming first, and 1894 instances of the subreddit team coming second. While this is not a particularly strong predictor, correctly predicting 57% of lists, it is one of the strongest we found, and a clear illustration of the Proximity Principle.", + "We can do a similar calculation with subreddit names, by looking between subreddits. While the team subreddits are not large enough for this calculation, many of the other subreddits are. We find that lists of subreddits in r/nba that include `r/nba' often start with `r/nba', and a similar result holds for r/nfl (Table TABREF25).", + "While NBA team subreddits show a fairly strong preference to name themselves first, this preference is slightly less strong among sport subreddits, and even less strong among politics subreddits. One potential factor here is that r/politics is a more general subreddit, while the rest are more specific \u2014 perhaps akin to r/nba and the team subreddits." + ], + [ + "In our case, political names are drawn from every congressperson (and their nicknames) in both houses of the US Congress through the 2018 election. It is worth noting that one of these people is Philadelph Van Trump. It is presumed that most references to `trump' refer to Donald Trump. There may be additional instances of mistaken identities. We restrict the names to only congresspeople that served before 1801 or after 1999, also including `trump'.", + "One might guess that political subreddits refer to politicians of their preferred party first. However, this was not the case, as Republicans are mentioned first only about 43%\u201346% of the time in all subreddits (Table TABREF27). On the other hand, the Proximity Principle does seem to come into play when discussing ideology. For instance, r/politics \u2014 a left-leaning subreddit \u2014 is more likely to say `democrats and republicans' while the other political subreddits in our study \u2014 which are right-leaning \u2014 are more likely to say `republicans and democrats'.", + "Another relevant measure for lists of proper nouns is the ratio of the number of list instances containing a name to the unigram frequency of that name. We restrict our investigation to names that are not also English words, and only names that have a unigram frequency of at least 30. The average ratio is 0.0535, but there is significant variation across names. It is conceivable that this list ratio is revealing about how often people are talked about alone instead of in company." + ], + [ + "While Reddit provides a very large corpus of informal text, McGuire and McGuire make a distinct separation between informal and formal text BIBREF28. As such, we briefly analyze highly stylized wine reviews and news articles from a diverse set of publications. Both data sets follow the same basic principles outlined above." + ], + [ + "Wine reviews are a highly stylized form of text. In this case reviews are often just a few sentences, and they use a specialized vocabulary meant for wine tasting. While one might hypothesize that such stylized text exhibits more frozen binomials, this is not the case (Tab TABREF28). There is some evidence of an additional freezing effect in binomials such as ('aromas', 'flavors') and ('scents', 'flavors') which both are frozen in the wine reviews, but are not frozen on Reddit. However, this does not seem to have a more general effect. Additionally, there are a number of binomials which appear frozen on Reddit, but have low asymmetry in the wine reviews, such as ['lemon', 'lime']." + ], + [ + "We focused our analysis on NYT, Buzzfeed, Reuters, CNN, the Washington Post, NPR, Breitbart, and the Atlantic. Much like in political subreddits, one might expect to see a split between various publications based upon ideology. However, this is not obviously the case. While there are certainly examples of binomials that seem to differ significantly for one publication or for a group of publications (Buzzfeed, in particular, frequently goes against the grain), there does not seem to be a sharp divide. Individual examples are difficult to draw conclusions from, but can suggest trends. (`China', `Russia') is a particularly controversial binomial. While the publications vary quite a bit, only Breitbart has an ordinality of above 0.5. In fact, country pairs are among the most controversial binomials within the publications (e.g. (`iraq', `syria'), (`afghanisatan', `iraq')), while most other highly controversial binomials reflect other political structures, such as (`house', `senate'), (`migrants', 'refugees'), and (`left', `right'). That so many controversial binomials reflect politics could point to subtle political or ideological differences between the publications. Additionally, the close similarity between Breitbart and more mainstream publications could be due to a similar effect we saw with r/The_Donald - mainly large amounts of quoted text." + ], + [ + "We can discover new structure in binomial orderings by taking a more global view. We do this by building directed graphs based on ordinality. In these graphs, nodes are words and an arrow from A to B indicates that there are at least 30 lists containing A and B and that those lists have order [A,B] at least 50% of the time. For our visualizations, the size of the node indicates how many distinct lists the word appears in,and color indicates how many list instances contain the word in total.", + "If we examine the global structure for r/nba, we can pinpoint a number of patterns (Fig. FIGREF31). First, most nodes within the purple circle correspond to names, while most nodes outside of it are not names. The cluster of circles in the lower left are a combination of numbers and years, where dark green corresponds to numbers, purple corresponds to years, and pink corresponds years represented as two-digit numbers (e.g., `96'). On the right, the brown circle contains adjectives, while above the blue circle contains heights (e.g., 6'5\"), and in the two circles in the lower middle, the left contains cities while the right contains team names. The darkest red node in the center of the graph corresponds to `lebron'.", + "Constructing a similar graph for our wines dataset, we can see clusters of words. In Fig FIGREF32, the colors represent clusters as formed through modularity. These clusters are quite distinct. Green nodes mostly refer to the structure or body of a wine, red are adjectives describing taste, teal and purple are fruits, dark green is wine varietals, gold is senses, and light blue is time (e.g. `year', `decade', etc.)", + "We can also consider the graph as we change the threshold of asymmetry for which an edge is included. If the asymmetry is large enough, the graph is acyclic, and we can consider how small the ordinality threshold must be in order to introduce a cycle. These cycles reveal the non-global ordering of binomials. The graph for r/nba begins to show cycles with a threshold asymmetry of 0.97. Three cycles exist at this threshold: [`ball', `catch', `shooter'], [`court', `pass', `set', `athleticism'], and [`court', `plays', `set', `athleticism'].", + "Restricting the nodes to be names is also revealing. Acyclic graphs in this context suggest a global partial hierarchy of individuals. For r/nba, the graph is no longer acyclic at an asymmetry threshold of 0.76, with the cycle [`blake', `jordan', `bryant', `kobe']. Similarly, the graph for r/nfl (only including names) is acyclic until the threshold reaches 0.73 with cycles [`tannehill', `miller', `jj watt', `aaron rodgers', `brady'], and [`hoyer', `savage', `watson', `hopkins', `miller', `jj watt', `aaron rodgers', `brady'].", + "Figure FIGREF33 shows these graphs for the three political subreddits, where the nodes are the 30 most common politician names. The graph visualizations immediately show that these communities view politicians differently. We can also consider cycles in these graphs and find that the graph is completely acyclic when the asymmetry threshold is at least 0.9. Again, this suggests that, at least among frozen binomials, there is in fact a global partial order of names that might signal hierarchy. (Including non-names, though, causes the r/politics graph to never be acyclic for any asymmetry threshold, since the cycle [`furious', `benghazi', `fast'] consists of completely frozen binomials.) We find similar results for r/Conservative and r/Libertarian, which are acyclic with thresholds of 0.58 and 0.66, respectively. Some of these cycles at high asymmetry might be due to English words that are also names (e.g. `law'), but one particularly notable cycle from r/Conservative is [`rubio', `bush', `obama', `trump', `cruz']." + ], + [ + "Binomials are the most studied type of list, but trinomials \u2014 lists of three \u2014 are also common enough in our dataset to analyze. Studying trinomials adds new aspects to the set of questions: for example, while binomials have only two possible orderings, trinomials have six possible orderings. However, very few trinomials show up in all six orderings. In fact, many trinomials show up in exactly one ordering: about 36% of trinomials being completely frozen amongst trinomials appearing at least 30 times in the data. To get a baseline comparison, we found an equal number of the most common binomials, and then subsampled instances of those binomials to equate the number of instances with the trinomials. In this case, only 21% of binomials are frozen. For trinomials that show up in at least two orderings, it is most common for the last word to keep the same position (e.g., [a, b, c] and [b, a, c]). For example, in our data, [`fraud', `waste', `abuse'] appears 34 times, and [`waste', `fraud', `abuse'] appears 20 times. This may partially be explained by many lists that contain words such as `other', `whatever', or `more'; for instance, [`smarter', `better', `more'] and [`better', `smarter', `more'] are the only two orderings we observe for this set of three words.", + "Additionally, each trinomial [a, b, c] contains three binomials within it: [a, b], [b, c], and [a, c]. It is natural to compare orderings of {a, b} in general with orderings of occurrences of {a, b} that lie inside trinomials. We use this comparison to define the compatibility of {a, b}, as follows.", + "Compatibility Let {a, b} be a binomial with dominant ordering [a, b]; that is, [a, b] is at least as frequent as [b, a]. We define the compatibility of {a, b} to be the fraction of instances of {a, b} occurring inside trinomials that have the order [a,b].", + "There are only a few cases where binomials have compatibility less than 0.5, and for most binomials, the asymmetry is remarkably consistent between binomials and trinomials (Fig. FIGREF37). In general, asymmetry is larger than compatibility \u2014 this occurs for 4569 binomials, compared to 3575 where compatibility was greater and 690 where the two values are the same. An extreme example is the binomial {`fairness', `accuracy'}, which has asymmetry 0.77 and compatibility 0.22. It would be natural to consider these questions for tetranomials and longer lists, but these are rarer in our data and correspondingly harder to draw conclusions from." + ], + [ + "Analyzing binomial orderings on a large scale has led to surprising results. Although most binomials are not frozen in the traditional sense, there is little movement in their ordinality across time or communities. A list that appears in the order [A, B] 60% of the time in one subreddit in one year is likely to show up as [A, B] very close to 60% of the time in all subreddits in all years. This suggests that binomial order should be predictable, but there is evidence that this is difficult: the most common theories on frozen binomial ordering were largely ineffective at predicting binomial ordering in general.", + "Given the challenge in predicting orderings, we searched for methods or principles that could yield better performance, and identified two promising approaches. First, models built on standard word embeddings produce predictions of binomial orders that are much more effective than simpler existing theories. Second, we established the Proximity Principle: the proper noun with which a speaker identifies more will tend to come first. This is evidenced when commenters refer to their sports team first, or politicians refer to their party first. Further analysis of the global structure of binomials reveals interesting patterns and a surprising acyclic nature in names. Analysis of longer lists in the form of multinomials suggests that the rules governing their orders may be different.", + "We have also found promising results in some special cases. We expect that more domain-specific studies will offer rich structure.", + "It is a challenge to adapt the long history of work on the question of frozen binomials to the large, messy environment of online text and social media. However, such data sources offer a unique opportunity to re-explore and redefine these questions. It seems that binomial orderings offer new insights into language, culture, and human cognition. Understanding what changes in these highly stable conventions mean \u2014 and whether or not they can be predicted \u2014 is an interesting avenue for future research." + ], + [ + "The authors thank members of the Cornell AI, Policy, and Practice Group, and (alphabetically by first name) Cristian Danescu-Niculescu-Mizil, Ian Lomeli, Justine Zhang, and Kate Donahue for aid in accessing data and their thoughtful insight. This research was supported by NSF Award DMS-1830274, ARO Award W911NF19-1-0057, a Simons Investigator Award, a Vannevar Bush Faculty Fellowship, and ARO MURI." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0553/environment/Dockerfile b/qasper-0553/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0553/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/* diff --git a/qasper-0553/instruction.md b/qasper-0553/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..b7f931a2d2d69b0c97c44a9511519a1d83f2ab2e --- /dev/null +++ b/qasper-0553/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Frozen Binomials on the Web: Word Ordering and Language Conventions in Online Text + +Question: What new model is proposed for binomial lists? \ No newline at end of file diff --git a/qasper-0554/environment/Dockerfile b/qasper-0554/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0554/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/* diff --git a/qasper-0554/instruction.md b/qasper-0554/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..8b7b71b0ae96a40c281aa614b8cadc9d64b60eb3 --- /dev/null +++ b/qasper-0554/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Frozen Binomials on the Web: Word Ordering and Language Conventions in Online Text + +Question: How was performance of previously proposed rules at very large scale? \ No newline at end of file diff --git a/qasper-0555/instruction.md b/qasper-0555/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..5066f8e3c9381091a12d1b283d5ec8211a03e272 --- /dev/null +++ b/qasper-0555/instruction.md @@ -0,0 +1,150 @@ +Name of Paper: Frozen Binomials on the Web: Word Ordering and Language Conventions in Online Text + +Question: What previously proposed rules for predicting binoial ordering are used? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Introduction ::: Related Work", + "Data", + "Dimensions of Binomials", + "Dimensions of Binomials ::: Definitions", + "Dimensions of Binomials ::: Dimensions", + "Models And Predictions", + "Models And Predictions ::: Stability of Asymmetry", + "Models And Predictions ::: Prediction Results", + "Proper Nouns and the Proximity Principle", + "Proper Nouns and the Proximity Principle ::: NBA Names", + "Proper Nouns and the Proximity Principle ::: Subreddit and team names", + "Proper Nouns and the Proximity Principle ::: Political Names", + "Formal Text", + "Formal Text ::: Wine", + "Formal Text ::: News", + "Global Structure", + "Multinomials", + "Discussion", + "Acknowledgements" + ], + "paragraphs": [ + [ + "Lists are extremely common in text and speech, and the ordering of items in a list can often reveal information. For instance, orderings can denote relative importance, such as on a to-do list, or signal status, as is the case for author lists of scholarly publications. In other cases, orderings might come from cultural or historical conventions. For example, `red, white, and blue' is a specific ordering of colors that is recognizable to those familiar with American culture.", + "The orderings of lists in text and speech is a subject that has been repeatedly touched upon for more than a century. By far the most frequently studied aspect of list ordering is the binomial, a list of two words usually separated by a conjunction such as `and' or `or', which is the focus of our paper. The academic treatment of binomial orderings dates back more than a century to Jespersen BIBREF0, who proposed in 1905 that the ordering of many common English binomials could be predicted by the rhythm of the words. In the case of a binomial consisting of a monosyllable and a disyllable, the prediction was that the monosyllable would appear first followed by the conjunction `and'. The idea was that this would give a much more standard and familiar syllable stress to the overall phrase, e.g., the binomial `bread and butter' would have the preferable rhythm compared to `butter and bread.'", + "This type of analysis is meaningful when the two words in the binomial nearly always appear in the same ordering. Binomials like this that appear in strictly one order (perhaps within the confines of some text corpus), are commonly termed frozen binomials BIBREF1, BIBREF2. Examples of frozen binomials include `salt and pepper' and `pros and cons', and explanations for their ordering in English and other languages have become increasingly complex. Early work focused almost exclusively on common frozen binomials, often drawn from everyday speech. More recent work has expanded this view to include nearly frozen binomials, binomials from large data sets such as books, and binomials of particular types such as food, names, and descriptors BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8. Additionally, explanations have increasingly focused on meaning rather than just sound, implying value systems inherent to the speaker or the culture of the language's speakers (one such example is that men are usually listed before women in English BIBREF9). The fact that purely phonetic explanations have been insufficient suggests that list orderings rely at least partially on semantics, and it has previously been suggested that these semantics could be revealing about the culture in which the speech takes place BIBREF3. Thus, it is possible that understanding these orderings could reveal biases or values held by the speaker.", + "Overall, this prior research has largely been confined to pristine examples, often relying on small samples of lists to form conclusions. Many early studies simply drew a small sample of what the author(s) considered some of the more representative or prominent binomials in whatever language they were studying BIBREF10, BIBREF1, BIBREF11, BIBREF0, BIBREF12, BIBREF13, BIBREF14, BIBREF15, BIBREF3. Other researchers have used books or news articles BIBREF2, BIBREF4, or small samples from the Web (web search results and Google books) BIBREF5. Many of these have lacked a large-scale text corpus and have relied on a focused set of statistics about word orderings.", + "Thus, despite the long history of this line of inquiry, there is an opportunity to extend it significantly by examining a broad range of questions about binomials coming from a large corpus of online text data produced organically by many people. Such an analysis could produce at least two types of benefits. First, such a study could help us learn about cultural phenomena embedded in word orderings and how they vary across communities and over time. Second, such an analysis could become a case study for the extension of theories developed at small scales in this domain to a much larger context.", + "The present work: Binomials in large-scale online text. In this work, we use data from large-scale Internet text corpora to study binomials at a massive scale, drawing on text created by millions of users. Our approach is more wholesale than prior work - we focus on all binomials of sufficient frequency, without first restricting to small samples of binomials that might be frozen. We draw our data from news publications, wine reviews, and Reddit, which in addition to large volume, also let us characterize binomials in new ways, and analyze differences in binomial orderings across communities and over time. Furthermore, the subject matter on Reddit leads to many lists about people and organizations that lets us study orderings of proper names \u2014 a key setting for word ordering which has been difficult to study by other means.", + "We begin our analysis by introducing several new key measures for the study of binomials, including a quantity we call asymmetry that measures how frequently a given binomial appears in some ordering. By looking at the distribution of asymmetries across a wide range of binomials, we find that most binomials are not frozen, barring a few strong exceptions. At the same time, there may still be an ordering preference. For example, `10 and 20' is not a frozen binomial; instead, the binomial ordering `10 and 20' appears 60% of the time and `20 and 10' appears 40% of time.", + "We also address temporal and community structure in collections of binomials. While it has been recognized that the orderings of binomials may change over time or between communities BIBREF5, BIBREF10, BIBREF1, BIBREF13, BIBREF14, BIBREF15, there has been little analysis of this change. We develop new metrics for the agreement of binomial orderings across communities and the movement of binomial orderings over time. Using subreddits as communities, these metrics reveal variations in orderings, some of which suggest cultural change influencing language. For example, in one community, we find that over a period of 10 years, the binomial `son and daughter' went from nearly frozen to appearing in that order only 64% of the time.", + "While these changes do happen, they are generally quite rare. Most binomials \u2014 frozen or not \u2014 are ordered in one way about the same percentage of the time, regardless of community or the year. We develop a null model to determine how much variation in binomial orderings we might expect across communities and across time, if binomial orderings were randomly ordered according to global asymmetry values. We find that there is less variation across time and communities in the data compared to this model, implying that binomial orderings are indeed remarkably stable.", + "Given this stability, one might expect that the dominant ordinality of a given binomial is still predictable, even if the binomial is not frozen. For example, one might expect that the global frequency of a single word or the number of syllables in a word would predict ordering in many cases. However, we find that these simple predictors are quite poor at determining binomial ordering.", + "On the other hand, we find that a notion of `proximity' is robust at predicting ordering in some cases. Here, the idea is that the person producing the text will list the word that is conceptually \u201ccloser\u201d to them first \u2014 a phenomenon related to a \u201cMe First\u201d principle of binomial orderings suggested by Cooper and Ross BIBREF3. One way in which we study this notion of proximity is through sports team subreddits. For example, we find that when two NBA team names form a binomial on a specific team's subreddit, the team that is the subject of the subreddit tends to appear first.", + "The other source of improved predictions comes from using word embeddings BIBREF16: we find that a model based on the positions of words in a standard pre-trained word embedding can be a remarkably reliable predictor of binomial orderings. While not applicable to all words, such as names, this type of model is strongly predictive in most cases.", + "Since binomial orderings are in general difficult to predict individually, we explore a new way of representing the global binomial ordering structure, we form a directed graph where an edge from $i$ to $j$ means that $i$ tends to come before $j$ in binomials. These graphs show tendencies across the English language and also reveal peculiarities in the language of particular communities. For instance, in a graph formed from the binomials in a sports community, the names of sports teams and cities are closely clustered, showing that they are often used together in binomials. Similarly, we identify clusters of names, numbers, and years. The presence of cycles in these graphs are also informative. For example, cycles are rare in graphs formed from proper names in politics, suggesting a possible hierarchy of names, and at the same time very common for other binomials. This suggests that no such hierarchy exists for most of the English language, further complicating attempts to predict binomial order.", + "Finally, we expand our work to include multinomials, which are lists of more than two words. There already appears to be more structure in trinomials (lists of three) compared to binomials. Trinomials are likely to appear in exactly one order, and when they appear in more than one order the last word is almost always the same across all instances. For instance, in one section of our Reddit data, `Fraud, Waste, and Abuse' appears 34 times, and `Waste, Fraud, and Abuse' appears 20 times. This could point to, for example, recency principles being more important in lists of three than in lists of two. While multinomials were in principle part of the scope of past research in this area, they were difficult to study in smaller corpora, suggesting another benefit of working at our current scale." + ], + [ + "Interest in list orderings spans the last century BIBREF10, BIBREF1, with a focus almost exclusively on binomials. This research has primarily investigated frozen binomials, also called irreversible binomials, fixed coordinates, and fixed conjuncts BIBREF11, although some work has also looked at non-coordinate freezes where the individual words are nonsensical by themselves (e.g., `dribs and drabs') BIBREF11. One study has directly addressed mostly frozen binomials BIBREF5, and we expand the scope of this paper by exploring the general question of how frequently binomials appear in a particular order. Early research investigated languages other than English BIBREF1, BIBREF10, but most recent research has worked almost exclusively with English. Overall, this prior research can be separated into three basic categories \u2014 phonological rules, semantic rules, and metadata rules.", + "Phonology. The earliest research on binomial orderings proposed mostly phonological explanations, particularly rhythm BIBREF0, BIBREF12. Another highly supported proposal is Panini's Law, which claims that words with fewer syllables come first BIBREF17; we find only very mild preference for this type of ordering. Cooper and Ross's work expands these to a large list of rules, many overlapping, and suggests that they can compound BIBREF3; a number of subsequent papers have expanded on their work BIBREF11, BIBREF15, BIBREF9, BIBREF17.", + "Semantics. There have also been a number of semantic explanations, mostly in the form of categorical tendencies (such as `desirable before undesirable') that may have cultural differences BIBREF10, BIBREF1. The most influential of these may be the `Me First' principle codified by Cooper and Ross. This suggests that the first word of a binomial tends to follow a hierarchy that favors `here', `now', present generation, adult, male, and positive. Additional hierarchies also include a hierarchy of food, plants vs. animals, etc. BIBREF3.", + "Frequency. More recently, it has been proposed that the more cognitively accessible word might come first, which often means the word the author sees or uses most frequently BIBREF18. There has also been debate on whether frequency may encompass most phonological and semantic rules that have been previously proposed BIBREF13, BIBREF4. We find that frequency is in general a poor predictor of word ordering.", + "Combinations. Given the number of theories, there have also been attempts to give a hierarchy of rules and study their interactions BIBREF4, BIBREF5. This research has complemented the proposals of Cooper and Ross BIBREF3. These types of hierarchies are also presented as explanations for the likelihood of a binomial becoming frozen BIBREF5.", + "Names. Work on the orderings of names has been dominated by a single phenomenon: men's names usually come before women's names. Explanations range from a power differential, to men being more `agentic' within `Me First', to men's names being more common or even exhibiting more of the phonological features of words that usually come first BIBREF8, BIBREF5, BIBREF18, BIBREF3, BIBREF13, BIBREF9, BIBREF19, BIBREF6. However, it has also been demonstrated that this preference may be affected by the author's own gender and relationship with the people named BIBREF6, BIBREF19, as well as context more generally BIBREF20.", + "Orderings on the Web. List orderings have also been explored in other Web data, specifically on the ordering of tags applied to images BIBREF21. There is evidence that these tags are ordered intentionally by users, and that a bias to order tag A before tag B may be influenced by historical precedent in that environment but also by the relative importance of A and B BIBREF21. Further work also demonstrates that exploiting the order of tags on images can improve models that rank those images BIBREF22." + ], + [ + "We take our data mostly from Reddit, a large social media website divided into subcommunities called `subreddits' or `subs'. Each subreddit has a theme (usually clearly expressed in its name), and we have focused our study on subreddits primarily in sports and politics, in part because of the richness of proper names in these domains: r/nba, r/nfl, r/politics, r/Conservative, r/Libertarian, r/The_Donald, r/food, along with a variety of NBA team subreddits (e.g., r/rockets for the Houston Rockets). Apart from the team-specific and food subreddits, these are among the largest and most heavily used subreddits BIBREF23. We gather text data from comments made by users in discussion threads. In all cases, we have data from when the subreddit started until mid-2018. (Data was contributed by Cristian Danescu-Niculescu-Mizil.) Reddit in general, and the subreddits we examined in particular, are rapidly growing, both in terms of number of users and number of comments.", + "Some of the subreddits we looked at (particularly sports subreddits) exhibited very distinctive `seasons', where commenting spikes (Fig. FIGREF2). These align with, e.g., the season of the given sport. When studying data across time, our convention is to bin the data by year, but we adjust the starting point of a year based on these seasons. Specifically, a year starts in May for r/nfl, August for r/nba, and February for all politics subreddits.", + "We use two methods to identify lists from user comments: `All Words' and `Names Only', with the latter focusing on proper names. In both cases, we collect a number of lists and discard lists for any pair of words that appear fewer than 30 times within the time frame that we examined (see Table TABREF3 for summary statistics).", + "The All Words method simply searches for two words $A$ and $B$ separated by `and' or `or', where a word is merely a series of characters separated by a space or punctuation. This process only captures lists of length two, or binomials. We then filter out lists containing words from a collection of stop-words that, by their grammatical role or formatting structure, are almost exclusively involved in false positive lists. No metadata is captured for these lists beyond the month and year of posting.", + "The Names Only method uses a curated list of full names relevant to the subreddit, focusing on sports and politics. For sports, we collected names of all NBA and NFL player active during 1980\u20132019 from basketball-reference.com and pro-football-reference.com. For politics, we collected the names of congresspeople from the @unitedstates project BIBREF24. To form lists, we search for any combination of any part of these names such that at least two partial names are separated by `and', `or', `v.s.', `vs', or `/' and the rest are separated by `,'. While we included a variety of separators, about 83% of lists include only `and', about 17% include `or' and the rest of the separators are negligible. Most lists that we retrieve in this way are of length 2, but we also found lists up to length 40 (Fig. FIGREF5). Finally, we also captured full metadata for these lists, including a timestamp, the user, any flairs attributed to the user (short custom text that appears next to the username), and other information.", + "We additionally used wine reviews and a variety of news paper articles for additional analysis. The wine data gives reviews of wine from WineEnthusiast and is hosted on Kaggle BIBREF25. While not specifically dated, the reviews were scraped between June and November of 2017. There are 20 different reviewers included, but the amount of reviews each has ranges from tens to thousands. The news data consists of news articles pulled from a variety of sources, including (in random order) the New York Times, Breitbart, CNN, the Atlantic, Buzzfeed News, National Review, New York Post, NPR, Reuters, and the Washington Post. The articles are primarily from 2016 and early 2017 with a few from 2015. The articles are scraped from home-page headline and RSS feeds BIBREF26. Metadata was limited for both of these data sets." + ], + [ + "In this paper we introduce a new framework to interpret binomials, based on three properties: asymmetry (how frozen a binomial is), movement (how binomial orderings change over time), and agreement (how consistent binomial orderings are between communities), which we will visualize as a cube with three dimensions. Again, prior work has focused essentially entirely on asymmetry, and we argue that this can only really be understood in the context of the other two dimensions.", + "For this paper we will use the convention {A,B} to refer to an unordered pair of words, and [A,B] to refer to an ordered pair where A comes before B. We say that [A,B] and [B,A] are the two possible orientations of {A,B}." + ], + [ + "Previous work has one main measure of binomials \u2014 their `frozen-ness'. A binomial is `frozen' if it always appears with a particular order. For example, if the pair {`arrow', `bow'} always occurs as [`bow', `arrow'] and never as [`arrow', `bow'], then it is frozen. This leaves open the question of how describe the large number of binomials that are not frozen. To address this point, we instead consider the ordinality of a list, or how often the list is `in order' according to some arbitrary underlying reference order. Unless otherwise specified, the underlying order is assumed to be alphabetical. If the list [`cat', `dog'] appears 40 times and the list [`dog', `cat'] 10 times, then the list {`cat', `dog'} would have an ordinality of 0.8.", + "Let $n_{x,y}$ be the number of times the ordered list $[x,y]$ appears, and let $f_{x,y} = n_{x,y} / (n_{x,y} + n_{y,x})$ be the fraction of times that the unordered version of the list appears in that order. We formalize ordinality as follows. [Ordinality] Given an ordering $<$ on words (by default, we assume alphabetical ordering), the ordinality $o_{x,y}$ of the pair $\\lbrace x,y\\rbrace $ is equal to $f_{x,y}$ if $x < y$ and $f_{y,x}$ otherwise.", + "Similarly, we introduce the concept of asymmetry in the context of binomials, which is how often the word appears in its dominant order. In our framework, a `frozen' list is one with ordinality 0 or 1 and would be considered a high asymmetry list, with asymmetry of 1. A list that appears as [`A', `B'] half of the time and [`B', `A'] half of the time (or with ordinality 0.5) would be considered a low asymmetry list, with asymmetry of 0.", + "[Asymmetry] The asymmetry of an unordered list $\\lbrace x,y\\rbrace $ is $A_{x,y} = 2 \\cdot \\vert o_{x,y} - 0.5 \\vert $.", + "The Reddit data described above gives us access to new dimensions of binomials not previously addressed. We define movement as how the ordinality of a list changes over time [Movement] Let $o_{x,y,t}$ be the ordinality of an unordered list $\\lbrace x,y\\rbrace $ for data in year $t \\in T$. The movement of $\\lbrace x,y\\rbrace $ is $M_{x,y} = \\max _{t \\in T} o_{x,y,t} - \\min _{t \\in T} o_{x,y,t}$. And agreement describes how the ordinality of a list differs between different communities. [Agreement] Let $o_{x,y,c}$ be the ordinality of an unordered list ${x,y}$ for data in community (subreddit) $c \\in C$. The agreement of $\\lbrace x,y\\rbrace $ is $A_{x,y} = 1 - (\\max _{c \\in C} o_{x,y,c} - \\min _{c \\in C} o_{x,y,c})$." + ], + [ + "Let the point $(A,M,G)_{x,y}$ be a vector of the asymmetry, movement, and agreement for some unordered list $\\lbrace x,y\\rbrace $. These vectors then define a 3-dimensional space in which each list occupies a point. Since our measures for asymmetry, agreement, and movement are all defined from 0 to 1, their domains form a unit cube (Fig. FIGREF8). The corners of this cube correspond to points with coordinates are entirely made up of 0s or 1s. By examining points near the corners of this cube, we can get a better understanding of the range of binomials. Some corners are natural \u2014 it is easy to imagine a high asymmetry, low movement, high agreement binomial \u2014 such as {`arrow', `bow'} from earlier. On the other hand, we have found no good examples of a high asymmetry, low movement, low agreement binomial. There are a few unusual examples, such as {10, 20}, which has 0.4 asymmetry, 0.2 movement, and 0.1 agreement and is clearly visible as an isolated point in Fig. FIGREF8.", + "Asymmetry. While a majority of binomials have low asymmetry, almost all previous work has focused exclusively on high-asymmetry binomials. In fact, asymmetry is roughly normally distributed across binomials with an additional increase of highly asymmetric binomials (Fig. FIGREF9). This implies that previous work has overlooked the vast majority of binomials, and an investigation into whether rules proposed for highly asymmetric binomials also functions for other binomials is a core piece of our analysis.", + "Movement. The vast majority of binomials have low movement. However, the exceptions to this can be very informative. Within r/nba a few of these pairs show clear change in linguistics and/or culture. The binomial [`rpm', `vorp'] (a pair of basketball statistics) started at 0.74 ordinality and within three years dropped to 0.32 ordinality, showing a potential change in users' representation of how these statistics relate to each other. In r/politics, [`daughter', `son'] moved from 0.07 ordinality to 0.36 ordinality over ten years. This may represent a cultural shift in how users refer to children, or a shift in topics discussed relating to children. And in r/politics, ['dems', 'obama'] went from 0.75 ordinality to 0.43 ordinality from 2009\u20132018, potentially reflecting changes in Obama's role as a defining feature of the Democratic Party. Meanwhile the ratio of unigram frequency of `dems' to `obama' actually increased from 10% to 20% from 2010 to 2017. Similarly, [`fdr', `lincoln'] moved from 0.49 ordinality to 0.17 ordinality from 2015\u20132018. This is particularly interesting, since in 2016 `fdr' had a unigram frequency 20% higher than `lincoln', but in 2017 they are almost the same. This suggests that movement could be unrelated to unigram frequency changes. Note also that the covariance for movement across subreddits is quite low TABREF10, and movement in one subreddit is not necessarily reflected by movement in another.", + "Agreement. Most binomials have high agreement (Table TABREF11) but again the counterexamples are informative. For instance, [`score', `kick'] has ordinality of 0.921 in r/nba and 0.204 in r/nfl. This likely points to the fact that American football includes field goals. A less obvious example is the list [`ceiling', `floor']. In r/nba and r/nfl, it has ordinality 0.44, and in r/politics, it has ordinality 0.27.", + "There are also differences among proper nouns. One example is [`france', `israel'], which has ordinality 0.6 in r/politics, 0.16 in r/Libertarian, and 0.51 in r/The_Donald (and the list does not appear in r/Conservative). And the list [`romney', `trump'] has ordinality 0.48 in r/poltics, 0.55 in r/The_Donald, and 0.73 in r/Conservative." + ], + [ + "In this section, we establish a null model under which different communities or time slices have the same probability of ordering a binomial in a particular way. With this, we would expect to see variation in binomial asymmetry. We find that our data shows smaller variation than this null model predicts, suggesting that binomial orderings are extremely stable across communities and time. From this, we might also expect that orderings are predictable; but we find that standard predictors in fact have limited success." + ], + [ + "Recall that the asymmetry of binomials with respect to alphabetic order (excluding frozen binomials) is roughly normal centered around $0.5$ (Fig. FIGREF9). One way of seeing this type of distribution would be if binomials are ordered randomly, with $p=0.5$ for each order. In this case, if each instance $l$ of a binomial $\\lbrace x,y\\rbrace $ takes value 0 (non-alphabetical ordering) or 1 (alphabetical ordering), then $l \\sim \\text{Bernoulli}(0.5)$. If $\\lbrace x,y\\rbrace $ appears $n$ times, then the number of instances of value 1 is distributed by $W \\sim \\text{Bin}(n, 0.5)$, and $W / n$ is approximately normally distributed with mean 0.5.", + "One way to test this behavior is to first estimate $p$ for each list within each community. If the differences in these estimates are not normal, then the above model is incorrect. We first omit frozen binomials before any analysis. Let $L$ be a set of unordered lists and $C$ be a set of communities. We estimate $p$ for list $l \\in L$ in community $c \\in C$ by $\\hat{p}_{l,c} = o_{l,c}$, the ordinality of $l$ in $C$. Next, for all $l \\in L$ let $p^*_{l} = \\max _{c \\in C}(\\hat{p}_{l, c}) - \\min _{ c \\in C}(\\hat{p}_{l, c})$. The distribution of $p^*_{l}$ over $l \\in L$ has median 0, mean 0.0145, and standard deviation 0.0344. We can perform a similar analysis over time. Define $Y$ as our set of years, and $\\hat{p}_{l, y} = o_{l,y}$ for $y \\in Y$ our estimates. The distribution of $p^{\\prime }_{l} = \\max _{y \\in Y}(\\hat{p}_{l, y}) - \\min _{y \\in Y}(\\hat{p}_{l, y})$ over $l \\in L$ has median 0.0216, mean 0.0685, and standard deviation 0.0856. The fact that $p$ varies very little across both time and communities suggests that there is some $p_l$ for each $l \\in L$ that is consistent across time and communities, which is not the case in the null model, where these values would be normally distributed.", + "We also used a bootstrapping technique to understand the mean variance in ordinality for lists over communities and years. Specifically, let $o_{l, c, y}$ be the ordinality of list $l$ in community $c$ and year $y$, $O_l$ be the set of $o_{l,c,y}$ for a given list $l$, and $s_l$ be the standard deviation of $O_l$. Finally, let $\\bar{s}$ be the average of the $s_l$. We re-sample data by randomizing the order of each binomial instance, sampling its orderings by a binomial random variable with success probability equal to its ordinality across all seasons and communities ($p_l$). We repeated this process to get samples estimates $\\lbrace \\bar{s}_1, \\ldots , \\bar{s}_{k}\\rbrace $, where $k$ is the size of the set of seasons and communities. These averages range from 0.0277 to 0.0278 and are approximately normally distributed (each is a mean over an approximately normal scaled Binomial random variable). However, $\\bar{s} = 0.0253$ for our non-randomized data. This is significantly smaller than the randomized data and implies that the true variation in $p_l$ across time and communities is even smaller than a binomial distribution would predict. One possible explanation for this is that each instance of $l$ is not actually independent, but is in fact anti-correlated, violating one of the conditions of the binomial distribution. An explanation for that could be that users attempt to draw attention by intentionally going against the typical ordering BIBREF1, but it is an open question what the true model is and why the variation is so low. Regardless, it is clear that the orientation of binomials varies very little across years and communities (Fig. FIGREF13)." + ], + [ + "Given the stability of binomials within our data, we now try to predict their ordering. We consider deterministic or rule-based methods that predict the order for a given binomial. We use two classes of evaluation measures for success on this task: (i) by token \u2014 judging each instance of a binomial separately; and (ii) by type \u2014 judging all instances of a particular binomial together. We further characterize these into weighted and unweighted.", + "To formalize these notions, first consider any unordered list $\\lbrace x,y\\rbrace $ that appears $n_{x,y}$ times in the orientation $[x,y]$ and $n_{y,x}$ times in the orientation $[y,x]$. Since we can only guess one order, we will have either $n_{x,y}$ or $n_{y,x}$ successful guesses for $\\lbrace x,y\\rbrace $ when guessing by token. The unweighted token score (UO) and weighted token score (WO) are the macro and micro averages of this accuracy.", + "If predicting by type, let $S$ be the lists such that the by-token prediction is successful at least half of the time. Then the unweighted type score (UT) and weighted type score (WT) are the macro and micro averages of $S$.", + "Basic Features. We first use predictors based on rules that have previously been proposed in the literature: word length, number of phonemes, number of syllables, alphabetical order, and frequency. We collect all binomials but make predictions only on binomials appearing at least 30 times total, stratified by subreddit. However, none of these features appear to be particularly predictive across the board (Table TABREF15). A simple linear regression model predicts close to random, which bolsters the evidence that these classical rules for frozen binomials are not predictive for general binomials.", + "Perhaps the oldest suggestion to explain binomial orderings is that if there are two words A and B, and A is monosyllabic and B is disyllabic, then A comes before B BIBREF0. Within r/politics, we gathered an estimate of number of syllables for each word as given by a variation on the CMU Pronouncing Dictionary BIBREF27 (Tables TABREF16 and TABREF17). In a weak sense, Jespersen was correct that monosyllabic words come before disyllabic words more often than not; and more generally, shorter words come before longer words more often than not. However, as predictors, these principles are close to random guessing.", + "Paired Predictions. Another measure of predictive power is predicting which of two binomials has higher asymmetry. In this case, we take two binomials with very different asymmetry and try to predict which has higher asymmetry by our measures (we use the top-1000 and bottom-1000 binomials in terms of asymmetry for these tasks). For instance, we may predict that [`red', `turquoise'] is more asymmetric than [`red', `blue'] because the differences in lengths is more extreme. Overall, the basic predictors from the literature are not very successful (Table TABREF18).", + "Word Embeddings. If we turn to more modern approaches to text analysis, one of the most common is word embeddings BIBREF16. Word embeddings assign a vector $x_i$ to each word $i$ in the corpus, such that the relative position of these vectors in space encode information lingustically relevant relationships among the words. Using the Google News word embeddings, via a simple logistic model, we produce a vector $v^*$ and predict the ordering of a binomial on words $i$ and $j$ from $v^* \\cdot (x_i - x_j)$. In this sense, $v^*$ can be thought of as a \u201csweep-line\u201d direction through the space containing the word vectors, such that the ordering along this sweep-line is the predicted ordering of all binomials in the corpus. This yields surprisingly accurate results, with accuracy ranging from 70% to 85% across various subreddits (Table TABREF20), and 80-100% accuracy on frozen binomials. This is by far the best prediction method we tested. It is important to note that not all words in our binomials could be associated with an embedding, so it was necessary to remove binomials containing words such as names or slang. However, retesting our basic features on this data set did not show any improvement, implying that the drastic change in predictive power is not due to the changed data set." + ], + [ + "Proper nouns, and names in particular, have been a focus within the literature on frozen binomials BIBREF8, BIBREF5, BIBREF18, BIBREF3, BIBREF13, BIBREF9, BIBREF6, BIBREF19, BIBREF20, BIBREF28, but these studies have largely concentrated on the effect of gender in ordering BIBREF8, BIBREF5, BIBREF18, BIBREF3, BIBREF13, BIBREF9, BIBREF6, BIBREF19, BIBREF20. With Reddit data, however, we have many conversations about large numbers of celebrities, with significant background information on each. As such, we can investigate proper nouns in three subreddits: r/nba, r/nfl, and r/politics. The names we used are from NBA and NFL players (1970\u20132019) and congresspeople (pre-1800 and 2000\u20132019) respectively. We also investigated names of entities for which users might feel a strong sense of identification, such as a team or political group they support, or a subreddit to which they subscribe. We hypothesized that the group with which the user identifies the most would come first in binomial orderings. Inspired by the `Me First Principle', we call this the Proximity Principle." + ], + [ + "First, we examined names in r/nba. One advantage of using NBA players is that we have detailed statistics for ever player in every year. We tested a number of these statistics, and while all of them predicted statistically significant numbers ($p <$ 1e-6) of binomials, they were still not very predictive in a practical sense (Table TABREF23). The best predictor was actually how often the player's team was mentioned. Interestingly, the unigram frequency (number of times the player's name was mentioned overall) was not a good predictor. It is relevant to these observations that some team subreddits (and thus, presumably, fanbases) are significantly larger than others." + ], + [ + "Additionally, we also investigated lists of names of sports teams and subreddits as proper nouns. In this case we exploit an interesting structure of the r/nba subreddit which is not evident at scale in other subreddits we examined. In addition to r/nba, there exists a number of subreddits that are affiliated with a particular NBA team, with the purpose of allowing discussion between fans of that team. This implies that most users in a team subreddit are fans of that team. We are then able to look for lists of NBA teams by name, city, and abbreviation. We found 2520 instances of the subreddit team coming first, and 1894 instances of the subreddit team coming second. While this is not a particularly strong predictor, correctly predicting 57% of lists, it is one of the strongest we found, and a clear illustration of the Proximity Principle.", + "We can do a similar calculation with subreddit names, by looking between subreddits. While the team subreddits are not large enough for this calculation, many of the other subreddits are. We find that lists of subreddits in r/nba that include `r/nba' often start with `r/nba', and a similar result holds for r/nfl (Table TABREF25).", + "While NBA team subreddits show a fairly strong preference to name themselves first, this preference is slightly less strong among sport subreddits, and even less strong among politics subreddits. One potential factor here is that r/politics is a more general subreddit, while the rest are more specific \u2014 perhaps akin to r/nba and the team subreddits." + ], + [ + "In our case, political names are drawn from every congressperson (and their nicknames) in both houses of the US Congress through the 2018 election. It is worth noting that one of these people is Philadelph Van Trump. It is presumed that most references to `trump' refer to Donald Trump. There may be additional instances of mistaken identities. We restrict the names to only congresspeople that served before 1801 or after 1999, also including `trump'.", + "One might guess that political subreddits refer to politicians of their preferred party first. However, this was not the case, as Republicans are mentioned first only about 43%\u201346% of the time in all subreddits (Table TABREF27). On the other hand, the Proximity Principle does seem to come into play when discussing ideology. For instance, r/politics \u2014 a left-leaning subreddit \u2014 is more likely to say `democrats and republicans' while the other political subreddits in our study \u2014 which are right-leaning \u2014 are more likely to say `republicans and democrats'.", + "Another relevant measure for lists of proper nouns is the ratio of the number of list instances containing a name to the unigram frequency of that name. We restrict our investigation to names that are not also English words, and only names that have a unigram frequency of at least 30. The average ratio is 0.0535, but there is significant variation across names. It is conceivable that this list ratio is revealing about how often people are talked about alone instead of in company." + ], + [ + "While Reddit provides a very large corpus of informal text, McGuire and McGuire make a distinct separation between informal and formal text BIBREF28. As such, we briefly analyze highly stylized wine reviews and news articles from a diverse set of publications. Both data sets follow the same basic principles outlined above." + ], + [ + "Wine reviews are a highly stylized form of text. In this case reviews are often just a few sentences, and they use a specialized vocabulary meant for wine tasting. While one might hypothesize that such stylized text exhibits more frozen binomials, this is not the case (Tab TABREF28). There is some evidence of an additional freezing effect in binomials such as ('aromas', 'flavors') and ('scents', 'flavors') which both are frozen in the wine reviews, but are not frozen on Reddit. However, this does not seem to have a more general effect. Additionally, there are a number of binomials which appear frozen on Reddit, but have low asymmetry in the wine reviews, such as ['lemon', 'lime']." + ], + [ + "We focused our analysis on NYT, Buzzfeed, Reuters, CNN, the Washington Post, NPR, Breitbart, and the Atlantic. Much like in political subreddits, one might expect to see a split between various publications based upon ideology. However, this is not obviously the case. While there are certainly examples of binomials that seem to differ significantly for one publication or for a group of publications (Buzzfeed, in particular, frequently goes against the grain), there does not seem to be a sharp divide. Individual examples are difficult to draw conclusions from, but can suggest trends. (`China', `Russia') is a particularly controversial binomial. While the publications vary quite a bit, only Breitbart has an ordinality of above 0.5. In fact, country pairs are among the most controversial binomials within the publications (e.g. (`iraq', `syria'), (`afghanisatan', `iraq')), while most other highly controversial binomials reflect other political structures, such as (`house', `senate'), (`migrants', 'refugees'), and (`left', `right'). That so many controversial binomials reflect politics could point to subtle political or ideological differences between the publications. Additionally, the close similarity between Breitbart and more mainstream publications could be due to a similar effect we saw with r/The_Donald - mainly large amounts of quoted text." + ], + [ + "We can discover new structure in binomial orderings by taking a more global view. We do this by building directed graphs based on ordinality. In these graphs, nodes are words and an arrow from A to B indicates that there are at least 30 lists containing A and B and that those lists have order [A,B] at least 50% of the time. For our visualizations, the size of the node indicates how many distinct lists the word appears in,and color indicates how many list instances contain the word in total.", + "If we examine the global structure for r/nba, we can pinpoint a number of patterns (Fig. FIGREF31). First, most nodes within the purple circle correspond to names, while most nodes outside of it are not names. The cluster of circles in the lower left are a combination of numbers and years, where dark green corresponds to numbers, purple corresponds to years, and pink corresponds years represented as two-digit numbers (e.g., `96'). On the right, the brown circle contains adjectives, while above the blue circle contains heights (e.g., 6'5\"), and in the two circles in the lower middle, the left contains cities while the right contains team names. The darkest red node in the center of the graph corresponds to `lebron'.", + "Constructing a similar graph for our wines dataset, we can see clusters of words. In Fig FIGREF32, the colors represent clusters as formed through modularity. These clusters are quite distinct. Green nodes mostly refer to the structure or body of a wine, red are adjectives describing taste, teal and purple are fruits, dark green is wine varietals, gold is senses, and light blue is time (e.g. `year', `decade', etc.)", + "We can also consider the graph as we change the threshold of asymmetry for which an edge is included. If the asymmetry is large enough, the graph is acyclic, and we can consider how small the ordinality threshold must be in order to introduce a cycle. These cycles reveal the non-global ordering of binomials. The graph for r/nba begins to show cycles with a threshold asymmetry of 0.97. Three cycles exist at this threshold: [`ball', `catch', `shooter'], [`court', `pass', `set', `athleticism'], and [`court', `plays', `set', `athleticism'].", + "Restricting the nodes to be names is also revealing. Acyclic graphs in this context suggest a global partial hierarchy of individuals. For r/nba, the graph is no longer acyclic at an asymmetry threshold of 0.76, with the cycle [`blake', `jordan', `bryant', `kobe']. Similarly, the graph for r/nfl (only including names) is acyclic until the threshold reaches 0.73 with cycles [`tannehill', `miller', `jj watt', `aaron rodgers', `brady'], and [`hoyer', `savage', `watson', `hopkins', `miller', `jj watt', `aaron rodgers', `brady'].", + "Figure FIGREF33 shows these graphs for the three political subreddits, where the nodes are the 30 most common politician names. The graph visualizations immediately show that these communities view politicians differently. We can also consider cycles in these graphs and find that the graph is completely acyclic when the asymmetry threshold is at least 0.9. Again, this suggests that, at least among frozen binomials, there is in fact a global partial order of names that might signal hierarchy. (Including non-names, though, causes the r/politics graph to never be acyclic for any asymmetry threshold, since the cycle [`furious', `benghazi', `fast'] consists of completely frozen binomials.) We find similar results for r/Conservative and r/Libertarian, which are acyclic with thresholds of 0.58 and 0.66, respectively. Some of these cycles at high asymmetry might be due to English words that are also names (e.g. `law'), but one particularly notable cycle from r/Conservative is [`rubio', `bush', `obama', `trump', `cruz']." + ], + [ + "Binomials are the most studied type of list, but trinomials \u2014 lists of three \u2014 are also common enough in our dataset to analyze. Studying trinomials adds new aspects to the set of questions: for example, while binomials have only two possible orderings, trinomials have six possible orderings. However, very few trinomials show up in all six orderings. In fact, many trinomials show up in exactly one ordering: about 36% of trinomials being completely frozen amongst trinomials appearing at least 30 times in the data. To get a baseline comparison, we found an equal number of the most common binomials, and then subsampled instances of those binomials to equate the number of instances with the trinomials. In this case, only 21% of binomials are frozen. For trinomials that show up in at least two orderings, it is most common for the last word to keep the same position (e.g., [a, b, c] and [b, a, c]). For example, in our data, [`fraud', `waste', `abuse'] appears 34 times, and [`waste', `fraud', `abuse'] appears 20 times. This may partially be explained by many lists that contain words such as `other', `whatever', or `more'; for instance, [`smarter', `better', `more'] and [`better', `smarter', `more'] are the only two orderings we observe for this set of three words.", + "Additionally, each trinomial [a, b, c] contains three binomials within it: [a, b], [b, c], and [a, c]. It is natural to compare orderings of {a, b} in general with orderings of occurrences of {a, b} that lie inside trinomials. We use this comparison to define the compatibility of {a, b}, as follows.", + "Compatibility Let {a, b} be a binomial with dominant ordering [a, b]; that is, [a, b] is at least as frequent as [b, a]. We define the compatibility of {a, b} to be the fraction of instances of {a, b} occurring inside trinomials that have the order [a,b].", + "There are only a few cases where binomials have compatibility less than 0.5, and for most binomials, the asymmetry is remarkably consistent between binomials and trinomials (Fig. FIGREF37). In general, asymmetry is larger than compatibility \u2014 this occurs for 4569 binomials, compared to 3575 where compatibility was greater and 690 where the two values are the same. An extreme example is the binomial {`fairness', `accuracy'}, which has asymmetry 0.77 and compatibility 0.22. It would be natural to consider these questions for tetranomials and longer lists, but these are rarer in our data and correspondingly harder to draw conclusions from." + ], + [ + "Analyzing binomial orderings on a large scale has led to surprising results. Although most binomials are not frozen in the traditional sense, there is little movement in their ordinality across time or communities. A list that appears in the order [A, B] 60% of the time in one subreddit in one year is likely to show up as [A, B] very close to 60% of the time in all subreddits in all years. This suggests that binomial order should be predictable, but there is evidence that this is difficult: the most common theories on frozen binomial ordering were largely ineffective at predicting binomial ordering in general.", + "Given the challenge in predicting orderings, we searched for methods or principles that could yield better performance, and identified two promising approaches. First, models built on standard word embeddings produce predictions of binomial orders that are much more effective than simpler existing theories. Second, we established the Proximity Principle: the proper noun with which a speaker identifies more will tend to come first. This is evidenced when commenters refer to their sports team first, or politicians refer to their party first. Further analysis of the global structure of binomials reveals interesting patterns and a surprising acyclic nature in names. Analysis of longer lists in the form of multinomials suggests that the rules governing their orders may be different.", + "We have also found promising results in some special cases. We expect that more domain-specific studies will offer rich structure.", + "It is a challenge to adapt the long history of work on the question of frozen binomials to the large, messy environment of online text and social media. However, such data sources offer a unique opportunity to re-explore and redefine these questions. It seems that binomial orderings offer new insights into language, culture, and human cognition. Understanding what changes in these highly stable conventions mean \u2014 and whether or not they can be predicted \u2014 is an interesting avenue for future research." + ], + [ + "The authors thank members of the Cornell AI, Policy, and Practice Group, and (alphabetically by first name) Cristian Danescu-Niculescu-Mizil, Ian Lomeli, Justine Zhang, and Kate Donahue for aid in accessing data and their thoughtful insight. This research was supported by NSF Award DMS-1830274, ARO Award W911NF19-1-0057, a Simons Investigator Award, a Vannevar Bush Faculty Fellowship, and ARO MURI." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0562/environment/Dockerfile b/qasper-0562/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0562/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/* diff --git a/qasper-0562/instruction.md b/qasper-0562/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..e1aefb9843b525cc613194cac9afc78ebddfeb64 --- /dev/null +++ b/qasper-0562/instruction.md @@ -0,0 +1,100 @@ +Name of Paper: Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation + +Question: Which datasets are used to evaluate performance? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Motivation", + "Hierarchical Sequence Generation for DST", + "Encoding Module", + "Conditional Memory Relation Decoder", + "Experimental Setting", + "Implementation Details", + "Results", + "Ablation Study", + "Qualitative Analysis", + "Related Work", + "Conclusion" + ], + "paragraphs": [ + [ + "A Dialogue State Tracker (DST) is a core component of a modular task-oriented dialogue system BIBREF7 . For each dialogue turn, a DST module takes a user utterance and the dialogue history as input, and outputs a belief estimate of the dialogue state. Then a machine action is decided based on the dialogue state according to a dialogue policy module, after which a machine response is generated.", + "Traditionally, a dialogue state consists of a set of requests and joint goals, both of which are represented by a set of slot-value pairs (e.g. (request, phone), (area, north), (food, Japanese)) BIBREF8 . In a recently proposed multi-domain dialogue state tracking dataset, MultiWoZ BIBREF9 , a representation of dialogue state consists of a hierarchical structure of domain, slot, and value is proposed. This is a more practical scenario since dialogues often include multiple domains simultaneously.", + "Many recently proposed DSTs BIBREF2 , BIBREF10 are based on pre-defined ontology lists that specify all possible slot values in advance. To generate a distribution over the candidate set, previous works often take each of the slot-value pairs as input for scoring. However, in real-world scenarios, it is often not practical to enumerate all possible slot value pairs and perform scoring from a large dynamically changing knowledge base BIBREF11 . To tackle this problem, a popular direction is to build a fixed-length candidate set that is dynamically updated throughout the dialogue development. cpt briefly summaries the inference time complexity of multiple state-of-the-art DST models following this direction. Since the inference complexity of all of previous model is at least proportional to the number of the slots, these models will struggle to scale to multi-domain datasets with much larger numbers of pre-defined slots.", + "In this work, we formulate the dialogue state tracking task as a sequence generation problem, instead of formulating the task as a pair-wise prediction problem as in existing work. We propose the COnditional MEmory Relation Network (COMER), a scalable and accurate dialogue state tracker that has a constant inference time complexity. ", + "Specifically, our model consists of an encoder-decoder network with a hierarchically stacked decoder to first generate the slot sequences in the belief state and then for each slot generate the corresponding value sequences. The parameters are shared among all of our decoders for the scalability of the depth of the hierarchical structure of the belief states. COMER applies BERT contextualized word embeddings BIBREF12 and BPE BIBREF13 for sequence encoding to ensure the uniqueness of the representations of the unseen words. The word embeddings for sequence generation are initialized and fixed with the static word embeddings generated from BERT to have the potential of generating unseen words." + ], + [ + "f1 shows a multi-domain dialogue in which the user wants the system to first help book a train and then reserve a hotel. For each turn, the DST will need to track the slot-value pairs (e.g. (arrive by, 20:45)) representing the user goals as well as the domain that the slot-value pairs belongs to (e.g. train, hotel). Instead of representing the belief state via a hierarchical structure, one can also combine the domain and slot together to form a combined slot-value pair (e.g. (train; arrive by, 20:45) where the combined slot is \u201ctrain; arrive by\"), which ignores the subordination relationship between the domain and the slots.", + "A typical fallacy in dialogue state tracking datasets is that they make an assumption that the slot in a belief state can only be mapped to a single value in a dialogue turn. We call this the single value assumption. Figure 2 shows an example of this fallacy from the WoZ2.0 dataset: Based on the belief state label (food, seafood), it will be impossible for the downstream module in the dialogue system to generate sample responses that return information about Chinese restaurants. A correct representation of the belief state could be (food, seafood $>$ chinese). This would tell the system to first search the database for information about seafood and then Chinese restaurants. The logical operator \u201c $>$ \" indicates which retrieved information should have a higher priority to be returned to the user. Thus we are interested in building DST modules capable of generating structured sequences, since this kind of sequence representation of the value is critical for accurately capturing the belief states of a dialogue." + ], + [ + "Given a dialogue $D$ which consists of $T$ turns of user utterances and system actions, our target is to predict the state at each turn. Different from previous methods which formulate multi-label state prediction as a collection of binary prediction problems, COMER adapts the task into a sequence generation problem via a Seq2Seq framework.", + "As shown in f3, COMER consists of three encoders and three hierarchically stacked decoders. We propose a novel Conditional Memory Relation Decoder (CMRD) for sequence decoding. Each encoder includes an embedding layer and a BiLSTM. The encoders take in the user utterance, the previous system actions, and the previous belief states at the current turn, and encodes them into the embedding space. The user encoder and the system encoder use the fixed BERT model as the embedding layer.", + "Since the slot value pairs are un-ordered set elements of a domain in the belief states, we first order the sequence of domain according to their frequencies as they appear in the training set BIBREF14 , and then order the slot value pairs in the domain according to the slot's frequencies of as they appear in a domain. After the sorting of the state elements, We represent the belief states following the paradigm: (Domain1- Slot1, Value1; Slot2, Value2; ... Domain2- Slot1, Value1; ...) for a more concise representation compared with the nested tuple representation.", + "All the CMRDs take the same representations from the system encoder, user encoder and the belief encoder as part of the input. In the procedure of hierarchical sequence generation, the first CMRD takes a zero vector for its condition input $\\mathbf {c}$ , and generates a sequence of the domains, $D$ , as well as the hidden representation of domains $H_D$ . For each $d$ in $D$ , the second CMRD then takes the corresponding $h_d$ as the condition input and generates the slot sequence $S_d$ , and representations, $H_{S,d}$ . Then for each $s$ in $S$ , the third CMRD generates the value sequence $D$0 based on the corresponding $D$1 . We update the belief state with the new $D$2 pairs and perform the procedure iteratively until a dialogue is completed. All the CMR decoders share all of their parameters.", + "Since our model generates domains and slots instead of taking pre-defined slots as inputs, and the number of domains and slots generated each turn is only related to the complexity of the contents covered in a specific dialogue, the inference time complexity of COMER is $O(1)$ with respect to the number of pre-defined slots and values." + ], + [ + "Let $X$ represent a user utterance or system transcript consisting of a sequence of words $\\lbrace w_1,\\ldots ,w_T\\rbrace $ . The encoder first passes the sequence $\\lbrace \\mathit {[CLS]},w_1,\\ldots ,w_T,\\mathit {[SEP]}\\rbrace $ into a pre-trained BERT model and obtains its contextual embeddings $E_{X}$ . Specifically, we leverage the output of all layers of BERT and take the average to obtain the contextual embeddings.", + "For each domain/slot appeared in the training set, if it has more than one word, such as `price range', `leave at', etc., we feed it into BERT and take the average of the word vectors to form the extra slot embedding $E_{s}$ . In this way, we map each domain/slot to a fixed embedding, which allows us to generate a domain/slot as a whole instead of a token at each time step of domain/slot sequence decoding. We also construct a static vocabulary embedding $E_{v}$ by feeding each token in the BERT vocabulary into BERT. The final static word embedding $E$ is the concatenation of the $E_{v}$ and $E_{s}$ .", + "After we obtain the contextual embeddings for the user utterance, system action, and the static embeddings for the previous belief state, we feed each of them into a Bidirectional LSTM BIBREF15 . ", + "$$\\begin{aligned}\n\\mathbf {h}_{a_t} & = \\textrm {BiLSTM}(\\mathbf {e}_{X_{a_t}}, \\mathbf {h}_{a_{t-1}}) \\\\\n\\mathbf {h}_{u_t} & = \\textrm {BiLSTM}(\\mathbf {e}_{X_{u_t}}, \\mathbf {h}_{u_{t-1}}) \\\\\n\\mathbf {h}_{b_t} & = \\textrm {BiLSTM}(\\mathbf {e}_{X_{b_t}}, \\mathbf {h}_{b_{t-1}}) \\\\\n\\mathbf {h}_{a_0} & = \\mathbf {h}_{u_0} = \\mathbf {h}_{b_0} = c_{0}, \\\\\n\\end{aligned}$$ (Eq. 7) ", + "where $c_{0}$ is the zero-initialized hidden state for the BiLSTM. The hidden size of the BiLSTM is $d_m/2$ . We concatenate the forward and the backward hidden representations of each token from the BiLSTM to obtain the token representation $\\mathbf {h}_{k_t}\\in R^{d_m}$ , $k\\in \\lbrace a,u,b\\rbrace $ at each time step $t$ . The hidden states of all time steps are concatenated to obtain the final representation of $H_{k}\\in R^{T \\times d_m}, k \\in \\lbrace a,u,B\\rbrace $ . The parameters are shared between all of the BiLSTMs." + ], + [ + "Inspired by Residual Dense Networks BIBREF16 , End-to-End Memory Networks BIBREF17 and Relation Networks BIBREF18 , we here propose the Conditional Memory Relation Decoder (CMRD). Given a token embedding, $\\mathbf {e}_x$ , CMRD outputs the next token, $s$ , and the hidden representation, $h_s$ , with the hierarchical memory access of different encoded information sources, $H_B$ , $H_a$ , $H_u$ , and the relation reasoning under a certain given condition $\\mathbf {c}$ , $\n\\mathbf {s}, \\mathbf {h}_s= \\textrm {CMRD}(\\mathbf {e}_x, \\mathbf {c}, H_B, H_a, H_u),\n$ ", + "the final output matrices $S,H_s \\in R^{l_s\\times d_m}$ are concatenations of all generated $\\mathbf {s}$ and $\\mathbf {h}_s$ (respectively) along the sequence length dimension, where $d_m$ is the model size, and $l_s$ is the generated sequence length. The general structure of the CMR decoder is shown in Figure 4 . Note that the CMR decoder can support additional memory sources by adding the residual connection and the attention block, but here we only show the structure with three sources: belief state representation ( $H_B$ ), system transcript representation ( $H_a$ ), and user utterance representation ( $H_u$ ), corresponding to a dialogue state tracking scenario. Since we share the parameters between all of the decoders, thus CMRD is actually a 2-dimensional auto-regressive model with respect to both the condition generation and the sequence generation task.", + "At each time step $t$ , the CMR decoder first embeds the token $x_t$ with a fixed token embedding $E\\in R^{d_e\\times d_v}$ , where $d_e$ is the embedding size and $d_v$ is the vocabulary size. The initial token $x_0$ is \u201c[CLS]\". The embedded vector $\\textbf {e}_{x_t}$ is then encoded with an LSTM, which emits a hidden representation $\\textbf {h}_0 \\in R^{d_m}$ , $\n\\textbf {h}_0= \\textrm {LSTM}(\\textbf {e}_{x_t},\\textbf {q}_{t-1}).\n$ ", + "where $\\textbf {q}_t$ is the hidden state of the LSTM. $\\textbf {q}_0$ is initialized with an average of the hidden states of the belief encoder, the system encoder and the user encoder which produces $H_B$ , $H_a$ , $H_u$ respectively.", + " $\\mathbf {h}_0$ is then summed (element-wise) with the condition representation $\\mathbf {c}\\in R^{d_m}$ to produce $\\mathbf {h}_1$ , which is (1) fed into the attention module; (2) used for residual connection; and (3) concatenated with other $\\mathbf {h}_i$ , ( $i>1$ ) to produce the concatenated working memory, $\\mathbf {r_0}$ , for relation reasoning, $\n\\mathbf {h}_1 & =\\mathbf {h}_0+\\mathbf {c},\\\\\n\\mathbf {h}_2 & =\\mathbf {h}_1+\\text{Attn}_{\\text{belief}}(\\mathbf {h}_1,H_e),\\\\\n\\mathbf {h}_3 & = \\mathbf {h}_2+\\text{Attn}_{\\text{sys}}(\\mathbf {h}_2,H_a),\\\\\n\\mathbf {h}_4 & = \\mathbf {h}_3+\\text{Attn}_{\\text{usr}}(\\mathbf {h}_3,H_u),\\\\\n\\mathbf {r} & = \\mathbf {h}_1\\oplus \\mathbf {h}_2\\oplus \\mathbf {h}_3\\oplus \\mathbf {h}_4 \\in R^{4d_m},\n$ ", + " where $\\text{Attn}_k$ ( $k\\in \\lbrace \\text{belief}, \\text{sys},\\text{usr}\\rbrace $ ) are the attention modules applied respectively to $H_B$ , $H_a$ , $H_u$ , and $\\oplus $ means the concatenation operator. The gradients are blocked for $ \\mathbf {h}_1,\\mathbf {h}_2,\\mathbf {h}_3$ during the back-propagation stage, since we only need them to work as the supplementary memories for the relation reasoning followed.", + "The attention module takes a vector, $\\mathbf {h}\\in R^{d_m}$ , and a matrix, $H\\in R^{d_m\\times l}$ as input, where $l$ is the sequence length of the representation, and outputs $\\mathbf {h}_a$ , a weighted sum of the column vectors in $H$ . $\n\\mathbf {a} & =W_1^T\\mathbf {h}+\\mathbf {b}_1& &\\in R^{d_m},\\\\\n\\mathbf {c} &=\\text{softmax}(H^Ta)& &\\in R^l,\\\\\n\\mathbf {h} &=H\\mathbf {c}& &\\in R^{d_m},\\\\\n\\mathbf {h}_a &=W_2^T\\mathbf {h}+\\mathbf {b}_2& &\\in R^{d_m},\n$ ", + " where the weights $W_1\\in R^{d_m \\times d_m}$ , $W_2\\in R^{d_m \\times d_m}$ and the bias $b_1\\in R^{d_m}$ , $b_2\\in R^{d_m}$ are the learnable parameters.", + "The order of the attention modules, i.e., first attend to the system and the user and then the belief, is decided empirically. We can interpret this hierarchical structure as the internal order for the memory processing, since from the daily life experience, people tend to attend to the most contemporary memories (system/user utterance) first and then attend to the older history (belief states). All of the parameters are shared between the attention modules.", + "The concatenated working memory, $\\mathbf {r}_0$ , is then fed into a Multi-Layer Perceptron (MLP) with four layers, $\n\\mathbf {r}_1 & =\\sigma (W_1^T\\mathbf {r}_0+\\mathbf {b}_1),\\\\\n\\mathbf {r}_2 & =\\sigma (W_2^T\\mathbf {r}_1+\\mathbf {b}_2),\\\\\n\\mathbf {r}_3 & = \\sigma (W_3^T\\mathbf {r}_2+\\mathbf {b}_3),\\\\\n\\mathbf {h}_s & = \\sigma (W_4^T\\mathbf {r}_3+\\mathbf {b}_4),\n$ ", + " where $\\sigma $ is a non-linear activation, and the weights $W_1 \\in R^{4d_m \\times d_m}$ , $W_i \\in R^{d_m \\times d_m}$ and the bias $b_1 \\in R^{d_m}$ , $b_i \\in R^{d_m}$ are learnable parameters, and $2\\le i\\le 4$ . The number of layers for the MLP is decided by the grid search.", + "The hidden representation of the next token, $\\mathbf {h}_s$ , is then (1) emitted out of the decoder as a representation; and (2) fed into a dropout layer with drop rate $p$ , and a linear layer to generate the next token, $\n\\mathbf {h}_k & =\\text{dropout}(\\mathbf {h}_s)& &\\in R^{d_m},\\\\\n\\mathbf {h}_o & =W_k^T\\mathbf {h}_k+\\mathbf {b}_k& &\\in R^{d_e},\\\\\n\\mathbf {p}_s & =\\text{softmax}(E^T\\mathbf {h}_o)& &\\in R^{d_v},\\\\\ns & =\\text{argmax}(\\mathbf {p}_s)& &\\in R,\n$ ", + " where the weight $W_k\\in R^{d_m \\times d_e}$ and the bias $b_k\\in R^{d_e}$ are learnable parameters. Since $d_e$ is the embedding size and the model parameters are independent of the vocabulary size, the CMR decoder can make predictions on a dynamic vocabulary and implicitly supports the generation of unseen words. When training the model, we minimize the cross-entropy loss between the output probabilities, $\\mathbf {p}_s$ , and the given labels." + ], + [ + "We first test our model on the single domain dataset, WoZ2.0 BIBREF19 . It consists of 1,200 dialogues from the restaurant reservation domain with three pre-defined slots: food, price range, and area. Since the name slot rarely occurs in the dataset, it is not included in our experiments, following previous literature BIBREF3 , BIBREF20 . Our model is also tested on the multi-domain dataset, MultiWoZ BIBREF9 . It has a more complex ontology with 7 domains and 25 predefined slots. Since the combined slot-value pairs representation of the belief states has to be applied for the model with $O(n)$ ITC, the total number of slots is 35. The statistics of these two datsets are shown in Table 2 .", + "Based on the statistics from these two datasets, we can calculate the theoretical Inference Time Multiplier (ITM), $K$ , as a metric of scalability. Given the inference time complexity, ITM measures how many times a model will be slower when being transferred from the WoZ2.0 dataset, $d_1$ , to the MultiWoZ dataset, $d_2$ , $\nK= h(t)h(s)h(n)h(m)\\\\\n$ $\nh(x)=\\left\\lbrace \n\\begin{array}{lcl}\n1 & &O(x)=O(1),\\\\\n\\frac{x_{d_2}}{x_{d_1}}& & \\text{otherwise},\\\\\n\\end{array}\\right.\n\n$ ", + "where $O(x)$ means the Inference Time Complexity (ITC) of the variable $x$ . For a model having an ITC of $O(1)$ with respect to the number of slots $n$ , and values $m$ , the ITM will be a multiplier of 2.15x, while for an ITC of $O(n)$ , it will be a multiplier of 25.1, and 1,143 for $O(mn)$ .", + "As a convention, the metric of joint goal accuracy is used to compare our model to previous work. The joint goal accuracy only regards the model making a successful belief state prediction if all of the slots and values predicted are exactly matched with the labels provided. This metric gives a strict measurement that tells how often the DST module will not propagate errors to the downstream modules in a dialogue system. In this work, the model with the highest joint accuracy on the validation set is evaluated on the test set for the test joint accuracy measurement." + ], + [ + "We use the $\\text{BERT}_\\text{large}$ model for both contextual and static embedding generation. All LSTMs in the model are stacked with 2 layers, and only the output of the last layer is taken as a hidden representation. ReLU non-linearity is used for the activation function, $\\sigma $ .", + "The hyper-parameters of our model are identical for both the WoZ2.0 and the MultiwoZ datasets: dropout rate $p=0.5$ , model size $d_m=512$ , embedding size $d_e=1024$ . For training on WoZ2.0, the model is trained with a batch size of 32 and the ADAM optimizer BIBREF21 for 150 epochs, while for MultiWoZ, the AMSGrad optimizer BIBREF22 and a batch size of 16 is adopted for 15 epochs of training. For both optimizers, we use a learning rate of 0.0005 with a gradient clip of 2.0. We initialize all weights in our model with Kaiming initialization BIBREF23 and adopt zero initialization for the bias. All experiments are conducted on a single NVIDIA GTX 1080Ti GPU." + ], + [ + "To measure the actual inference time multiplier of our model, we evaluate the runtime of the best-performing models on the validation sets of both the WoZ2.0 and MultiWoZ datasets. During evaluation, we set the batch size to 1 to avoid the influence of data parallelism and sequence padding. On the validation set of WoZ2.0, we obtain a runtime of 65.6 seconds, while on MultiWoZ, the runtime is 835.2 seconds. Results are averaged across 5 runs. Considering that the validation set of MultiWoZ is 5 times larger than that of WoZ2.0, the actual inference time multiplier is 2.54 for our model. Since the actual inference time multiplier roughly of the same magnitude as the theoretical value of 2.15, we can confirm empirically that we have the $O(1)$ inference time complexity and thus obtain full scalability to the number of slots and values pre-defined in an ontology.", + "c compares our model with the previous state-of-the-art on both the WoZ2.0 test set and the MultiWoZ test set. For the WoZ2.0 dataset, we maintain performance at the level of the state-of-the-art, with a marginal drop of 0.3% compared with previous work. Considering the fact that WoZ2.0 is a relatively small dataset, this small difference does not represent a significant big performance drop. On the muli-domain dataset, MultiWoZ, our model achieves a joint goal accuracy of 45.72%, which is significant better than most of the previous models other than TRADE which applies the copy mechanism and gains better generalization ability on named entity coping." + ], + [ + "To prove the effectiveness of our structure of the Conditional Memory Relation Decoder (CMRD), we conduct ablation experiments on the WoZ2.0 dataset. We observe an accuracy drop of 1.95% after removing residual connections and the hierarchical stack of our attention modules. This proves the effectiveness of our hierarchical attention design. After the MLP is replaced with a linear layer of hidden size 512 and the ReLU activation function, the accuracy further drops by 3.45%. This drop is partly due to the reduction of the number of the model parameters, but it also proves that stacking more layers in an MLP can improve the relational reasoning performance given a concatenation of multiple representations from different sources.", + "We also conduct the ablation study on the MultiWoZ dataset for a more precise analysis on the hierarchical generation process. For joint domain accuracy, we calculate the probability that all domains generated in each turn are exactly matched with the labels provided. The joint domain-slot accuracy further calculate the probability that all domains and slots generated are correct, while the joint goal accuracy requires all the domains, slots and values generated are exactly matched with the labels. From abm, We can further calculate that given the correct slot prediction COMER has 83.52% chance to make the correct value prediction. While COMER has done great job on domain prediction (95.53%) and value prediction (83.52%), the accuracy of the slot prediction given the correct domain is only 57.30%. We suspect that this is because we only use the previous belief state to represent the dialogue history, and the inter-turn reasoning ability on the slot prediction suffers from the limited context and the accuracy is harmed due to the multi-turn mapping problem BIBREF4 . We can also see that the JDS Acc. has an absolute boost of 5.48% when we switch from the combined slot representation to the nested tuple representation. This is because the subordinate relationship between the domains and the slots can be captured by the hierarchical sequence generation, while this relationship is missed when generating the domain and slot together via the combined slot representation." + ], + [ + "f5 shows an example of the belief state prediction result in one turn of a dialogue on the MultiWoZ test set. The visualization includes the CMRD attention scores over the belief states, system transcript and user utterance during the decoding stage of the slot sequence.", + "From the system attention (top right), since it is the first attention module and no previous context information is given, it can only find the information indicating the slot \u201cdeparture\u201d from the system utterance under the domain condition, and attend to the evidence \u201cleaving\u201d correctly during the generation step of \u201cdeparture\u201d. From the user attention, we can see that it captures the most helpful keywords that are necessary for correct prediction, such as \u201cafter\" for \u201cday\" and \u201cleave at\u201d, \u201cto\" for \u201cdestination\". Moreover, during the generation step of \u201cdeparture\u201d, the user attention successfully discerns that, based on the context, the word \u201cleave\u201d is not the evidence that need to be accumulated and choose to attend nothing in this step. For the belief attention, we can see that the belief attention module correctly attends to a previous slot for each generation step of a slot that has been presented in the previous state. For the generation step of the new slot \u201cdestination\", since the previous state does not have the \u201cdestination\" slot, the belief attention module only attends to the `-' mark after the `train' domain to indicate that the generated word should belong to this domain." + ], + [ + "Semi-scalable Belief Tracker BIBREF1 proposed an approach that can generate fixed-length candidate sets for each of the slots from the dialogue history. Although they only need to perform inference for a fixed number of values, they still need to iterate over all slots defined in the ontology to make a prediction for a given dialogue turn. In addition, their method needs an external language understanding module to extract the exact entities from a dialogue to form candidates, which will not work if the label value is an abstraction and does not have the exact match with the words in the dialogue.", + "StateNet BIBREF3 achieves state-of-the-art performance with the property that its parameters are independent of the number of slot values in the candidate set, and it also supports online training or inference with dynamically changing slots and values. Given a slot that needs tracking, it only needs to perform inference once to make the prediction for a turn, but this also means that its inference time complexity is proportional to the number of slots.", + "TRADE BIBREF4 achieves state-of-the-art performance on the MultiWoZ dataset by applying the copy mechanism for the value sequence generation. Since TRADE takes $n$ combinations of the domains and slots as the input, the inference time complexity of TRADE is $O(n)$ . The performance improvement achieved by TRADE is mainly due to the fact that it incorporates the copy mechanism that can boost the accuracy on the \u2018name\u2019 slot, which mainly needs the ability in copying names from the dialogue history. However, TRADE does not report its performance on the WoZ2.0 dataset which does not have the \u2018name\u2019 slot.", + "DSTRead BIBREF6 formulate the dialogue state tracking task as a reading comprehension problem by asking slot specified questions to the BERT model and find the answer span in the dialogue history for each of the pre-defined combined slot. Thus its inference time complexity is still $O(n)$ . This method suffers from the fact that its generation vocabulary is limited to the words occurred in the dialogue history, and it has to do a manual combination strategy with another joint state tracking model on the development set to achieve better performance.", + "Contextualized Word Embedding (CWE) was first proposed by BIBREF25 . Based on the intuition that the meaning of a word is highly correlated with its context, CWE takes the complete context (sentences, passages, etc.) as the input, and outputs the corresponding word vectors that are unique under the given context. Recently, with the success of language models (e.g. BIBREF12 ) that are trained on large scale data, contextualizeds word embedding have been further improved and can achieve the same performance compared to (less flexible) finely-tuned pipelines.", + "Sequence Generation Models. Recently, sequence generation models have been successfully applied in the realm of multi-label classification (MLC) BIBREF14 . Different from traditional binary relevance methods, they proposed a sequence generation model for MLC tasks which takes into consideration the correlations between labels. Specifically, the model follows the encoder-decoder structure with an attention mechanism BIBREF26 , where the decoder generates a sequence of labels. Similar to language modeling tasks, the decoder output at each time step will be conditioned on the previous predictions during generation. Therefore the correlation between generated labels is captured by the decoder." + ], + [ + "In this work, we proposed the Conditional Memory Relation Network (COMER), the first dialogue state tracking model that has a constant inference time complexity with respect to the number of domains, slots and values pre-defined in an ontology. Besides its scalability, the joint goal accuracy of our model also achieve the similar performance compared with the state-of-the-arts on both the MultiWoZ dataset and the WoZ dataset. Due to the flexibility of our hierarchical encoder-decoder framework and the CMR decoder, abundant future research direction remains as applying the transformer structure, incorporating open vocabulary and copy mechanism for explicit unseen words generation, and inventing better dialogue history access mechanism to accommodate efficient inter-turn reasoning.", + "Acknowledgements. This work is partly supported by NSF #1750063. We thank all the reviewers for their constructive suggestions. We also want to thank Zhuowen Tu and Shengnan Zhang for the early discussions of the project." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0563/instruction.md b/qasper-0563/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..783105b732695d29219e159139d3ab52980b805d --- /dev/null +++ b/qasper-0563/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Siamese recurrent networks learn first-order logic reasoning and exhibit zero-shot compositional generalization + +Question: How does the automatic theorem prover infer the relation? \ No newline at end of file diff --git a/qasper-0564/instruction.md b/qasper-0564/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..c7c4400df6e45ffa229bdb8b0cec28ca68892ac7 --- /dev/null +++ b/qasper-0564/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Siamese recurrent networks learn first-order logic reasoning and exhibit zero-shot compositional generalization + +Question: If these model can learn the first-order logic on artificial language, why can't it lear for natural language? \ No newline at end of file diff --git a/qasper-0565/environment/Dockerfile b/qasper-0565/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0565/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/* diff --git a/qasper-0565/instruction.md b/qasper-0565/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..da77e3d2dc9f081d7f252812e67491f92337755e --- /dev/null +++ b/qasper-0565/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Siamese recurrent networks learn first-order logic reasoning and exhibit zero-shot compositional generalization + +Question: How many samples did they generate for the artificial language? \ No newline at end of file diff --git a/qasper-0590/instruction.md b/qasper-0590/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..7a638d0f857305d262196be6ea4d08129f826b4a --- /dev/null +++ b/qasper-0590/instruction.md @@ -0,0 +1,98 @@ +Name of Paper: HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization + +Question: Is the baseline a non-heirarchical model like BERT? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Model", + "Document Representation", + "Pre-training", + "Extractive Summarization", + "Experiments", + "Datasets", + "Implementation Details", + "Evaluations", + "Results", + "Conclusions" + ], + "paragraphs": [ + [ + "Automatic document summarization is the task of rewriting a document into its shorter form while still retaining its important content. Over the years, many paradigms for document summarization have been explored (see Nenkova:McKeown:2011 for an overview). The most popular two among them are extractive approaches and abstractive approaches. As the name implies, extractive approaches generate summaries by extracting parts of the original document (usually sentences), while abstractive methods may generate new words or phrases which are not in the original document.", + "Extractive summarization is usually modeled as a sentence ranking problem with length constraints (e.g., max number of words or sentences). Top ranked sentences (under constraints) are selected as summaries. Early attempts mostly leverage manually engineered features BIBREF1 . Based on these sparse features, sentence are selected using a classifier or a regression model. Later, the feature engineering part in this paradigm is replaced with neural networks. cheng:2016:acl propose a hierarchical long short-term memory network (LSTM; BIBREF2 ) to encode a document and then use another LSTM to predict binary labels for each sentence in the document. This architecture is widely adopted recently BIBREF3 , BIBREF4 , BIBREF5 . Our model also employs a hierarchical document encoder, but we adopt a hierarchical transformer BIBREF6 rather a hierarchical LSTM. Because recent studies BIBREF6 , BIBREF0 show the transformer model performs better than LSTM in many tasks.", + "Abstractive models do not attract much attention until recently. They are mostly based on sequence to sequence (seq2seq) models BIBREF7 , where a document is viewed a sequence and its summary is viewed as another sequence. Although seq2seq based summarizers can be equipped with copy mechanism BIBREF8 , BIBREF9 , coverage model BIBREF9 and reinforcement learning BIBREF10 , there is still no guarantee that the generated summaries are grammatical and convey the same meaning as the original document does. It seems that extractive models are more reliable than their abstractive counterparts.", + "However, extractive models require sentence level labels, which are usually not included in most summarization datasets (most datasets only contain document-summary pairs). Sentence labels are usually obtained by rule-based methods (e.g., maximizing the ROUGE score between a set of sentences and reference summaries) and may not be accurate. Extractive models proposed recently BIBREF11 , BIBREF3 employ hierarchical document encoders and even have neural decoders, which are complex. Training such complex neural models with inaccurate binary labels is challenging. We observed in our initial experiments on one of our dataset that our extractive model (see Section \"Extractive Summarization\" for details) overfits to the training set quickly after the second epoch, which indicates the training set may not be fully utilized. Inspired by the recent pre-training work in natural language processing BIBREF12 , BIBREF13 , BIBREF0 , our solution to this problem is to first pre-train the \u201ccomplex\u201d' part (i.e., the hierarchical encoder) of the extractive model on unlabeled data and then we learn to classify sentences with our model initialized from the pre-trained encoder. In this paper, we propose Hibert, which stands for HIerachical Bidirectional Encoder Representations from Transformers. We design an unsupervised method to pre-train Hibert for document modeling. We apply the pre-trained Hibert to the task of document summarization and achieve state-of-the-art performance on both the CNN/Dailymail and New York Times dataset." + ], + [ + "In this section, we introduce work on extractive summarization, abstractive summarization and pre-trained natural language processing models. For a more comprehensive review of summarization, we refer the interested readers to Nenkova:McKeown:2011 and Mani:01." + ], + [ + "In this section, we present our model Hibert. We first introduce how documents are represented in Hibert. We then describe our method to pre-train Hibert and finally move on to the application of Hibert to summarization." + ], + [ + "Let $\\mathcal {D} = (S_1, S_2, \\dots , S_{| \\mathcal {D} |})$ denote a document, where $S_i = (w_1^i, w_2^i, \\dots , w_{|S_i|}^i)$ is a sentence in $\\mathcal {D}$ and $w_j^i$ a word in $S_i$ . Note that following common practice in natural language processing literatures, $w_{|S_i|}^i$ is an artificial EOS (End Of Sentence) token. To obtain the representation of $\\mathcal {D}$ , we use two encoders: a sentence encoder to transform each sentence in $\\mathcal {D}$ to a vector and a document encoder to learn sentence representations given their surrounding sentences as context. Both the sentence encoder and document encoder are based on the Transformer encoder described in vaswani:2017:nips. As shown in Figure 1 , they are nested in a hierarchical fashion. A transformer encoder usually has multiple layers and each layer is composed of a multi-head self attentive sub-layer followed by a feed-forward sub-layer with residual connections BIBREF30 and layer normalizations BIBREF31 . For more details of the Transformer encoder, we refer the interested readers to vaswani:2017:nips. To learn the representation of $S_i$ , $S_i= (w_1^i, w_2^i, \\dots , w_{|S_i|}^i)$ is first mapped into continuous space ", + "$$\\begin{split}\n\\mathbf {E}_i = (\\mathbf {e}_1^i, \\mathbf {e}_2^i, \\dots , \\mathbf {e}_{|S_i|}^i) \\\\\n\\quad \\quad \\text{where} \\quad \\mathbf {e}_j^i = e(w_j^i) + \\mathbf {p}_j\n\\end{split}$$ (Eq. 6) ", + " where $e(w_j^i)$ and $\\mathbf {p}_j$ are the word and positional embeddings of $w_j^i$ , respectively. The word embedding matrix is randomly initialized and we adopt the sine-cosine positional embedding BIBREF6 . Then the sentence encoder (a Transformer) transforms $\\mathbf {E}_i$ into a list of hidden representations $(\\mathbf {h}_1^i, \\mathbf {h}_2^i, \\dots , \\mathbf {h}_{|S_i|}^i)$ . We take the last hidden representation $\\mathbf {h}_{|S_i|}^i$ (i.e., the representation at the EOS token) as the representation of sentence $S_i$ . Similar to the representation of each word in $S_i$ , we also take the sentence position into account. The final representation of $S_i$ is ", + "$$\\hat{\\mathbf {h}}_i = \\mathbf {h}_{|S_i|}^i + \\mathbf {p}_i$$ (Eq. 8) ", + "Note that words and sentences share the same positional embedding matrix.", + "In analogy to the sentence encoder, as shown in Figure 1 , the document encoder is yet another Transformer but applies on the sentence level. After running the Transformer on a sequence of sentence representations $( \\hat{\\mathbf {h}}_1, \\hat{\\mathbf {h}}_2, \\dots , \\hat{\\mathbf {h}}_{|\\mathcal {D}|} )$ , we obtain the context sensitive sentence representations $( \\mathbf {d}_1, \\mathbf {d}_2, \\dots , \\mathbf {d}_{|\\mathcal {D}|} )$ . Now we have finished the encoding of a document with a hierarchical bidirectional transformer encoder Hibert. Note that in previous work, document representation are also learned with hierarchical models, but each hierarchy is a Recurrent Neural Network BIBREF3 , BIBREF21 or Convolutional Neural Network BIBREF11 . We choose the Transformer because it outperforms CNN and RNN in machine translation BIBREF6 , semantic role labeling BIBREF32 and other NLP tasks BIBREF0 . In the next section we will introduce how we train Hibert with an unsupervised training objective." + ], + [ + "Most recent encoding neural models used in NLP (e.g., RNNs, CNNs or Transformers) can be pre-trained by predicting a word in a sentence (or a text span) using other words within the same sentence (or span). For example, ELMo BIBREF12 and OpenAI-GPT BIBREF13 predict a word using all words on its left (or right); while word2vec BIBREF33 predicts one word with its surrounding words in a fixed window and BERT BIBREF0 predicts (masked) missing words in a sentence given all the other words.", + "All the models above learn the representation of a sentence, where its basic units are words. Hibert aims to learn the representation of a document, where its basic units are sentences. Therefore, a natural way of pre-training a document level model (e.g., Hibert) is to predict a sentence (or sentences) instead of a word (or words). We could predict a sentence in a document with all the sentences on its left (or right) as in a (document level) language model. However, in summarization, context on both directions are available. We therefore opt to predict a sentence using all sentences on both its left and right.", + "Specifically, suppose $\\mathcal {D} = (S_1, S_2, \\dots , S_{| \\mathcal {D} |})$ is a document, where $S_i = (w_1^i, w_2^i, \\dots , w_{|S_i|}^i)$ is a sentence in it. We randomly select 15% of the sentences in $\\mathcal {D}$ and mask them. Then, we predict these masked sentences. The prediction task here is similar with the Cloze task BIBREF34 , BIBREF0 , but the missing part is a sentence. However, during test time the input document is not masked, to make our model can adapt to documents without masks, we do not always mask the selected sentences. Once a sentence is selected (as one of the 15% selected masked sentences), we transform it with one of three methods below. We will use an example to demonstrate the transformation. For instance, we have the following document and the second sentence is selected:", + "William Shakespeare is a poet . He died in 1616 . He is regarded as the greatest writer .", + "In 80% of the cases, we mask the selected sentence (i.e., we replace each word in the sentence with a mask token [MASK]). The document above becomes William Shakespeare is a poet . [MASK] [MASK] [MASK] [MASK] [MASK] He is regarded as the greatest writer . (where \u201cHe died in 1616 . \u201d is masked).", + "In 10% of the cases, we keep the selected sentence as it is. This strategy is to simulate the input document during test time (with no masked sentences).", + "In the rest 10% cases, we replace the selected sentence with a random sentence. In this case, the document after transformation is William Shakespeare is a poet . Birds can fly . He is regarded as the greatest writer . The second sentence is replaced with \u201cBirds can fly .\u201d This strategy intends to add some noise during training and make the model more robust.", + "After the application of the above procedures to a document $\\mathcal {D} = (S_1, S_2, \\dots , S_{| \\mathcal {D} |})$ , we obtain the masked document $\\widetilde{ \\mathcal {D} }= (\\tilde{S_1}, \\tilde{S_2}, \\dots , \\tilde{S_{| \\mathcal {D} |}})$ . Let $\\mathcal {K} $ denote the set of indicies of selected sentences in $\\mathcal {D}$ . Now we are ready to predict the masked sentences $\\mathcal {M} = \\lbrace S_k | k \\in \\mathcal {K} \\rbrace $ using $\\widetilde{ \\mathcal {D} }$ . We first apply the hierarchical encoder Hibert in Section \"Conclusions\" to $\\widetilde{ \\mathcal {D} }$ and obtain its context sensitive sentence representations $( \\tilde{ \\mathbf {d}_1 }, \\tilde{ \\mathbf {d}_2 }, \\dots , \\tilde{ \\mathbf {d}_{| \\mathcal {D} |} } )$ . We will demonstrate how we predict the masked sentence $S_k = (w_0^k, w_1^k, w_2^k, \\dots , w_{|S_k|}^k)$ one word per step ( $w_0^k$ is an artificially added BOS token). At the $\\widetilde{ \\mathcal {D} }= (\\tilde{S_1}, \\tilde{S_2}, \\dots , \\tilde{S_{| \\mathcal {D} |}})$0 th step, we predict $\\widetilde{ \\mathcal {D} }= (\\tilde{S_1}, \\tilde{S_2}, \\dots , \\tilde{S_{| \\mathcal {D} |}})$1 given $\\widetilde{ \\mathcal {D} }= (\\tilde{S_1}, \\tilde{S_2}, \\dots , \\tilde{S_{| \\mathcal {D} |}})$2 and $\\widetilde{ \\mathcal {D} }= (\\tilde{S_1}, \\tilde{S_2}, \\dots , \\tilde{S_{| \\mathcal {D} |}})$3 . $\\widetilde{ \\mathcal {D} }= (\\tilde{S_1}, \\tilde{S_2}, \\dots , \\tilde{S_{| \\mathcal {D} |}})$4 already encodes the information of $\\widetilde{ \\mathcal {D} }= (\\tilde{S_1}, \\tilde{S_2}, \\dots , \\tilde{S_{| \\mathcal {D} |}})$5 with a focus around its $\\widetilde{ \\mathcal {D} }= (\\tilde{S_1}, \\tilde{S_2}, \\dots , \\tilde{S_{| \\mathcal {D} |}})$6 th sentence $\\widetilde{ \\mathcal {D} }= (\\tilde{S_1}, \\tilde{S_2}, \\dots , \\tilde{S_{| \\mathcal {D} |}})$7 . As shown in Figure 1 , we employ a Transformer decoder BIBREF6 to predict $\\widetilde{ \\mathcal {D} }= (\\tilde{S_1}, \\tilde{S_2}, \\dots , \\tilde{S_{| \\mathcal {D} |}})$8 with $\\widetilde{ \\mathcal {D} }= (\\tilde{S_1}, \\tilde{S_2}, \\dots , \\tilde{S_{| \\mathcal {D} |}})$9 as its additional input. The transformer decoder we used here is slightly different from the original one. The original decoder employs two multi-head attention layers to include both the context in encoder and decoder, while we only need one to learn the decoder context, since the context in encoder is a vector (i.e., $\\mathcal {K} $0 ). Specifically, after applying the word and positional embeddings to ( $\\mathcal {K} $1 ), we obtain $\\mathcal {K} $2 (also see Equation 6 ). Then we apply multi-head attention sub-layer to $\\mathcal {K} $3 : ", + "$$\\begin{split}\n\\tilde{\\mathbf {h}_{j-1}} &= \\text{MultiHead}(\\mathbf {q}_{j-1}, \\mathbf {K}_{j-1}, \\mathbf {V}_{j-1}) \\\\\n\\mathbf {q}_{j-1} &= \\mathbf {W}^Q \\: \\tilde{\\mathbf {e}_{j-1}^k} \\\\\n\\mathbf {K}_{j-1} &= \\mathbf {W}^K \\: \\widetilde{ \\mathbf {E} }^k_{1:j-1} \\\\\n\\mathbf {K}_{j-1} &= \\mathbf {W}^V \\: \\widetilde{ \\mathbf {E} }^k_{1:j-1}\n\\end{split}$$ (Eq. 13) ", + " where $\\mathbf {q}_{j-1}$ , $\\mathbf {K}_{j-1}$ , $\\mathbf {V}_{j-1}$ are the input query, key and value matrices of the multi-head attention function BIBREF6 $\\text{MultiHead}(\\cdot , \\cdot , \\cdot )$ , respectively. $\\mathbf {W}^Q \\in \\mathbb {R}^{d \\times d}$ , $\\mathbf {W}^K \\in \\mathbb {R}^{d \\times d}$ and $\\mathbf {W}^V \\in \\mathbb {R}^{d \\times d}$ are weight matrices.", + "Then we include the information of $\\widetilde{ \\mathcal {D} }$ by addition: ", + "$$\\tilde{\\mathbf {x}_{j-1}} = \\tilde{\\mathbf {h}_{j-1}} + \\tilde{ \\mathbf {d}_k }$$ (Eq. 14) ", + "We also follow a feedforward sub-layer (one hidden layer with ReLU BIBREF35 activation function) after $\\tilde{\\mathbf {x}_{j-1}}$ as in vaswani:2017:nips: ", + "$$\\tilde{\\mathbf {g}_{j-1}} = \\mathbf {W}^{ff}_2 \\max (0, \\mathbf {W}^{ff}_1 \\tilde{\\mathbf {x}_{j-1}} + \\mathbf {b}_1) + \\mathbf {b}_2$$ (Eq. 15) ", + "Note that the transformer decoder can have multiple layers by applying Equation ( 13 ) to ( 15 ) multiple times and we only show the computation of one layer for simplicity.", + "The probability of $w_j^k$ given $w_0^k,\\dots ,w_{j-1}^k$ and $\\widetilde{ \\mathcal {D} }$ is: ", + "$$p( w_j^k | w_{0:j-1}^k, \\widetilde{ \\mathcal {D} } ) = \\text{softmax}( \\mathbf {W}^O \\: \\tilde{\\mathbf {g}_{j-1}} )$$ (Eq. 16) ", + "Finally the probability of all masked sentences $ \\mathcal {M} $ given $\\widetilde{ \\mathcal {D} }$ is ", + "$$p(\\mathcal {M} | \\widetilde{ \\mathcal {D} }) = \\prod _{k \\in \\mathcal {K}} \\prod _{j=1}^{|S_k|} p(w_j^k | w_{0:j-1}^k, \\widetilde{ \\mathcal {D} })$$ (Eq. 17) ", + "The model above can be trained by minimizing the negative log-likelihood of all masked sentences given their paired documents. We can in theory have unlimited amount of training data for Hibert, since they can be generated automatically from (unlabeled) documents. Therefore, we can first train Hibert on large amount of data and then apply it to downstream tasks. In the next section, we will introduce its application to document summarization." + ], + [ + "Extractive summarization selects the most important sentences in a document as its summary. In this section, summarization is modeled as a sequence labeling problem. Specifically, a document is viewed as a sequence of sentences and a summarization model is expected to assign a True or False label for each sentence, where True means this sentence should be included in the summary. In the following, we will introduce the details of our summarization model based Hibert.", + "Let $\\mathcal {D} = (S_1, S_2, \\dots , S_{| \\mathcal {D} |})$ denote a document and $Y = (y_1, y_2, \\dots , y_{| \\mathcal {D} |})$ its sentence labels (methods for obtaining these labels are in Section \"Datasets\" ). As shown in Figure 2 , we first apply the hierarchical bidirectional transformer encoder Hibert to $\\mathcal {D}$ and yields the context dependent representations for all sentences $( \\mathbf {d}_1, \\mathbf {d}_2, \\dots , \\mathbf {d}_{|\\mathcal {D}|} )$ . The probability of the label of $S_i$ can be estimated using an additional linear projection and a softmax: ", + "$$p( y_i | \\mathcal {D} ) = \\text{softmax}(\\mathbf {W}^S \\: \\mathbf {d}_i)$$ (Eq. 20) ", + "where $\\mathbf {W}^S \\in \\mathbb {R}^{2 \\times d}$ . The summarization model can be trained by minimizing the negative log-likelihood of all sentence labels given their paired documents." + ], + [ + "In this section we assess the performance of our model on the document summarization task. We first introduce the dataset we used for pre-training and the summarization task and give implementation details of our model. We also compare our model against multiple previous models." + ], + [ + "We conducted our summarization experiments on the non-anonymous version CNN/Dailymail (CNNDM) dataset BIBREF36 , BIBREF9 , and the New York Times dataset BIBREF37 , BIBREF38 . For the CNNDM dataset, we preprocessed the dataset using the scripts from the authors of see:2017:acl. The resulting dataset contains 287,226 documents with summaries for training, 13,368 for validation and 11,490 for test. Following BIBREF38 , BIBREF37 , we created the NYT50 dataset by removing the documents whose summaries are shorter than 50 words from New York Times dataset. We used the same training/validation/test splits as in xu:2019:arxiv, which contain 137,778 documents for training, 17,222 for validation and 17,223 for test. To create sentence level labels for extractive summarization, we used a strategy similar to nallapati:2017:aaai. We label the subset of sentences in a document that maximizes Rouge BIBREF39 (against the human summary) as True and all other sentences as False.", + "To unsupervisedly pre-train our document model Hibert (see Section \"Pre-training\" for details), we created the GIGA-CM dataset (totally 6,626,842 documents and 2,854 million words), which includes 6,339,616 documents sampled from the English Gigaword dataset and the training split of the CNNDM dataset. We used the validation set of CNNDM as the validation set of GIGA-CM as well. As in see:2017:acl, documents and summaries in CNNDM, NYT50 and GIGA-CM are all segmented and tokenized using Stanford CoreNLP toolkit BIBREF40 . To reduce the vocabulary size, we applied byte pair encoding (BPE; BIBREF41 ) to all of our datasets. To limit the memory consumption during training, we limit the length of each sentence to be 50 words (51th word and onwards are removed) and split documents with more than 30 sentences into smaller documents with each containing at most 30 sentences." + ], + [ + "Our model is trained in three stages, which includes two pre-training stages and one finetuning stage. The first stage is the open-domain pre-training and in this stage we train Hibert with the pre-training objective (Section \"Pre-training\" ) on GIGA-CM dataset. In the second stage, we perform the in-domain pre-training on the CNNDM (or NYT50) dataset still with the same pre-training objective. In the final stage, we finetune Hibert in the summarization model (Section \"Extractive Summarization\" ) to predict extractive sentence labels on CNNDM (or NYT50).", + "The sizes of the sentence and document level Transformers as well as the Transformer decoder in Hibert are the same. Let $L$ denote the number of layers in Transformer, $H$ the hidden size and $A$ the number of attention heads. As in BIBREF6 , BIBREF0 , the hidden size of the feedforward sublayer is $4H$ . We mainly trained two model sizes: $\\text{\\sc Hibert}_S$ ( $L=6$ , $H=512$ and $A=8$ ) and $\\text{\\sc Hibert}_M$ ( $L=6$ , $H$0 and $H$1 ). We trained both $H$2 and $H$3 on a single machine with 8 Nvidia Tesla V100 GPUs with a batch size of 256 documents. We optimized our models using Adam with learning rate of 1e-4, $H$4 , $H$5 , L2 norm of 0.01, learning rate warmup 10,000 steps and learning rate decay afterwards using the strategies in vaswani:2017:nips. The dropout rate in all layers are 0.1. In pre-training stages, we trained our models until validation perplexities do not decrease significantly (around 45 epochs on GIGA-CM dataset and 100 to 200 epochs on CNNDM and NYT50). Training $H$6 for one epoch on GIGA-CM dataset takes approximately 20 hours.", + "Our models during fine-tuning stage can be trained on a single GPU. The hyper-parameters are almost identical to these in the pre-training stages except that the learning rate is 5e-5, the batch size is 32, the warmup steps are 4,000 and we train our models for 5 epochs. During inference, we rank sentences using $p( y_i | \\mathcal {D} ) $ (Equation ( 20 )) and choose the top $K$ sentences as summary, where $K$ is tuned on the validation set." + ], + [ + "We evaluated the quality of summaries from different systems automatically using ROUGE BIBREF39 . We reported the full length F1 based ROUGE-1, ROUGE-2 and ROUGE-L on the CNNDM and NYT50 datasets. We compute ROUGE scores using the ROUGE-1.5.5.pl script.", + "Additionally, we also evaluated the generated summaries by eliciting human judgments. Following BIBREF11 , BIBREF4 , we randomly sampled 20 documents from the CNNDM test set. Participants were presented with a document and a list of summaries produced by different systems. We asked subjects to rank these summaries (ties allowed) by taking informativeness (is the summary capture the important information from the document?) and fluency (is the summary grammatical?) into account. Each document is annotated by three different subjects." + ], + [ + "Our main results on the CNNDM dataset are shown in Table 1 , with abstractive models in the top block and extractive models in the bottom block. Pointer+Coverage BIBREF9 , Abstract-ML+RL BIBREF10 and DCA BIBREF42 are all sequence to sequence learning based models with copy and coverage modeling, reinforcement learning and deep communicating agents extensions. SentRewrite BIBREF26 and InconsisLoss BIBREF25 all try to decompose the word by word summary generation into sentence selection from document and \u201csentence\u201d level summarization (or compression). Bottom-Up BIBREF27 generates summaries by combines a word prediction model with the decoder attention model. The extractive models are usually based on hierarchical encoders (SummaRuNNer; BIBREF3 and NeuSum; BIBREF11 ). They have been extended with reinforcement learning (Refresh; BIBREF4 and BanditSum; BIBREF20 ), Maximal Marginal Relevance (NeuSum-MMR; BIBREF21 ), latent variable modeling (LatentSum; BIBREF5 ) and syntactic compression (JECS; BIBREF38 ). Lead3 is a baseline which simply selects the first three sentences. Our model $\\text{\\sc Hibert}_S$ (in-domain), which only use one pre-training stage on the in-domain CNNDM training set, outperforms all of them and differences between them are all significant with a 0.95 confidence interval (estimated with the ROUGE script). Note that pre-training $\\text{\\sc Hibert}_S$ (in-domain) is very fast and it only takes around 30 minutes for one epoch on the CNNDM training set. Our models with two pre-training stages ( $\\text{\\sc Hibert}_S$ ) or larger size ( $\\text{\\sc Hibert}_M$ ) perform even better and $\\text{\\sc Hibert}_M$ outperforms BERT by 0.5 ROUGE. We also implemented two baselines. One is the hierarchical transformer summarization model (HeriTransfomer; described in \"Extractive Summarization\" ) without pre-training. Note the setting for HeriTransfomer is ( $L=4$ , $H=300$ and $A=4$ ) . We can see that the pre-training (details in Section \"Pre-training\" ) leads to a +1.25 ROUGE improvement. Another baseline is based on a pre-trained BERT BIBREF0 and finetuned on the CNNDM dataset. We used the $\\text{BERT}_{\\text{base}}$ model because our 16G RAM V100 GPU cannot fit $\\text{BERT}_{\\text{large}}$ for the summarization task even with batch size of 1. The positional embedding of BERT supports input length up to 512 words, we therefore split documents with more than 10 sentences into multiple blocks (each block with 10 sentences). We feed each block (the BOS and EOS tokens of each sentence are replaced with [CLS] and [SEP] tokens) into BERT and use the representation at [CLS] token to classify each sentence. Our model $\\text{\\sc Hibert}_S$1 outperforms BERT by 0.4 to 0.5 ROUGE despite with only half the number of model parameters ( $\\text{\\sc Hibert}_S$2 54.6M v.s. BERT 110M). Results on the NYT50 dataset show the similar trends (see Table 2 ). EXTRACTION is a extractive model based hierarchical LSTM and we use the numbers reported by xu:2019:arxiv. The improvement of $\\text{\\sc Hibert}_S$3 over the baseline without pre-training (HeriTransformer) becomes 2.0 ROUGE. $\\text{\\sc Hibert}_S$4 (in-domain), $\\text{\\sc Hibert}_S$5 (in-domain), $\\text{\\sc Hibert}_S$6 and $\\text{\\sc Hibert}_S$7 all outperform BERT significantly according to the ROUGE script.", + "We also conducted human experiment with 20 randomly sampled documents from the CNNDM test set. We compared our model $\\text{\\sc Hibert}_M$ against Lead3, DCA, Latent, BERT and the human reference (Human). We asked the subjects to rank the outputs of these systems from best to worst. As shown in Table 4 , the output of $\\text{\\sc Hibert}_M$ is selected as the best in 30% of cases and we obtained lower mean rank than all systems except for Human. We also converted the rank numbers into ratings (rank $i$ to $7-i$ ) and applied student $t$ -test on the ratings. $\\text{\\sc Hibert}_M$ is significantly different from all systems in comparison ( $p < 0.05$ ), which indicates our model still lags behind Human, but is better than all other systems.", + "As mentioned earlier, our pre-training includes two stages. The first stage is the open-domain pre-training stage on the GIGA-CM dataset and the following stage is the in-domain pre-training on the CNNDM (or NYT50) dataset. As shown in Table 3 , we pretrained $\\text{\\sc Hibert}_S$ using only open-domain stage (Open-Domain), only in-domain stage (In-Domain) or both stages (Open+In-Domain) and applied it to the CNNDM summarization task. Results on the validation set of CNNDM indicate the two-stage pre-training process is necessary." + ], + [ + "The core part of a neural extractive summarization model is the hierarchical document encoder. We proposed a method to pre-train document level hierarchical bidirectional transformer encoders on unlabeled data. When we only pre-train hierarchical transformers on the training sets of summarization datasets with our proposed objective, application of the pre-trained hierarchical transformers to extractive summarization models already leads to wide improvement of summarization performance. Adding the large open-domain dataset to pre-training leads to even better performance. In the future, we plan to apply models to other tasks that also require hierarchical document encodings (e.g., document question answering). We are also interested in improving the architectures of hierarchical document encoders and designing other objectives to train hierarchical transformers." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0591/environment/Dockerfile b/qasper-0591/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0591/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/* diff --git a/qasper-0591/instruction.md b/qasper-0591/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..8866118d1b50334c7a041ae476e0ccae03255668 --- /dev/null +++ b/qasper-0591/instruction.md @@ -0,0 +1,164 @@ +Name of Paper: Shallow Discourse Annotation for Chinese TED Talks + +Question: Do they build a model to recognize discourse relations on their dataset? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related work", + "PDTB and our Annotation Scheme", + "PDTB and our Annotation Scheme ::: Arguments", + "PDTB and our Annotation Scheme ::: Relations", + "PDTB and our Annotation Scheme ::: Senses", + "Annotation Procedure", + "Annotation Procedure ::: Annotator training", + "Annotation Procedure ::: Corpus building", + "Annotation Procedure ::: Agreement study", + "Results", + "Conclusions and Future Work", + "Acknowledgement" + ], + "paragraphs": [ + [ + "", + "Researchers have recognized that performance improvements in natural language processing (NLP) tasks such as summarization BIBREF0, question answering BIBREF1, and machine translation BIBREF2 can come from recognizing discourse-level properties of text. These include properties such as the how new entities are introduced into the text, how entities are subsequently referenced (e.g., coreference chains), and how clauses and sentences relate to one another. Corpora in which such properties have been manually annotated by experts can be used as training data for such tasks, or seed data for creating additional \"silver annotated\u201d data. Penn Discourse Treebank (PDTB), a lexically grounded method for annotation, is a shallow approach to discourse structure which can be adapted to different genres. Annotating discourse relations both within and across sentences, it aims to have wide application in the field of natural language processing. PDTB can effectively help extract discourse semantic features, thus serving as a useful substrate for the development and evaluation of neural models in many downstream NLP applications.", + "Few Chinese corpora are both annotated for discourse properties and publicly available. The available annotated texts are primarily newspaper articles. The work described here annotates another type of text \u2013 the planned monologues found in TED talks, following the annotation style used in the Penn Discourse TreeBank, but adapted to take account of properties of Chinese described in Section 3.", + "TED talks (TED is short for technology, entertainment, design), as examples of planned monologues delivered to a live audience BIBREF3, are scrupulously translated to various languages. Although TED talks have been annotated for discourse relations in several languages BIBREF4, this is the first attempt to annotate TED talks in Chinese (either translated into Chinese, or presented in Chinese), providing data on features of Chinese spoken discourse. Our annotation by and large follows the annotation scheme in the PDTB-3, adapted to features of Chinese spoken discourse described below.", + "The rest of the paper is organized as follows: in Section 2, we review the related existing discourse annotation work. In Section 3, we briefly introduce PDTB-3 BIBREF5 and our adapted annotation scheme by examples. In Section 4, we elaborate our annotation process and the results of our inteannotator-agreement study. Finally, in Section 5, we display the results of our annotation and preliminarily analyze corpus statistics, which we compare to the relation distribution of the CUHK Discourse TreeBank for Chinese. (CUHK-DTBC)BIBREF6." + ], + [ + "Following the release of the Penn Discourse Treebank (PDTB-2) in 2008 BIBREF7, several remarkable Chinese discourse corpora have since adapted the PDTB framework BIBREF8, including the Chinese Discourse Treebank BIBREF9, HIT Chinese Discourse Treebank (HIT-CDTB) zhou2014cuhk, and the Discourse Treebank for Chinese (DTBC) BIBREF6. Specifically, Xue proposed the Chinese Discourse Treebank (CDTB) Project BIBREF10. From their annotation work, they discussed the matters such as features of Chinese discourse connectives, definition and scope of arguments, and senses disambiguation, and they argued that determining the argument scope is the most challenging part of the annotation. To further promote their research, zhou2012pdtb presented a PDTB-style discourse corpus for Chinese. They also discussed the key characteristics of Chinese text which differs from English, e.g., the parallel connectives, comma-delimited intra-sentential implicit relations etc. Their data set contains 98 documents from the Chinese Treebank BIBREF10. In 2015, Zhou and Xue expanded their corpus to 164 documents, with more than 5000 relations being annotated. huang-chen-2011-chinese constructed a Chinese discourse corpus with 81 articles. They adopted the top-level senses from PDTB sense hierarchy and focused on the annotation of inter-sentential discourse relations. zhang2014chinese analyzed the differences between Chinese and English, and then presented a new Chinese discourse relation hierarchy based on the PDTB system, in which the discourse relations are divided into 6 types: temporal, causal, condition, comparison, expansion and conjunction. And they constructed a Chinese Discourse Relation corpus called HIT-CDTB based on this hierarchy. Then, zhou2014cuhk presented the first open discourse treebank for Chinese, the CUHK Discourse Treebank for Chinese. They adapted the annotation scheme of Penn Discourse Treebank 2 (PDTB-2) to Chinese language and made adjustments to 3 aspects according to the previous study of Chinese linguistics. However, they just reannotated the documents of the Chinese Treebank and did not annotate inter-sentence level discourse relations.", + "It is worth noting that, all these corpora display a similar unbalanced distribution that is likely to be associated with them all being limited to text from the same NEWS genre. In particular, these two senses (Expansion and Conjunction) represent 80 % of the relations annotated in the CDTB.", + "In addition, although annotating spoken TED talks has been done on other several languages before BIBREF4, to our knowledge, there is no recent annotation work for Chinese spoken discourses, or particularly for Chinese Ted talks. However, there is some evidence that noticeable differences in the use of discourse connectives and discourse relations can be found between written and spoken discourses BIBREF11. Here, by using the new PDTB-3 sense hierarchy and annotator, which has not been used for Chinese annotation before, we annotated Chinese Ted talks to help others be aware of the differences between the Chinese discourse structure of written and spoken texts and will make our corpus publicly available to benefit the discourse-level NLP researches for spoken discourses." + ], + [ + "The annotation scheme we adopted in this work is based on the framework of PDTB, incorporating the most recent PDTB (PDTB-3) relational taxonomy and sense hierarchy BIBREF5, shown in Table 1. PDTB follows a lexically grounded approach to the representation of discourse relations BIBREF12. Discourse relations are taken to hold between two abstract object arguments, named Arg1 and Arg2 using syntactic conventions, and are triggered either by explicit connectives or, otherwise, by adjacency between clauses and sentences. As we can see from Table 1, the PDTB-3 sense hierarchy has 4 top-level senses (Expansion, Temporal, Contingency, Contrast) and second- and third-level senses for some cases. With obvious differences ranging from the conventions used in annotation, to differences in senses hierarchy, PDTB-3 gives rigorous attention to achieving as much consistency as possible while annotating discourse relations.", + "Previously, all Chinese annotation work using PDTB style followed the settings of PDTB-2. Some researchers tried to adapt it in lines of the Chinese characteristics. For example, zhou2012pdtb annotated the parallel connectives continuously rather than discontinuously due to the greater use of parallel connectives in Chinese and a reduced use of explicit connectives in general. zhou2014cuhk added some additional senses into the hierarchy. However, PDTB-3, as a new and enriched version, not only has paid greater attention to intra-sentential senses, but also has incorporated some of those additional senses. Therefore, we just made several modifications including removing, adding, or disambiguating for the practical use of PDTB-3 into our Chinese annotation.", + "In practice, using the PDTB annotator tool, we annotated an explicit connective, identified its two arguments in which the connective occurs, and then labeled the sense. For implicit relations, when we inferred the type of relation between two arguments, we tried to insert a connective for this relation, and also the inserted connective is not so strictly restricted, extending to expressions that can convey the sense of the arguments. If a connective conveys more than one sense or more than one relation can be inferred, multiple senses would be assigned to the token. Our adaptations towards PDTB-3 will be introduced from the perspectives of arguments, relations and senses as follows." + ], + [ + "The argument-labelling conventions used in the PDTB-2 had to be modified to deal with the wider variety of discourse relations that needed to be annotated consistently within sentences in the PDTB-3. In particular, in labelling intra-sentential discourse relations, a distinction was made between relations whose arguments were in coordinating syntactic structures and ones whose arguments were in subordinating syntactic structures. For coordinating structures, arguments were labelled by position (Arg1 first, then Arg2), while for subordinating structures, the argument in subordinate position was labelled Arg2, and the other, Arg1, independent of position.", + "For discourse in Chinese, this can introduce an unwanted ambiguity. Example 1 is a typical example for illustrate this phenomenon. In the examples throughout the paper, explicit connectives are underlined, while implicit Discourse Connectives and the lexicalizing expression for Alternative Lexicalizations are shown in parentheses and square brackets respectively. The position of the arguments is indicated by the attached composite labels to the right square brackets, and the relation lables and sense lables can be seen in the parentheses at the end of arguments. When the arguments, relations or senses are ambiguous, there may be no corresponding labels shown in the examples.", + "UTF8gbsn", + "\u56e0\u4e3a \u4f60 \u8ba9 \u6211 \u751f\u6c14\uff0c \u6240\u4ee5\uff0c\u6211 \u8981\u8ba9", + "Because you make me angry, so I want", + "\u4f60 \u66f4\u96be\u8fc7\u3002(Explicit, Cause.Result)", + "you to be sadder.", + "\u201cYou made me angry, so I return it double back.\u201d", + "While\u201cbecause\u201dand\u201cso\u201dare rarely found together as connectives in a sentence in English, it is not uncommon to find them used concurrently as a paired connective in Chinese. Therefore, due to this difference, the annotators tend to have no idea about which clause is subordinate. Therefore, if we regard the first clause as subordinating structure and \u201c\u56e0 \u4e3a\u201d(because)as connective, then the sense would be Contingency.Cause.Reason. By contrast, the sense would be Contingency.Cause.Result, when the second clause is regarded as Arg2. To get rid of this kind of ambiguity, we just take the first as Arg1 and the second Arg2 regardless of the fact that the parallel connectives are surbodinating or coordinating." + ], + [ + "There are two new types of relation in PDTB-3: AltlexC and Hypophora. Hypophora is an explicitly marked question-response pairs, first used in annotating the TED- MDB BIBREF4. In Hypophora relations, Arg1 expresses a question and Arg2 offers an answer, with no explicit or implicit connective being annotated (Example 2). Because of the nature of TED talks, many relations in both the TED-MDB and in our Chinese TED talks are examples of \u201cHypophora\u201d. However, not all discourse relations whose first argument is a question are Hypophora. Example 3, instead of seeking information and giving answer, is just a rhetorical question expressing negation by imposing a dramatic effect.", + "[\u6211\u5230\u5e95 \u8981 \u8bb2 \u4ec0\u4e48Arg1]?", + "I on earth am going to talk about what ?", + "[\u6700\u540e \u6211\u51b3\u5b9a \u8981 \u8bb2 \u6559\u80b2Arg2]\u3002(Hypophora)", + "Finally, I decided to talk about education .", + "\u201cwhat am I gonna say? Finally, I decided to talk about education.\u201d", + "\u4ed6\u8bf4 \uff1a \u201c \u6211 \u662f \u4e09 \u5929 \u4e00 \u5c0f \u54ed \u3001 \u4e94 \u5929", + "He said, \" I am three days a little cry, five days", + "\u4e00 \u5927 \u54ed \u3002 \" \u8fd9\u6837 \u4f60 \u6709 \u6bd4\u8f83 \u5065\u5eb7 \u5417 \uff1f", + "a lot cry.\" In this way, you are more healthier?", + "\u90fd \u662f \u60b2\u4f24 \uff0c \u5e76 \u4e0d \u662f \u6bcf \u4e00 \u4e2a \u4eba \uff0c\u6bcf \u4e00 \u6b21", + "All are sadness, not everyone, every time", + "\u611f\u53d7 \u5230 \u60b2\u4f24 \u7684 \u65f6\u5019 \uff0c\u90fd \u4e00\u5b9a \u4f1a \u6d41\u6cea \u3001 \u751a\u81f3 \u5927\u54ed \u3002", + "feel sad 's time, would shed tears\u3001even cry.", + "\u201cHe said, \"Three times I cry a little, and five times I cry a lot.\" Is that healthier? Everyone gets sad, but that's not to say that whenever someone feels sad, they necessarily will cry.\u201d", + "In addition, we found a new issue when identifying Hypophora, which is shown in Example 4. In this example, we have a series of questions, rather than a series of assertions or a question-response pair. We attempted to capture the rhetorical links by taking advantage of our current inventory of discourse relations. Here, two implicit relations were annotated for this example, and the senses are Arg2-as-detail and Result+SpeechAct respectively. Therefore, when there are subsequent texts related to a question or a sequence of questions, we would not just annotated them as Hypophora but had to do such analysis as what we did for the examples shown.", + "[\u60c5\u7eea \uff0c \u5b83 \u5230\u5e95 \u662f \u4ec0\u4e48Rel1-Arg1 ]? (\u5177\u4f53\u6765\u8bf4)", + "Emotion, it on earth is what? (Specially)", + "[\u5b83 \u662f \u597d\u8fd8\u662f \u4e0d \u597dRel1-Arg2,Rel2-Arg1]\uff1f(Implicit\uff0cArg2-as-detail)", + "It is good or bad?", + "(\u6240\u4ee5)[\u4f60 \u4f1a \u60f3\u8981 \u62e5\u6709 \u5b83 \u5417Rel2-Arg2]\uff1f (Implicit\uff0cResult+SpeechAct)", + "(So) You want to have it ?", + "\u201cWhat is it exactly? Is it good or bad? Do you want to have them?\u201d", + "Besides, it is widely accepted that the ellipsis of subject or object are frequently seen in Chinese. Then for EntRel, if facing this situation where one of the entities in Arg1 or Arg2 is omitted, we still need to annotate this as EntRel (Example 5). In this following example, we can see in Arg2, the pronoun which means \u201cthat\u201dis omitted, but in fact which refers to the phenomenon mentioned in Arg1, so here there is still an EntRel relation between this pair of arguments.", + "[\u6211\u4eec\u4f1a\u4ee5\u8bbd\u523a\u7684\u53e3\u543b\u6765\u8c08\u8bba\uff0c \u5e76\u4e14\u4f1a", + "We in ironic terms talk about, and", + "\u52a0\u4e0a\u5f15\u53f7 : \u201c\u8fdb\u6b65\u201dArg1 ]", + "add quotes: \u201cProgress\u201d.", + "[\u6211\u60f3\u662f\u6709\u539f\u56e0\u7684\uff0c \u6211\u4eec\u4e5f\u77e5\u9053 \u662f \u4ec0\u4e48", + "I think there are reasons, we also know are what", + "\u539f\u56e0Arg2]\u3002(EntRel)", + "reasons.", + "\u201cWe talk about it in ironic terms with little quotes around it:\u201cProgress.\u201dOkay, there are reasons for that, and I think we know what those reasons are.\u201d" + ], + [ + "The great improvement in the sense hierarchy in PDTB-3 enables us to capture more senses with additional types and assign the senses more clearly. For example, the senses under the category of Expansion such as level of detail, manner, disjunction and similarity are indispensable to our annotation. Therefore, we nearly adopted the sense hierarchy in PDTB-3, just with few adaptations. On the one hand, we removed the third level sense \u201cNegative condition+SpeechAct\u201d, since it was not used to label anything in the corpus. On the other hand, we added the Level-2 sense \u201cExpansion.Progression\u201d. This type of sense applies when Arg1 and Arg2 are coordinating structure with different emphasis. The first argument is annotated as Arg1 and the second as Arg2. This sense is usually conveyed by such typical connectives as \u201c\u4e0d \u4f46 (not only)... \u800c \u4e14 (but also)...\u201d, \u201c\u751a \u81f3 (even)... \u4f55 \u51b5 (let alone)...\u201d,\u201c... \u66f4 (even more)...\u201d(Example 6).", + "[\u6211 \u53bb \u4e86 \u804b\u4eba \u4ff1\u4e50\u90e8 \uff0c\u89c2\u770b \u4e86 \u804b\u4eba \u7684", + "I went to deaf clubs, saw the deaf person\u2019s", + "\u8868\u6f14 Arg1]\u3002[\u6211\u751a\u81f3 \u53bb \u4e86 \u7530\u7eb3\u897f\u5dde \u7eb3\u4ec0\u7ef4\u5c14\u7684", + "performances. I even went to the Nashville \u2019s", + "\u201c \u7f8e\u56fd \u804b\u4eba \u5c0f\u59d0 \u201d \u9009\u79c0\u8d5bArg2]\u3002(Explicit, Progression.Arg2-as-progr)", + "\u201cthe Miss Deaf\u201d America contest.", + "\u201c I went to deaf clubs. I saw performances of deaf theater and of deaf poetry. I even went to the Miss Deaf America contest in Nashville.\u201d", + "Another issue about sense is the inconsistency when we annotated the implicit relations. zhou2012pdtb did not insert connective for implicit relations, but we did this for further researches with regard to implicit relations. However, we found that in some cases where different connectives can be inserted into the same arguments to express the same relation, the annotators found themselves in a dilemma. We can see that Example 7 and Example 8 respectively insert \u201cso\u201d and \u201cbecause\u201d into the arguments between which there is a causal relation, but the senses in these two examples would be Cause.Result and Cause.Reason. The scheme we adopted for this is that we only take the connectives that we would insert into account, and the position and sense relations of arguments would depend on the inserted connectives.", + "[\u201c\u514b\u670d \u9006\u5883 \u201d \u8fd9\u4e00\u8bf4\u6cd5 \u5bf9\u6211", + "\u201cOvercome the adversity\u201d this phrase for me", + "\u6839\u672c \u4e0d \u6210\u7acb Arg1]\uff0c\uff08\u6240\u4ee5\uff09[\u522b\u4eba \u8ba9 \u6211", + "completely not justified, (so) others asked me", + "\u5c31 \u8fd9\u4e00\u8bdd\u9898 \u8bf4 \u51e0 \u53e5 \u7684\u65f6\u5019\uff0c \u6211\u5f88\u4e0d\u81ea\u5728Arg2]\u3002(Implicit, Cause.Result)", + "to this topic talk about some, I felt uneasy.", + "\uff08\u56e0\u4e3a\uff09[\u201c\u514b\u670d \u9006\u5883 \u201d \u8fd9\u4e00\u8bf4\u6cd5", + "(Because) \u201covercome the adversity\u201d this phrase", + "\u5bf9 \u6211\u6765\u8bf4\u6839\u672c \u4e0d \u6210\u7acbArg2], [ \u522b\u4eba", + "for me completely not justified, (so) others", + "\u8ba9 \u6211\u5c31 \u8fd9\u4e00\u8bdd\u9898 \u8bf4\u51e0\u53e5\u7684\u65f6\u5019\uff0c\u6211\u5f88", + "asked me to this topic, talk about some, I felt", + "\u4e0d\u81ea\u5728Arg1]\u3002(Implicit, Cause.Reason)", + "uneasy.", + "\u201c\u201covercome the adversity\u201d this phrase never sat right with me, and I always felt uneasy trying to answer people's questions about it.\u201d" + ], + [ + "In this section, we describe our annotation process in creating the Chinese TED discourse treebank. To ensure annotation quality, the whole annotation process has three stages: annotators training, annotation, post-annotation. The training process intends to improve the annotators\u2019 annotation ability, while after the formal annotation, the annotated work was carefully checked by the supervisor, and the possible errors and inconsistencies were dealt with through discussions and further study." + ], + [ + "The annotator team consists of a professor as the supervisor, an experienced annotator and a researcher of PDTB as counselors, two master degree candidates as annotators. Both of the annotators have a certain theoretical foundation of linguistics. To guarantee annotation quality, the annotators were trained through the following steps: firstly, the annotators read the PDTB-3 annotation manual, the PDTB-2 annotation manual and also other related papers carefully; next, the annotators tried to independently annotate same texts, finding out their own uncertainties or problems respectively and discussing these issues together; then, the annotators were asked to create sample annotations on TED talks transcripts for each sense from the top level to the third. They discussed the annotations with the researchers of the team and tried to settle disputes. When sample annotations are created, this part of process is completed; based on the manuals, previous annotation work and also the annotators\u2019 own pre-annotation work, they made a Chinese tutorial on PDTB guidelines, in which major difficulties and perplexities, such as the position and the span of the arguments, the insert of connectives, and the distinction of different categories of relations and senses, are explained clearly in detail by typical samples. This Chinese tutorial is beneficial for those who want to carry out similar Chinese annotation, so we made this useful tutorial available to those who want to carry out similar annotation; finally, to guarantee annotation consistency, the annotators were required to repeat their annotation-discussion process until their annotation results show the Kappa value > 0.8 for each of the indicators for agreement." + ], + [ + "At present, our corpus has been released publiclyFOOTREF19. Our corpus consists of two parts with equal number of texts: (1) 8 English TED talks translated into Chinese, just like the talks in the TED-MDB, all of which were originally presented in English and translated into other languages (including German, Lithuanian, Portuguese,Polish, Russian and Turkish) BIBREF4. (2) 8 Chinese TED talks originally presented in Taipei and translated into English. We got the texts by means of extracting Chinese and English subtitles from TED talks videos . Firstly, we just annotated the talks given in English and translated in Chinese. But after considering the possible divergencies between translated texts and the original texts, we did our annotation for the Taipei TED talks, which were delivered in Chinese. The parallel English texts are also being annotated for discourse relations, but they are not ready for carrying out a systematic comparison between them. At the current stage, we annotated 3212 relations for the TED talks transcripts with 55307 words\uff0cand the length of each talk (in words) and the number of annotated", + "relations in each talks can be found from Table 2. These TED talks we annotated were prudently selected from dozens of candidate texts. The quality of texts which is principally embodied in content, logic, punctuation and the translation are the major concerns for us. Moreover, when selecting the texts from the Taipei talks, we ruled out those texts which are heavy in dialogues. Some speakers try to interact with the audience, asking the questions, and then commenting on how they have replied. However, what we were annotating was not dialogues. In spite of critically picking over the texts, we still spent considerable time on dealing with them before annotation such as inserting punctuation and correcting the translation. Moreover, before annotation, we did word segmentation by using Stanford Segmenter and corrected improper segmentation.", + "While annotating, we assigned the vast majority of the relations a single sense and a small proportion of relations multiple senses. Unlike previous similar Chinese corpora which primarily or just annotated the relations between sentences, we annotated not only discourse relations between sentences but intra-sentential discourse relations as well. To ensure building a high-quality corpus, the annotators regularly discussed their difficulties and confusions with the researcher and the experienced annotator in the whole process of annotation. After discussion, the annotators reached agreement or retained the differences for few ambiguities." + ], + [ + "We measured intra-annotator agreement between two annotators in three aspects: relations, senses, arguments. To be specific, the annotators\u2019 consistency in annotating the type of a specific relation or sense and the position and scope of arguments are measured. To assess the consistency of annotations and also eliminate coincidental annotations, we used agreement rates, which is calculated by dividing the number of senses under each category where the annotators annotate consistently by the total number of each kind of sense. And considering the potential impact of unbalanced distribution of senses, we also used the Kappa value. And the final agreement study was carried out for the first 300 relations in our corpus. We obtained high agreement results and Kappa value for the discourse relation type and top-level senses ($\\ge {0.9} $ ). However, what we did was more than this, and we also achieved great results on the second-level and third-level senses for the sake of our self-demand for high-quality, finally achieving agreement of 0.85 and Kappa value of 0.83 for these two deeper levels of senses.", + "Table 3 also shows that agreement on argument order is almost 1.0 (kappa = 0.99). This means that the guidelines were sufficiently clear that the annotators rarely had difficulty in deciding the location of Arg1 and Arg2 when the senses are determined. Concerning the scope of arguments, which is seen as the most challenging part in the annotation work BIBREF10, our agreement and Kappa value on this are 0.88 and 0.86 respectively, while the agreement of the scope of arguments depends on whether the scopes of two arguments the anotators annotated are completely the same. Under such strict requirement, our consistency in this respect is still significantly higher than that of other annotation work done before, for we strictly obeyed the rules of \u201cminimality principle\u201d mentioned in the PDTB-3 annotation manual and got a clearer perspective of supplementary information. Therefore, the annotators are better at excluding the information that do not fall within the scope of the discourse relation.", + "It is useful to determine where the annotators disagreed most with each other. The three senses where most disagreement occurred are shown in Table 4. The disagreements were primarily in labelling implicit relations. The highest level of disagreement occurred with Expansion.Conjunction and Expansion.Detail, accounting for 12.5 % among all the inconsistent senses. It is because, more often than not, the annotators failed to judge whether the two arguments make the same contribution with respect to that situation or both arguments describing the same has different level of details. The second highest level of disagreement is reflected in Conjunction and Asynchronous, accounting for 9.3 %. Besides, Contrast and Concession are two similar senses, which are usually signaled by the same connectives like \u201c\u4f46\u662f\u201d, \u201c\u800c\u201d, \u201c\u4e0d\u8fc7\u201d, and all these words can be translated into\u201cbut\u201din English. Hence, the annotators sometimes tend to be inconsistent when distinguishing them." + ], + [ + "In regard to discourse relations, there are 3212 relations, of which 1237 are explicit relations (39%) and 1174 are implicit relation (37%) (Figure 1). The remaining 801 relations include Hypophora, AltLex, EntRel, and NoRel. Among these 4 kinds of relations, what is worth mentioning is AltLex(Alternative Lexicalizations ),which only constitutes 3% but is of tremendous significance, for we are able to discover inter- or intra-sentential relations when there is no explicit expressions but AltLex expressions conveying the relations. but AltLex expressions(eg, \u8fd9\u5bfc\u81f4\u4e86(this cause), \u4e00\u4e2a\u4f8b\u5b50\u662f(one example is... ), \u539f \u56e0 \u662f (the reason is), etc.). Originally in English, AltLex is supposed to contain both an anaphoric or deictic reference to an actual argument and an indication of the type of sense BIBREF13. While for Chinese, the instances of Altlex do not differ significantly from those annotated in English. To prove this, two examples are given as below (Example 9 and Example 10). From our annotation, we realized that Altlex deserves more attention, for which can effectively help to recogonize types of discourse relations automatically.", + "[\u201c\u56fd\u5185 \u8bb8\u591a\u88ab\u622a\u80a2\u8005, \u65e0\u6cd5\u4f7f\u7528", + "in this country, many of the amputees, cannot use", + "\u4ed6\u4eec\u7684\u5047\u80a2Arg1],[\u8fd9\u5176\u4e2d\u7684\u539f\u56e0\u662f] [\u4ed6\u4eec\u7531\u4e8e", + "their prostheses, the reason was their", + "\u5047\u80a2\u63a5\u53d7\u8154 \u65e0\u6cd5 \u4e0e\u6b8b\u80a2 \u9002\u914d \u800c", + "prosthetic sockets cannot their leg fit well so that", + "\u611f\u5230\u75bc\u75dbArg2].(AltLex, Cause.Reason)", + "felt painful.]", + "\u201cMany of the amputees in the country would not use their prostheses. The reason, I would come to find out, was that their prosthetic sockets were painful because they did not fit well.\u201d", + "\u4e09\u5e74\u7ea7\u7684\u65f6\u5019\u8003\u8fdb\u79c0\u6717\u5c0f\u5b66\u7684\u6e38\u6cf3\u73ed,", + "in third grade, got in the swimming class at Xiu Lang", + "[ \u8fd9\u4e2a\u73ed\u6bcf\u5929\u7684 \u6e38\u6cf3", + "elementary school, this class everyday\u2019s swimming", + "\u8bad\u7ec3\u91cf\u9ad8\u8fbe 3000 \u7c73 Arg1], \u6211\u53d1\u73b0 [\u8fd9\u6837\u7684\u8bad\u7ec3\u91cf", + "volumm reach 3000 meters, I realized the training load", + "\u4f7f][\u6211 \u65e0\u6cd5\u540c\u65f6\u517c\u987e\u4e24\u79cd\u4e50\u5668 Arg2]\u3002(AltLex,", + "Cause.Result)", + "make me cannot learn the two instruments at the same time", + "\u201cI got in the swimming class at Xiu Lang elementary school when I was in third grade. We had to swim up to 3000 meters every day. I realized the training load was too much for me to learn the two instruments at the same time.\u201d", + "Obviously, there is approximately the same number of explicit and implicit relations in the corpus. This may indicate that explicit connectives and relations are more likely to present in Chinese spoken texts rather than Chinese written texts.", + "The figures shown by Table 4 illustrate the distributions of class level senses. We make a comparison for the class level senses between our corpus and the CUHK Discourse Treebank for Chinese (CUHK-DTBC). CUHK Discourse Treebank for Chinese is a corpus annotating news reports. Therefore, our comparison with it may shed light on the differences of discourse structures in different genres. According to the statistics of CUHK-DTBC for 400 documents and our corpus, while more than half of the senses is Expansion in CUHK-DTBC, it just represents 37.5% in our corpus. In addition, it is highlighted that the ranks of the class level senses are the same in both corpora, although all of the other three senses in our corpus are more than those in CUHK-DTBC.", + "The most frequent second-level senses in our corpus can be seen from Table 5. We can find that 20% of the senses is Cause (including Reason and Result), followed by Conjunction and Concession, each with 13%. The top 10 most frequent senses take up 86% of all senses annotated, which reveals that other senses also can validate their existence in our corpus. Therefore, these findings show that, compared with other corpora about Chinese shallow relations where the majority of the documents are news report, our corpus evidently show a more balanced and varied distribution from perspectives of both relations and senses, which in large measure proves the differences in discourse relations between Chinese written texts and Chinese spoken texts." + ], + [ + "In this paper, we describe our scheme and process in annotating shallow discourse relations using PDTB-style. In view of the differences between English and Chinese, we made adaptations for the PDTB-3 scheme such as removing AltLexC and adding Progression into our sense hierarchy. To ensure the annotation quality, we formulated detailed annotation criteria and quality assurance strategies. After serious training, we annotated 3212 discourse relations, and we achieved a satisfactory consistency of labelling with a Kappa value of greater than 0.85 for most of the indicators. Finally, we display our annotation results in which the distribution of discourse relations and senses differ from that in other corpora which annotate news report or newspaper texts. Our corpus contains more Contingency, Temporal and Comparison relations instead of being governed by Expansion.", + "In future work, we are planning to 1) expand our corpus by annotating more TED talks or other spoken texts; 2) build a richer and diverse connective set and AltLex expressions set; 3) use the corpus in developing a shallow discourse parser for Chinese spoken discourses; 4) also explore automatic approaches for implicit discourse relations recognition." + ], + [ + "The present research was supported by the National Natural Science Foundation of China (Grant No. 61861130364) and the Royal Society (London) (NAF$\\backslash $R1$\\backslash $180122). We would like to thank the anonymous reviewers for their insightful comments." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0596/environment/Dockerfile b/qasper-0596/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0596/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/* diff --git a/qasper-0596/instruction.md b/qasper-0596/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..9f509117665f7794a62ef74967b26072d378f513 --- /dev/null +++ b/qasper-0596/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: The Role of Pragmatic and Discourse Context in Determining Argument Impact + +Question: What models that rely only on claim-specific linguistic features are used as baselines? \ No newline at end of file diff --git a/qasper-0597/instruction.md b/qasper-0597/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..ca2ae30bd90dc5591a9d0d59be3e907094270930 --- /dev/null +++ b/qasper-0597/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: The Role of Pragmatic and Discourse Context in Determining Argument Impact + +Question: How is pargmative and discourse context added to the dataset? \ No newline at end of file diff --git a/qasper-0598/environment/Dockerfile b/qasper-0598/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0598/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/* diff --git a/qasper-0598/instruction.md b/qasper-0598/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..807c745a9bc009bd3195a422847ecdff728f48f9 --- /dev/null +++ b/qasper-0598/instruction.md @@ -0,0 +1,109 @@ +Name of Paper: The Role of Pragmatic and Discourse Context in Determining Argument Impact + +Question: What annotations are available in the dataset? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Dataset", + "Methodology ::: Hypothesis and Task Description", + "Methodology ::: Baseline Models ::: Majority", + "Methodology ::: Baseline Models ::: SVM with RBF kernel", + "Methodology ::: Baseline Models ::: FastText", + "Methodology ::: Baseline Models ::: BiLSTM with Attention", + "Methodology ::: Fine-tuned BERT model", + "Methodology ::: Fine-tuned BERT model ::: Claim with no context", + "Methodology ::: Fine-tuned BERT model ::: Claim with parent representation", + "Methodology ::: Fine-tuned BERT model ::: Incorporating larger context", + "Results and Analysis", + "Conclusion", + "Acknowledgements" + ], + "paragraphs": [ + [ + "Previous work in the social sciences and psychology has shown that the impact and persuasive power of an argument depends not only on the language employed, but also on the credibility and character of the communicator (i.e. ethos) BIBREF0, BIBREF1, BIBREF2; the traits and prior beliefs of the audience BIBREF3, BIBREF4, BIBREF5, BIBREF6; and the pragmatic context in which the argument is presented (i.e. kairos) BIBREF7, BIBREF8.", + "Research in Natural Language Processing (NLP) has only partially corroborated these findings. One very influential line of work, for example, develops computational methods to automatically determine the linguistic characteristics of persuasive arguments BIBREF9, BIBREF10, BIBREF11, but it does so without controlling for the audience, the communicator or the pragmatic context.", + "Very recent work, on the other hand, shows that attributes of both the audience and the communicator constitute important cues for determining argument strength BIBREF12, BIBREF13. They further show that audience and communicator attributes can influence the relative importance of linguistic features for predicting the persuasiveness of an argument. These results confirm previous findings in the social sciences that show a person's perception of an argument can be influenced by his background and personality traits.", + "To the best of our knowledge, however, no NLP studies explicitly investigate the role of kairos \u2014 a component of pragmatic context that refers to the context-dependent \u201ctimeliness\" and \u201cappropriateness\" of an argument and its claims within an argumentative discourse \u2014 in argument quality prediction. Among the many social science studies of attitude change, the order in which argumentative claims are shared with the audience has been studied extensively: 10.1086/209393, for example, summarize studies showing that the argument-related claims a person is exposed to beforehand can affect his perception of an alternative argument in complex ways. article-3 similarly find that changes in an argument's context can have a big impact on the audience's perception of the argument.", + "Some recent studies in NLP have investigated the effect of interactions on the overall persuasive power of posts in social media BIBREF10, BIBREF14. However, in social media not all posts have to express arguments or stay on topic BIBREF15, and qualitative evaluation of the posts can be influenced by many other factors such as interactions between the individuals BIBREF16. Therefore, it is difficult to measure the effect of argumentative pragmatic context alone in argument quality prediction without the effect of these confounding factors using the datasets and models currently available in this line of research.", + "In this paper, we study the role of kairos on argument quality prediction by examining the individual claims of an argument for their timeliness and appropriateness in the context of a particular line of argument. We define kairos as the sequence of argumentative text (e.g. claims) along a particular line of argumentative reasoning.", + "To start, we present a dataset extracted from kialo.com of over 47,000 claims that are part of a diverse collection of arguments on 741 controversial topics. The structure of the website dictates that each argument must present a supporting or opposing claim for its parent claim, and stay within the topic of the main thesis. Rather than being posts on a social media platform, these are community-curated claims. Furthermore, for each presented claim, the audience votes on its impact within the given line of reasoning. Critically then, the dataset includes the argument context for each claim, allowing us to investigate the characteristics associated with impactful arguments.", + "With the dataset in hand, we propose the task of studying the characteristics of impactful claims by (1) taking the argument context into account, (2) studying the extent to which this context is important, and (3) determining the representation of context that is more effective. To the best of our knowledge, ours is the first dataset that includes claims with both impact votes and the corresponding context of the argument." + ], + [ + "Recent studies in computational argumentation have mainly focused on the tasks of identifying the structure of the arguments such as argument structure parsing BIBREF17, BIBREF18, and argument component classification BIBREF19, BIBREF20. More recently, there is an increased research interest to develop computational methods that can automatically evaluate qualitative characteristic of arguments, such as their impact and persuasive power BIBREF9, BIBREF10, BIBREF21, BIBREF22, BIBREF23, BIBREF24, BIBREF25, BIBREF26, BIBREF27, BIBREF28. Consistent with findings in the social sciences and psychology, some of the work in NLP has shown that the impact and persuasive power of the arguments are not simply related to the linguistic characteristics of the language, but also on characteristics the source (ethos) BIBREF16 and the audience BIBREF12, BIBREF13. These studies suggest that perception of the arguments can be influenced by the credibility of the source, and the background of the audience.", + "It has also been shown, in social science studies, that kairos, which refers to the \u201ctimeliness\u201d and \u201cappropropriateness\u201d of arguments and claims, is important to consider in studies of argument impact and persuasiveness BIBREF7, BIBREF8. One recent study in NLP has investigated the role of argument sequencing in argument persuasion looking at BIBREF14 Change My View, which is a social media platform where users post their views, and challenge other users to present arguments in an attempt to change their them. However, as stated in BIBREF15 many posts on social media platforms either do not express an argument, or diverge from the main topic of conversation. Therefore, it is difficult to measure the effect of pragmatic context in argument impact and persuasion, without confounding factors from using noisy social media data. In contrast, we provide a dataset of claims along with their structured argument path, which only consists of claims and corresponds to a particular line of reasoning for the given controversial topic. This structure enables us to study the characteristics of impactful claims, accounting for the effect of the pragmatic context.", + "Consistent with previous findings in the social sciences, we find that incorporating pragmatic and discourse context is important in computational studies of persuasion, as predictive models that with the context representation outperform models that only incorporate claim-specific linguistic features, in predicting the impact of a claim. Such a system that can predict the impact of a claim given an argumentative discourse, for example, could potentially be employed by argument retrieval and generation models which aims to pick or generate the most appropriate possible claim given the discourse." + ], + [ + "Claims and impact votes. We collected 47,219 claims from kialo.com for 741 controversial topics and their corresponding impact votes. Impact votes are provided by the users of the platform to evaluate how impactful a particular claim is. Users can pick one of 5 possible impact labels for a particular claim: no impact, low impact, medium impact, high impact and very high impact. While evaluating the impact of a claim, users have access to the full argument context and therefore, they can assess how impactful a claim is in the given context of an argument. An interesting observation is that, in this dataset, the same claim can have different impact labels depending on the context in which it is presented.", + "Figure FIGREF1 shows a partial argument tree for the argument thesis \u201cPhysical torture of prisoners is an acceptable interrogation tool.\u201d. Each node in the argument tree corresponds to a claim, and these argument trees are constructed and edited collaboratively by the users of the platform.", + "Except the thesis, every claim in the argument tree either opposes or supports its parent claim. Each path from the root to leaf nodes corresponds to an argument path which represents a particular line of reasoning on the given controversial topic.", + "Moreover, each claim has impact votes assigned by the users of the platform. The impact votes evaluate how impactful a claim is within its context, which consists of its predecessor claims from the thesis of the tree. For example, claim O1 \u201cIt is morally wrong to harm a defenseless person\u201d is an opposing claim for the thesis and it is an impactful claim since most of its impact votes belong to the category of very high impact. However, claim S3 \u201cIt is illegitimate for state actors to harm someone without the process\u201d is a supporting claim for its parent O1 and it is a less impactful claim since most of the impact votes belong to the no impact and low impact categories.", + "Distribution of impact votes. The distribution of claims with the given range of number of impact votes are shown in Table TABREF5. There are 19,512 claims in total with 3 or more votes. Out of the claims with 3 or more votes, majority of them have 5 or more votes. We limit our study to the claims with at least 5 votes to have a more reliable assignment for the accumulated impact label for each claim.", + "Impact label statistics. Table TABREF7 shows the distribution of the number of votes for each of the impact categories. The claims have $241,884$ total votes. The majority of the impact votes belong to medium impact category. We observe that users assign more high impact and very high impact votes than low impact and no impact votes respectively. When we restrict the claims to the ones with at least 5 impact votes, we have $213,277$ votes in total.", + "Agreement for the impact votes. To determine the agreement in assigning the impact label for a particular claim, for each claim, we compute the percentage of the votes that are the same as the majority impact vote for that claim. Let $c_{i}$ denote the count of the claims with the class labels C=[no impact, low impact, medium impact, high impact, very high impact] for the impact label $l$ at index $i$.", + "For example, for claim S1 in Figure FIGREF1, the agreement score is $100 * \\frac{30}{90}\\%=33.33\\%$ since the majority class (no impact) has 30 votes and there are 90 impact votes in total for this particular claim. We compute the agreement score for the cases where (1) we treat each impact label separately (5-class case) and (2) we combine the classes high impact and very high impact into a one class: impactful and no impact and low impact into a one class: not impactful (3-class case).", + "Table TABREF6 shows the number of claims with the given agreement score thresholds when we include the claims with at least 5 votes. We see that when we combine the low impact and high impact classes, there are more claims with high agreement score. This may imply that distinguishing between no impact-low impact and high impact-very high impact classes is difficult. To decrease the sparsity issue, in our experiments, we use 3-class representation for the impact labels. Moreover, to have a more reliable assignment of impact labels, we consider only the claims with have more than 60% agreement.", + "Context. In an argument tree, the claims from the thesis node (root) to each leaf node, form an argument path. This argument path represents a particular line of reasoning for the given thesis. Similarly, for each claim, all the claims along the path from the thesis to the claim, represent the context for the claim. For example, in Figure FIGREF1, the context for O1 consists of only the thesis, whereas the context for S3 consists of both the thesis and O1 since S3 is provided to support the claim O1 which is an opposing claim for the thesis.", + "The claims are not constructed independently from their context since they are written in consideration with the line of reasoning so far. In most cases, each claim elaborates on the point made by its parent and presents cases to support or oppose the parent claim's points. Similarly, when users evaluate the impact of a claim, they consider if the claim is timely and appropriate given its context. There are cases in the dataset where the same claim has different impact labels, when presented within a different context. Therefore, we claim that it is not sufficient to only study the linguistic characteristic of a claim to determine its impact, but it is also necessary to consider its context in determining the impact.", + "Context length ($\\text{C}_{l}$) for a particular claim C is defined by number of claims included in the argument path starting from the thesis until the claim C. For example, in Figure FIGREF1, the context length for O1 and S3 are 1 and 2 respectively. Table TABREF8 shows number of claims with the given range of context length for the claims with more than 5 votes and $60\\%$ agreement score. We observe that more than half of these claims have 3 or higher context length." + ], + [ + "Similar to prior work, our aim is to understand the characteristics of impactful claims in argumentation. However, we hypothesize that the qualitative characteristics of arguments is not independent of the context in which they are presented. To understand the relationship between argument context and the impact of a claim, we aim to incorporate the context along with the claim itself in our predictive models.", + "Prediction task. Given a claim, we want to predict the impact label that is assigned to it by the users: not impactful, medium impact, or impactful.", + "Preprocessing. We restrict our study to claims with at least 5 or more votes and greater than $60\\%$ agreement, to have a reliable impact label assignment. We have $7,386$ claims in the dataset satisfying these constraints. We see that the impact class impacful is the majority class since around $58\\%$ of the claims belong to this category.", + "For our experiments, we split our data to train (70%), validation (15%) and test (15%) sets." + ], + [ + "The majority baseline assigns the most common label of the training examples (high impact) to every test example." + ], + [ + "Similar to BIBREF9, we experiment with SVM with RBF kernel, with features that represent (1) the simple characteristics of the argument tree and (2) the linguistic characteristics of the claim.", + "The features that represent the simple characteristics of the claim's argument tree include the distance and similarity of the claim to the thesis, the similarity of a claim with its parent, and the impact votes of the claim's parent claim. We encode the similarity of a claim to its parent and the thesis claim with the cosine similarity of their tf-idf vectors. The distance and similarity metrics aim to model whether claims which are more similar (i.e. potentially more topically relevant) to their parent claim or the thesis claim, are more impactful.", + "We encode the quality of the parent claim as the number of votes for each impact class, and incorporate it as a feature to understand if it is more likely for a claim to impactful given an impactful parent claim.", + "Linguistic features. To represent each claim, we extracted the linguistic features proposed by BIBREF9 such as tf-idf scores for unigrams and bigrams, ratio of quotation marks, exclamation marks, modal verbs, stop words, type-token ratio, hedging BIBREF29, named entity types, POS n-grams, sentiment BIBREF30 and subjectivity scores BIBREF31, spell-checking, readibility features such as Coleman-Liau BIBREF32, Flesch BIBREF33, argument lexicon features BIBREF34 and surface features such as word lengths, sentence lengths, word types, and number of complex words." + ], + [ + "joulin-etal-2017-bag introduced a simple, yet effective baseline for text classification, which they show to be competitive with deep learning classifiers in terms of accuracy. Their method represents a sequence of text as a bag of n-grams, and each n-gram is passed through a look-up table to get its dense vector representation. The overall sequence representation is simply an average over the dense representations of the bag of n-grams, and is fed into a linear classifier to predict the label. We use the code released by joulin-etal-2017-bag to train a classifier for argument impact prediction, based on the claim text." + ], + [ + "Another effective baseline BIBREF35, BIBREF36 for text classification consists of encoding the text sequence using a bidirectional Long Short Term Memory (LSTM) BIBREF37, to get the token representations in context, and then attending BIBREF38 over the tokens to get the sequence representation. For the query vector for attention, we use a learned context vector, similar to yang-etal-2016-hierarchical. We picked our hyperparameters based on performance on the validation set, and report our results for the best set of hyperparameters. We initialized our word embeddings with glove vectors BIBREF39 pre-trained on Wikipedia + Gigaword, and used the Adam optimizer BIBREF40 with its default settings." + ], + [ + "devlin2018bert fine-tuned a pre-trained deep bi-directional transformer language model (which they call BERT), by adding a simple classification layer on top, and achieved state of the art results across a variety of NLP tasks. We employ their pre-trained language models for our task and compare it to our baseline models. For all the architectures described below, we finetune for 10 epochs, with a learning rate of 2e-5. We employ an early stopping procedure based on the model performance on a validation set." + ], + [ + "In this setting, we attempt to classify the impact of the claim, based on the text of the claim only. We follow the fine-tuning procedure for sequence classification detailed in BIBREF41, and input the claim text as a sequence of tokens preceded by the special [CLS] token and followed by the special [SEP] token. We add a classification layer on top of the BERT encoder, to which we pass the representation of the [CLS] token, and fine-tune this for argument impact prediction." + ], + [ + "In this setting, we use the parent claim's text, in addition to the target claim text, in order to classify the impact of the target claim. We treat this as a sequence pair classification task, and combine both the target claim and parent claim as a single sequence of tokens, separated by the special separator [SEP]. We then follow the same procedure above, for fine-tuning." + ], + [ + "In this setting, we consider incorporating a larger context from the discourse, in order to assess the impact of a claim. In particular, we consider up to four previous claims in the discourse (for a total context length of 5). We attempt to incorporate larger context into the BERT model in three different ways.", + "Flat representation of the path. The first, simple approach is to represent the entire path (claim + context) as a single sequence, where each of the claims is separated by the [SEP] token. BERT was trained on sequence pairs, and therefore the pre-trained encoders only have two segment embeddings BIBREF41. So to fit multiple sequences into this framework, we indicate all tokens of the target claim as belonging to segment A and the tokens for all the claims in the discourse context as belonging to segment B. This way of representing the input, requires no additional changes to the architecture or retraining, and we can just finetune in a similar manner as above. We refer to this representation of the context as a flat representation, and denote the model as $\\text{Context}_{f}(i)$, where $i$ indicates the length of the context that is incorporated into the model.", + "Attention over context. Recent work in incorporating argument sequence in predicting persuasiveness BIBREF14 has shown that hierarchical representations are effective in representing context. Similarly, we consider hierarchical representations for representing the discourse. We first encode each claim using the pre-trained BERT model as the claim encoder, and use the representation of the [CLS] token as claim representation. We then employ dot-product attention BIBREF38, to get a weighted representation for the context. We use a learned context vector as the query, for computing attention scores, similar to yang-etal-2016-hierarchical. The attention score $\\alpha _c$ is computed as shown below:", + "Where $V_c$ is the claim representation that was computed with the BERT encoder as described above, $V_l$ is the learned context vector that is used for computing attention scores, and $D$ is the set of claims in the discourse. After computing the attention scores, the final context representation $v_d$ is computed as follows:", + "We then concatenate the context representation with the target claim representation $[V_d, V_r]$ and pass it to the classification layer to predict the quality. We denote this model as $\\text{Context}_{a}(i)$.", + "GRU to encode context Similar to the approach above, we consider a hierarchical representation for representing the context. We compute the claim representations, as detailed above, and we then feed the discourse claims' representations (in sequence) into a bidirectional Gated Recurrent Unit (GRU) BIBREF42, to compute the context representation. We concatenate this with the target claim representation and use this to predict the claim impact. We denote this model as $\\text{Context}_{gru}(i)$." + ], + [ + "Table TABREF21 shows the macro precision, recall and F1 scores for the baselines as well as the BERT models with and without context representations.", + "We see that parent quality is a simple yet effective feature and SVM model with this feature can achieve significantly higher ($p<0.001$) F1 score ($46.61\\%$) than distance from the thesis and linguistic features. Claims with higher impact parents are more likely to be have higher impact. Similarity with the parent and thesis is not significantly better than the majority baseline. Although the BiLSTM model with attention and FastText baselines performs better than the SVM with distance from the thesis and linguistic features, it has similar performance to the parent quality baseline.", + "We find that the BERT model with claim only representation performs significantly better ($p<0.001$) than the baseline models. Incorporating the parent representation only along with the claim representation does not give significant improvement over representing the claim only. However, incorporating the flat representation of the larger context along with the claim representation consistently achieves significantly better ($p<0.001$) performance than the claim representation alone. Similarly, attention representation over the context with the learned query vector achieves significantly better performance then the claim representation only ($p<0.05$).", + "We find that the flat representation of the context achieves the highest F1 score. It may be more difficult for the models with a larger number of parameters to perform better than the flat representation since the dataset is small. We also observe that modeling 3 claims on the argument path before the target claim achieves the best F1 score ($55.98\\%$).", + "To understand for what kinds of claims the best performing contextual model is more effective, we evaluate the BERT model with flat context representation for claims with context length values 1, 2, 3 and 4 separately. Table TABREF26 shows the F1 score of the BERT model without context and with flat context representation with different lengths of context. For the claims with context length 1, adding $\\text{Context}_{f}(3)$ and $\\text{Context}_{f}(4)$ representation along with the claim achieves significantly better $(p<0.05)$ F1 score than modeling the claim only. Similarly for the claims with context length 3 and 4, $\\text{Context}_{f}(4)$ and $\\text{Context}_{f}(3)$ perform significantly better than BERT with claim only ($(p<0.05)$ and $(p<0.01)$ respectively). We see that models with larger context are helpful even for claims which have limited context (e.g. $\\text{C}_{l}=1$). This may suggest that when we train the models with larger context, they learn how to represent the claims and their context better." + ], + [ + "In this paper, we present a dataset of claims with their corresponding impact votes, and investigate the role of argumentative discourse context in argument impact classification. We experiment with various models to represent the claims and their context and find that incorporating the context information gives significant improvement in predicting argument impact. In our study, we find that flat representation of the context gives the best improvement in the performance and our analysis indicates that the contextual models perform better even for the claims with limited context." + ], + [ + "This work was supported in part by NSF grants IIS-1815455 and SES-1741441. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of NSF or the U.S. Government." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0599/instruction.md b/qasper-0599/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..de2542a5cd51528c238d40f66d956c09d8f83057 --- /dev/null +++ b/qasper-0599/instruction.md @@ -0,0 +1,108 @@ +Name of Paper: Textual Data for Time Series Forecasting + +Question: How big is dataset used for training/testing? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Presentation of the data", + "Presentation of the data ::: Time Series", + "Presentation of the data ::: Text", + "Modeling and forecasting framework", + "Modeling and forecasting framework ::: Numerical Encoding of the Text", + "Modeling and forecasting framework ::: Machine Learning Algorithms", + "Modeling and forecasting framework ::: Hyperparameter Tuning", + "Experiments", + "Experiments ::: Feature selection", + "Experiments ::: Main results", + "Experiments ::: Interpretability of the models", + "Experiments ::: Interpretability of the models ::: TF-IDF representation", + "Experiments ::: Interpretability of the models ::: Vector embedding representation", + "Conclusion", + "" + ], + "paragraphs": [ + [ + "Whether it is in the field of energy, finance or meteorology, accurately predicting the behavior of time series is nowadays of paramount importance for optimal decision making or profit. While the field of time series forecasting is extremely prolific from a research point-of-view, up to now it has narrowed its efforts on the exploitation of regular numerical features extracted from sensors, data bases or stock exchanges. Unstructured data such as text on the other hand remains underexploited for prediction tasks, despite its potentially valuable informative content. Empirical studies have already proven that textual sources such as news articles or blog entries can be correlated to stock exchange time series and have explanatory power for their variations BIBREF0, BIBREF1. This observation has motivated multiple extensive experiments to extract relevant features from textual documents in different ways and use them for prediction, notably in the field of finance. In Lavrenko et al. BIBREF2, language models (considering only the presence of a word) are used to estimate the probability of trends such as surges or falls of 127 different stock values using articles from Biz Yahoo!. Their results show that this text driven approach could be used to make profit on the market. One of the most conventional ways for text representation is the TF-IDF (Term Frequency - Inverse Document Frequency) approach. Authors have included such features derived from news pieces in multiple traditional machine learning algorithms such as support vector machines (SVM) BIBREF3 or logistic regression BIBREF4 to predict the variations of financial series again. An alternative way to encode the text is through latent Dirichlet allocation (LDA) BIBREF5. It assigns topic probabilities to a text, which can be used as inputs for subsequent tasks. This is for instance the case in Wang's aforementioned work (alongside TF-IDF). In BIBREF6, the authors used Reuters news encoded by LDA to predict if NASDAQ and Dow Jones closing prices increased or decreased compared to the opening ones. Their empirical results show that this approach was efficient to improve the prediction of stock volatility. More recently Kanungsukkasem et al. BIBREF7 introduced a variant of the LDA graphical model, named FinLDA, to craft probabilities that are specifically tailored for a financial time series prediction task (although their approach could be generalized to other ones). Their results showed that indeed performance was better when using probabilities from their alternative than those of the original LDA. Deep learning with its natural ability to work with text through word embeddings has also been used for time series prediction with text. Combined with traditional time series features, the authors of BIBREF8 derived sentiment features from a convolutional neural network (CNN) to reduce the prediction error of oil prices. Akita et al. BIBREF9 represented news articles through the use of paragraph vectors BIBREF10 in order to predict 10 closing stock values from the Nikkei 225. While in the case of financial time series the existence of specialized press makes it easy to decide which textual source to use, it is much more tedious in other fields. Recently in Rodrigues et al. BIBREF11, short description of events (such as concerts, sports matches, ...) are leveraged through a word embedding and neural networks in addition to more traditional features. Their experiments show that including the text can bring an improvement of up to 2% of root mean squared error compared to an approach without textual information. Although the presented studies conclude on the usefulness of text to improve predictions, they never thoroughly analyze which aspects of the text are of importance, keeping the models as black-boxes.", + "The field of electricity consumption is one where expert knowledge is broad. It is known that the major phenomena driving the load demand are calendar (time of the year, day of the week, ...) and meteorological. For instance generalized additive models (GAM) BIBREF12 representing the consumption as a sum of functions of the time of the year, temperature and wind speed (among others) typically yield less than 1.5% of relative error for French national electricity demand and 8% for local one BIBREF13, BIBREF14. Neural networks and their variants, with their ability to extract patterns from heterogeneous types of data have also obtained state-of-the-art results BIBREF15, BIBREF16, BIBREF17. However to our knowledge no exploratory work using text has been conducted yet. Including such data in electricity demand forecasting models would not only contribute to close the gap with other domains, but also help to understand better which aspects of text are useful, how the encoding of the text influences forecasts and to which extend a prediction algorithm can extract relevant information from unstructured data. Moreover the major drawback of all the aforementioned approaches is that they require meteorological data that may be difficult to find, unavailable in real time or expensive. Textual sources such as weather reports on the other hand are easy to find, usually available on a daily basis and free.", + "The main contribution of our paper is to suggest the use of a certain type of textual documents, namely daily weather report, to build forecasters of the daily national electricity load, average temperature and wind speed for both France and the United-Kingdom (UK). Consequently this work represents a significant break with traditional methods, and we do not intend to best state-of-the-art approaches. Textual information is naturally more fuzzy than numerical one, and as such the same accuracy is not expected from the presented approaches. With a single text, we were already able to predict the electricity consumption with a relative error of less than 5% for both data sets. Furthermore, the quality of our predictions of temperature and wind speed is satisfying enough to replace missing or unavailable data in traditional models. Two different approaches are considered to represent the text numerically, as well as multiple forecasting algorithms. Our empirical results are consistent across encoding, methods and language, thus proving the intrinsic value weather reports have for the prediction of the aforementioned time series. Moreover, a major distinction between previous works is our interpretation of the models. We quantify the impact of a word on the forecast and analyze the geometric properties of the word embedding we trained ourselves. Note that although multiple time series are discussed in our paper, the main focus of this paper remains electricity consumption. As such, emphasis is put on the predictive results on the load demand time series.", + "The rest of this paper is organized as follows. The following section introduces the two data sets used to conduct our study. Section 3 presents the different machine learning approaches used and how they were tuned. Section 4 highlights the main results of our study, while section 5 concludes this paper and gives insight on future possible work." + ], + [ + "In order to prove the consistency of our work, experiments have been conducted on two data sets, one for France and the other for the UK. In this section details about the text and time series data are given, as well as the major preprocessing steps." + ], + [ + "Three types of time series are considered in our work: national net electricity consumption (also referred as load or demand), national temperature and wind speed. The load data sets were retrieved on the websites of the respective grid operators, respectively RTE (R\u00e9seau et Transport d'\u00c9lectricit\u00e9) for France and National Grid for the UK. For France, the available data ranges from January the 1st 2007 to August the 31st 2018. The default temporal resolution is 30 minutes, but it is averaged to a daily one. For the UK, it is available from January the 1st 2006 to December the 31st 2018 with the same temporal resolution and thus averaging. Due to social factors such as energy policies or new usages of electricity (e.g. Electric Vehicles), the net consumption usually has a long-term trend (fig. FIGREF2). While for France it seems marginal (fig. FIGREF2), there is a strong decreasing trend for the United-Kingdom (fig. FIGREF2). Such a strong non-stationarity of the time series would cause problems for the forecasting process, since the learnt demand levels would differ significantly from the upcoming ones. Therefore a linear regression was used to approximate the decreasing trend of the net consumption in the UK. It is then subtracted before the training of the methods, and then re-added a posteriori for prediction.", + "As for the weather time series, they were extracted from multiple weather stations around France and the UK. The national average is obtained by combining the data from all stations with a weight proportional to the city population the station is located in. For France the stations' data is provided by the French meteorological office, M\u00e9t\u00e9o France, while the British ones are scrapped from stations of the National Oceanic and Atmospheric Administration (NOAA). Available on the same time span as the consumption, they usually have a 3 hours temporal resolution but are averaged to a daily one as well. Finally the time series were scaled to the range $[0,1]$ before the training phase, and re-scaled during prediction time." + ], + [ + "Our work aims at predicting time series using exclusively text. Therefore for both countries the inputs of all our models consist only of written daily weather reports. Under their raw shape, those reports take the form of PDF documents giving a short summary of the country's overall weather, accompanied by pressure, temperature, wind, etc. maps. Note that those reports are written a posteriori, although they could be written in a predictive fashion as well. The reports are published by M\u00e9t\u00e9o France and the Met Office, its British counterpart. They are publicly available on the respective websites of the organizations. Both corpora span on the same period as the corresponding time series and given their daily nature, it yields a total of 4,261 and 4,748 documents respectively. An excerpt for each language may be found in tables TABREF6 and TABREF7. The relevant text was extracted from the PDF documents using the Python library PyPDF2.", + "As emphasized in many studies, preprocessing of the text can ease the learning of the methods and improve accuracy BIBREF18. Therefore the following steps are applied: removal of non-alphabetic characters, removal of stop-words and lowercasing. While it was often highlighted that word lemmatization and stemming improve results, initial experiments showed it was not the case for our study. This is probably due to the technical vocabulary used in both corpora pertaining to the field of meteorology. Already limited in size, the aforementioned preprocessing operations do not yield a significant vocabulary size reduction and can even lead to a loss of linguistic meaning. Finally, extremely frequent or rare words may not have high explanatory power and may reduce the different models' accuracy. That is why words appearing less than 7 times or in more than 40% of the (learning) corpus are removed as well. Figure FIGREF8 represents the distribution of the document lengths after preprocessing, while table TABREF11 gives descriptive statistics on both corpora. Note that the preprocessing steps do not heavily rely on the considered language: therefore our pipeline is easily adaptable for other languages." + ], + [ + "A major target of our work is to show the reports contain an intrinsic information relevant for time series, and that the predictive results do not heavily depend on the encoding of the text or the machine learning algorithm used. Therefore in this section we present the text encoding approaches, as well as the forecasting methods used with them." + ], + [ + "Machines and algorithms cannot work with raw text directly. Thus one major step when working with text is the choice of its numerical representation. In our work two significantly different encoding approaches are considered. The first one is the TF-IDF approach. It embeds a corpus of $N$ documents and $V$ words into a matrix $X$ of size $N \\times V$. As such, every document is represented by a vector of size $V$. For each word $w$ and document $d$ the associated coefficient $x_{d,w}$ represents the frequency of that word in that document, penalized by its overall frequency in the rest of the corpus. Thus very common words will have a low TF-IDF value, whereas specific ones which will appear often in a handful of documents will have a large TF-IDF score. The exact formula to calculate the TF-IDF value of word $w$ in document $d$ is:", + "where $f_{d,w}$ is the number of appearances of $w$ in $d$ adjusted by the length of $d$ and $\\#\\lbrace d: w \\in d \\rbrace $ is the number of documents in which the word $w$ appears. In our work we considered only individual words, also commonly referred as 1-grams in the field of natural language processing (NLP). The methodology can be easily extended to $n$-grams (groups of $n$ consecutive words), but initial experiments showed that it did not bring any significant improvement over 1-grams.", + "The second representation is a neural word embedding. It consists in representing every word in the corpus by a real-valued vector of dimension $q$. Such models are usually obtained by learning a vector representation from word co-occurrences in a very large corpus (typically hundred thousands of documents, such as Wikipedia articles for example). The two most popular embeddings are probably Google's Word2Vec BIBREF19 and Standford's GloVe BIBREF20. In the former, a neural network is trained to predict a word given its context (continuous bag of word model), whereas in the latter a matrix factorization scheme on the log co-occurences of words is applied. In any case, the very nature of the objective function allows the embedding models to learn to translate linguistic similarities into geometric properties in the vector space. For instance the vector $\\overrightarrow{king} - \\overrightarrow{man} + \\overrightarrow{woman}$ is expected to be very close to the vector $\\overrightarrow{queen}$. However in our case we want a vector encoding which is tailored for the technical vocabulary of our weather reports and for the subsequent prediction task. This is why we decided to train our own word embedding from scratch during the learning phase of our recurrent or convolutional neural network. Aside from the much more restricted size of our corpora, the major difference with the aforementioned embeddings is that in our case it is obtained by minimizing a squared loss on the prediction. In that framework there is no explicit reason for our representation to display any geometric structure. However as detailed in section SECREF36, our word vectors nonetheless display geometric properties pertaining to the behavior of the time series." + ], + [ + "Multiple machine learning algorithms were applied on top of the encoded textual documents. For the TF-IDF representation, the following approaches are applied: random forests (RF), LASSO and multilayer perceptron (MLP) neural networks (NN). We chose these algorithms combined to the TF-IDF representation due to the possibility of interpretation they give. Indeed, considering the novelty of this work, the understanding of the impact of the words on the forecast is of paramount importance, and as opposed to embeddings, TF-IDF has a natural interpretation. Furthermore the RF and LASSO methods give the possibility to interpret marginal effects and analyze the importance of features, and thus to find the words which affect the time series the most.", + "As for the word embedding, recurrent or convolutional neural networks (respectively RNN and CNN) were used with them. MLPs are not used, for they would require to concatenate all the vector representations of a sentence together beforehand and result in a network with too many parameters to be trained correctly with our number of available documents. Recall that we decided to train our own vector representation of words instead of using an already available one. In order to obtain the embedding, the texts are first converted into a sequence of integers: each word is given a number ranging from 1 to $V$, where $V$ is the vocabulary size (0 is used for padding or unknown words in the test set). One must then calculate the maximum sequence length $S$, and sentences of length shorter than $S$ are then padded by zeros. During the training process of the network, for each word a $q$ dimensional real-valued vector representation is calculated simultaneously to the rest of the weights of the network. Ergo a sentence of $S$ words is translated into a sequence of $S$ $q$-sized vectors, which is then fed into a recurrent neural unit. For both languages, $q=20$ seemed to yield the best results. In the case of recurrent units two main possibilities arise, with LSTM (Long Short-Term Memory) BIBREF21 and GRU (Gated Recurrent Unit) BIBREF22. After a few initial trials, no significant performance differences were noticed between the two types of cells. Therefore GRU were systematically used for recurrent networks, since their lower amount of parameters makes them easier to train and reduces overfitting. The output of the recurrent unit is afterwards linked to a fully connected (also referred as dense) layer, leading to the final forecast as output. The rectified linear unit (ReLU) activation in dense layers systematically gave the best results, except on the output layer where we used a sigmoid one considering the time series' normalization. In order to tone down overfitting, dropout layers BIBREF23 with probabilities of 0.25 or 0.33 are set in between the layers. Batch normalization BIBREF24 is also used before the GRU since it stabilized training and improved performance. Figure FIGREF14 represents the architecture of our RNN.", + "The word embedding matrix is therefore learnt jointly with the rest of the parameters of the neural network by minimization of the quadratic loss with respect to the true electricity demand. Note that while above we described the case of the RNN, the same procedure is considered for the case of the CNN, with only the recurrent layers replaced by a combination of 1D convolution and pooling ones. As for the optimization algorithms of the neural networks, traditional stochastic gradient descent with momentum or ADAM BIBREF25 together with a quadratic loss are used. All of the previously mentioned methods were coded with Python. The LASSO and RF were implemented using the library Scikit Learn BIBREF26, while Keras BIBREF27 was used for the neural networks." + ], + [ + "While most parameters are trained during the learning optimization process, all methods still involve a certain number of hyperparameters that must be manually set by the user. For instance for random forests it can correspond to the maximum depth of the trees or the fraction of features used at each split step, while for neural networks it can be the number of layers, neurons, the embedding dimension or the activation functions used. This is why the data is split into three sets:", + "The training set, using all data available up to the 31st of December 2013 (2,557 days for France and 2,922 for the UK). It is used to learn the parameters of the algorithms through mathematical optimization.", + "The years 2014 and 2015 serve as validation set (730 days). It is used to tune the hyperparameters of the different approaches.", + "All the data from January the 1st 2016 (974 days for France and 1,096 for the UK) is used as test set, on which the final results are presented.", + "Grid search is applied to find the best combination of values: for each hyperparameter, a range of values is defined, and all the possible combinations are successively tested. The one yielding the lowest RMSE (see section SECREF4) on the validation set is used for the final results on the test one. While relatively straightforward for RFs and the LASSO, the extreme number of possibilities for NNs and their extensive training time compelled us to limit the range of architectures possible. The hyperparameters are tuned per method and per country: ergo the hyperparameters of a given algorithm will be the same for the different time series of a country (e.g. the RNN architecture for temperature and load for France will be the same, but different from the UK one). Finally before application on the testing set, all the methods are re-trained from scratch using both the training and validation data." + ], + [ + "The goal of our experiments is to quantify how close one can get using textual data only when compared to numerical data. However the inputs of the numerical benchmark should be hence comparable to the information contained in the weather reports. Considering they mainly contain calendar (day of the week and month) as well as temperature and wind information, the benchmark of comparison is a random forest trained on four features only: the time of the year (whose value is 0 on January the 1st and 1 on December the 31st with a linear growth in between), the day of the week, the national average temperature and wind speed. The metrics of evaluation are the Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and the $R^2$ coefficient given by:", + "where $T$ is the number of test samples, $y_t$ and $\\hat{y}_t$ are respectively the ground truth and the prediction for the document of day $t$, and $\\overline{y}$ is the empirical average of the time series over the test sample. A known problem with MAPE is that it unreasonably increases the error score for values close to 0. While for the load it isn't an issue at all, it can be for the meteorological time series. Therefore for the temperature, the MAPE is calculated only when the ground truth is above the 5% empirical quantile. Although we aim at achieving the highest accuracy possible, we focus on the interpretability of our models as well." + ], + [ + "Many words are obviously irrelevant to the time series in our texts. For instance the day of the week, while playing a significant role for the load demand, is useless for temperature or wind. Such words make the training harder and may decrease the accuracy of the prediction. Therefore a feature selection procedure similar to BIBREF28 is applied to select a subset of useful features for the different algorithms, and for each type of time series. Random forests are naturally able to calculate feature importance through the calculation of error increase in the out-of-bag (OOB) samples. Therefore the following process is applied to select a subset of $V^*$ relevant words to keep:", + "A RF is trained on the whole training & validation set. The OOB feature importance can thus be calculated.", + "The features are then successively added to the RF in decreasing order of feature importance.", + "This process is repeated $B=10$ times to tone down the randomness. The number $V^*$ is then set to the number of features giving the highest median OOB $R^2$ value.", + "The results of this procedure for the French data is represented in figure FIGREF24. The best median $R^2$ is achieved for $V^* = 52$, although one could argue that not much gain is obtained after 36 words. The results are very similar for the UK data set, thus for the sake of simplicity the same value $V^* = 52$ is used. Note that the same subset of words is used for all the different forecasting models, which could be improved in further work using other selection criteria (e.g. mutual information, see BIBREF29). An example of normalized feature importance is given in figure. FIGREF32." + ], + [ + "Note that most of the considered algorithms involve randomness during the training phase, with the subsampling in the RFs or the gradient descent in the NNs for instance. In order to tone it down and to increase the consistency of our results, the different models are run $B=10$ times. The results presented hereafter correspond to the average and standard-deviation on those runs. The RF model denoted as \"sel\" is the one with the reduced number of features, whereas the other RF uses the full vocabulary. We also considered an aggregated forecaster (abridged Agg), consisting of the average of the two best individual ones in terms of RMSE. All the neural network methods have a reduced vocabulary size $V^*$. The results for the French and UK data are respectively given by tables TABREF26 and TABREF27.", + "Our empirical results show that for the electricity consumption prediction task, the order of magnitude of the relative error is around 5%, independently of the language, encoding and machine learning method, thus proving the intrinsic value of the information contained in the textual documents for this time series. As expected, all text based methods perform poorer than when using explicitly numerical input features. Indeed, despite containing relevant information, the text is always more fuzzy and less precise than an explicit value for the temperature or the time of the year for instance. Again the aim of this work is not to beat traditional methods with text, but quantifying how close one can come to traditional approaches when using text exclusively. As such achieving less than 5% of MAPE was nonetheless deemed impressive by expert electricity forecasters. Feature selection brings significant improvement in the French case, although it does not yield any improvement in the English one. The reason for this is currently unknown. Nevertheless the feature selection procedure also helps the NNs by dramatically reducing the vocabulary size, and without it the training of the networks was bound to fail. While the errors accross methods are roughly comparable and highlight the valuable information contained within the reports, the best method nonetheless fluctuates between languages. Indeed in the French case there is a hegemony of the NNs, with the embedding RNN edging the MLP TF-IDF one. However for the UK data set the RFs yield significantly better results on the test set than the NNs. This inversion of performance of the algorithms is possibly due to a change in the way the reports were written by the Met Office after August 2017, since the results of the MLP and RNN on the validation set (not shown here) were satisfactory and better than both RFs. For the two languages both the CNN and the LASSO yielded poor results. For the former, it is because despite grid search no satisfactory architecture was found, whereas the latter is a linear approach and was used more for interpretation purposes than strong performance. Finally the naive aggregation of the two best experts always yields improvement, especially for the French case where the two different encodings are combined. This emphasises the specificity of the two representations leading to different types of errors. An example of comparison between ground truth and forecast for the case of electricity consumption is given for the French language with fig. FIGREF29, while another for temperature may be found in the appendix FIGREF51. The sudden \"spikes\" in the forecast are due to the presence of winter related words in a summer report. This is the case when used in comparisons, such as \"The flood will be as severe as in January\" in a June report and is a limit of our approach. Finally, the usual residual $\\hat{\\varepsilon }_t = y_t - \\hat{y}_t$ analyses procedures were applied: Kolmogorov normality test, QQplots comparaison to gaussian quantiles, residual/fit comparison... While not thoroughly gaussian, the residuals were close to normality nonetheless and displayed satisfactory properties such as being generally independent from the fitted and ground truth values. Excerpts of this analysis for France are given in figure FIGREF52 of the appendix. The results for the temperature and wind series are given in appendix. Considering that they have a more stochastic behavior and are thus more difficult to predict, the order of magnitude of the errors differ (the MAPE being around 15% for temperature for instance) but globally the same observations can be made." + ], + [ + "While accuracy is the most relevant metric to assess forecasts, interpretability of the models is of paramount importance, especially in the field of professional electricity load forecasting and considering the novelty of our work. Therefore in this section we discuss the properties of the RF and LASSO models using the TF-IDF encoding scheme, as well as the RNN word embedding." + ], + [ + "One significant advantage of the TF-IDF encoding when combined with random forests or the LASSO is that it is possible to interpret the behavior of the models. For instance, figure FIGREF32 represents the 20 most important features (in the RF OOB sense) for both data sets when regressing over electricity demand data. As one can see, the random forest naturally extracts calendar information contained in the weather reports, since months or week-end days are among the most important ones. For the former, this is due to the periodic behavior of electricity consumption, which is higher in winter and lower in summer. This is also why characteristic phenomena of summer and winter, such as \"thunderstorms\", \"snow\" or \"freezing\" also have a high feature importance. The fact that August has a much more important role than July also concurs with expert knowledge, especially for France: indeed it is the month when most people go on vacations, and thus when the load drops the most. As for the week-end names, it is due to the significantly different consumer behavior during Saturdays and especially Sundays when most of the businesses are closed and people are usually at home. Therefore the relevant words selected by the random forest are almost all in agreement with expert knowledge.", + "We also performed the analysis of the relevant words for the LASSO. In order to do that, we examined the words $w$ with the largest associated coefficients $\\beta _w$ (in absolute value) in the regression. Since the TF-IDF matrix has positive coefficients, it is possible to interpret the sign of the coefficient $\\beta _w$ as its impact on the time series. For instance if $\\beta _w > 0$ then the presence of the word $w$ causes a rise the time series (respectively if $\\beta _w < 0$, it entails a decline). The results are plotted fig. FIGREF35 for the the UK. As one can see, the winter related words have positive coefficients, and thus increase the load demand as expected whereas the summer related ones decrease it. The value of the coefficients also reflects the impact on the load demand. For example January and February have the highest and very similar values, which concurs with the similarity between the months. Sunday has a much more negative coefficient than Saturday, since the demand significantly drops during the last day of the week. The important words also globally match between the LASSO and the RF, which is a proof of the consistency of our results (this is further explored afterwards in figure FIGREF43). Although not presented here, the results are almost identical for the French load, with approximately the same order of relevancy. The important words logically vary in function of the considered time series, but are always coherent. For instance for the wind one, terms such as \"gales\", \"windy\" or \"strong\" have the highest positive coefficients, as seen in the appendix figure FIGREF53. Those results show that a text based approach not only extracts the relevant information by itself, but it may eventually be used to understand which phenomena are relevant to explain the behavior of a time series, and to which extend." + ], + [ + "Word vector embeddings such as Word2Vec and GloVe are known for their vectorial properties translating linguistic ones. However considering the objective function of our problem, there was no obvious reason for such attributes to appear in our own. Nevertheless for both languages we conducted an analysis of the geometric properties of our embedding matrix. We investigated the distances between word vectors, the relevant metric being the cosine distance given by:", + "where $\\overrightarrow{w_1}$ and $\\overrightarrow{w_2}$ are given word vectors. Thus a cosine distance lower than 1 means similarity between word vectors, whereas a greater than 1 corresponds to opposition.", + "The initial analyses of the embedding matrices for both the UK and France revealed that in general, words were grouped by context or influence on the electricity consumption. For instance, we observed that winter words were together and far away from summer ones. Week days were grouped as well and far from week-end days. However considering the vocabulary was reduced to $V^* = 52$ words, those results lacked of consistency. Therefore for both languages we decided to re-train the RNNs using the same architecture, but with a larger vocabulary of the $V=300$ most relevant words (still in the RF sense) and on all the available data (i.e. everything is used as training) to compensate for the increased size of the vocabulary. We then calculated the distance of a few prominent words to the others. The analysis of the average cosine distance over $B=10$ runs for three major words is given by tables TABREF38 and TABREF39, and three other examples are given in the appendix tables TABREF57 and TABREF58. The first row corresponds to the reference word vector $\\overrightarrow{w_1}$ used to calculate the distance from (thus the distance is always zero), while the following ones are the 9 closest to it. The two last rows correspond to words we deemed important to check the distance with (an antagonistic one or relevant one not in the top 9 for instance).", + "The results of the experiments are very similar for both languages again. Indeed, the words are globally embedded in the vector space by topic: winter related words such as \"January\" (\"janvier\"), \"February\" (\"f\u00e9vrier\"), \"snow\" (\"neige\"), \"freezing\" (\"glacial\") are close to each other and almost opposite to summer related ones such as \"July\" (\"juillet\"), \"August\" (\"ao\u00fbt\"), \"hot\" (\"chaud\"). For both cases the week days Monday (\"lundi\") to Friday (\"vendredi\") are grouped very closely to each other, while significantly separated from the week-end ones \"Saturday\" (\"samedi\") and \"Sunday\" (\"dimanche\"). Despite these observations, a few seemingly unrelated words enter the lists of top 10, especially for the English case (such as \"pressure\" or \"dusk\" for \"February\"). In fact the French language embedding seems of better quality, which is perhaps linked to the longer length of the French reports in average. This issue could probably be addressed with more data. Another observation made is that the importance of a word $w$ seems related to its euclidean norm in the embedding space ${\\overrightarrow{w}}_2$. For both languages the list of the 20 words with the largest norm is given fig. FIGREF40. As one can see, it globally matches the selected ones from the RF or the LASSO (especially for the French language), although the order is quite different. This is further supported by the Venn diagram of common words among the top 50 ones for each word selection method represented in figure FIGREF43 for France. Therefore this observation could also be used as feature selection procedure for the RNN or CNN in further work.", + "In order to achieve a global view of the embeddings, the t-SNE algorithm BIBREF30 is applied to project an embedding matrix into a 2 dimensional space, for both languages. The observations for the few aforementioned words are confirmed by this representation, as plotted in figure FIGREF44. Thematic clusters can be observed, roughly corresponding to winter, summer, week-days, week-end days for both languages. Globally summer and winter seem opposed, although one should keep in mind that the t-SNE representation does not preserve the cosine distance. The clusters of the French embedding appear much more compact than the UK one, comforting the observations made when explicitly calculating the cosine distances." + ], + [ + "In this study, a novel pipeline to predict three types of time series using exclusively a textual source was proposed. Making use of publicly available daily weather reports, we were able to predict the electricity consumption with less than 5% of MAPE for both France and the United-Kingdom. Moreover our average national temperature and wind speed predictions displayed sufficient accuracy to be used to replace missing data or as first approximation in traditional models in case of unavailability of meteorological features.", + "The texts were encoded numerically using either TF-IDF or our own neural word embedding. A plethora of machine learning algorithms such as random forests or neural networks were applied on top of those representations. Our results were consistent over language, numerical representation of the text and prediction algorithm, proving the intrinsic value of the textual sources for the three considered time series. Contrarily to previous works in the field of textual data for time series forecasting, we went in depth and quantified the impact of words on the variations of the series. As such we saw that all the algorithms naturally extract calendar and meteorological information from the texts, and that words impact the time series in the expected way (e.g. winter words increase the consumption and summer ones decrease it). Despite being trained on a regular quadratic loss, our neural word embedding spontaneously builds geometric properties. Not only does the norm of a word vector reflect its significance, but the words are also grouped by topic with for example winter, summer or day of the week clusters.", + "Note that this study was a preliminary work on the use of textual information for time series prediction, especially electricity demand one. The long-term goal is to include multiple sources of textual information to improve the accuracy of state-of-the-art methods or to build a text based forecaster which can be used to increase the diversity in a set of experts for electricity consumption BIBREF31. However due to the redundancy of the information of the considered weather reports with meteorological features, it may be necessary to consider alternative textual sources. The use of social media such as Facebook, Twitter or Instagram may give interesting insight and will therefore be investigated in future work." + ], + [ + "Additional results for the prediction tasks on temperature and wind speed can be found in tables TABREF47 to TABREF50. An example of forecast for the French temperature is given in figure FIGREF51.", + "While not strictly normally distributed, the residuals for the French electricity demand display an acceptable behavior. This holds also true for the British consumption, and both temperature time series, but is of lesser quality for the wind one.", + "The the UK wind LASSO regression, the words with the highest coefficients $\\beta _w$ are indeed related to strong wind phenomena, whereas antagonistic ones such as \"fog\" or \"mist\" have strongly negative ones as expected (fig. FIGREF53).", + "For both languages we represented the evolution of the (normalized) losses for the problem of load regression in fig. FIGREF54. The aspect is a typical one, with the validation loss slightly above the training one. The slightly erratic behavior of the former one is possibly due to a lack of data to train the embeddings.", + "The cosine distances for three other major words and for both corpora have been calculated as well. The results are given in tables TABREF57 and TABREF58. For both languages, the three summer months are grouped together, and so are the two week-end days. However again the results are less clear for the English language. They are especially mediocre for \"hot\", considering that only \"warm\" seems truly relevant and that \"August\" for instance is quite far away. For the French language instead of \"hot\" the distances to \"thunderstorms\" were calculated. The results are quite satisfactory, with \"orageux\"/\"orageuse\" (\"thundery\") coming in the two first places and related meteorological phenomena (\"cumulus\" and \"gr\u00eale\", meaning \"hail\") relatively close as well. For the French case, Saturday and Sunday are very close to summer related words. This observation probably highlights the fact that the RNN groups load increasing and decreasing words in opposite parts of the embedding space." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0607/instruction.md b/qasper-0607/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..58726a369abfafd6f2965ccfddbb303f8d90b9e7 --- /dev/null +++ b/qasper-0607/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis + +Question: What are the datasets used for training? \ No newline at end of file diff --git a/qasper-0612/instruction.md b/qasper-0612/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..06fcddedfee5ddd62b94814ce531f10c94ca4335 --- /dev/null +++ b/qasper-0612/instruction.md @@ -0,0 +1,129 @@ +Name of Paper: Mapping (Dis-)Information Flow about the MH17 Plane Crash + +Question: What recommendations are made to improve the performance in future? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Introduction ::: MH17 Related (Dis-)Information Flow on Twitter", + "Introduction ::: Contributions", + "Competing Narratives about the MH17 Crash", + "Dataset", + "Classification Models", + "Classification Models ::: Hashtag-Based Baseline", + "Classification Models ::: Logistic Regression Classifier", + "Classification Models ::: Convolutional Neural Network Classifier", + "Experimental Setup", + "Experimental Setup ::: Tweet Preprocessing", + "Experimental Setup ::: Evaluation Metrics", + "Results", + "Results ::: Comparison Between Models", + "Results ::: Per-Class Performance", + "Data Augmentation Experiments using Cross-Lingual Transfer", + "Error Analysis", + "Error Analysis ::: Category I Errors", + "Error Analysis ::: Category II Errors", + "Error Analysis ::: Category III Errors", + "Integrating Automatic Predictions into the Retweet Network", + "Integrating Automatic Predictions into the Retweet Network ::: Predicting Polarized Edges", + "Conclusion", + "Acknowledgements" + ], + "paragraphs": [ + [ + "Digital media enables fast sharing of information, including various forms of false or deceptive information. Hence, besides bringing the obvious advantage of broadening information access for everyone, digital media can also be misused for campaigns that spread disinformation about specific events, or campaigns that are targeted at specific individuals or governments. Disinformation, in this case, refers to intentionally misleading content BIBREF0. A prominent case of a disinformation campaign are the efforts of the Russian government to control information during the Russia-Ukraine crisis BIBREF1. One of the most important events during the crisis was the crash of Malaysian Airlines (MH17) flight on July 17, 2014. The plane crashed on its way from Amsterdam to Kuala Lumpur over Ukrainian territory, causing the death of 298 civilians. The event immediately led to the circulation of competing narratives about who was responsible for the crash (see Section SECREF2), with the two most prominent narratives being that the plane was either shot down by the Ukrainian military, or by Russian separatists in Ukraine supported by the Russian government BIBREF2. The latter theory was confirmed by findings of an international investigation team. In this work, information that opposes these findings by promoting other theories about the crash is considered disinformation. When studying disinformation, however, it is important to acknowledge that our fact checkers (in this case the international investigation team) may be wrong, which is why we focus on both of the narratives in our study.", + "MH17 is a highly important case in the context of international relations, because the tragedy has not only increased Western, political pressure against Russia, but may also continue putting the government's global image at stake. In 2020, at least four individuals connected to the Russian separatist movement will face murder charges for their involvement in the MH17 crash BIBREF3, which is why one can expect the waves of disinformation about MH17 to continue spreading. The purpose of this work is to develop an approach that may help both practitioners and scholars of political science, international relations and political communication to detect and measure the scope of MH17-related disinformation.", + "Several studies analyse the framing of the crash and the spread of (dis)information about the event in terms of pro-Russian or pro-Ukrainian framing. These studies analyse information based on manually labeled content, such as television transcripts BIBREF2 or tweets BIBREF4, BIBREF5. Restricting the analysis to manually labeled content ensures a high quality of annotations, but prohibits analysis from being extended to the full amount of available data. Another widely used method for classifying misleading content is to use distant annotations, for example to classify a tweet based on the domain of a URL that is shared by the tweet, or a hashtag that is contained in the tweet BIBREF6, BIBREF7, BIBREF8. Often, this approach treats content from uncredible sources as misleading (e.g. misinformation, disinformation or fake news). This methods enables researchers to scale up the number of observations without having to evaluate the fact value of each piece of content from low-quality sources. However, the approach fails to address an important issue: Not all content from uncredible sources is necessarily misleading or false and not all content from credible sources is true. As often emphasized in the propaganda literature, established media outlets too are vulnerable to state-driven disinformation campaigns, even if they are regarded as credible sources BIBREF9, BIBREF10, BIBREF11.", + "In order to scale annotations that go beyond metadata to larger datasets, Natural Language Processing (NLP) models can be used to automatically label text content. For example, several works developed classifiers for annotating text content with frame labels that can subsequently be used for large-scale content analysis BIBREF12, BIBREF13, BIBREF14, BIBREF15, BIBREF16, BIBREF17, BIBREF18, BIBREF19. Similarly, automatically labeling attitudes expressed in text BIBREF20, BIBREF21, BIBREF22, BIBREF23 can aid the analysis of disinformation and misinformation spread BIBREF24. In this work, we examine to which extent such classifiers can be used to detect pro-Russian framing related to the MH17 crash, and to which extent classifier predictions can be relied on for analysing information flow on Twitter." + ], + [ + "We focus our classification efforts on a Twitter dataset introduced in BIBREF4, that was collected to investigate the flow of MH17-related information on Twitter, focusing on the question who is distributing (dis-)information. In their analysis, the authors found that citizens are active distributors, which contradicts the widely adopted view that the information campaign is only driven by the state and that citizens do not have an active role.", + "To arrive at this conclusion, the authors manually labeled a subset of the tweets in the dataset with pro-Russian/pro-Ukrainian frames and build a retweet network, which has Twitter users as nodes and edges between two nodes if a retweet occurred between the two associated users. An edge was considered as polarized (either pro-Russian or pro-Ukrainian), if at least one retweet between the two users connected by the edge was pro-Russian/pro-Ukrainian. Then, the amount of polarized edges between users with different profiles (e.g. citizen, journalist, state organ) was computed.", + "Labeling more data via automatic classification (or computer-assisted annotation) of tweets could serve an analysis as the one presented in BIBREF4 in two ways. First, more edges could be labeled. Second, edges could be labeled with higher precision, i.e. by taking more tweets comprised by the edge into account. For example, one could decide to only label an edge as polarized if at least half of the retweets between the users were pro-Ukrainian/pro-Russian." + ], + [ + "We evaluate different classifiers that predict frames for unlabeled tweets in BIBREF4's dataset, in order to increase the number of polarized edges in the retweet network derived from the data. This is challenging due to a skewed data distribution and the small amount of training data for the pro-Russian class. We try to combat the data sparsity using a data augmentation approach, but have to report a negative result as we find that data augmentation in this particular case does not improve classification results. While our best neural classifier clearly outperforms a hashtag-based baseline, generating high quality predictions for the pro-Russian class is difficult: In order to make predictions at a precision level of 80%, recall has to be decreased to 23%. Finally, we examine the applicability of the classifier for finding new polarized edges in a retweet network and show how, with manual filtering, the number of pro-Russian edges can be increased by 29%. We make our code, trained models and predictions publicly available." + ], + [ + "We briefly summarize the timeline around the crash of MH17 and some of the dominant narratives present in the dataset. On July 17, 2014, the MH17 flight crashed over Donetsk Oblast in Ukraine. The region was at that time part of an armed conflict between pro-Russian separatists and the Ukrainian military, one of the unrests following the Ukrainian revolution and the annexation of Crimea by the Russian government. The territory in which the plane fell down was controlled by pro-Russian separatists.", + "Right after the crash, two main narratives were propagated: Western media claimed that the plane was shot down by pro-Russian separatists, whereas the Russian government claimed that the Ukrainian military was responsible. Two organisations were tasked with investigating the causes of the crash, the Dutch Safety Board (DSB) and the Dutch-led joint investigation team (JIT). Their final reports were released in October 2015 and September 2016, respectively, and conclude that the plane had been shot down by a missile launched by a BUK surface-to-air system. The BUK was stationed in an area controlled by pro-Russian separatists when the missile was launched, and had been transported there from Russia and returned to Russia after the incident. These findings are denied by the Russian government until now. There are several other crash-related reports that are frequently mentioned throughout the dataset. One is a report by Almaz-Antey, the Russian company that manufactured the BUK, which rejects the DSB findings based on mismatch of technical evidence. Several reports backing up the Dutch findings were released by the investigative journalism website Bellingcat.", + "The crash also sparked the circulation of several alternative theories, many of them promoted in Russian media BIBREF2, e.g. that the plane was downed by Ukrainian SU25 military jets, that the plane attack was meant to hit Putin\u2019s plane that was allegedly traveling the same route earlier that day, and that the bodies found in the plane had already been dead before the crash." + ], + [ + "For our classification experiments, we use the MH17 Twitter dataset introduced by BIBREF4, a dataset collected in order to study the flow of (dis)information about the MH17 plane crash on Twitter. It contains tweets collected based on keyword search that were posted between July 17, 2014 (the day of the plane crash) and December 9, 2016.", + "BIBREF4 provide annotations for a subset of the English tweets contained in the dataset. A tweet is annotated with one of three classes that indicate the framing of the tweet with respect to responsibility for the plane crash. A tweet can either be pro-Russian (Ukrainian authorities, NATO or EU countries are explicitly or implicitly held responsible, or the tweet states that Russia is not responsible), pro-Ukrainian (the Russian Federation or Russian separatists in Ukraine are explicitly or implicitly held responsible, or the tweet states that Ukraine is not responsible) or neutral (neither Ukraine nor Russia or any others are blamed). Example tweets for each category can be found in Table TABREF9. These examples illustrate that the framing annotations do not reflect general polarity, but polarity with respect to responsibility to the crash. For example, even though the last example in the table is in general pro-Ukrainian, as it displays the separatists in a bad light, the tweet does not focus on responsibility for the crash. Hence the it is labeled as neutral. Table TABREF8 shows the label distribution of the annotated portion of the data as well as the total amount of original tweets, and original tweets plus their retweets/duplicates in the network. A retweet is a repost of another user's original tweet, indicated by a specific syntax (RT @username: ). We consider as duplicate a tweet with text that is identical to an original tweet after preprocessing (see Section SECREF18). For our classification experiments, we exclusively consider original tweets, but model predictions can then be propagated to retweets and duplicates." + ], + [ + "For our classification experiments, we compare three classifiers, a hashtag-based baseline, a logistic regression classifier and a convolutional neural network (CNN)." + ], + [ + "Hashtags are often used as a means to assess the content of a tweet BIBREF25, BIBREF26, BIBREF27. We identify hashtags indicative of a class in the annotated dataset using the pointwise mutual information (pmi) between a hashtag $hs$ and a class $c$, which is defined as", + "We then predict the class for unseen tweets as the class that has the highest pmi score for the hashtags contained in the tweet. Tweets without hashtag (5% of the tweets in the development set) or with multiple hashtags leading to conflicting predictions (5% of the tweets in the development set) are labeled randomly. We refer to to this baseline as hs_pmi." + ], + [ + "As non-neural baseline we use a logistic regression model. We compute input representations for tweets as the average over pre-trained word embedding vectors for all words in the tweet. We use fasttext embeddings BIBREF28 that were pre-trained on Wikipedia." + ], + [ + "As neural classification model, we use a convolutional neural network (CNN) BIBREF29, which has previously shown good results for tweet classification BIBREF30, BIBREF27. The model performs 1d convolutions over a sequence of word embeddings. We use the same pre-trained fasttext embeddings as for the logistic regression model. We use a model with one convolutional layer and a relu activation function, and one max pooling layer. The number of filters is 100 and the filter size is set to 4." + ], + [ + "We evaluate the classification models using 10-fold cross validation, i.e. we produce 10 different datasplits by randomly sampling 60% of the data for training, 20% for development and 20% for testing. For each fold, we train each of the models described in Section SECREF4 on the training set and measure performance on the test set. For the CNN and LogReg models, we upsample the training examples such that each class has as many instances as the largest class (Neutral). The final reported scores are averages over the 10 splits." + ], + [ + "Before embedding the tweets, we replace urls, retweet syntax (RT @user_name: ) and @mentions (@user_name) by placeholders. We lowercase all text and tokenize sentences using the StandfordNLP pipeline BIBREF31. If a tweet contains multiple sentences, these are concatenated. Finally, we remove all tokens that contain non-alphanumeric symbols (except for dashes and hashtags) and strip the hashtags from each token, in order to increase the number of words that are represented by a pre-trained word embedding." + ], + [ + "We report performance as F1-scores, which is the harmonic mean between precision and recall. As the class distribution is highly skewed and we are mainly interested in accurately classifying the classes with low support (pro-Russian and pro-Ukrainian), we report macro-averages over the classes. In addition to F1-scores, we report the area under the precision-recall curve (AUC). We compute an AUC score for each class by converting the classification task into a one-vs-all classification task." + ], + [ + "The results of our classification experiments are presented in Table TABREF21. Figure FIGREF22 shows the per-class precision-recall curves for the LogReg and CNN models as well as the confusion matrices between classes." + ], + [ + "We observe that the hashtag baseline performs poorly and does not improve over the random baseline. The CNN classifier outperforms the baselines as well as the LogReg model. It shows the highest improvement over the LogReg for the pro-Russian class. Looking at the confusion matrices, we observe that for the LogReg model, the fraction of True Positives is equal between the pro-Russian and the pro-Ukrainian class. The CNN model produces a higher amount of correct predictions for the pro-Ukrainian than for the pro-Russian class. The absolute number of pro-Russian True Positives is lower for the CNN, but so is in return the amount of misclassifications between the pro-Russian and pro-Ukrainian class." + ], + [ + "With respect to the per class performance, we observe a similar trend across models, which is that the models perform best for the neutral class, whereas performance is lower for the pro-Ukrainian and pro-Russian classes. All models perform worst on the pro-Russian class, which might be due to the fact that it is the class with the fewest instances in the dataset.", + "Considering these results, we conclude that the CNN is the best performing model and also the classifier that best serves our goals, as we want to produce accurate predictions for the pro-Russian and pro-Ukrainian class without confusing between them. Even though the CNN can improve over the other models, the classification performance for the pro-Russian and pro-Ukrainian class is rather low. One obvious reason for this might be the small amount of training data, in particular for the pro-Russian class.", + "In the following, we briefly report a negative result on an attempt to combat the data sparseness with cross-lingual transfer. We then perform an error analysis on the CNN classifications to shed light on the difficulties of the task." + ], + [ + "The annotations in the MH17 dataset are highly imbalanced, with as few as 512 annotated examples for the pro-Russian class. As the annotated examples were sampled from the dataset at random, we assume that there are only few tweets with pro-Russian stance in the dataset. This observation is in line with studies that showed that the amount of disinformation on Twitter is in fact small BIBREF6, BIBREF8. In order to find more pro-Russian training examples, we turn to a resource that we expect to contain large amounts of pro-Russian (dis)information. The Elections integrity dataset was released by Twitter in 2018 and contains the tweets and account information for 3,841 accounts that are believed to be Russian trolls financed by the Russian government. While most tweets posted after late 2014 are in English language and focus on topics around the US elections, the earlier tweets in the dataset are primarily in Russian language and focus on the Ukraine crisis BIBREF33. One feature of the dataset observed by BIBREF33 is that several hashtags show high peakedness BIBREF34, i.e. they are posted with high frequency but only during short intervals, while others are persistent during time.", + "We find two hashtags in the Elections integrity dataset with high peakedness that were exclusively posted within 2 days after the MH17 crash and that seem to be pro-Russian in the context of responsibility for the MH17 crash: russian #\u041a\u0438\u0435\u0432\u0421\u043a\u0430\u0436\u0438\u041f\u0440\u0430\u0432\u0434\u0443 (Kiew tell the truth) and russian #\u041a\u0438\u0435\u0432\u0441\u0431\u0438\u043b\u0431\u043e\u0438\u043d\u0433 (Kiew made the plane go down). We collect all tweets with these two hashtags, resulting in 9,809 Russian tweets that we try to use as additional training data for the pro-Russian class in the MH17 dataset. We experiment with cross-lingual transfer by embedding tweets via aligned English and Russian word embeddings. However, so far results for the cross-lingual models do not improve over the CNN model trained on only English data. This might be due to the fact that the additional Russian tweets rather contain a general pro-Russian frame than specifically talking about the crash, but needs further investigation." + ], + [ + "In order to integrate automatically labeled examples into a network analysis that studies the flow of polarized information in the network, we need to produce high precision predictions for the pro-Russian and the pro-Ukrainian class. Polarized tweets that are incorrectly classified as neutral will hurt an analysis much less than neutral tweets that are erroneously classified as pro-Russian or pro-Ukrainian. However, the worst type of confusion is between the pro-Russian and pro-Ukrainian class. In order to gain insights into why these confusions happen, we manually inspect incorrectly predicted examples that are confused between the pro-Russian and pro-Ukrainian class. We analyse the misclassifications in the development set of all 10 runs, which results in 73 False Positives of pro-Ukrainian tweets being classified as pro-Russian (referred to as pro-Russian False Positives), and 88 False Positives of pro-Russian tweets being classified as pro-Ukrainian (referred to as pro-Ukrainian False Positives). We can identify three main cases for which the model produces an error:", + "the correct class can be directly inferred from the text content easily, even without background knowledge", + "the correct class can be inferred from the text content, given that event-specific knowledge is provided", + "the correct class can be inferred from the text content if the text is interpreted correctly", + "For the pro-Russian False Positives, we find that 42% of the errors are category I and II errors, respectively, and 15% of category III. For the pro-Ukrainian False Positives, we find 48% category I errors, 33% category II errors and and 13% category III errors. Table TABREF28 presents examples for each of the error categories in both sets which we will discuss in the following." + ], + [ + "Category I errors could easily be classified by humans following the annotation guidelines (see Section SECREF3). One difficulty can be seen in example f). Even though no background knowledge is needed to interpret the content, interpretation is difficult because of the convoluted syntax of the tweet. For the other examples it is unclear why the model would have difficulties with classifying them." + ], + [ + "Category II errors can only be classified with event-specific background knowledge. Examples g), i) and k) relate to the theory that a Ukrainian SU25 fighter jet shot down the plane in air. Correct interpretation of these tweets depends on knowledge about the SU25 fighter jet. In order to correctly interpret example j) as pro-Russian, it has to be known that the bellingcat report is pro-Ukrainian. Example l) relates to the theory that the shoot down was a false flag operation run by Western countries and the bodies in the plane were already dead before the crash. In order to correctly interpret example m), the identity of Kolomoisky has to be known. He is an anti-separatist Ukrainian billionaire, hence his involvement points to the Ukrainian government being responsible for the crash." + ], + [ + "Category III errors occur for examples that can only be classified by correctly interpreting the tweet authors' intention. Interpretation is difficult due to phenomena such as irony as in examples n) and o). While the irony is indicated in example n) through the use of the hashtag #LOL, there is no explicit indication in example o).", + "Interpretation of example q) is conditioned on world knowledge as well as the understanding of the speakers beliefs. Example r) is pro-Russian as it questions the validity of the assumption AC360 is making, but we only know that because we know that the assumption is absurd. Example s) requires to evaluate that the speaker thinks people on site are trusted more than people at home.", + "From the error analysis, we conclude that category I errors need further investigation, as here the model makes mistakes on seemingly easy instances. This might be due to the model not being able to correctly represent Twitter specific language or unknown words, such as Eukraine in example e). Category II and III errors are harder to avoid and could be improved by applying reasoning BIBREF36 or irony detection methods BIBREF37." + ], + [ + "Finally, we apply the CNN classifier to label new edges in BIBREF4's retweet network, which is shown in Figure FIGREF35. The retweet network is a graph that contains users as nodes and an edge between two users if the users are retweeting each other. In order to track the flow of polarized information, BIBREF4 label an edge as polarized if at least one tweet contained in the edge was manually annotated as pro-Russian or pro-Ukrainian. While the network shows a clear polarization, only a small subset of the edges present in the network are labeled (see Table TABREF38).", + "Automatic polarity prediction of tweets can help the analysis in two ways. Either, we can label a previously unlabeled edge, or we can verify/confirm the manual labeling of an edge, by labeling additional tweets that are comprised in the edge." + ], + [ + "In order to get high precision predictions for unlabeled tweets, we choose the probability thresholds for predicting a pro-Russian or pro-Ukrainian tweet such that the classifier would achieve 80% precision on the test splits (recall at this precision level is 23%). Table TABREF38 shows the amount of polarized edges we can predict at this precision level. Upon manual inspection, we however find that the quality of predictions is lower than estimated. Hence, we manually re-annotate the pro-Russian and pro-Ukrainian predictions according to the official annotation guidelines used by BIBREF4. This way, we can label 77 new pro-Russian edges by looking at 415 tweets, which means that 19% of the candidates are hits. For the pro-Ukrainian class, we can label 110 new edges by looking at 611 tweets (18% hits). Hence even though the quality of the classifier predictions is too low to be integrated into the network analysis right away, the classifier drastically facilitates the annotation process for human annotators compared to annotating unfiltered tweets (from the original labels we infer that for unfiltered tweets, only 6% are hits for the pro-Russian class, and 11% for the pro-Ukrainian class)." + ], + [ + "In this work, we investigated the usefulness of text classifiers to detect pro-Russian and pro-Ukrainian framing in tweets related to the MH17 crash, and to which extent classifier predictions can be relied on for producing high quality annotations. From our classification experiments, we conclude that the real-world applicability of text classifiers for labeling polarized tweets in a retweet network is restricted to pre-filtering tweets for manual annotation. However, if used as a filter, the classifier can significantly speed up the annotation process, making large-scale content analysis more feasible." + ], + [ + "We thank the anonymous reviewers for their helpful comments. The research was carried out as part of the \u2018Digital Disinformation\u2019 project, which was directed by Rebecca Adler-Nissen and funded by the Carlsberg Foundation (project number CF16-0012)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0613/instruction.md b/qasper-0613/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..13d1196f9b75bece6fe736cd999c0d5491b62bff --- /dev/null +++ b/qasper-0613/instruction.md @@ -0,0 +1,129 @@ +Name of Paper: Mapping (Dis-)Information Flow about the MH17 Plane Crash + +Question: What type of errors do the classifiers use? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Introduction ::: MH17 Related (Dis-)Information Flow on Twitter", + "Introduction ::: Contributions", + "Competing Narratives about the MH17 Crash", + "Dataset", + "Classification Models", + "Classification Models ::: Hashtag-Based Baseline", + "Classification Models ::: Logistic Regression Classifier", + "Classification Models ::: Convolutional Neural Network Classifier", + "Experimental Setup", + "Experimental Setup ::: Tweet Preprocessing", + "Experimental Setup ::: Evaluation Metrics", + "Results", + "Results ::: Comparison Between Models", + "Results ::: Per-Class Performance", + "Data Augmentation Experiments using Cross-Lingual Transfer", + "Error Analysis", + "Error Analysis ::: Category I Errors", + "Error Analysis ::: Category II Errors", + "Error Analysis ::: Category III Errors", + "Integrating Automatic Predictions into the Retweet Network", + "Integrating Automatic Predictions into the Retweet Network ::: Predicting Polarized Edges", + "Conclusion", + "Acknowledgements" + ], + "paragraphs": [ + [ + "Digital media enables fast sharing of information, including various forms of false or deceptive information. Hence, besides bringing the obvious advantage of broadening information access for everyone, digital media can also be misused for campaigns that spread disinformation about specific events, or campaigns that are targeted at specific individuals or governments. Disinformation, in this case, refers to intentionally misleading content BIBREF0. A prominent case of a disinformation campaign are the efforts of the Russian government to control information during the Russia-Ukraine crisis BIBREF1. One of the most important events during the crisis was the crash of Malaysian Airlines (MH17) flight on July 17, 2014. The plane crashed on its way from Amsterdam to Kuala Lumpur over Ukrainian territory, causing the death of 298 civilians. The event immediately led to the circulation of competing narratives about who was responsible for the crash (see Section SECREF2), with the two most prominent narratives being that the plane was either shot down by the Ukrainian military, or by Russian separatists in Ukraine supported by the Russian government BIBREF2. The latter theory was confirmed by findings of an international investigation team. In this work, information that opposes these findings by promoting other theories about the crash is considered disinformation. When studying disinformation, however, it is important to acknowledge that our fact checkers (in this case the international investigation team) may be wrong, which is why we focus on both of the narratives in our study.", + "MH17 is a highly important case in the context of international relations, because the tragedy has not only increased Western, political pressure against Russia, but may also continue putting the government's global image at stake. In 2020, at least four individuals connected to the Russian separatist movement will face murder charges for their involvement in the MH17 crash BIBREF3, which is why one can expect the waves of disinformation about MH17 to continue spreading. The purpose of this work is to develop an approach that may help both practitioners and scholars of political science, international relations and political communication to detect and measure the scope of MH17-related disinformation.", + "Several studies analyse the framing of the crash and the spread of (dis)information about the event in terms of pro-Russian or pro-Ukrainian framing. These studies analyse information based on manually labeled content, such as television transcripts BIBREF2 or tweets BIBREF4, BIBREF5. Restricting the analysis to manually labeled content ensures a high quality of annotations, but prohibits analysis from being extended to the full amount of available data. Another widely used method for classifying misleading content is to use distant annotations, for example to classify a tweet based on the domain of a URL that is shared by the tweet, or a hashtag that is contained in the tweet BIBREF6, BIBREF7, BIBREF8. Often, this approach treats content from uncredible sources as misleading (e.g. misinformation, disinformation or fake news). This methods enables researchers to scale up the number of observations without having to evaluate the fact value of each piece of content from low-quality sources. However, the approach fails to address an important issue: Not all content from uncredible sources is necessarily misleading or false and not all content from credible sources is true. As often emphasized in the propaganda literature, established media outlets too are vulnerable to state-driven disinformation campaigns, even if they are regarded as credible sources BIBREF9, BIBREF10, BIBREF11.", + "In order to scale annotations that go beyond metadata to larger datasets, Natural Language Processing (NLP) models can be used to automatically label text content. For example, several works developed classifiers for annotating text content with frame labels that can subsequently be used for large-scale content analysis BIBREF12, BIBREF13, BIBREF14, BIBREF15, BIBREF16, BIBREF17, BIBREF18, BIBREF19. Similarly, automatically labeling attitudes expressed in text BIBREF20, BIBREF21, BIBREF22, BIBREF23 can aid the analysis of disinformation and misinformation spread BIBREF24. In this work, we examine to which extent such classifiers can be used to detect pro-Russian framing related to the MH17 crash, and to which extent classifier predictions can be relied on for analysing information flow on Twitter." + ], + [ + "We focus our classification efforts on a Twitter dataset introduced in BIBREF4, that was collected to investigate the flow of MH17-related information on Twitter, focusing on the question who is distributing (dis-)information. In their analysis, the authors found that citizens are active distributors, which contradicts the widely adopted view that the information campaign is only driven by the state and that citizens do not have an active role.", + "To arrive at this conclusion, the authors manually labeled a subset of the tweets in the dataset with pro-Russian/pro-Ukrainian frames and build a retweet network, which has Twitter users as nodes and edges between two nodes if a retweet occurred between the two associated users. An edge was considered as polarized (either pro-Russian or pro-Ukrainian), if at least one retweet between the two users connected by the edge was pro-Russian/pro-Ukrainian. Then, the amount of polarized edges between users with different profiles (e.g. citizen, journalist, state organ) was computed.", + "Labeling more data via automatic classification (or computer-assisted annotation) of tweets could serve an analysis as the one presented in BIBREF4 in two ways. First, more edges could be labeled. Second, edges could be labeled with higher precision, i.e. by taking more tweets comprised by the edge into account. For example, one could decide to only label an edge as polarized if at least half of the retweets between the users were pro-Ukrainian/pro-Russian." + ], + [ + "We evaluate different classifiers that predict frames for unlabeled tweets in BIBREF4's dataset, in order to increase the number of polarized edges in the retweet network derived from the data. This is challenging due to a skewed data distribution and the small amount of training data for the pro-Russian class. We try to combat the data sparsity using a data augmentation approach, but have to report a negative result as we find that data augmentation in this particular case does not improve classification results. While our best neural classifier clearly outperforms a hashtag-based baseline, generating high quality predictions for the pro-Russian class is difficult: In order to make predictions at a precision level of 80%, recall has to be decreased to 23%. Finally, we examine the applicability of the classifier for finding new polarized edges in a retweet network and show how, with manual filtering, the number of pro-Russian edges can be increased by 29%. We make our code, trained models and predictions publicly available." + ], + [ + "We briefly summarize the timeline around the crash of MH17 and some of the dominant narratives present in the dataset. On July 17, 2014, the MH17 flight crashed over Donetsk Oblast in Ukraine. The region was at that time part of an armed conflict between pro-Russian separatists and the Ukrainian military, one of the unrests following the Ukrainian revolution and the annexation of Crimea by the Russian government. The territory in which the plane fell down was controlled by pro-Russian separatists.", + "Right after the crash, two main narratives were propagated: Western media claimed that the plane was shot down by pro-Russian separatists, whereas the Russian government claimed that the Ukrainian military was responsible. Two organisations were tasked with investigating the causes of the crash, the Dutch Safety Board (DSB) and the Dutch-led joint investigation team (JIT). Their final reports were released in October 2015 and September 2016, respectively, and conclude that the plane had been shot down by a missile launched by a BUK surface-to-air system. The BUK was stationed in an area controlled by pro-Russian separatists when the missile was launched, and had been transported there from Russia and returned to Russia after the incident. These findings are denied by the Russian government until now. There are several other crash-related reports that are frequently mentioned throughout the dataset. One is a report by Almaz-Antey, the Russian company that manufactured the BUK, which rejects the DSB findings based on mismatch of technical evidence. Several reports backing up the Dutch findings were released by the investigative journalism website Bellingcat.", + "The crash also sparked the circulation of several alternative theories, many of them promoted in Russian media BIBREF2, e.g. that the plane was downed by Ukrainian SU25 military jets, that the plane attack was meant to hit Putin\u2019s plane that was allegedly traveling the same route earlier that day, and that the bodies found in the plane had already been dead before the crash." + ], + [ + "For our classification experiments, we use the MH17 Twitter dataset introduced by BIBREF4, a dataset collected in order to study the flow of (dis)information about the MH17 plane crash on Twitter. It contains tweets collected based on keyword search that were posted between July 17, 2014 (the day of the plane crash) and December 9, 2016.", + "BIBREF4 provide annotations for a subset of the English tweets contained in the dataset. A tweet is annotated with one of three classes that indicate the framing of the tweet with respect to responsibility for the plane crash. A tweet can either be pro-Russian (Ukrainian authorities, NATO or EU countries are explicitly or implicitly held responsible, or the tweet states that Russia is not responsible), pro-Ukrainian (the Russian Federation or Russian separatists in Ukraine are explicitly or implicitly held responsible, or the tweet states that Ukraine is not responsible) or neutral (neither Ukraine nor Russia or any others are blamed). Example tweets for each category can be found in Table TABREF9. These examples illustrate that the framing annotations do not reflect general polarity, but polarity with respect to responsibility to the crash. For example, even though the last example in the table is in general pro-Ukrainian, as it displays the separatists in a bad light, the tweet does not focus on responsibility for the crash. Hence the it is labeled as neutral. Table TABREF8 shows the label distribution of the annotated portion of the data as well as the total amount of original tweets, and original tweets plus their retweets/duplicates in the network. A retweet is a repost of another user's original tweet, indicated by a specific syntax (RT @username: ). We consider as duplicate a tweet with text that is identical to an original tweet after preprocessing (see Section SECREF18). For our classification experiments, we exclusively consider original tweets, but model predictions can then be propagated to retweets and duplicates." + ], + [ + "For our classification experiments, we compare three classifiers, a hashtag-based baseline, a logistic regression classifier and a convolutional neural network (CNN)." + ], + [ + "Hashtags are often used as a means to assess the content of a tweet BIBREF25, BIBREF26, BIBREF27. We identify hashtags indicative of a class in the annotated dataset using the pointwise mutual information (pmi) between a hashtag $hs$ and a class $c$, which is defined as", + "We then predict the class for unseen tweets as the class that has the highest pmi score for the hashtags contained in the tweet. Tweets without hashtag (5% of the tweets in the development set) or with multiple hashtags leading to conflicting predictions (5% of the tweets in the development set) are labeled randomly. We refer to to this baseline as hs_pmi." + ], + [ + "As non-neural baseline we use a logistic regression model. We compute input representations for tweets as the average over pre-trained word embedding vectors for all words in the tweet. We use fasttext embeddings BIBREF28 that were pre-trained on Wikipedia." + ], + [ + "As neural classification model, we use a convolutional neural network (CNN) BIBREF29, which has previously shown good results for tweet classification BIBREF30, BIBREF27. The model performs 1d convolutions over a sequence of word embeddings. We use the same pre-trained fasttext embeddings as for the logistic regression model. We use a model with one convolutional layer and a relu activation function, and one max pooling layer. The number of filters is 100 and the filter size is set to 4." + ], + [ + "We evaluate the classification models using 10-fold cross validation, i.e. we produce 10 different datasplits by randomly sampling 60% of the data for training, 20% for development and 20% for testing. For each fold, we train each of the models described in Section SECREF4 on the training set and measure performance on the test set. For the CNN and LogReg models, we upsample the training examples such that each class has as many instances as the largest class (Neutral). The final reported scores are averages over the 10 splits." + ], + [ + "Before embedding the tweets, we replace urls, retweet syntax (RT @user_name: ) and @mentions (@user_name) by placeholders. We lowercase all text and tokenize sentences using the StandfordNLP pipeline BIBREF31. If a tweet contains multiple sentences, these are concatenated. Finally, we remove all tokens that contain non-alphanumeric symbols (except for dashes and hashtags) and strip the hashtags from each token, in order to increase the number of words that are represented by a pre-trained word embedding." + ], + [ + "We report performance as F1-scores, which is the harmonic mean between precision and recall. As the class distribution is highly skewed and we are mainly interested in accurately classifying the classes with low support (pro-Russian and pro-Ukrainian), we report macro-averages over the classes. In addition to F1-scores, we report the area under the precision-recall curve (AUC). We compute an AUC score for each class by converting the classification task into a one-vs-all classification task." + ], + [ + "The results of our classification experiments are presented in Table TABREF21. Figure FIGREF22 shows the per-class precision-recall curves for the LogReg and CNN models as well as the confusion matrices between classes." + ], + [ + "We observe that the hashtag baseline performs poorly and does not improve over the random baseline. The CNN classifier outperforms the baselines as well as the LogReg model. It shows the highest improvement over the LogReg for the pro-Russian class. Looking at the confusion matrices, we observe that for the LogReg model, the fraction of True Positives is equal between the pro-Russian and the pro-Ukrainian class. The CNN model produces a higher amount of correct predictions for the pro-Ukrainian than for the pro-Russian class. The absolute number of pro-Russian True Positives is lower for the CNN, but so is in return the amount of misclassifications between the pro-Russian and pro-Ukrainian class." + ], + [ + "With respect to the per class performance, we observe a similar trend across models, which is that the models perform best for the neutral class, whereas performance is lower for the pro-Ukrainian and pro-Russian classes. All models perform worst on the pro-Russian class, which might be due to the fact that it is the class with the fewest instances in the dataset.", + "Considering these results, we conclude that the CNN is the best performing model and also the classifier that best serves our goals, as we want to produce accurate predictions for the pro-Russian and pro-Ukrainian class without confusing between them. Even though the CNN can improve over the other models, the classification performance for the pro-Russian and pro-Ukrainian class is rather low. One obvious reason for this might be the small amount of training data, in particular for the pro-Russian class.", + "In the following, we briefly report a negative result on an attempt to combat the data sparseness with cross-lingual transfer. We then perform an error analysis on the CNN classifications to shed light on the difficulties of the task." + ], + [ + "The annotations in the MH17 dataset are highly imbalanced, with as few as 512 annotated examples for the pro-Russian class. As the annotated examples were sampled from the dataset at random, we assume that there are only few tweets with pro-Russian stance in the dataset. This observation is in line with studies that showed that the amount of disinformation on Twitter is in fact small BIBREF6, BIBREF8. In order to find more pro-Russian training examples, we turn to a resource that we expect to contain large amounts of pro-Russian (dis)information. The Elections integrity dataset was released by Twitter in 2018 and contains the tweets and account information for 3,841 accounts that are believed to be Russian trolls financed by the Russian government. While most tweets posted after late 2014 are in English language and focus on topics around the US elections, the earlier tweets in the dataset are primarily in Russian language and focus on the Ukraine crisis BIBREF33. One feature of the dataset observed by BIBREF33 is that several hashtags show high peakedness BIBREF34, i.e. they are posted with high frequency but only during short intervals, while others are persistent during time.", + "We find two hashtags in the Elections integrity dataset with high peakedness that were exclusively posted within 2 days after the MH17 crash and that seem to be pro-Russian in the context of responsibility for the MH17 crash: russian #\u041a\u0438\u0435\u0432\u0421\u043a\u0430\u0436\u0438\u041f\u0440\u0430\u0432\u0434\u0443 (Kiew tell the truth) and russian #\u041a\u0438\u0435\u0432\u0441\u0431\u0438\u043b\u0431\u043e\u0438\u043d\u0433 (Kiew made the plane go down). We collect all tweets with these two hashtags, resulting in 9,809 Russian tweets that we try to use as additional training data for the pro-Russian class in the MH17 dataset. We experiment with cross-lingual transfer by embedding tweets via aligned English and Russian word embeddings. However, so far results for the cross-lingual models do not improve over the CNN model trained on only English data. This might be due to the fact that the additional Russian tweets rather contain a general pro-Russian frame than specifically talking about the crash, but needs further investigation." + ], + [ + "In order to integrate automatically labeled examples into a network analysis that studies the flow of polarized information in the network, we need to produce high precision predictions for the pro-Russian and the pro-Ukrainian class. Polarized tweets that are incorrectly classified as neutral will hurt an analysis much less than neutral tweets that are erroneously classified as pro-Russian or pro-Ukrainian. However, the worst type of confusion is between the pro-Russian and pro-Ukrainian class. In order to gain insights into why these confusions happen, we manually inspect incorrectly predicted examples that are confused between the pro-Russian and pro-Ukrainian class. We analyse the misclassifications in the development set of all 10 runs, which results in 73 False Positives of pro-Ukrainian tweets being classified as pro-Russian (referred to as pro-Russian False Positives), and 88 False Positives of pro-Russian tweets being classified as pro-Ukrainian (referred to as pro-Ukrainian False Positives). We can identify three main cases for which the model produces an error:", + "the correct class can be directly inferred from the text content easily, even without background knowledge", + "the correct class can be inferred from the text content, given that event-specific knowledge is provided", + "the correct class can be inferred from the text content if the text is interpreted correctly", + "For the pro-Russian False Positives, we find that 42% of the errors are category I and II errors, respectively, and 15% of category III. For the pro-Ukrainian False Positives, we find 48% category I errors, 33% category II errors and and 13% category III errors. Table TABREF28 presents examples for each of the error categories in both sets which we will discuss in the following." + ], + [ + "Category I errors could easily be classified by humans following the annotation guidelines (see Section SECREF3). One difficulty can be seen in example f). Even though no background knowledge is needed to interpret the content, interpretation is difficult because of the convoluted syntax of the tweet. For the other examples it is unclear why the model would have difficulties with classifying them." + ], + [ + "Category II errors can only be classified with event-specific background knowledge. Examples g), i) and k) relate to the theory that a Ukrainian SU25 fighter jet shot down the plane in air. Correct interpretation of these tweets depends on knowledge about the SU25 fighter jet. In order to correctly interpret example j) as pro-Russian, it has to be known that the bellingcat report is pro-Ukrainian. Example l) relates to the theory that the shoot down was a false flag operation run by Western countries and the bodies in the plane were already dead before the crash. In order to correctly interpret example m), the identity of Kolomoisky has to be known. He is an anti-separatist Ukrainian billionaire, hence his involvement points to the Ukrainian government being responsible for the crash." + ], + [ + "Category III errors occur for examples that can only be classified by correctly interpreting the tweet authors' intention. Interpretation is difficult due to phenomena such as irony as in examples n) and o). While the irony is indicated in example n) through the use of the hashtag #LOL, there is no explicit indication in example o).", + "Interpretation of example q) is conditioned on world knowledge as well as the understanding of the speakers beliefs. Example r) is pro-Russian as it questions the validity of the assumption AC360 is making, but we only know that because we know that the assumption is absurd. Example s) requires to evaluate that the speaker thinks people on site are trusted more than people at home.", + "From the error analysis, we conclude that category I errors need further investigation, as here the model makes mistakes on seemingly easy instances. This might be due to the model not being able to correctly represent Twitter specific language or unknown words, such as Eukraine in example e). Category II and III errors are harder to avoid and could be improved by applying reasoning BIBREF36 or irony detection methods BIBREF37." + ], + [ + "Finally, we apply the CNN classifier to label new edges in BIBREF4's retweet network, which is shown in Figure FIGREF35. The retweet network is a graph that contains users as nodes and an edge between two users if the users are retweeting each other. In order to track the flow of polarized information, BIBREF4 label an edge as polarized if at least one tweet contained in the edge was manually annotated as pro-Russian or pro-Ukrainian. While the network shows a clear polarization, only a small subset of the edges present in the network are labeled (see Table TABREF38).", + "Automatic polarity prediction of tweets can help the analysis in two ways. Either, we can label a previously unlabeled edge, or we can verify/confirm the manual labeling of an edge, by labeling additional tweets that are comprised in the edge." + ], + [ + "In order to get high precision predictions for unlabeled tweets, we choose the probability thresholds for predicting a pro-Russian or pro-Ukrainian tweet such that the classifier would achieve 80% precision on the test splits (recall at this precision level is 23%). Table TABREF38 shows the amount of polarized edges we can predict at this precision level. Upon manual inspection, we however find that the quality of predictions is lower than estimated. Hence, we manually re-annotate the pro-Russian and pro-Ukrainian predictions according to the official annotation guidelines used by BIBREF4. This way, we can label 77 new pro-Russian edges by looking at 415 tweets, which means that 19% of the candidates are hits. For the pro-Ukrainian class, we can label 110 new edges by looking at 611 tweets (18% hits). Hence even though the quality of the classifier predictions is too low to be integrated into the network analysis right away, the classifier drastically facilitates the annotation process for human annotators compared to annotating unfiltered tweets (from the original labels we infer that for unfiltered tweets, only 6% are hits for the pro-Russian class, and 11% for the pro-Ukrainian class)." + ], + [ + "In this work, we investigated the usefulness of text classifiers to detect pro-Russian and pro-Ukrainian framing in tweets related to the MH17 crash, and to which extent classifier predictions can be relied on for producing high quality annotations. From our classification experiments, we conclude that the real-world applicability of text classifiers for labeling polarized tweets in a retweet network is restricted to pre-filtering tweets for manual annotation. However, if used as a filter, the classifier can significantly speed up the annotation process, making large-scale content analysis more feasible." + ], + [ + "We thank the anonymous reviewers for their helpful comments. The research was carried out as part of the \u2018Digital Disinformation\u2019 project, which was directed by Rebecca Adler-Nissen and funded by the Carlsberg Foundation (project number CF16-0012)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0614/instruction.md b/qasper-0614/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..4cbe91c1373ededf8e788440426f725e0bb64174 --- /dev/null +++ b/qasper-0614/instruction.md @@ -0,0 +1,129 @@ +Name of Paper: Mapping (Dis-)Information Flow about the MH17 Plane Crash + +Question: What neural classifiers are used? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Introduction ::: MH17 Related (Dis-)Information Flow on Twitter", + "Introduction ::: Contributions", + "Competing Narratives about the MH17 Crash", + "Dataset", + "Classification Models", + "Classification Models ::: Hashtag-Based Baseline", + "Classification Models ::: Logistic Regression Classifier", + "Classification Models ::: Convolutional Neural Network Classifier", + "Experimental Setup", + "Experimental Setup ::: Tweet Preprocessing", + "Experimental Setup ::: Evaluation Metrics", + "Results", + "Results ::: Comparison Between Models", + "Results ::: Per-Class Performance", + "Data Augmentation Experiments using Cross-Lingual Transfer", + "Error Analysis", + "Error Analysis ::: Category I Errors", + "Error Analysis ::: Category II Errors", + "Error Analysis ::: Category III Errors", + "Integrating Automatic Predictions into the Retweet Network", + "Integrating Automatic Predictions into the Retweet Network ::: Predicting Polarized Edges", + "Conclusion", + "Acknowledgements" + ], + "paragraphs": [ + [ + "Digital media enables fast sharing of information, including various forms of false or deceptive information. Hence, besides bringing the obvious advantage of broadening information access for everyone, digital media can also be misused for campaigns that spread disinformation about specific events, or campaigns that are targeted at specific individuals or governments. Disinformation, in this case, refers to intentionally misleading content BIBREF0. A prominent case of a disinformation campaign are the efforts of the Russian government to control information during the Russia-Ukraine crisis BIBREF1. One of the most important events during the crisis was the crash of Malaysian Airlines (MH17) flight on July 17, 2014. The plane crashed on its way from Amsterdam to Kuala Lumpur over Ukrainian territory, causing the death of 298 civilians. The event immediately led to the circulation of competing narratives about who was responsible for the crash (see Section SECREF2), with the two most prominent narratives being that the plane was either shot down by the Ukrainian military, or by Russian separatists in Ukraine supported by the Russian government BIBREF2. The latter theory was confirmed by findings of an international investigation team. In this work, information that opposes these findings by promoting other theories about the crash is considered disinformation. When studying disinformation, however, it is important to acknowledge that our fact checkers (in this case the international investigation team) may be wrong, which is why we focus on both of the narratives in our study.", + "MH17 is a highly important case in the context of international relations, because the tragedy has not only increased Western, political pressure against Russia, but may also continue putting the government's global image at stake. In 2020, at least four individuals connected to the Russian separatist movement will face murder charges for their involvement in the MH17 crash BIBREF3, which is why one can expect the waves of disinformation about MH17 to continue spreading. The purpose of this work is to develop an approach that may help both practitioners and scholars of political science, international relations and political communication to detect and measure the scope of MH17-related disinformation.", + "Several studies analyse the framing of the crash and the spread of (dis)information about the event in terms of pro-Russian or pro-Ukrainian framing. These studies analyse information based on manually labeled content, such as television transcripts BIBREF2 or tweets BIBREF4, BIBREF5. Restricting the analysis to manually labeled content ensures a high quality of annotations, but prohibits analysis from being extended to the full amount of available data. Another widely used method for classifying misleading content is to use distant annotations, for example to classify a tweet based on the domain of a URL that is shared by the tweet, or a hashtag that is contained in the tweet BIBREF6, BIBREF7, BIBREF8. Often, this approach treats content from uncredible sources as misleading (e.g. misinformation, disinformation or fake news). This methods enables researchers to scale up the number of observations without having to evaluate the fact value of each piece of content from low-quality sources. However, the approach fails to address an important issue: Not all content from uncredible sources is necessarily misleading or false and not all content from credible sources is true. As often emphasized in the propaganda literature, established media outlets too are vulnerable to state-driven disinformation campaigns, even if they are regarded as credible sources BIBREF9, BIBREF10, BIBREF11.", + "In order to scale annotations that go beyond metadata to larger datasets, Natural Language Processing (NLP) models can be used to automatically label text content. For example, several works developed classifiers for annotating text content with frame labels that can subsequently be used for large-scale content analysis BIBREF12, BIBREF13, BIBREF14, BIBREF15, BIBREF16, BIBREF17, BIBREF18, BIBREF19. Similarly, automatically labeling attitudes expressed in text BIBREF20, BIBREF21, BIBREF22, BIBREF23 can aid the analysis of disinformation and misinformation spread BIBREF24. In this work, we examine to which extent such classifiers can be used to detect pro-Russian framing related to the MH17 crash, and to which extent classifier predictions can be relied on for analysing information flow on Twitter." + ], + [ + "We focus our classification efforts on a Twitter dataset introduced in BIBREF4, that was collected to investigate the flow of MH17-related information on Twitter, focusing on the question who is distributing (dis-)information. In their analysis, the authors found that citizens are active distributors, which contradicts the widely adopted view that the information campaign is only driven by the state and that citizens do not have an active role.", + "To arrive at this conclusion, the authors manually labeled a subset of the tweets in the dataset with pro-Russian/pro-Ukrainian frames and build a retweet network, which has Twitter users as nodes and edges between two nodes if a retweet occurred between the two associated users. An edge was considered as polarized (either pro-Russian or pro-Ukrainian), if at least one retweet between the two users connected by the edge was pro-Russian/pro-Ukrainian. Then, the amount of polarized edges between users with different profiles (e.g. citizen, journalist, state organ) was computed.", + "Labeling more data via automatic classification (or computer-assisted annotation) of tweets could serve an analysis as the one presented in BIBREF4 in two ways. First, more edges could be labeled. Second, edges could be labeled with higher precision, i.e. by taking more tweets comprised by the edge into account. For example, one could decide to only label an edge as polarized if at least half of the retweets between the users were pro-Ukrainian/pro-Russian." + ], + [ + "We evaluate different classifiers that predict frames for unlabeled tweets in BIBREF4's dataset, in order to increase the number of polarized edges in the retweet network derived from the data. This is challenging due to a skewed data distribution and the small amount of training data for the pro-Russian class. We try to combat the data sparsity using a data augmentation approach, but have to report a negative result as we find that data augmentation in this particular case does not improve classification results. While our best neural classifier clearly outperforms a hashtag-based baseline, generating high quality predictions for the pro-Russian class is difficult: In order to make predictions at a precision level of 80%, recall has to be decreased to 23%. Finally, we examine the applicability of the classifier for finding new polarized edges in a retweet network and show how, with manual filtering, the number of pro-Russian edges can be increased by 29%. We make our code, trained models and predictions publicly available." + ], + [ + "We briefly summarize the timeline around the crash of MH17 and some of the dominant narratives present in the dataset. On July 17, 2014, the MH17 flight crashed over Donetsk Oblast in Ukraine. The region was at that time part of an armed conflict between pro-Russian separatists and the Ukrainian military, one of the unrests following the Ukrainian revolution and the annexation of Crimea by the Russian government. The territory in which the plane fell down was controlled by pro-Russian separatists.", + "Right after the crash, two main narratives were propagated: Western media claimed that the plane was shot down by pro-Russian separatists, whereas the Russian government claimed that the Ukrainian military was responsible. Two organisations were tasked with investigating the causes of the crash, the Dutch Safety Board (DSB) and the Dutch-led joint investigation team (JIT). Their final reports were released in October 2015 and September 2016, respectively, and conclude that the plane had been shot down by a missile launched by a BUK surface-to-air system. The BUK was stationed in an area controlled by pro-Russian separatists when the missile was launched, and had been transported there from Russia and returned to Russia after the incident. These findings are denied by the Russian government until now. There are several other crash-related reports that are frequently mentioned throughout the dataset. One is a report by Almaz-Antey, the Russian company that manufactured the BUK, which rejects the DSB findings based on mismatch of technical evidence. Several reports backing up the Dutch findings were released by the investigative journalism website Bellingcat.", + "The crash also sparked the circulation of several alternative theories, many of them promoted in Russian media BIBREF2, e.g. that the plane was downed by Ukrainian SU25 military jets, that the plane attack was meant to hit Putin\u2019s plane that was allegedly traveling the same route earlier that day, and that the bodies found in the plane had already been dead before the crash." + ], + [ + "For our classification experiments, we use the MH17 Twitter dataset introduced by BIBREF4, a dataset collected in order to study the flow of (dis)information about the MH17 plane crash on Twitter. It contains tweets collected based on keyword search that were posted between July 17, 2014 (the day of the plane crash) and December 9, 2016.", + "BIBREF4 provide annotations for a subset of the English tweets contained in the dataset. A tweet is annotated with one of three classes that indicate the framing of the tweet with respect to responsibility for the plane crash. A tweet can either be pro-Russian (Ukrainian authorities, NATO or EU countries are explicitly or implicitly held responsible, or the tweet states that Russia is not responsible), pro-Ukrainian (the Russian Federation or Russian separatists in Ukraine are explicitly or implicitly held responsible, or the tweet states that Ukraine is not responsible) or neutral (neither Ukraine nor Russia or any others are blamed). Example tweets for each category can be found in Table TABREF9. These examples illustrate that the framing annotations do not reflect general polarity, but polarity with respect to responsibility to the crash. For example, even though the last example in the table is in general pro-Ukrainian, as it displays the separatists in a bad light, the tweet does not focus on responsibility for the crash. Hence the it is labeled as neutral. Table TABREF8 shows the label distribution of the annotated portion of the data as well as the total amount of original tweets, and original tweets plus their retweets/duplicates in the network. A retweet is a repost of another user's original tweet, indicated by a specific syntax (RT @username: ). We consider as duplicate a tweet with text that is identical to an original tweet after preprocessing (see Section SECREF18). For our classification experiments, we exclusively consider original tweets, but model predictions can then be propagated to retweets and duplicates." + ], + [ + "For our classification experiments, we compare three classifiers, a hashtag-based baseline, a logistic regression classifier and a convolutional neural network (CNN)." + ], + [ + "Hashtags are often used as a means to assess the content of a tweet BIBREF25, BIBREF26, BIBREF27. We identify hashtags indicative of a class in the annotated dataset using the pointwise mutual information (pmi) between a hashtag $hs$ and a class $c$, which is defined as", + "We then predict the class for unseen tweets as the class that has the highest pmi score for the hashtags contained in the tweet. Tweets without hashtag (5% of the tweets in the development set) or with multiple hashtags leading to conflicting predictions (5% of the tweets in the development set) are labeled randomly. We refer to to this baseline as hs_pmi." + ], + [ + "As non-neural baseline we use a logistic regression model. We compute input representations for tweets as the average over pre-trained word embedding vectors for all words in the tweet. We use fasttext embeddings BIBREF28 that were pre-trained on Wikipedia." + ], + [ + "As neural classification model, we use a convolutional neural network (CNN) BIBREF29, which has previously shown good results for tweet classification BIBREF30, BIBREF27. The model performs 1d convolutions over a sequence of word embeddings. We use the same pre-trained fasttext embeddings as for the logistic regression model. We use a model with one convolutional layer and a relu activation function, and one max pooling layer. The number of filters is 100 and the filter size is set to 4." + ], + [ + "We evaluate the classification models using 10-fold cross validation, i.e. we produce 10 different datasplits by randomly sampling 60% of the data for training, 20% for development and 20% for testing. For each fold, we train each of the models described in Section SECREF4 on the training set and measure performance on the test set. For the CNN and LogReg models, we upsample the training examples such that each class has as many instances as the largest class (Neutral). The final reported scores are averages over the 10 splits." + ], + [ + "Before embedding the tweets, we replace urls, retweet syntax (RT @user_name: ) and @mentions (@user_name) by placeholders. We lowercase all text and tokenize sentences using the StandfordNLP pipeline BIBREF31. If a tweet contains multiple sentences, these are concatenated. Finally, we remove all tokens that contain non-alphanumeric symbols (except for dashes and hashtags) and strip the hashtags from each token, in order to increase the number of words that are represented by a pre-trained word embedding." + ], + [ + "We report performance as F1-scores, which is the harmonic mean between precision and recall. As the class distribution is highly skewed and we are mainly interested in accurately classifying the classes with low support (pro-Russian and pro-Ukrainian), we report macro-averages over the classes. In addition to F1-scores, we report the area under the precision-recall curve (AUC). We compute an AUC score for each class by converting the classification task into a one-vs-all classification task." + ], + [ + "The results of our classification experiments are presented in Table TABREF21. Figure FIGREF22 shows the per-class precision-recall curves for the LogReg and CNN models as well as the confusion matrices between classes." + ], + [ + "We observe that the hashtag baseline performs poorly and does not improve over the random baseline. The CNN classifier outperforms the baselines as well as the LogReg model. It shows the highest improvement over the LogReg for the pro-Russian class. Looking at the confusion matrices, we observe that for the LogReg model, the fraction of True Positives is equal between the pro-Russian and the pro-Ukrainian class. The CNN model produces a higher amount of correct predictions for the pro-Ukrainian than for the pro-Russian class. The absolute number of pro-Russian True Positives is lower for the CNN, but so is in return the amount of misclassifications between the pro-Russian and pro-Ukrainian class." + ], + [ + "With respect to the per class performance, we observe a similar trend across models, which is that the models perform best for the neutral class, whereas performance is lower for the pro-Ukrainian and pro-Russian classes. All models perform worst on the pro-Russian class, which might be due to the fact that it is the class with the fewest instances in the dataset.", + "Considering these results, we conclude that the CNN is the best performing model and also the classifier that best serves our goals, as we want to produce accurate predictions for the pro-Russian and pro-Ukrainian class without confusing between them. Even though the CNN can improve over the other models, the classification performance for the pro-Russian and pro-Ukrainian class is rather low. One obvious reason for this might be the small amount of training data, in particular for the pro-Russian class.", + "In the following, we briefly report a negative result on an attempt to combat the data sparseness with cross-lingual transfer. We then perform an error analysis on the CNN classifications to shed light on the difficulties of the task." + ], + [ + "The annotations in the MH17 dataset are highly imbalanced, with as few as 512 annotated examples for the pro-Russian class. As the annotated examples were sampled from the dataset at random, we assume that there are only few tweets with pro-Russian stance in the dataset. This observation is in line with studies that showed that the amount of disinformation on Twitter is in fact small BIBREF6, BIBREF8. In order to find more pro-Russian training examples, we turn to a resource that we expect to contain large amounts of pro-Russian (dis)information. The Elections integrity dataset was released by Twitter in 2018 and contains the tweets and account information for 3,841 accounts that are believed to be Russian trolls financed by the Russian government. While most tweets posted after late 2014 are in English language and focus on topics around the US elections, the earlier tweets in the dataset are primarily in Russian language and focus on the Ukraine crisis BIBREF33. One feature of the dataset observed by BIBREF33 is that several hashtags show high peakedness BIBREF34, i.e. they are posted with high frequency but only during short intervals, while others are persistent during time.", + "We find two hashtags in the Elections integrity dataset with high peakedness that were exclusively posted within 2 days after the MH17 crash and that seem to be pro-Russian in the context of responsibility for the MH17 crash: russian #\u041a\u0438\u0435\u0432\u0421\u043a\u0430\u0436\u0438\u041f\u0440\u0430\u0432\u0434\u0443 (Kiew tell the truth) and russian #\u041a\u0438\u0435\u0432\u0441\u0431\u0438\u043b\u0431\u043e\u0438\u043d\u0433 (Kiew made the plane go down). We collect all tweets with these two hashtags, resulting in 9,809 Russian tweets that we try to use as additional training data for the pro-Russian class in the MH17 dataset. We experiment with cross-lingual transfer by embedding tweets via aligned English and Russian word embeddings. However, so far results for the cross-lingual models do not improve over the CNN model trained on only English data. This might be due to the fact that the additional Russian tweets rather contain a general pro-Russian frame than specifically talking about the crash, but needs further investigation." + ], + [ + "In order to integrate automatically labeled examples into a network analysis that studies the flow of polarized information in the network, we need to produce high precision predictions for the pro-Russian and the pro-Ukrainian class. Polarized tweets that are incorrectly classified as neutral will hurt an analysis much less than neutral tweets that are erroneously classified as pro-Russian or pro-Ukrainian. However, the worst type of confusion is between the pro-Russian and pro-Ukrainian class. In order to gain insights into why these confusions happen, we manually inspect incorrectly predicted examples that are confused between the pro-Russian and pro-Ukrainian class. We analyse the misclassifications in the development set of all 10 runs, which results in 73 False Positives of pro-Ukrainian tweets being classified as pro-Russian (referred to as pro-Russian False Positives), and 88 False Positives of pro-Russian tweets being classified as pro-Ukrainian (referred to as pro-Ukrainian False Positives). We can identify three main cases for which the model produces an error:", + "the correct class can be directly inferred from the text content easily, even without background knowledge", + "the correct class can be inferred from the text content, given that event-specific knowledge is provided", + "the correct class can be inferred from the text content if the text is interpreted correctly", + "For the pro-Russian False Positives, we find that 42% of the errors are category I and II errors, respectively, and 15% of category III. For the pro-Ukrainian False Positives, we find 48% category I errors, 33% category II errors and and 13% category III errors. Table TABREF28 presents examples for each of the error categories in both sets which we will discuss in the following." + ], + [ + "Category I errors could easily be classified by humans following the annotation guidelines (see Section SECREF3). One difficulty can be seen in example f). Even though no background knowledge is needed to interpret the content, interpretation is difficult because of the convoluted syntax of the tweet. For the other examples it is unclear why the model would have difficulties with classifying them." + ], + [ + "Category II errors can only be classified with event-specific background knowledge. Examples g), i) and k) relate to the theory that a Ukrainian SU25 fighter jet shot down the plane in air. Correct interpretation of these tweets depends on knowledge about the SU25 fighter jet. In order to correctly interpret example j) as pro-Russian, it has to be known that the bellingcat report is pro-Ukrainian. Example l) relates to the theory that the shoot down was a false flag operation run by Western countries and the bodies in the plane were already dead before the crash. In order to correctly interpret example m), the identity of Kolomoisky has to be known. He is an anti-separatist Ukrainian billionaire, hence his involvement points to the Ukrainian government being responsible for the crash." + ], + [ + "Category III errors occur for examples that can only be classified by correctly interpreting the tweet authors' intention. Interpretation is difficult due to phenomena such as irony as in examples n) and o). While the irony is indicated in example n) through the use of the hashtag #LOL, there is no explicit indication in example o).", + "Interpretation of example q) is conditioned on world knowledge as well as the understanding of the speakers beliefs. Example r) is pro-Russian as it questions the validity of the assumption AC360 is making, but we only know that because we know that the assumption is absurd. Example s) requires to evaluate that the speaker thinks people on site are trusted more than people at home.", + "From the error analysis, we conclude that category I errors need further investigation, as here the model makes mistakes on seemingly easy instances. This might be due to the model not being able to correctly represent Twitter specific language or unknown words, such as Eukraine in example e). Category II and III errors are harder to avoid and could be improved by applying reasoning BIBREF36 or irony detection methods BIBREF37." + ], + [ + "Finally, we apply the CNN classifier to label new edges in BIBREF4's retweet network, which is shown in Figure FIGREF35. The retweet network is a graph that contains users as nodes and an edge between two users if the users are retweeting each other. In order to track the flow of polarized information, BIBREF4 label an edge as polarized if at least one tweet contained in the edge was manually annotated as pro-Russian or pro-Ukrainian. While the network shows a clear polarization, only a small subset of the edges present in the network are labeled (see Table TABREF38).", + "Automatic polarity prediction of tweets can help the analysis in two ways. Either, we can label a previously unlabeled edge, or we can verify/confirm the manual labeling of an edge, by labeling additional tweets that are comprised in the edge." + ], + [ + "In order to get high precision predictions for unlabeled tweets, we choose the probability thresholds for predicting a pro-Russian or pro-Ukrainian tweet such that the classifier would achieve 80% precision on the test splits (recall at this precision level is 23%). Table TABREF38 shows the amount of polarized edges we can predict at this precision level. Upon manual inspection, we however find that the quality of predictions is lower than estimated. Hence, we manually re-annotate the pro-Russian and pro-Ukrainian predictions according to the official annotation guidelines used by BIBREF4. This way, we can label 77 new pro-Russian edges by looking at 415 tweets, which means that 19% of the candidates are hits. For the pro-Ukrainian class, we can label 110 new edges by looking at 611 tweets (18% hits). Hence even though the quality of the classifier predictions is too low to be integrated into the network analysis right away, the classifier drastically facilitates the annotation process for human annotators compared to annotating unfiltered tweets (from the original labels we infer that for unfiltered tweets, only 6% are hits for the pro-Russian class, and 11% for the pro-Ukrainian class)." + ], + [ + "In this work, we investigated the usefulness of text classifiers to detect pro-Russian and pro-Ukrainian framing in tweets related to the MH17 crash, and to which extent classifier predictions can be relied on for producing high quality annotations. From our classification experiments, we conclude that the real-world applicability of text classifiers for labeling polarized tweets in a retweet network is restricted to pre-filtering tweets for manual annotation. However, if used as a filter, the classifier can significantly speed up the annotation process, making large-scale content analysis more feasible." + ], + [ + "We thank the anonymous reviewers for their helpful comments. The research was carried out as part of the \u2018Digital Disinformation\u2019 project, which was directed by Rebecca Adler-Nissen and funded by the Carlsberg Foundation (project number CF16-0012)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0615/instruction.md b/qasper-0615/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..c1f2e6163ef55ade8157c67bb548a5a59e42ec80 --- /dev/null +++ b/qasper-0615/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Mapping (Dis-)Information Flow about the MH17 Plane Crash + +Question: What is the hashtags does the hashtag-based baseline use? \ No newline at end of file diff --git a/qasper-0622/instruction.md b/qasper-0622/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..dd775377423b61eb14ff22040f5786e8af31d021 --- /dev/null +++ b/qasper-0622/instruction.md @@ -0,0 +1,32 @@ +Name of Paper: Conversational Intent Understanding for Passengers in Autonomous Vehicles + +Question: What intents does the paper explore? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Methodology", + "Experimental Results", + "Conclusion" + ], + "paragraphs": [ + [ + "Understanding passenger intents and extracting relevant slots are important building blocks towards developing a contextual dialogue system responsible for handling certain vehicle-passenger interactions in autonomous vehicles (AV). When the passengers give instructions to AMIE (Automated-vehicle Multimodal In-cabin Experience), the agent should parse such commands properly and trigger the appropriate functionality of the AV system. In our AMIE scenarios, we describe usages and support various natural commands for interacting with the vehicle. We collected a multimodal in-cabin data-set with multi-turn dialogues between the passengers and AMIE using a Wizard-of-Oz scheme. We explored various recent Recurrent Neural Networks (RNN) based techniques and built our own hierarchical models to recognize passenger intents along with relevant slots associated with the action to be performed in AV scenarios. Our experimental results achieved F1-score of 0.91 on utterance-level intent recognition and 0.96 on slot extraction models." + ], + [ + "Our AV in-cabin data-set includes 30 hours of multimodal data collected from 30 passengers (15 female, 15 male) in 20 rides/sessions. 10 types of passenger intents are identified and annotated as: Set/Change Destination, Set/Change Route (including turn-by-turn instructions), Go Faster, Go Slower, Stop, Park, Pull Over, Drop Off, Open Door, and Other (turn music/radio on/off, open/close window/trunk, change AC/temp, show map, etc.). Relevant slots are identified and annotated as: Location, Position/Direction, Object, Time-Guidance, Person, Gesture/Gaze (this, that, over there, etc.), and None. In addition to utterance-level intent types and their slots, word-level intent keywords are annotated as Intent as well. We obtained 1260 unique utterances having commands to AMIE from our in-cabin data-set. We expanded this data-set via Amazon Mechanical Turk and ended up with 3347 utterances having intents. The annotations for intents and slots are obtained on the transcribed utterances by majority voting of 3 annotators.", + "For slot filling and intent keywords extraction tasks, we experimented with seq2seq LSTMs and GRUs, and also Bidirectional LSTM/GRUs. The passenger utterance is fed into a Bi-LSTM network via an embedding layer as a sequence of words, which are transformed into word vectors. We also experimented with GloVe, word2vec, and fastText as pre-trained word embeddings. To prevent overfitting, a dropout layer is used for regularization. Best performing results are obtained with Bi-LSTMs and GloVe embeddings (6B tokens, 400K vocab size, dim 100).", + "For utterance-level intent detection, we experimented with mainly 5 models: (1) Hybrid: RNN + Rule-based, (2) Separate: Seq2one Bi-LSTM + Attention, (3) Joint: Seq2seq Bi-LSTM for slots/intent keywords & utterance-level intents, (4) Hierarchical + Separate, (5) Hierarchical + Joint. For (1), we extract intent keywords/slots (Bi-LSTM) and map them into utterance-level intent types (rule-based via term frequencies for each intent). For (2), we feed the whole utterance as input sequence and intent-type as single target. For (3), we experiment with the joint learning models BIBREF0 , BIBREF1 , BIBREF2 where we jointly train word-level intent keywords/slots and utterance-level intents (adding / terms to the start/end of utterances with intent types). For (4) and (5), we experiment with the hierarchical models BIBREF3 , BIBREF4 , BIBREF5 where we extract intent keywords/slots first, and then only feed the predicted keywords/slots as a sequence into (2) and (3), respectively." + ], + [ + "The slot extraction and intent keywords extraction results are given in Table TABREF1 and Table TABREF2 , respectively. Table TABREF3 summarizes the results of various approaches we investigated for utterance-level intent understanding. Table TABREF4 shows the intent-wise detection results for our AMIE scenarios with the best performing utterance-level intent recognizer." + ], + [ + "After exploring various recent Recurrent Neural Networks (RNN) based techniques, we built our own hierarchical joint models to recognize passenger intents along with relevant slots associated with the action to be performed in AV scenarios. Our experimental results outperformed certain competitive baselines and achieved overall F1-scores of 0.91 for utterance-level intent recognition and 0.96 for slot extraction tasks." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0623/instruction.md b/qasper-0623/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..d0f5877ffee8f85f375cb5552999f3da2a80f276 --- /dev/null +++ b/qasper-0623/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models + +Question: What kind of features are used by the HMM models, and how interpretable are those? \ No newline at end of file diff --git a/qasper-0624/instruction.md b/qasper-0624/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..1960d542ac3a824b6e050e1a95343258a26f1ea6 --- /dev/null +++ b/qasper-0624/instruction.md @@ -0,0 +1,60 @@ +Name of Paper: Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models + +Question: What kind of information do the HMMs learn that the LSTMs don't? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Methods", + "LSTM models", + "Hidden Markov models", + "Hybrid models", + "Experiments", + "Conclusion and future work" + ], + "paragraphs": [ + [ + "Following the recent progress in deep learning, researchers and practitioners of machine learning are recognizing the importance of understanding and interpreting what goes on inside these black box models. Recurrent neural networks have recently revolutionized speech recognition and translation, and these powerful models could be very useful in other applications involving sequential data. However, adoption has been slow in applications such as health care, where practitioners are reluctant to let an opaque expert system make crucial decisions. If we can make the inner workings of RNNs more interpretable, more applications can benefit from their power.", + "There are several aspects of what makes a model or algorithm understandable to humans. One aspect is model complexity or parsimony. Another aspect is the ability to trace back from a prediction or model component to particularly influential features in the data BIBREF0 BIBREF1 . This could be useful for understanding mistakes made by neural networks, which have human-level performance most of the time, but can perform very poorly on seemingly easy cases. For instance, convolutional networks can misclassify adversarial examples with very high confidence BIBREF2 , and made headlines in 2015 when the image tagging algorithm in Google Photos mislabeled African Americans as gorillas. It's reasonable to expect recurrent networks to fail in similar ways as well. It would thus be useful to have more visibility into where these sorts of errors come from, i.e. which groups of features contribute to such flawed predictions.", + "Several promising approaches to interpreting RNNs have been developed recently. BIBREF3 have approached this by using gradient boosting trees to predict LSTM output probabilities and explain which features played a part in the prediction. They do not model the internal structure of the LSTM, but instead approximate the entire architecture as a black box. BIBREF4 showed that in LSTM language models, around 10% of the memory state dimensions can be interpreted with the naked eye by color-coding the text data with the state values; some of them track quotes, brackets and other clearly identifiable aspects of the text. Building on these results, we take a somewhat more systematic approach to looking for interpretable hidden state dimensions, by using decision trees to predict individual hidden state dimensions (Figure 2 ). We visualize the overall dynamics of the hidden states by coloring the training data with the k-means clusters on the state vectors (Figures 3 , 3 ).", + "We explore several methods for building interpretable models by combining LSTMs and HMMs. The existing body of literature mostly focuses on methods that specifically train the RNN to predict HMM states BIBREF5 or posteriors BIBREF6 , referred to as hybrid or tandem methods respectively. We first investigate an approach that does not require the RNN to be modified in order to make it understandable, as the interpretation happens after the fact. Here, we model the big picture of the state changes in the LSTM, by extracting the hidden states and approximating them with a continuous emission hidden Markov model (HMM). We then take the reverse approach where the HMM state probabilities are added to the output layer of the LSTM (see Figure 1 ). The LSTM model can then make use of the information from the HMM, and fill in the gaps when the HMM is not performing well, resulting in an LSTM with a smaller number of hidden state dimensions that could be interpreted individually (Figures 3 , 3 )." + ], + [ + "We compare a hybrid HMM-LSTM approach with a continuous emission HMM (trained on the hidden states of a 2-layer LSTM), and a discrete emission HMM (trained directly on data)." + ], + [ + "We use a character-level LSTM with 1 layer and no dropout, based on the Element-Research library. We train the LSTM for 10 epochs, starting with a learning rate of 1, where the learning rate is halved whenever $\\exp (-l_t) > \\exp (-l_{t-1}) + 1$ , where $l_t$ is the log likelihood score at epoch $t$ . The $L_2$ -norm of the parameter gradient vector is clipped at a threshold of 5." + ], + [ + "The HMM training procedure is as follows:", + "Initialization of HMM hidden states:", + "(Discrete HMM) Random multinomial draw for each time step (i.i.d. across time steps).", + "(Continuous HMM) K-means clusters fit on LSTM states, to speed up convergence relative to random initialization.", + "At each iteration:", + "Sample states using Forward Filtering Backwards Sampling algorithm (FFBS, BIBREF7 ).", + "Sample transition parameters from a Multinomial-Dirichlet posterior. Let $n_{ij}$ be the number of transitions from state $i$ to state $j$ . Then the posterior distribution of the $i$ -th row of transition matrix $T$ (corresponding to transitions from state $i$ ) is: $T_i \\sim \\text{Mult}(n_{ij} | T_i) \\text{Dir}(T_i | \\alpha )$ ", + "where $\\alpha $ is the Dirichlet hyperparameter.", + "(Continuous HMM) Sample multivariate normal emission parameters from Normal-Inverse-Wishart posterior for state $i$ : $ \\mu _i, \\Sigma _i \\sim N(y|\\mu _i, \\Sigma _i) N(\\mu _i |0, \\Sigma _i) \\text{IW}(\\Sigma _i) $ ", + "(Discrete HMM) Sample the emission parameters from a Multinomial-Dirichlet posterior.", + "Evaluation:", + "We evaluate the methods on how well they predict the next observation in the validation set. For the HMM models, we do a forward pass on the validation set (no backward pass unlike the full FFBS), and compute the HMM state distribution vector $p_t$ for each time step $t$ . Then we compute the predictive likelihood for the next observation as follows: $ P(y_{t+1} | p_t) =\\sum _{x_t=1}^n \\sum _{x_{t+1}=1}^n p_{tx_t} \\cdot T_{x_t, x_{t+1}} \\cdot P(y_{t+1} | x_{t+1})$ ", + "where $n$ is the number of hidden states in the HMM." + ], + [ + "Our main hybrid model is put together sequentially, as shown in Figure 1 . We first run the discrete HMM on the data, outputting the hidden state distributions obtained by the HMM's forward pass, and then add this information to the architecture in parallel with a 1-layer LSTM. The linear layer between the LSTM and the prediction layer is augmented with an extra column for each HMM state. The LSTM component of this architecture can be smaller than a standalone LSTM, since it only needs to fill in the gaps in the HMM's predictions. The HMM is written in Python, and the rest of the architecture is in Torch.", + "We also build a joint hybrid model, where the LSTM and HMM are simultaneously trained in Torch. We implemented an HMM Torch module, optimized using stochastic gradient descent rather than FFBS. Similarly to the sequential hybrid model, we concatenate the LSTM outputs with the HMM state probabilities." + ], + [ + "We test the models on several text data sets on the character level: the Penn Tree Bank (5M characters), and two data sets used by BIBREF4 , Tiny Shakespeare (1M characters) and Linux Kernel (5M characters). We chose $k=20$ for the continuous HMM based on a PCA analysis of the LSTM states, as the first 20 components captured almost all the variance.", + "Table 1 shows the predictive log likelihood of the next text character for each method. On all text data sets, the hybrid algorithm performs a bit better than the standalone LSTM with the same LSTM state dimension. This effect gets smaller as we increase the LSTM size and the HMM makes less difference to the prediction (though it can still make a difference in terms of interpretability). The hybrid algorithm with 20 HMM states does better than the one with 10 HMM states. The joint hybrid algorithm outperforms the sequential hybrid on Shakespeare data, but does worse on PTB and Linux data, which suggests that the joint hybrid is more helpful for smaller data sets. The joint hybrid is an order of magnitude slower than the sequential hybrid, as the SGD-based HMM is slower to train than the FFBS-based HMM.", + "We interpret the HMM and LSTM states in the hybrid algorithm with 10 LSTM state dimensions and 10 HMM states in Figures 3 and 3 , showing which features are identified by the HMM and LSTM components. In Figures 3 and 3 , we color-code the training data with the 10 HMM states. In Figures 3 and 3 , we apply k-means clustering to the LSTM state vectors, and color-code the training data with the clusters. The HMM and LSTM states pick up on spaces, indentation, and special characters in the data (such as comment symbols in Linux data). We see some examples where the HMM and LSTM complement each other, such as learning different things about spaces and comments on Linux data, or punctuation on the Shakespeare data. In Figure 2 , we see that some individual LSTM hidden state dimensions identify similar features, such as comment symbols in the Linux data." + ], + [ + "Hybrid HMM-RNN approaches combine the interpretability of HMMs with the predictive power of RNNs. Sometimes, a small hybrid model can perform better than a standalone LSTM of the same size. We use visualizations to show how the LSTM and HMM components of the hybrid algorithm complement each other in terms of features learned in the data." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0625/instruction.md b/qasper-0625/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..34b055ef8625819fe4764ecbb36620d33c7b45cb --- /dev/null +++ b/qasper-0625/instruction.md @@ -0,0 +1,60 @@ +Name of Paper: Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models + +Question: Which methods do the authors use to reach the conclusion that LSTMs and HMMs learn complementary information? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Methods", + "LSTM models", + "Hidden Markov models", + "Hybrid models", + "Experiments", + "Conclusion and future work" + ], + "paragraphs": [ + [ + "Following the recent progress in deep learning, researchers and practitioners of machine learning are recognizing the importance of understanding and interpreting what goes on inside these black box models. Recurrent neural networks have recently revolutionized speech recognition and translation, and these powerful models could be very useful in other applications involving sequential data. However, adoption has been slow in applications such as health care, where practitioners are reluctant to let an opaque expert system make crucial decisions. If we can make the inner workings of RNNs more interpretable, more applications can benefit from their power.", + "There are several aspects of what makes a model or algorithm understandable to humans. One aspect is model complexity or parsimony. Another aspect is the ability to trace back from a prediction or model component to particularly influential features in the data BIBREF0 BIBREF1 . This could be useful for understanding mistakes made by neural networks, which have human-level performance most of the time, but can perform very poorly on seemingly easy cases. For instance, convolutional networks can misclassify adversarial examples with very high confidence BIBREF2 , and made headlines in 2015 when the image tagging algorithm in Google Photos mislabeled African Americans as gorillas. It's reasonable to expect recurrent networks to fail in similar ways as well. It would thus be useful to have more visibility into where these sorts of errors come from, i.e. which groups of features contribute to such flawed predictions.", + "Several promising approaches to interpreting RNNs have been developed recently. BIBREF3 have approached this by using gradient boosting trees to predict LSTM output probabilities and explain which features played a part in the prediction. They do not model the internal structure of the LSTM, but instead approximate the entire architecture as a black box. BIBREF4 showed that in LSTM language models, around 10% of the memory state dimensions can be interpreted with the naked eye by color-coding the text data with the state values; some of them track quotes, brackets and other clearly identifiable aspects of the text. Building on these results, we take a somewhat more systematic approach to looking for interpretable hidden state dimensions, by using decision trees to predict individual hidden state dimensions (Figure 2 ). We visualize the overall dynamics of the hidden states by coloring the training data with the k-means clusters on the state vectors (Figures 3 , 3 ).", + "We explore several methods for building interpretable models by combining LSTMs and HMMs. The existing body of literature mostly focuses on methods that specifically train the RNN to predict HMM states BIBREF5 or posteriors BIBREF6 , referred to as hybrid or tandem methods respectively. We first investigate an approach that does not require the RNN to be modified in order to make it understandable, as the interpretation happens after the fact. Here, we model the big picture of the state changes in the LSTM, by extracting the hidden states and approximating them with a continuous emission hidden Markov model (HMM). We then take the reverse approach where the HMM state probabilities are added to the output layer of the LSTM (see Figure 1 ). The LSTM model can then make use of the information from the HMM, and fill in the gaps when the HMM is not performing well, resulting in an LSTM with a smaller number of hidden state dimensions that could be interpreted individually (Figures 3 , 3 )." + ], + [ + "We compare a hybrid HMM-LSTM approach with a continuous emission HMM (trained on the hidden states of a 2-layer LSTM), and a discrete emission HMM (trained directly on data)." + ], + [ + "We use a character-level LSTM with 1 layer and no dropout, based on the Element-Research library. We train the LSTM for 10 epochs, starting with a learning rate of 1, where the learning rate is halved whenever $\\exp (-l_t) > \\exp (-l_{t-1}) + 1$ , where $l_t$ is the log likelihood score at epoch $t$ . The $L_2$ -norm of the parameter gradient vector is clipped at a threshold of 5." + ], + [ + "The HMM training procedure is as follows:", + "Initialization of HMM hidden states:", + "(Discrete HMM) Random multinomial draw for each time step (i.i.d. across time steps).", + "(Continuous HMM) K-means clusters fit on LSTM states, to speed up convergence relative to random initialization.", + "At each iteration:", + "Sample states using Forward Filtering Backwards Sampling algorithm (FFBS, BIBREF7 ).", + "Sample transition parameters from a Multinomial-Dirichlet posterior. Let $n_{ij}$ be the number of transitions from state $i$ to state $j$ . Then the posterior distribution of the $i$ -th row of transition matrix $T$ (corresponding to transitions from state $i$ ) is: $T_i \\sim \\text{Mult}(n_{ij} | T_i) \\text{Dir}(T_i | \\alpha )$ ", + "where $\\alpha $ is the Dirichlet hyperparameter.", + "(Continuous HMM) Sample multivariate normal emission parameters from Normal-Inverse-Wishart posterior for state $i$ : $ \\mu _i, \\Sigma _i \\sim N(y|\\mu _i, \\Sigma _i) N(\\mu _i |0, \\Sigma _i) \\text{IW}(\\Sigma _i) $ ", + "(Discrete HMM) Sample the emission parameters from a Multinomial-Dirichlet posterior.", + "Evaluation:", + "We evaluate the methods on how well they predict the next observation in the validation set. For the HMM models, we do a forward pass on the validation set (no backward pass unlike the full FFBS), and compute the HMM state distribution vector $p_t$ for each time step $t$ . Then we compute the predictive likelihood for the next observation as follows: $ P(y_{t+1} | p_t) =\\sum _{x_t=1}^n \\sum _{x_{t+1}=1}^n p_{tx_t} \\cdot T_{x_t, x_{t+1}} \\cdot P(y_{t+1} | x_{t+1})$ ", + "where $n$ is the number of hidden states in the HMM." + ], + [ + "Our main hybrid model is put together sequentially, as shown in Figure 1 . We first run the discrete HMM on the data, outputting the hidden state distributions obtained by the HMM's forward pass, and then add this information to the architecture in parallel with a 1-layer LSTM. The linear layer between the LSTM and the prediction layer is augmented with an extra column for each HMM state. The LSTM component of this architecture can be smaller than a standalone LSTM, since it only needs to fill in the gaps in the HMM's predictions. The HMM is written in Python, and the rest of the architecture is in Torch.", + "We also build a joint hybrid model, where the LSTM and HMM are simultaneously trained in Torch. We implemented an HMM Torch module, optimized using stochastic gradient descent rather than FFBS. Similarly to the sequential hybrid model, we concatenate the LSTM outputs with the HMM state probabilities." + ], + [ + "We test the models on several text data sets on the character level: the Penn Tree Bank (5M characters), and two data sets used by BIBREF4 , Tiny Shakespeare (1M characters) and Linux Kernel (5M characters). We chose $k=20$ for the continuous HMM based on a PCA analysis of the LSTM states, as the first 20 components captured almost all the variance.", + "Table 1 shows the predictive log likelihood of the next text character for each method. On all text data sets, the hybrid algorithm performs a bit better than the standalone LSTM with the same LSTM state dimension. This effect gets smaller as we increase the LSTM size and the HMM makes less difference to the prediction (though it can still make a difference in terms of interpretability). The hybrid algorithm with 20 HMM states does better than the one with 10 HMM states. The joint hybrid algorithm outperforms the sequential hybrid on Shakespeare data, but does worse on PTB and Linux data, which suggests that the joint hybrid is more helpful for smaller data sets. The joint hybrid is an order of magnitude slower than the sequential hybrid, as the SGD-based HMM is slower to train than the FFBS-based HMM.", + "We interpret the HMM and LSTM states in the hybrid algorithm with 10 LSTM state dimensions and 10 HMM states in Figures 3 and 3 , showing which features are identified by the HMM and LSTM components. In Figures 3 and 3 , we color-code the training data with the 10 HMM states. In Figures 3 and 3 , we apply k-means clustering to the LSTM state vectors, and color-code the training data with the clusters. The HMM and LSTM states pick up on spaces, indentation, and special characters in the data (such as comment symbols in Linux data). We see some examples where the HMM and LSTM complement each other, such as learning different things about spaces and comments on Linux data, or punctuation on the Shakespeare data. In Figure 2 , we see that some individual LSTM hidden state dimensions identify similar features, such as comment symbols in the Linux data." + ], + [ + "Hybrid HMM-RNN approaches combine the interpretability of HMMs with the predictive power of RNNs. Sometimes, a small hybrid model can perform better than a standalone LSTM of the same size. We use visualizations to show how the LSTM and HMM components of the hybrid algorithm complement each other in terms of features learned in the data." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0640/instruction.md b/qasper-0640/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..fc954c0d428c2ad3df9ee51debc8ead9805f6b83 --- /dev/null +++ b/qasper-0640/instruction.md @@ -0,0 +1,95 @@ +Name of Paper: Crowdsourcing a High-Quality Gold Standard for QA-SRL + +Question: How was quality measured? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Background \u2014 QA-SRL ::: Specifications", + "Background \u2014 QA-SRL ::: Corpora", + "Annotation and Evaluation Methods ::: Crowdsourcing Methodology ::: Screening and Training", + "Annotation and Evaluation Methods ::: Crowdsourcing Methodology ::: Annotation", + "Annotation and Evaluation Methods ::: Crowdsourcing Methodology ::: Guidelines Refinements", + "Annotation and Evaluation Methods ::: Crowdsourcing Methodology ::: Data & Cost", + "Annotation and Evaluation Methods ::: Evaluation Metrics", + "Annotation and Evaluation Methods ::: Evaluation Metrics ::: Evaluating Redundant Annotations", + "Dataset Quality Analysis ::: Inter-Annotator Agreement (IAA)", + "Dataset Quality Analysis ::: Dataset Assessment and Comparison", + "Dataset Quality Analysis ::: Agreement with PropBank Data", + "Baseline Parser Evaluation", + "Baseline Parser Evaluation ::: Error Analysis", + "Conclusion", + "Supplemental Material ::: The Question Template", + "Supplemental Material ::: Annotation Pipeline", + "Supplemental Material ::: Redundant Parser Output" + ], + "paragraphs": [ + [ + "Semantic Role Labeling (SRL) provides explicit annotation of predicate-argument relations, which have been found useful in various downstream tasks BIBREF0, BIBREF1, BIBREF2, BIBREF3. Question-Answer driven Semantic Role Labeling (QA-SRL) BIBREF4 is an SRL scheme in which roles are captured by natural language questions, while arguments represent their answers, making the annotations intuitive, semantically rich, and easily attainable by laymen. For example, in Table TABREF4, the question Who cut something captures the traditional \u201cagent\u201d role.", + "Previous attempts to annotate QA-SRL initially involved trained annotators BIBREF4 but later resorted to crowdsourcing BIBREF5 to achieve scalability. Naturally, employing crowd workers raises challenges when annotating semantic structures like SRL. As BIBREF5 acknowledged, the main shortage of the large-scale 2018 dataset is the lack of recall, estimated by experts to be in the lower 70s.", + "In light of this and other annotation inconsistencies, we propose an improved QA-SRL crowdsourcing protocol for high-quality annotation, allowing for substantially more reliable performance evaluation of QA-SRL parsers. To address worker quality, we systematically screen workers, provide concise yet effective guidelines, and perform a short training procedure, all within a crowd-sourcing platform. To address coverage, we employ two independent workers plus an additional one for consolidation \u2014 similar to conventional expert-annotation practices. In addition to yielding 25% more roles, our coverage gain is demonstrated by evaluating against expertly annotated data and comparison with PropBank (Section SECREF4). To foster future research, we release an assessed high-quality gold dataset along with our reproducible protocol and evaluation scheme, and report the performance of the existing parser BIBREF5 as a baseline." + ], + [ + "In QA-SRL, a role question adheres to a 7-slot template, with slots corresponding to a WH-word, the verb, auxiliaries, argument placeholders (SUBJ, OBJ), and prepositions, where some slots are optional BIBREF4 (see appendix for examples). Such question captures the corresponding semantic role with a natural easily understood expression. The set of all non-overlapping answers for the question is then considered as the set of arguments associated with that role. This broad question-based definition of roles captures traditional cases of syntactically-linked arguments, but also additional semantic arguments clearly implied by the sentence meaning (see example (2) in Table TABREF4)." + ], + [ + "The original 2015 QA-SRL dataset BIBREF4 was annotated by non-expert workers after completing a brief training procedure. They annotated 7.8K verbs, reporting an average of 2.4 QA pairs per predicate. Even though multiple annotators were shown to produce greater coverage, their released dataset was produced using only a single annotator per verb. In subsequent work, BIBREF5 constructed a large-scale corpus and used it to train a parser. They crowdsourced 133K verbs with 2.0 QA pairs per verb on average. Since crowd-workers had no prior training, quality was established using an additional validation step, where workers had to ascertain the validity of the question, but not of its answers. Instead, the validator provided additional answers, independent of the other annotators. Each verb in the corpus was annotated by a single QA-generating worker and validated by two others.", + "In a reserved part of the corpus (Dense), targeted for parser evaluation, verbs were densely validated with 5 workers, approving questions judged as valid by at least 4/5 validators. Notably, adding validators to the Dense annotation pipeline accounts mostly for precision errors, while role coverage solely relies upon the single generator's set of questions. As both 2015 and 2018 datasets use a single question generator, both struggle with maintaining coverage. Also noteworthy, is that while traditional SRL annotations contain a single authoritative and non-redundant annotation, the 2018 dataset provides the raw annotations of all annotators. These include many overlapping or noisy answers, without settling on consolidation procedures to provide a single gold reference.", + "We found that these characteristics of the dataset impede its utility for future development of parsers." + ], + [ + "Our pool of annotators is selected after several short training rounds, with up to 15 predicates per round, in which they received extensive personal feedback. 1 out of 3 participants were selected after exhibiting good performance, tested against expert annotations." + ], + [ + "We adopt the annotation machinery of BIBREF5 implemented using Amazon's Mechanical Turk, and annotate each predicate by 2 trained workers independently, while a third consolidates their annotations into a final set of roles and arguments. In this consolidation task, the worker validates questions, merges, splits or modifies answers for the same role according to guidelines, and removes redundant roles by picking the more naturally phrased questions. For example, in Table TABREF4 ex. 1, one worker could have chosen \u201c47 people\u201d, while another chose \u201cthe councillor\u201d; in this case the consolidator would include both of those answers. In Section SECREF4, we show that this process yields better coverage. For example annotations, please refer to the appendix." + ], + [ + "We refine the previous guidelines by emphasizing several semantic features: correctly using modal verbs and negations in the question, and choosing answers that coincide with a single entity (example 1 in Table TABREF4)." + ], + [ + "We annotated a sample taken from the Dense set on Wikinews and Wikipedia domains, each with 1000 sentences, equally divided between development and test. QA generating annotators are paid the same as in fitz2018qasrl, while the consolidator is rewarded 5\u00a2 per verb and 3\u00a2 per question. Per predicate, on average, our cost is 54.2\u00a2, yielding 2.9 roles, compared to reported 2.3 valid roles with an approximated cost of 51\u00a2 per predicate for Dense." + ], + [ + "Evaluation in QA-SRL involves aligning predicted and ground truth argument spans and evaluating role label equivalence. Since detecting question paraphrases is still an open challenge, we propose both unlabeled and labeled evaluation metrics.", + "Unlabeled Argument Detection (UA) Inspired by the method presented in BIBREF5, arguments are matched using a span matching criterion of intersection over union $\\ge 0.5$ . To credit each argument only once, we employ maximal bipartite matching between the two sets of arguments, drawing an edge for each pair that passes the above mentioned criterion. The resulting maximal matching determines the true-positive set, while remaining non-aligned arguments become false-positives or false-negatives.", + "Labeled Argument Detection (LA) All aligned arguments from the previous step are inspected for label equivalence, similar to the joint evaluation reported in BIBREF5. There may be many correct questions for a role. For example, What was given to someone? and What has been given by someone? both refer to the same semantic role but diverge in grammatical tense, voice, and presence of a syntactical object or subject. Aiming to avoid judging non-equivalent roles as equivalent, we propose Strict-Match to be an equivalence on the following template slots: WH, SUBJ, OBJ, as well as on negation, voice, and modality extracted from the question. Final reported numbers on labelled argument detection rates are based on bipartite aligned arguments passing Strict-Match. We later manually estimate the rate of correct equivalences missed by this conservative method.", + "As we will see, our evaluation heuristics, adapted from those in BIBREF5, significantly underestimate agreement between annotations, hence reflecting performance lower bounds. Devising more tight evaluation measures remains a challenge for future research." + ], + [ + "We extend our metric for evaluating manual or automatic redundant annotations, like the Dense dataset or the parser in BIBREF5, which predicts argument spans independently of each other. To that end, we ignore predicted arguments that match ground-truth but are not selected by the bipartite matching due to redundancy. After connecting unmatched predicted arguments that overlap, we count one false positive for every connected component to avoid penalizing precision too harshly when predictions are redundant." + ], + [ + "To estimate dataset consistency across different annotations, we measure F1 using our UA metric with 5 generators per predicate. Individual worker-vs-worker agreement yields 79.8 F1 over 10 experiments with 150 predicates, indicating high consistency across our annotators, inline with results by other structured semantic annotations (e.g. BIBREF6). Overall consistency of the dataset is assessed by measuring agreement between different consolidated annotations, obtained by disjoint triplets of workers, which achieves F1 of 84.1 over 4 experiments, each with 35 distinct predicates. Notably, consolidation boosts agreement, suggesting it is a necessity for semantic annotation consistency." + ], + [ + "We assess both our gold standard set and the recent Dense set against an integrated expert annotated sample of 100 predicates. To construct the expert set, we blindly merged the Dense set with our worker annotations and manually corrected them. We further corrected the evaluation decisions, accounting for some automatic evaluation mistakes introduced by the span-matching and question paraphrasing criteria. As seen in Table TABREF19, our gold set yields comparable precision with significantly higher recall, which is in line with our 25% higher yield.", + "Examining disagreements between our gold and Dense, we observe that our workers successfully produced more roles, both implied and explicit. To a lesser extent, they split more arguments into independent answers, as emphasized by our guidelines, an issue which was left under-specified in the previous annotation guidelines." + ], + [ + "It is illuminating to observe the agreement between QA-SRL and PropBank (CoNLL-2009) annotations BIBREF7. In Table TABREF22, we replicate the experiments in BIBREF4 for both our gold set and theirs, over a sample of 200 sentences from Wall Street Journal (agreement evaluation is automatic and the metric is somewhat similar to our UA). We report macro-averaged (over predicates) precision and recall for all roles, including core and adjuncts, while considering the PropBank data as the reference set. Our recall of the PropBank roles is notably high, reconfirming the coverage obtained by our annotation protocol.", + "The measured precision with respect to PropBank is low for adjuncts due to the fact that our annotators were capturing many correct arguments not covered in PropBank. To examine this, we analyzed 100 false positive arguments. Only 32 of those were due to wrong or incomplete QA annotations in our gold, while most others were outside of PropBank's scope, capturing either implied arguments or roles not covered in PropBank. Extrapolating from this manual analysis estimates our true precision (on all roles) to be about 91%, which is consistent with the 88% precision figure in Table TABREF19. Compared with 2015, our QA-SRL gold yielded 1593 annotations, with 989 core and 604 adjuncts, while theirs yielded 1315 annotations, 979 core and 336 adjuncts. Overall, the comparison to PropBank reinforces the quality of our gold dataset and shows its better coverage relative to the 2015 dataset." + ], + [ + "To illustrate the effectiveness of our new gold-standard, we use its Wikinews development set to evaluate the currently available parser from BIBREF5. For each predicate, the parser classifies every span for being an argument, independently of the other spans. Unlike many other SRL systems, this policy often produces outputs with redundant arguments (see appendix for examples). Results for 1200 predicates are reported in Table TABREF23, demonstrating reasonable performance along with substantial room for improvement, especially with respect to coverage. As expected, the parser's recall against our gold is substantially lower than the 84.2 recall reported in BIBREF5 against Dense, due to the limited recall of Dense relative to our gold set." + ], + [ + "We sample and evaluate 50 predicates to detect correct argument and paraphrase pairs that are skipped by the IOU and Strict-Match criteria. Based on this inspection, the parser completely misses 23% of the 154 roles present in the gold-data, out of which, 17% are implied. While the parser correctly predicts 82% of non-implied roles, it skips half of the implied ones." + ], + [ + "We introduced a refined crowdsourcing pipeline and a corresponding evaluation methodology for QA-SRL. It enabled us to release a new gold standard for evaluations, notably of much higher coverage of core and implied roles than the previous Dense evaluation dataset. We believe that our annotation methodology and dataset would facilitate future research on natural semantic annotations and QA-SRL parsing." + ], + [ + "For completeness, we include several examples with some questions restructured into its 7 template slots in Table TABREF26" + ], + [ + "As described in section 3 The consolidator receives two sets of QA annotations and merges them according to the guidelines to produce an exhaustive and consistent QA set. See Table TABREF28 for examples." + ], + [ + "As mentioned in the paper body, the Fitzgerald et al. parser generates redundant role questions and answers. The first two rows in Table TABREF30 illustrate different, partly redundant, argument spans for the same question. The next two rows illustrate two paraphrased questions for the same role. Generating such redundant output might complicate downstream use of the parser output as well as evaluation methodology." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0641/instruction.md b/qasper-0641/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..36a692ca2daaa181128574a9387b0c58e94dd082 --- /dev/null +++ b/qasper-0641/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Crowdsourcing a High-Quality Gold Standard for QA-SRL + +Question: How was the corpus obtained? \ No newline at end of file diff --git a/qasper-0646/instruction.md b/qasper-0646/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..c38f1de15a2985df6028be72c4516115631b8a4f --- /dev/null +++ b/qasper-0646/instruction.md @@ -0,0 +1,73 @@ +Name of Paper: Zero-Shot Cross-lingual Classification Using Multilingual Neural Machine Translation + +Question: Do the other multilingual baselines make use of the same amount of training data? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Proposed Method", + "Multilingual Representations Using NMT", + "Multilingual Encoder-Classifier", + "Corpora", + "Model and Training Details", + "Transfer Learning Results", + "Zero-Shot Classification Results", + "Analyses", + "Conclusion" + ], + "paragraphs": [ + [ + "Transfer learning has been shown to work well in Computer Vision where pre-trained components from a model trained on ImageNet BIBREF0 are used to initialize models for other tasks BIBREF1 . In most cases, the other tasks are related to and share architectural components with the ImageNet task, enabling the use of such pre-trained models for feature extraction. With this transfer capability, improvements have been obtained on other image classification datasets, and on other tasks such as object detection, action recognition, image segmentation, etc BIBREF2 . Analogously, we propose a method to transfer a pre-trained component - the multilingual encoder from an NMT system - to other NLP tasks.", + "In NLP, initializing word embeddings with pre-trained word representations obtained from Word2Vec BIBREF3 or GloVe BIBREF4 has become a common way of transferring information from large unlabeled data to downstream tasks. Recent work has further shown that we can improve over this approach significantly by considering representations in context, i.e. modeled depending on the sentences that contain them, either by taking the outputs of an encoder in MT BIBREF5 or by obtaining representations from the internal states of a bi-directional Language Model (LM) BIBREF6 . There has also been successful recent work in transferring sentence representations from resource-rich tasks to improve resource-poor tasks BIBREF7 , however, most of the above transfer learning examples have focused on transferring knowledge across tasks for a single language, in English.", + "Cross-lingual or multilingual NLP, the task of transferring knowledge from one language to another, serves as a good test bed for evaluating various transfer learning approaches. For cross-lingual NLP, the most widely studied approach is to use multilingual embeddings as features in neural network models. However, research has shown that representations learned in context are more effective BIBREF5 , BIBREF6 ; therefore, we aim at doing better than just using multilingual embeddings in the cross-lingual tasks. Recent progress in multilingual NMT provides a compelling opportunity for obtaining contextualized multilingual representations, as multilingual NMT systems are capable of generalizing to an unseen language direction, i.e. zero-shot translation. There is also evidence that the encoder of a multilingual NMT system learns language agnostic, universal interlingua representations, which can be further exploited BIBREF8 .", + "In this paper, we focus on using the representations obtained from a multilingual NMT system to enable cross-lingual transfer learning on downstream NLP tasks. Our contributions are three-fold:" + ], + [ + "We propose an Encoder-Classifier model, where the Encoder, leveraging the representations learned by a multilingual NMT model, converts an input sequence ${\\mathbf {x}}$ into a set of vectors C, and the Classifier predicts a class label $y$ given the encoding of the input sequence, C." + ], + [ + "Although there has been a large body of work in building multilingual NMT models which can translate between multiple languages at the same time BIBREF29 , BIBREF30 , BIBREF31 , BIBREF8 , zero-shot capabilities of such multilingual representations have only been tested for MT BIBREF8 . We propose a simple yet effective solution - reuse the encoder of a multilingual NMT model to initialize the encoder for other NLP tasks. To be able to achieve promising zero-shot classification performance, we consider two factors: (1) The ability to encode multiple source languages with the same encoder and (2) The ability to learn language agnostic representations of the source sequence. Based on the literature, both requirements can be satisfied by training a multilingual NMT model having a shared encoder BIBREF32 , BIBREF8 , and a separate decoder and attention mechanism for each target language BIBREF30 . After training such a multilingual NMT model, the decoder and the corresponding attention mechanisms (which are target-language specific) are discarded, while the multilingual encoder is used to initialize the encoder of our proposed Encoder-Classifier model." + ], + [ + "In order to leverage pre-trained multilingual representations introduced in Section \"Analyses\" , our encoder strictly follows the structure of a regular Recurrent Neural Network (RNN) based NMT encoder BIBREF33 with a stacked layout BIBREF34 . Given an input sequence ${\\mathbf {x}} = (x_{1}, x_{2}, \\ldots , x_{T_x})$ of length $T_x$ , our encoder contextualizes or encodes the input sequence into a set of vectors C, by first applying a bi-directional RNN BIBREF35 , followed by a stack of uni-directional RNNs. The hidden states of the final layer RNN, $h_i^l$ , form the set C $~=\\lbrace h_i^l \\rbrace _{i=1}^{T_x}$ of context vectors which will be used by the classifier, where $l$ denotes the number of RNN layers in the stacked encoder.", + "The task of the classifier is to predict a class label $y$ given the context set C. To ease this classification task given a variable length input set C, a common approach in the literature is to extract a single sentence vector $\\mathbf {q}$ by making use of pooling over time BIBREF36 . Further, to increase the modeling capacity, the pooling operation can be parameterized using pre- and post-pooling networks. Formally, given the context set C, we extract a sentence vector $\\mathbf {q}$ in three steps, using three networks, (1) pre-pooling feed-forward network $f_{pre}$ , (2) pooling network $f_{pool}$ and (3) post-pooling feed-forward network $f_{post}$ , $\n\\mathbf {q} = f_{post}( f_{pool} ( f_{pre} (\\textbf {C}) ) ).\n$ ", + " Finally, given the sentence vector $\\mathbf {q}$ , a class label $y$ is predicted by employing a softmax function." + ], + [ + "We evaluate the proposed method on three common NLP tasks: Amazon Reviews, SST and SNLI. We utilize parallel data to train our multilingual NMT system, as detailed below.", + "For the MT task, we use the WMT 2014 En $\\leftrightarrow $ Fr parallel corpus. The dataset contains 36 million En $\\rightarrow $ Fr sentence pairs. We swapped the source and target sentences to obtain parallel data for the Fr $\\rightarrow $ En translation task. We use these two datasets (72 million sentence pairs) to train a single multilingual NMT model to learn both these translation directions simultaneously. We generated a shared sub-word vocabulary BIBREF37 , BIBREF38 of 32K units from all source and target training data. We use this sub-word vocabulary for all of our experiments below.", + "The Amazon reviews dataset BIBREF39 is a multilingual sentiment classification dataset, providing data for four languages - English (En), French (Fr), German (De), and Japanese. We use the English and French datasets in our experiments. The dataset contains 6,000 documents in the train and test portions for each language. Each review consists of a category label, a title, a review, and a star rating (5-point scale). We only use the review text in our experiments. Following BIBREF39 , we mapped the reviews with lower scores (1 and 2) to negative examples and the reviews with higher scores (4 and 5) to positive examples, thereby turning it into a binary classification problem. Reviews with score 3 are dropped. We split the training dataset into 10% for development and the rest for training, and we truncate each example and keep the first 200 words in the review. Note that, since the data for each language was obtained by crawling different product pages, the data is not aligned across languages.", + "The sentiment classification task proposed in BIBREF9 is also a binary classification problem where each sentence and phrase is associated with either a positive or a negative sentiment. We ignore phrase-level annotations and sentence-level neutral examples in our experiments. The dataset contains 6920, 872, and 1821 examples for training, development and testing, respectively. Since SST does not provide a multilingual test set, we used the public translation engine Google Translate to translate the SST test set to French. Previous work by BIBREF40 has shown that replacing the human translated test set with a synthetic set (obtained by using Google Translate) produces only a small difference of around 1% absolute accuracy on their human-translated French SNLI test set. Therefore, the performance measured on our `pseudo' French SST test set is expected to be a good indicator of zero-shot performance.", + "Natural language inference is a task that aims to determine whether a natural language hypothesis $\\mathbf {h}$ can justifiably be inferred from a natural language premise $\\mathbf {p}$ . SNLI BIBREF10 is one of the largest datasets for a natural language inference task in English and contains multiple sentence pairs with a sentence-level entailment label. Each pair of sentences can have one of three labels - entailment, contradiction, and neutral, which are annotated by multiple humans. The dataset contains 550K training, 10K validation, and 10K testing examples. To enable research on multilingual SNLI, BIBREF40 chose a subset of the SNLI test set (1332 sentences) and professionally translated it into four major languages - Arabic, French, Russian, and Spanish. We use the French test set for evaluation in Section \"Zero-Shot Classification Results\" and \"Analyses\" ." + ], + [ + "Here, we first describe the model and training details of the base multilingual NMT model whose encoder is reused in all other tasks. Then we provide details about the task-specific classifiers. For each task, we provide the specifics of $f_{pre}$ , $f_{pool}$ and $f_{post}$ nets that build the task-specific classifier.", + "All the models in our experiments are trained using Adam optimizer BIBREF42 with label smoothing BIBREF43 and unless otherwise stated below, layer normalization BIBREF44 is applied to all LSTM gates and feed-forward layer inputs. We apply L2 regularization to the model weights and dropout to layer activations and sub-word embeddings. Hyper-parameters, such as mixing ratio $\\lambda $ of L2 regularization, dropout rates, label smoothing uncertainty, batch sizes, learning rate of optimizers and initialization ranges of weights are tuned on the development sets provided for each task separately.", + "Our multilingual NMT model consists of a shared multilingual encoder and two decoders, one for English and the other for French. The multilingual encoder uses one bi-directional LSTM, followed by three stacked layers of uni-directional LSTMs in the encoder. Each decoder consists of four stacked LSTM layers, with the first LSTM layers intertwined with additive attention networks BIBREF33 to learn a source-target alignment function. All the uni-directional LSTMs are equipped with residual connections BIBREF45 to ease the optimization, both in the encoder and the decoders. LSTM hidden units and the shared source-target embedding dimensions are set to 512.", + "Similar to BIBREF30 , multilingual NMT model is trained in a multi-task learning setup, where each decoder is augmented with a task-specific loss, minimizing the negative conditional log-likelihood of the target sequence given the source sequence. During training, mini-batches of En $\\rightarrow $ Fr and Fr $\\rightarrow $ En examples are interleaved. We picked the best model based on the best average development set BLEU score on both of the language pairs.", + "The Encoder-Classifier model here uses the encoder defined previously. With regards to the classifier, the pre- and post-pooling networks ( $f_{pre}$ , $f_{post}$ ) are both one-layer feed forward networks to cast the dimension size from 512 to 128 and from 128 to 32, respectively. We used max-pooling operator for the $f_{pool}$ network to pool the activation over time.", + "We extended the proposed Encoder-Classifier model to a multi-source model BIBREF46 since SNLI is an inference task of relations between two input sentences, \u201cpremise\" and \u201chypothesis\". For the two sources, we use two separate encoders, which are initialized with the same pre-trained multilingual NMT encoder, to obtain their representations. Following our notation, the encoder outputs are processed using $f_{pre}$ , $f_{pool}$ and $f_{post}$ nets, again with two separate network blocks. Specifically, $f_{pre}$ consists of a co-attention layer BIBREF47 followed by a two-layer feed-forward neural network with residual connections. We use max pooling over time for $f_{pool}$ and again a two-layer feed-forward neural network with residual connections as $f_{post}$ . After processing two sentence encodings using two network blocks, we obtain two vectors representing premise $\\mathbf {h}_{premise}$ and hypothesis $\\mathbf {h}_{hypothesis}$ . Following BIBREF48 , we compute two types of relational vectors with $\\mathbf {h}_{-} = |\\mathbf {h}_{premise} - \\mathbf {h}_{hypothesis}|,$ and $\\mathbf {h}_{\\times } = \\mathbf {h}_{premise} \\odot \\mathbf {h}_{hypothesis}$ , where $f_{pool}$0 denotes the element-wise multiplication between two vectors. The final relation vector is obtained by concatenating $f_{pool}$1 and $f_{pool}$2 . For both \u201cpremise\" and \u201chypothesis\" feed-forward networks we used 512 hidden dimensions.", + "For Amazon Reviews, SST and SNLI tasks, we picked the best model based on the highest development set accuracy." + ], + [ + "In this section, we report our results for the three tasks - Amazon Reviews (English and French), SST, and SNLI. For each task, we first build a baseline system using the proposed Encoder-Classifier architecture described in Section \"Proposed Method\" where the encoder is initialized randomly. Next, we experiment with using the pre-trained multilingual NMT encoder to initialize the system as described in Section \"Analyses\" . Finally, we perform an experiment where we freeze the encoder after initialization and only update the classifier component of the system.", + "Table 1 summarizes the accuracy of our proposed system for these three different approaches and the state-of-the-art results on all the tasks. The first row in the table shows the baseline accuracy of our system for all four datasets. The second row shows the result from initializing with a pre-trained multilingual NMT encoder. It can be seen that this provides a significant improvement in accuracy, an average of 4.63%, across all the tasks. This illustrates that the multilingual NMT encoder has successfully learned transferable contextualized representations that are leveraged by the classifier component of our proposed system. These results are in line with the results in BIBREF5 where the authors used the representations from the top NMT encoder layer as an additional input to the task-specific system. However, in our setup we reused all of the layers of the encoder as a single pre-trained component in the task-specific system. The third row shows the results from freezing the pre-trained encoder after initialization and only training the classifier component. For the Amazon English and French tasks, freezing the encoder after initialization significantly improves the performance further. We hypothesize that since the Amazon dataset is a document level classification task, the long input sequences are very different from the short sequences consumed by the NMT system and hence freezing the encoder seems to have a positive effect. This hypothesis is also supported by the SNLI and SST results, which contain sentence-level input sequences, where we did not find any significant difference between freezing and not freezing the encoder." + ], + [ + "In this section, we explore the zero-shot classification task in French for our systems. We assume that we do not have any French training data for all the three tasks and test how well our proposed method can generalize to the unseen French language without any further training. Specifically, we reuse the three proposed systems from Table 1 after being trained only on the English classification task and test the systems on data from an unseen language (e.g. French). A reasonable upper bound to which zero-shot performance should be compared to is bridging - translating a French test text to English and then applying the English classifier on the translated text. If we assume the translation to be perfect, we should expect this approach to perform as well as the English classifier.", + "The Amazon Reviews and SNLI tasks have a French test set available, and we evaluate the performance of the bridged and zero-shot systems on each French set. However, the SST dataset does not have a French test set, hence the `pseudo French' test set described in Section UID14 is used to evaluate the zero-shot performance. We use the English accuracy scores from the SST column in Table 1 as a high-quality proxy for the SST bridged system. We do this since translating the `pseudo French' back to English will result in two distinct translation steps and hence more errors.", + "Table 2 summarizes all of our zero-shot results for French classification on the three tasks. It can be seen that just by using the pre-trained NMT encoder, the zero-shot performance increases drastically from almost random to within 10% of the bridged system. Freezing the encoder further pushes this performance closer to the bridged system. On the Amazon Review task, our zero-shot system is within 2% of the best bridged system. On the SST task, our zero-shot system obtains an accuracy of 83.14% which is within 1.5% of the bridged equivalent (in this case the English system).", + "Finally, on SNLI, we compare our best zero-shot system with bilingual and multilingual embedding based methods evaluated on the same French test set in BIBREF40 . As illustrated in Table 3 , our best zero-shot system obtains the highest accuracy of 73.88%. INVERT BIBREF23 uses inverted indexing over a parallel corpus to obtain crosslingual word representations. BiCVM BIBREF25 learns bilingual compositional representations from sentence-aligned parallel corpora. In RANDOM BIBREF24 , bilingual embeddings are trained on top of parallel sentences with randomly shuffled tokens using skip-gram with negative sampling, and RATIO is similar to RANDOM with the one difference being that the tokens in the parallel sentences are not randomly shuffled. Our system significantly outperforms all methods listed in the second column by 10.66% to 15.24% and demonstrates the effectiveness of our proposed approach." + ], + [ + "In this section, we try to analyze why our simple Encoder-Classifier system is effective at zero-shot classification. We perform a series of experiments to better understand this phenomenon. In particular, we study (1) the effect of shared sub-word vocabulary, (2) the amount of multilingual training data to measure the influence of multilinguality, (3) encoder/classifier capacity to measure the influence of representation power, and (4) model behavior on different training phases to assess the relation between generalization performance on English and zero-shot performance on French." + ], + [ + "In this paper, we have demonstrated a simple yet effective approach to perform cross-lingual transfer learning using representations from a multilingual NMT model. Our proposed approach of reusing the encoder from a multilingual NMT system as a pre-trained component provides significant improvements on three downstream tasks. Further, our approach enables us to perform surprisingly competitive zero-shot classification on an unseen language and outperforms cross-lingual embedding base methods. Finally, we end with a series of analyses which shed light on the factors that contribute to the zero-shot phenomenon. We hope that these results showcase the efficacy of multilingual NMT to learn transferable contextualized representations for many downstream tasks." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0647/instruction.md b/qasper-0647/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..8f54e05ca74e8a1ee3db07d2bb65ed6de318d2da --- /dev/null +++ b/qasper-0647/instruction.md @@ -0,0 +1,73 @@ +Name of Paper: Zero-Shot Cross-lingual Classification Using Multilingual Neural Machine Translation + +Question: How big is the impact of training data size on the performance of the multilingual encoder? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Proposed Method", + "Multilingual Representations Using NMT", + "Multilingual Encoder-Classifier", + "Corpora", + "Model and Training Details", + "Transfer Learning Results", + "Zero-Shot Classification Results", + "Analyses", + "Conclusion" + ], + "paragraphs": [ + [ + "Transfer learning has been shown to work well in Computer Vision where pre-trained components from a model trained on ImageNet BIBREF0 are used to initialize models for other tasks BIBREF1 . In most cases, the other tasks are related to and share architectural components with the ImageNet task, enabling the use of such pre-trained models for feature extraction. With this transfer capability, improvements have been obtained on other image classification datasets, and on other tasks such as object detection, action recognition, image segmentation, etc BIBREF2 . Analogously, we propose a method to transfer a pre-trained component - the multilingual encoder from an NMT system - to other NLP tasks.", + "In NLP, initializing word embeddings with pre-trained word representations obtained from Word2Vec BIBREF3 or GloVe BIBREF4 has become a common way of transferring information from large unlabeled data to downstream tasks. Recent work has further shown that we can improve over this approach significantly by considering representations in context, i.e. modeled depending on the sentences that contain them, either by taking the outputs of an encoder in MT BIBREF5 or by obtaining representations from the internal states of a bi-directional Language Model (LM) BIBREF6 . There has also been successful recent work in transferring sentence representations from resource-rich tasks to improve resource-poor tasks BIBREF7 , however, most of the above transfer learning examples have focused on transferring knowledge across tasks for a single language, in English.", + "Cross-lingual or multilingual NLP, the task of transferring knowledge from one language to another, serves as a good test bed for evaluating various transfer learning approaches. For cross-lingual NLP, the most widely studied approach is to use multilingual embeddings as features in neural network models. However, research has shown that representations learned in context are more effective BIBREF5 , BIBREF6 ; therefore, we aim at doing better than just using multilingual embeddings in the cross-lingual tasks. Recent progress in multilingual NMT provides a compelling opportunity for obtaining contextualized multilingual representations, as multilingual NMT systems are capable of generalizing to an unseen language direction, i.e. zero-shot translation. There is also evidence that the encoder of a multilingual NMT system learns language agnostic, universal interlingua representations, which can be further exploited BIBREF8 .", + "In this paper, we focus on using the representations obtained from a multilingual NMT system to enable cross-lingual transfer learning on downstream NLP tasks. Our contributions are three-fold:" + ], + [ + "We propose an Encoder-Classifier model, where the Encoder, leveraging the representations learned by a multilingual NMT model, converts an input sequence ${\\mathbf {x}}$ into a set of vectors C, and the Classifier predicts a class label $y$ given the encoding of the input sequence, C." + ], + [ + "Although there has been a large body of work in building multilingual NMT models which can translate between multiple languages at the same time BIBREF29 , BIBREF30 , BIBREF31 , BIBREF8 , zero-shot capabilities of such multilingual representations have only been tested for MT BIBREF8 . We propose a simple yet effective solution - reuse the encoder of a multilingual NMT model to initialize the encoder for other NLP tasks. To be able to achieve promising zero-shot classification performance, we consider two factors: (1) The ability to encode multiple source languages with the same encoder and (2) The ability to learn language agnostic representations of the source sequence. Based on the literature, both requirements can be satisfied by training a multilingual NMT model having a shared encoder BIBREF32 , BIBREF8 , and a separate decoder and attention mechanism for each target language BIBREF30 . After training such a multilingual NMT model, the decoder and the corresponding attention mechanisms (which are target-language specific) are discarded, while the multilingual encoder is used to initialize the encoder of our proposed Encoder-Classifier model." + ], + [ + "In order to leverage pre-trained multilingual representations introduced in Section \"Analyses\" , our encoder strictly follows the structure of a regular Recurrent Neural Network (RNN) based NMT encoder BIBREF33 with a stacked layout BIBREF34 . Given an input sequence ${\\mathbf {x}} = (x_{1}, x_{2}, \\ldots , x_{T_x})$ of length $T_x$ , our encoder contextualizes or encodes the input sequence into a set of vectors C, by first applying a bi-directional RNN BIBREF35 , followed by a stack of uni-directional RNNs. The hidden states of the final layer RNN, $h_i^l$ , form the set C $~=\\lbrace h_i^l \\rbrace _{i=1}^{T_x}$ of context vectors which will be used by the classifier, where $l$ denotes the number of RNN layers in the stacked encoder.", + "The task of the classifier is to predict a class label $y$ given the context set C. To ease this classification task given a variable length input set C, a common approach in the literature is to extract a single sentence vector $\\mathbf {q}$ by making use of pooling over time BIBREF36 . Further, to increase the modeling capacity, the pooling operation can be parameterized using pre- and post-pooling networks. Formally, given the context set C, we extract a sentence vector $\\mathbf {q}$ in three steps, using three networks, (1) pre-pooling feed-forward network $f_{pre}$ , (2) pooling network $f_{pool}$ and (3) post-pooling feed-forward network $f_{post}$ , $\n\\mathbf {q} = f_{post}( f_{pool} ( f_{pre} (\\textbf {C}) ) ).\n$ ", + " Finally, given the sentence vector $\\mathbf {q}$ , a class label $y$ is predicted by employing a softmax function." + ], + [ + "We evaluate the proposed method on three common NLP tasks: Amazon Reviews, SST and SNLI. We utilize parallel data to train our multilingual NMT system, as detailed below.", + "For the MT task, we use the WMT 2014 En $\\leftrightarrow $ Fr parallel corpus. The dataset contains 36 million En $\\rightarrow $ Fr sentence pairs. We swapped the source and target sentences to obtain parallel data for the Fr $\\rightarrow $ En translation task. We use these two datasets (72 million sentence pairs) to train a single multilingual NMT model to learn both these translation directions simultaneously. We generated a shared sub-word vocabulary BIBREF37 , BIBREF38 of 32K units from all source and target training data. We use this sub-word vocabulary for all of our experiments below.", + "The Amazon reviews dataset BIBREF39 is a multilingual sentiment classification dataset, providing data for four languages - English (En), French (Fr), German (De), and Japanese. We use the English and French datasets in our experiments. The dataset contains 6,000 documents in the train and test portions for each language. Each review consists of a category label, a title, a review, and a star rating (5-point scale). We only use the review text in our experiments. Following BIBREF39 , we mapped the reviews with lower scores (1 and 2) to negative examples and the reviews with higher scores (4 and 5) to positive examples, thereby turning it into a binary classification problem. Reviews with score 3 are dropped. We split the training dataset into 10% for development and the rest for training, and we truncate each example and keep the first 200 words in the review. Note that, since the data for each language was obtained by crawling different product pages, the data is not aligned across languages.", + "The sentiment classification task proposed in BIBREF9 is also a binary classification problem where each sentence and phrase is associated with either a positive or a negative sentiment. We ignore phrase-level annotations and sentence-level neutral examples in our experiments. The dataset contains 6920, 872, and 1821 examples for training, development and testing, respectively. Since SST does not provide a multilingual test set, we used the public translation engine Google Translate to translate the SST test set to French. Previous work by BIBREF40 has shown that replacing the human translated test set with a synthetic set (obtained by using Google Translate) produces only a small difference of around 1% absolute accuracy on their human-translated French SNLI test set. Therefore, the performance measured on our `pseudo' French SST test set is expected to be a good indicator of zero-shot performance.", + "Natural language inference is a task that aims to determine whether a natural language hypothesis $\\mathbf {h}$ can justifiably be inferred from a natural language premise $\\mathbf {p}$ . SNLI BIBREF10 is one of the largest datasets for a natural language inference task in English and contains multiple sentence pairs with a sentence-level entailment label. Each pair of sentences can have one of three labels - entailment, contradiction, and neutral, which are annotated by multiple humans. The dataset contains 550K training, 10K validation, and 10K testing examples. To enable research on multilingual SNLI, BIBREF40 chose a subset of the SNLI test set (1332 sentences) and professionally translated it into four major languages - Arabic, French, Russian, and Spanish. We use the French test set for evaluation in Section \"Zero-Shot Classification Results\" and \"Analyses\" ." + ], + [ + "Here, we first describe the model and training details of the base multilingual NMT model whose encoder is reused in all other tasks. Then we provide details about the task-specific classifiers. For each task, we provide the specifics of $f_{pre}$ , $f_{pool}$ and $f_{post}$ nets that build the task-specific classifier.", + "All the models in our experiments are trained using Adam optimizer BIBREF42 with label smoothing BIBREF43 and unless otherwise stated below, layer normalization BIBREF44 is applied to all LSTM gates and feed-forward layer inputs. We apply L2 regularization to the model weights and dropout to layer activations and sub-word embeddings. Hyper-parameters, such as mixing ratio $\\lambda $ of L2 regularization, dropout rates, label smoothing uncertainty, batch sizes, learning rate of optimizers and initialization ranges of weights are tuned on the development sets provided for each task separately.", + "Our multilingual NMT model consists of a shared multilingual encoder and two decoders, one for English and the other for French. The multilingual encoder uses one bi-directional LSTM, followed by three stacked layers of uni-directional LSTMs in the encoder. Each decoder consists of four stacked LSTM layers, with the first LSTM layers intertwined with additive attention networks BIBREF33 to learn a source-target alignment function. All the uni-directional LSTMs are equipped with residual connections BIBREF45 to ease the optimization, both in the encoder and the decoders. LSTM hidden units and the shared source-target embedding dimensions are set to 512.", + "Similar to BIBREF30 , multilingual NMT model is trained in a multi-task learning setup, where each decoder is augmented with a task-specific loss, minimizing the negative conditional log-likelihood of the target sequence given the source sequence. During training, mini-batches of En $\\rightarrow $ Fr and Fr $\\rightarrow $ En examples are interleaved. We picked the best model based on the best average development set BLEU score on both of the language pairs.", + "The Encoder-Classifier model here uses the encoder defined previously. With regards to the classifier, the pre- and post-pooling networks ( $f_{pre}$ , $f_{post}$ ) are both one-layer feed forward networks to cast the dimension size from 512 to 128 and from 128 to 32, respectively. We used max-pooling operator for the $f_{pool}$ network to pool the activation over time.", + "We extended the proposed Encoder-Classifier model to a multi-source model BIBREF46 since SNLI is an inference task of relations between two input sentences, \u201cpremise\" and \u201chypothesis\". For the two sources, we use two separate encoders, which are initialized with the same pre-trained multilingual NMT encoder, to obtain their representations. Following our notation, the encoder outputs are processed using $f_{pre}$ , $f_{pool}$ and $f_{post}$ nets, again with two separate network blocks. Specifically, $f_{pre}$ consists of a co-attention layer BIBREF47 followed by a two-layer feed-forward neural network with residual connections. We use max pooling over time for $f_{pool}$ and again a two-layer feed-forward neural network with residual connections as $f_{post}$ . After processing two sentence encodings using two network blocks, we obtain two vectors representing premise $\\mathbf {h}_{premise}$ and hypothesis $\\mathbf {h}_{hypothesis}$ . Following BIBREF48 , we compute two types of relational vectors with $\\mathbf {h}_{-} = |\\mathbf {h}_{premise} - \\mathbf {h}_{hypothesis}|,$ and $\\mathbf {h}_{\\times } = \\mathbf {h}_{premise} \\odot \\mathbf {h}_{hypothesis}$ , where $f_{pool}$0 denotes the element-wise multiplication between two vectors. The final relation vector is obtained by concatenating $f_{pool}$1 and $f_{pool}$2 . For both \u201cpremise\" and \u201chypothesis\" feed-forward networks we used 512 hidden dimensions.", + "For Amazon Reviews, SST and SNLI tasks, we picked the best model based on the highest development set accuracy." + ], + [ + "In this section, we report our results for the three tasks - Amazon Reviews (English and French), SST, and SNLI. For each task, we first build a baseline system using the proposed Encoder-Classifier architecture described in Section \"Proposed Method\" where the encoder is initialized randomly. Next, we experiment with using the pre-trained multilingual NMT encoder to initialize the system as described in Section \"Analyses\" . Finally, we perform an experiment where we freeze the encoder after initialization and only update the classifier component of the system.", + "Table 1 summarizes the accuracy of our proposed system for these three different approaches and the state-of-the-art results on all the tasks. The first row in the table shows the baseline accuracy of our system for all four datasets. The second row shows the result from initializing with a pre-trained multilingual NMT encoder. It can be seen that this provides a significant improvement in accuracy, an average of 4.63%, across all the tasks. This illustrates that the multilingual NMT encoder has successfully learned transferable contextualized representations that are leveraged by the classifier component of our proposed system. These results are in line with the results in BIBREF5 where the authors used the representations from the top NMT encoder layer as an additional input to the task-specific system. However, in our setup we reused all of the layers of the encoder as a single pre-trained component in the task-specific system. The third row shows the results from freezing the pre-trained encoder after initialization and only training the classifier component. For the Amazon English and French tasks, freezing the encoder after initialization significantly improves the performance further. We hypothesize that since the Amazon dataset is a document level classification task, the long input sequences are very different from the short sequences consumed by the NMT system and hence freezing the encoder seems to have a positive effect. This hypothesis is also supported by the SNLI and SST results, which contain sentence-level input sequences, where we did not find any significant difference between freezing and not freezing the encoder." + ], + [ + "In this section, we explore the zero-shot classification task in French for our systems. We assume that we do not have any French training data for all the three tasks and test how well our proposed method can generalize to the unseen French language without any further training. Specifically, we reuse the three proposed systems from Table 1 after being trained only on the English classification task and test the systems on data from an unseen language (e.g. French). A reasonable upper bound to which zero-shot performance should be compared to is bridging - translating a French test text to English and then applying the English classifier on the translated text. If we assume the translation to be perfect, we should expect this approach to perform as well as the English classifier.", + "The Amazon Reviews and SNLI tasks have a French test set available, and we evaluate the performance of the bridged and zero-shot systems on each French set. However, the SST dataset does not have a French test set, hence the `pseudo French' test set described in Section UID14 is used to evaluate the zero-shot performance. We use the English accuracy scores from the SST column in Table 1 as a high-quality proxy for the SST bridged system. We do this since translating the `pseudo French' back to English will result in two distinct translation steps and hence more errors.", + "Table 2 summarizes all of our zero-shot results for French classification on the three tasks. It can be seen that just by using the pre-trained NMT encoder, the zero-shot performance increases drastically from almost random to within 10% of the bridged system. Freezing the encoder further pushes this performance closer to the bridged system. On the Amazon Review task, our zero-shot system is within 2% of the best bridged system. On the SST task, our zero-shot system obtains an accuracy of 83.14% which is within 1.5% of the bridged equivalent (in this case the English system).", + "Finally, on SNLI, we compare our best zero-shot system with bilingual and multilingual embedding based methods evaluated on the same French test set in BIBREF40 . As illustrated in Table 3 , our best zero-shot system obtains the highest accuracy of 73.88%. INVERT BIBREF23 uses inverted indexing over a parallel corpus to obtain crosslingual word representations. BiCVM BIBREF25 learns bilingual compositional representations from sentence-aligned parallel corpora. In RANDOM BIBREF24 , bilingual embeddings are trained on top of parallel sentences with randomly shuffled tokens using skip-gram with negative sampling, and RATIO is similar to RANDOM with the one difference being that the tokens in the parallel sentences are not randomly shuffled. Our system significantly outperforms all methods listed in the second column by 10.66% to 15.24% and demonstrates the effectiveness of our proposed approach." + ], + [ + "In this section, we try to analyze why our simple Encoder-Classifier system is effective at zero-shot classification. We perform a series of experiments to better understand this phenomenon. In particular, we study (1) the effect of shared sub-word vocabulary, (2) the amount of multilingual training data to measure the influence of multilinguality, (3) encoder/classifier capacity to measure the influence of representation power, and (4) model behavior on different training phases to assess the relation between generalization performance on English and zero-shot performance on French." + ], + [ + "In this paper, we have demonstrated a simple yet effective approach to perform cross-lingual transfer learning using representations from a multilingual NMT model. Our proposed approach of reusing the encoder from a multilingual NMT system as a pre-trained component provides significant improvements on three downstream tasks. Further, our approach enables us to perform surprisingly competitive zero-shot classification on an unseen language and outperforms cross-lingual embedding base methods. Finally, we end with a series of analyses which shed light on the factors that contribute to the zero-shot phenomenon. We hope that these results showcase the efficacy of multilingual NMT to learn transferable contextualized representations for many downstream tasks." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0648/instruction.md b/qasper-0648/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..0da7d5122d8e81373c01f1b4d0706150bfc08f69 --- /dev/null +++ b/qasper-0648/instruction.md @@ -0,0 +1,73 @@ +Name of Paper: Zero-Shot Cross-lingual Classification Using Multilingual Neural Machine Translation + +Question: What data were they used to train the multilingual encoder? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Proposed Method", + "Multilingual Representations Using NMT", + "Multilingual Encoder-Classifier", + "Corpora", + "Model and Training Details", + "Transfer Learning Results", + "Zero-Shot Classification Results", + "Analyses", + "Conclusion" + ], + "paragraphs": [ + [ + "Transfer learning has been shown to work well in Computer Vision where pre-trained components from a model trained on ImageNet BIBREF0 are used to initialize models for other tasks BIBREF1 . In most cases, the other tasks are related to and share architectural components with the ImageNet task, enabling the use of such pre-trained models for feature extraction. With this transfer capability, improvements have been obtained on other image classification datasets, and on other tasks such as object detection, action recognition, image segmentation, etc BIBREF2 . Analogously, we propose a method to transfer a pre-trained component - the multilingual encoder from an NMT system - to other NLP tasks.", + "In NLP, initializing word embeddings with pre-trained word representations obtained from Word2Vec BIBREF3 or GloVe BIBREF4 has become a common way of transferring information from large unlabeled data to downstream tasks. Recent work has further shown that we can improve over this approach significantly by considering representations in context, i.e. modeled depending on the sentences that contain them, either by taking the outputs of an encoder in MT BIBREF5 or by obtaining representations from the internal states of a bi-directional Language Model (LM) BIBREF6 . There has also been successful recent work in transferring sentence representations from resource-rich tasks to improve resource-poor tasks BIBREF7 , however, most of the above transfer learning examples have focused on transferring knowledge across tasks for a single language, in English.", + "Cross-lingual or multilingual NLP, the task of transferring knowledge from one language to another, serves as a good test bed for evaluating various transfer learning approaches. For cross-lingual NLP, the most widely studied approach is to use multilingual embeddings as features in neural network models. However, research has shown that representations learned in context are more effective BIBREF5 , BIBREF6 ; therefore, we aim at doing better than just using multilingual embeddings in the cross-lingual tasks. Recent progress in multilingual NMT provides a compelling opportunity for obtaining contextualized multilingual representations, as multilingual NMT systems are capable of generalizing to an unseen language direction, i.e. zero-shot translation. There is also evidence that the encoder of a multilingual NMT system learns language agnostic, universal interlingua representations, which can be further exploited BIBREF8 .", + "In this paper, we focus on using the representations obtained from a multilingual NMT system to enable cross-lingual transfer learning on downstream NLP tasks. Our contributions are three-fold:" + ], + [ + "We propose an Encoder-Classifier model, where the Encoder, leveraging the representations learned by a multilingual NMT model, converts an input sequence ${\\mathbf {x}}$ into a set of vectors C, and the Classifier predicts a class label $y$ given the encoding of the input sequence, C." + ], + [ + "Although there has been a large body of work in building multilingual NMT models which can translate between multiple languages at the same time BIBREF29 , BIBREF30 , BIBREF31 , BIBREF8 , zero-shot capabilities of such multilingual representations have only been tested for MT BIBREF8 . We propose a simple yet effective solution - reuse the encoder of a multilingual NMT model to initialize the encoder for other NLP tasks. To be able to achieve promising zero-shot classification performance, we consider two factors: (1) The ability to encode multiple source languages with the same encoder and (2) The ability to learn language agnostic representations of the source sequence. Based on the literature, both requirements can be satisfied by training a multilingual NMT model having a shared encoder BIBREF32 , BIBREF8 , and a separate decoder and attention mechanism for each target language BIBREF30 . After training such a multilingual NMT model, the decoder and the corresponding attention mechanisms (which are target-language specific) are discarded, while the multilingual encoder is used to initialize the encoder of our proposed Encoder-Classifier model." + ], + [ + "In order to leverage pre-trained multilingual representations introduced in Section \"Analyses\" , our encoder strictly follows the structure of a regular Recurrent Neural Network (RNN) based NMT encoder BIBREF33 with a stacked layout BIBREF34 . Given an input sequence ${\\mathbf {x}} = (x_{1}, x_{2}, \\ldots , x_{T_x})$ of length $T_x$ , our encoder contextualizes or encodes the input sequence into a set of vectors C, by first applying a bi-directional RNN BIBREF35 , followed by a stack of uni-directional RNNs. The hidden states of the final layer RNN, $h_i^l$ , form the set C $~=\\lbrace h_i^l \\rbrace _{i=1}^{T_x}$ of context vectors which will be used by the classifier, where $l$ denotes the number of RNN layers in the stacked encoder.", + "The task of the classifier is to predict a class label $y$ given the context set C. To ease this classification task given a variable length input set C, a common approach in the literature is to extract a single sentence vector $\\mathbf {q}$ by making use of pooling over time BIBREF36 . Further, to increase the modeling capacity, the pooling operation can be parameterized using pre- and post-pooling networks. Formally, given the context set C, we extract a sentence vector $\\mathbf {q}$ in three steps, using three networks, (1) pre-pooling feed-forward network $f_{pre}$ , (2) pooling network $f_{pool}$ and (3) post-pooling feed-forward network $f_{post}$ , $\n\\mathbf {q} = f_{post}( f_{pool} ( f_{pre} (\\textbf {C}) ) ).\n$ ", + " Finally, given the sentence vector $\\mathbf {q}$ , a class label $y$ is predicted by employing a softmax function." + ], + [ + "We evaluate the proposed method on three common NLP tasks: Amazon Reviews, SST and SNLI. We utilize parallel data to train our multilingual NMT system, as detailed below.", + "For the MT task, we use the WMT 2014 En $\\leftrightarrow $ Fr parallel corpus. The dataset contains 36 million En $\\rightarrow $ Fr sentence pairs. We swapped the source and target sentences to obtain parallel data for the Fr $\\rightarrow $ En translation task. We use these two datasets (72 million sentence pairs) to train a single multilingual NMT model to learn both these translation directions simultaneously. We generated a shared sub-word vocabulary BIBREF37 , BIBREF38 of 32K units from all source and target training data. We use this sub-word vocabulary for all of our experiments below.", + "The Amazon reviews dataset BIBREF39 is a multilingual sentiment classification dataset, providing data for four languages - English (En), French (Fr), German (De), and Japanese. We use the English and French datasets in our experiments. The dataset contains 6,000 documents in the train and test portions for each language. Each review consists of a category label, a title, a review, and a star rating (5-point scale). We only use the review text in our experiments. Following BIBREF39 , we mapped the reviews with lower scores (1 and 2) to negative examples and the reviews with higher scores (4 and 5) to positive examples, thereby turning it into a binary classification problem. Reviews with score 3 are dropped. We split the training dataset into 10% for development and the rest for training, and we truncate each example and keep the first 200 words in the review. Note that, since the data for each language was obtained by crawling different product pages, the data is not aligned across languages.", + "The sentiment classification task proposed in BIBREF9 is also a binary classification problem where each sentence and phrase is associated with either a positive or a negative sentiment. We ignore phrase-level annotations and sentence-level neutral examples in our experiments. The dataset contains 6920, 872, and 1821 examples for training, development and testing, respectively. Since SST does not provide a multilingual test set, we used the public translation engine Google Translate to translate the SST test set to French. Previous work by BIBREF40 has shown that replacing the human translated test set with a synthetic set (obtained by using Google Translate) produces only a small difference of around 1% absolute accuracy on their human-translated French SNLI test set. Therefore, the performance measured on our `pseudo' French SST test set is expected to be a good indicator of zero-shot performance.", + "Natural language inference is a task that aims to determine whether a natural language hypothesis $\\mathbf {h}$ can justifiably be inferred from a natural language premise $\\mathbf {p}$ . SNLI BIBREF10 is one of the largest datasets for a natural language inference task in English and contains multiple sentence pairs with a sentence-level entailment label. Each pair of sentences can have one of three labels - entailment, contradiction, and neutral, which are annotated by multiple humans. The dataset contains 550K training, 10K validation, and 10K testing examples. To enable research on multilingual SNLI, BIBREF40 chose a subset of the SNLI test set (1332 sentences) and professionally translated it into four major languages - Arabic, French, Russian, and Spanish. We use the French test set for evaluation in Section \"Zero-Shot Classification Results\" and \"Analyses\" ." + ], + [ + "Here, we first describe the model and training details of the base multilingual NMT model whose encoder is reused in all other tasks. Then we provide details about the task-specific classifiers. For each task, we provide the specifics of $f_{pre}$ , $f_{pool}$ and $f_{post}$ nets that build the task-specific classifier.", + "All the models in our experiments are trained using Adam optimizer BIBREF42 with label smoothing BIBREF43 and unless otherwise stated below, layer normalization BIBREF44 is applied to all LSTM gates and feed-forward layer inputs. We apply L2 regularization to the model weights and dropout to layer activations and sub-word embeddings. Hyper-parameters, such as mixing ratio $\\lambda $ of L2 regularization, dropout rates, label smoothing uncertainty, batch sizes, learning rate of optimizers and initialization ranges of weights are tuned on the development sets provided for each task separately.", + "Our multilingual NMT model consists of a shared multilingual encoder and two decoders, one for English and the other for French. The multilingual encoder uses one bi-directional LSTM, followed by three stacked layers of uni-directional LSTMs in the encoder. Each decoder consists of four stacked LSTM layers, with the first LSTM layers intertwined with additive attention networks BIBREF33 to learn a source-target alignment function. All the uni-directional LSTMs are equipped with residual connections BIBREF45 to ease the optimization, both in the encoder and the decoders. LSTM hidden units and the shared source-target embedding dimensions are set to 512.", + "Similar to BIBREF30 , multilingual NMT model is trained in a multi-task learning setup, where each decoder is augmented with a task-specific loss, minimizing the negative conditional log-likelihood of the target sequence given the source sequence. During training, mini-batches of En $\\rightarrow $ Fr and Fr $\\rightarrow $ En examples are interleaved. We picked the best model based on the best average development set BLEU score on both of the language pairs.", + "The Encoder-Classifier model here uses the encoder defined previously. With regards to the classifier, the pre- and post-pooling networks ( $f_{pre}$ , $f_{post}$ ) are both one-layer feed forward networks to cast the dimension size from 512 to 128 and from 128 to 32, respectively. We used max-pooling operator for the $f_{pool}$ network to pool the activation over time.", + "We extended the proposed Encoder-Classifier model to a multi-source model BIBREF46 since SNLI is an inference task of relations between two input sentences, \u201cpremise\" and \u201chypothesis\". For the two sources, we use two separate encoders, which are initialized with the same pre-trained multilingual NMT encoder, to obtain their representations. Following our notation, the encoder outputs are processed using $f_{pre}$ , $f_{pool}$ and $f_{post}$ nets, again with two separate network blocks. Specifically, $f_{pre}$ consists of a co-attention layer BIBREF47 followed by a two-layer feed-forward neural network with residual connections. We use max pooling over time for $f_{pool}$ and again a two-layer feed-forward neural network with residual connections as $f_{post}$ . After processing two sentence encodings using two network blocks, we obtain two vectors representing premise $\\mathbf {h}_{premise}$ and hypothesis $\\mathbf {h}_{hypothesis}$ . Following BIBREF48 , we compute two types of relational vectors with $\\mathbf {h}_{-} = |\\mathbf {h}_{premise} - \\mathbf {h}_{hypothesis}|,$ and $\\mathbf {h}_{\\times } = \\mathbf {h}_{premise} \\odot \\mathbf {h}_{hypothesis}$ , where $f_{pool}$0 denotes the element-wise multiplication between two vectors. The final relation vector is obtained by concatenating $f_{pool}$1 and $f_{pool}$2 . For both \u201cpremise\" and \u201chypothesis\" feed-forward networks we used 512 hidden dimensions.", + "For Amazon Reviews, SST and SNLI tasks, we picked the best model based on the highest development set accuracy." + ], + [ + "In this section, we report our results for the three tasks - Amazon Reviews (English and French), SST, and SNLI. For each task, we first build a baseline system using the proposed Encoder-Classifier architecture described in Section \"Proposed Method\" where the encoder is initialized randomly. Next, we experiment with using the pre-trained multilingual NMT encoder to initialize the system as described in Section \"Analyses\" . Finally, we perform an experiment where we freeze the encoder after initialization and only update the classifier component of the system.", + "Table 1 summarizes the accuracy of our proposed system for these three different approaches and the state-of-the-art results on all the tasks. The first row in the table shows the baseline accuracy of our system for all four datasets. The second row shows the result from initializing with a pre-trained multilingual NMT encoder. It can be seen that this provides a significant improvement in accuracy, an average of 4.63%, across all the tasks. This illustrates that the multilingual NMT encoder has successfully learned transferable contextualized representations that are leveraged by the classifier component of our proposed system. These results are in line with the results in BIBREF5 where the authors used the representations from the top NMT encoder layer as an additional input to the task-specific system. However, in our setup we reused all of the layers of the encoder as a single pre-trained component in the task-specific system. The third row shows the results from freezing the pre-trained encoder after initialization and only training the classifier component. For the Amazon English and French tasks, freezing the encoder after initialization significantly improves the performance further. We hypothesize that since the Amazon dataset is a document level classification task, the long input sequences are very different from the short sequences consumed by the NMT system and hence freezing the encoder seems to have a positive effect. This hypothesis is also supported by the SNLI and SST results, which contain sentence-level input sequences, where we did not find any significant difference between freezing and not freezing the encoder." + ], + [ + "In this section, we explore the zero-shot classification task in French for our systems. We assume that we do not have any French training data for all the three tasks and test how well our proposed method can generalize to the unseen French language without any further training. Specifically, we reuse the three proposed systems from Table 1 after being trained only on the English classification task and test the systems on data from an unseen language (e.g. French). A reasonable upper bound to which zero-shot performance should be compared to is bridging - translating a French test text to English and then applying the English classifier on the translated text. If we assume the translation to be perfect, we should expect this approach to perform as well as the English classifier.", + "The Amazon Reviews and SNLI tasks have a French test set available, and we evaluate the performance of the bridged and zero-shot systems on each French set. However, the SST dataset does not have a French test set, hence the `pseudo French' test set described in Section UID14 is used to evaluate the zero-shot performance. We use the English accuracy scores from the SST column in Table 1 as a high-quality proxy for the SST bridged system. We do this since translating the `pseudo French' back to English will result in two distinct translation steps and hence more errors.", + "Table 2 summarizes all of our zero-shot results for French classification on the three tasks. It can be seen that just by using the pre-trained NMT encoder, the zero-shot performance increases drastically from almost random to within 10% of the bridged system. Freezing the encoder further pushes this performance closer to the bridged system. On the Amazon Review task, our zero-shot system is within 2% of the best bridged system. On the SST task, our zero-shot system obtains an accuracy of 83.14% which is within 1.5% of the bridged equivalent (in this case the English system).", + "Finally, on SNLI, we compare our best zero-shot system with bilingual and multilingual embedding based methods evaluated on the same French test set in BIBREF40 . As illustrated in Table 3 , our best zero-shot system obtains the highest accuracy of 73.88%. INVERT BIBREF23 uses inverted indexing over a parallel corpus to obtain crosslingual word representations. BiCVM BIBREF25 learns bilingual compositional representations from sentence-aligned parallel corpora. In RANDOM BIBREF24 , bilingual embeddings are trained on top of parallel sentences with randomly shuffled tokens using skip-gram with negative sampling, and RATIO is similar to RANDOM with the one difference being that the tokens in the parallel sentences are not randomly shuffled. Our system significantly outperforms all methods listed in the second column by 10.66% to 15.24% and demonstrates the effectiveness of our proposed approach." + ], + [ + "In this section, we try to analyze why our simple Encoder-Classifier system is effective at zero-shot classification. We perform a series of experiments to better understand this phenomenon. In particular, we study (1) the effect of shared sub-word vocabulary, (2) the amount of multilingual training data to measure the influence of multilinguality, (3) encoder/classifier capacity to measure the influence of representation power, and (4) model behavior on different training phases to assess the relation between generalization performance on English and zero-shot performance on French." + ], + [ + "In this paper, we have demonstrated a simple yet effective approach to perform cross-lingual transfer learning using representations from a multilingual NMT model. Our proposed approach of reusing the encoder from a multilingual NMT system as a pre-trained component provides significant improvements on three downstream tasks. Further, our approach enables us to perform surprisingly competitive zero-shot classification on an unseen language and outperforms cross-lingual embedding base methods. Finally, we end with a series of analyses which shed light on the factors that contribute to the zero-shot phenomenon. We hope that these results showcase the efficacy of multilingual NMT to learn transferable contextualized representations for many downstream tasks." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0649/instruction.md b/qasper-0649/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..4cd35d436d78c58ee669718d0442159cb4d5aaf7 --- /dev/null +++ b/qasper-0649/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: An Analysis of Visual Question Answering Algorithms + +Question: From when are many VQA datasets collected? \ No newline at end of file diff --git a/qasper-0652/instruction.md b/qasper-0652/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..ce5c5ffb7b0061111ee5c5e8dc8e18538e12f0a7 --- /dev/null +++ b/qasper-0652/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Imitation Learning of Robot Policies by Combining Language, Vision and Demonstration + +Question: Does proposed end-to-end approach learn in reinforcement or supervised learning manner? \ No newline at end of file diff --git a/qasper-0653/instruction.md b/qasper-0653/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..e469e0cbc0f685c83464db75f311114cccf4e70f --- /dev/null +++ b/qasper-0653/instruction.md @@ -0,0 +1,119 @@ +Name of Paper: Overcoming the Rare Word Problem for Low-Resource Language Pairs in Neural Machine Translation + +Question: Are synonymous relation taken into account in the Japanese-Vietnamese task? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Neural Machine Translation", + "Rare Word translation", + "Rare Word translation ::: Low-frequency Word Translation", + "Rare Word translation ::: Reducing Unknown Words", + "Rare Word translation ::: Dealing with OOV using WordNet", + "Experiments", + "Experiments ::: Datasets", + "Experiments ::: Preprocessing", + "Experiments ::: Systems and Training", + "Experiments ::: Results", + "Experiments ::: Results ::: Japanese-Vietnamese Translation", + "Experiments ::: Results ::: English-Vietnamese Translation", + "Related Works", + "Conclusion", + "Acknowledgments" + ], + "paragraphs": [ + [ + "NMT systems have achieved better performance compared to statistical machine translation (SMT) systems in recent years not only on available data language pairs BIBREF1, BIBREF2, but also on low-resource language pairs BIBREF3, BIBREF4. Nevertheless, NMT still exists many challenges which have adverse effects on its effectiveness BIBREF0. One of these challenges is that NMT has biased tend in translating high-frequency words, thus words which have lower frequencies are often translated incorrectly. This challenge has also been confirmed again in BIBREF3, and they have proposed two strategies to tackle this problem with modifications on the model's output distribution: one for normalizing some matrices by fixing them to constants after several training epochs and another for adding a direct connection from source embeddings through a simple feed forward neural network (FFNN). These approaches increase the size and the training time of their NMT systems. In this work, we follow their second approach but simplify the computations by replacing FFNN with two single operations.", + "Despite above approaches can improve the prediction of rare words, however, NMT systems often use limited vocabularies in their sizes, from 30K to 80K most frequent words of the training data, in order to reduce computational complexity and the sizes of the models BIBREF5, BIBREF6, so the rare-word translation are still problematic in NMT. Even when we use a larger vocabulary, this situation still exists BIBREF7. A word which has not seen in the vocabulary of the input text (called unknown word) are presented by the $unk$ symbol in NMT systems. Inspired by alignments and phrase tables in phrase-based machine translation (SMT) as suggested by BIBREF8, BIBREF6 proposed to address OOV words using an annotated training corpus. They then used a dictionary generated from alignment model or maps between source and target words to determine the translations of $unks$ if translations are not found. BIBREF9 proposed to reduce unknown words using Gage's Byte Pair Encoding (BPE) algorithm BIBREF10, but NMT systems are less effective for low-resource language pairs due to the lack of data and also for other languages that sub-word are not the optimal translation unit. In this paper, we employ several techniques inspired by the works from NMT and the traditional SMT mentioned above. Instead of a loosely unsupervised approach, we suggest a supervised approach to solve this trouble using synonymous relation of word pairs from WordNet on Japanese$\\rightarrow $Vietnamese and English$\\rightarrow $Vietnamese systems. To leverage effectiveness of this relation in English, we transform variants of words in the source texts to their original forms by separating their affixes collected by hand.", + "Our contributes in this work are:", + "", + "We release the state-of-the-art for Japanese-Vietnamese NMT systems.", + "We proposed the approach to deal with the rare word translation by integrating source embeddings to the attention component of NMT.", + "We present a supervised algorithm to reduce the number of unknown words for the English$\\rightarrow $Vietnamese translation system.", + "We demonstrate the effectiveness of leveraging linguistic information from WordNet to alleviate the rare-word problem in NMT." + ], + [ + "Our NMT system use a bidirectional recurrent neural network (biRNN) as an encoder and a single-directional RNN as a decoder with input feeding of BIBREF11 and the attention mechanism of BIBREF5. The Encoder's biRNN are constructed by two RNNs with the hidden units in the LSTM cell, one for forward and the other for backward of the source sentence $\\mathbf {x}=(x_1, ...,x_n)$. Every word $x_i$ in sentence is first encoded into a continuous representation $E_s(x_i)$, called the source embedding. Then $\\mathbf {x}$ is transformed into a fixed-length hidden vector $\\mathbf {h}_i$ representing the sentence at the time step $i$, which called the annotation vector, combined by the states of forward $\\overrightarrow{\\mathbf {h}}_i$ and backward $\\overleftarrow{\\mathbf {h}}_i$:", + "$\\overrightarrow{\\mathbf {h}}_i=f(E_s(x_i),\\overrightarrow{\\mathbf {h}}_{i-1})$", + "$\\overleftarrow{\\mathbf {h}}_i=f(E_s(x_i),\\overleftarrow{\\mathbf {h}}_{i+1})$", + "The decoder generates the target sentence $\\mathbf {y}={(y_1, ..., y_m)}$, and at the time step $j$, the predicted probability of the target word $y_j$ is estimated as follows:", + "where $\\mathbf {z}_j$ is the output hidden states of the attention mechanism and computed by the previous output hidden states $\\mathbf {z}_{j-1}$, the embedding of previous target word $E_t(y_{j-1})$ and the context $\\mathbf {c}_j$:", + "$\\mathbf {z}_j=g(E_t(y_{j-1}), \\mathbf {z}_{j-1}, \\mathbf {c}_j)$", + "The source context $\\mathbf {c}_j$ is the weighted sum of the encoder's annotation vectors $\\mathbf {h}_i$:", + "$\\mathbf {c}_j=\\sum ^n_{i=1}\\alpha _{ij}\\mathbf {h}_i$", + "where $\\alpha _{ij}$ are the alignment weights, denoting the relevance between the current target word $y_j$ and all source annotation vectors $\\mathbf {h}_i$." + ], + [ + "In this section, we present the details about our approaches to overcome the rare word situation. While the first strategy augments the source context to translate low-frequency words, the remaining strategies reduce the number of OOV words in the vocabulary." + ], + [ + "The attention mechanism in RNN-based NMT maps the target word into source context corresponding through the annotation vectors $\\mathbf {h}_i$. In the recurrent hidden unit, $\\mathbf {h}_i$ is computed from the previous state $\\mathbf {h}_{t-1}$. Therefore, the information flow of the words in the source sentence may be diminished over time. This leads to the accuracy reduction when translating low-frequency words, since there is no direct connection between the target word and the source word. To alleviate the adverse impact of this problem, BIBREF3 combined the source embeddings with the predictive distribution over the output target word in several following steps:", + "Firstly, the weighted average vector of the source embeddings is computed as follows:", + "where $\\alpha _j(e)$ are alignment weights in the attention component and $f_e = E_s(x)$, are the embeddings of the source words.", + "Then $l_j$ is transformed through one-hidden-layer FFNN with residual connection proposed by BIBREF12:", + "Finally, the output distribution over the target word is calculated by:", + "The matrices $\\mathbf {W}_l$, $\\mathbf {W}_t$ and $\\mathbf {b}_t$ are trained together with other parameters of the NMT model.", + "This approach improves the performance of the NMT systems but introduces more computations as the model size increase due to the additional parameters $\\mathbf {W}_l$, $\\mathbf {W}_t$ and $\\mathbf {b}_t$. We simplify this method by using the weighted average of source embeddings directly in the softmax output layer:", + "Our method does not learn any additional parameters. Instead, it requires the source embedding size to be compatible with the decoder's hidden states. With the additional information provided from the source embeddings, we achieve similar improvements compared to the more expensive method described in BIBREF3." + ], + [ + "In our previous experiments for English$\\rightarrow $Vietnamese, BPE algorithm BIBREF9 applied to the source side does not significantly improves the systems despite it is able to reduce the number of unknown English words. We speculate that it might be due to the morphological differences between the source and the target languages (English and Vietnamese in this case). The unsupervised way of BPE while learning sub-words in English thus might be not explicit enough to provide the morphological information to the Vietnamese side. In this work, we would like to attempt a more explicit, supervised way. We collect 52 popular affixes (prefixes and suffixes) in English and then apply the separating affixes algorithm (called SAA) to reduce the number of unknown words as well as to force our NMT systems to learn better morphological mappings between two languages.", + "The main ideal of our SAA is to separate affixes of unknown words while ensuring that the rest of them still exists in the vocabulary. Let the vocabulary $V$ containing $K$ most frequency words from the training set $T1$, a set of prefixes $P$, a set of suffixes $S$, we call word $w^{\\prime }$ is the rest of an unknown word or rare word $w$ after delimiting its affixes. We iteratively pick a $w$ from $N$ words (including unknown words and rare words) of the source text $T2$ to consider if $w$ starts with a prefix $p$ in $P$ or ends with a suffix $s$ in $S$, we then determine splitting its affixes if $w^{\\prime }$ in $V$. A rare word in $V$ also can be separated its affixes if its frequency is less than the given threshold. We set this threshold by 2 in our experiments. Similarly to BPE approach, we also employ a pair of the special symbol $@$ for separating affixes from the word. Listing SECREF6 shows our SAA algorithm.", + "" + ], + [ + "WordNet is a lexical database grouping words into sets which share some semantic relations. Its version for English is proposed for the first time by BIBREF13. It becomes a useful resource for many tasks of natural language processing BIBREF14, BIBREF15, BIBREF16. WordNet are available mainly for English and German, the version for other languages are being developed including some Asian languages in such as Japanese, Chinese, Indonesian and Vietnamese. Several works have employed WordNet in SMT systemsBIBREF17, BIBREF18 but to our knowledge, none of the work exploits the benefits of WordNet in order to ease the rare word problem in NMT. In this work, we propose the learning synonymous algorithm (called LSW) from the WordNet of English and Japanese to handle unknown words in our NMT systems.", + "In WordNet, synonymous words are organized in groups which are called synsets. Our aim is to replace an OOV word by its synonym which appears in the vocabulary of the translation system. From the training set of the source language $T1$, we extract the vocabulary $V$ in size of $K$ most frequent words. For each OOV word from $T1$, we learn its synonyms which exist in the $V$ from the WordNet $W$. The synonyms are then arranged in the descending order of their frequencies to facilitate selection of the $n$ best words which have the highest frequencies. The output file $C$ of the algorithm contains OOV words and its corresponding synonyms and then it is applied to the input text $T2$. We also utilize a frequency threshold for rare words in the same way as in SAA algorithm. In practice, we set this threshold as 0, meaning no words on $V$ is replaced by its synonym. If a source sentence has $m$ unknown words and each of them has $n$ best synonyms, it would generate $m^n$ sentences. Translation process allow us to select the best hypothesis based on their scores. Because of each word in the WordNet can belong to many synsets with different meanings, thus an inappropriate word can be placed in the current source context. We will solve this situation in the further works. Our systems only use 1-best synonym for each OOV word. Listing SECREF7 presents the LSW algorithm.", + "" + ], + [ + "We evaluate our approaches on the English-Vietnamese and the Japanese-Vietnamese translation systems. Translation performance is measured in BLEU BIBREF19 by the multi-BLEU scripts from Moses." + ], + [ + "We consider two low-resource language pairs: Japanese-Vietnamese and English-Vietnamese. For Japanese-Vietnamese, we use the TED data provided by WIT3 BIBREF20 and compiled by BIBREF21. The training set includes 106758 sentence pairs, the validation and test sets are dev2010 (568 pairs) and tst2010 (1220 pairs). For English$\\rightarrow $Vietnamese, we use the dataset from IWSLT 2015 BIBREF22 with around 133K sentence pairs for the training set, 1553 pairs in tst2012 as the validation and 1268 pairs in tst2013 as the test sets.", + "For LSW algorithm, we crawled pairs of synonymous words from Japanese-English WordNet and achieved 315850 pairs for English and 1419948 pairs for Japanese." + ], + [ + "For English and Vietnamese, we tokenized the texts and then true-cased the tokenized texts using Moses script. We do not use any word segmentation tool for Vietnamese. For comparison purpose, Sennrich's BPE algorithm is applied for English texts. Following the same preprocessing steps for Japanese (JPBPE) in BIBREF21, we use KyTea BIBREF23 to tokenize texts and then apply BPE on those texts. The number of BPE merging operators are 50k for both Japanese and English." + ], + [ + "We implement our NMT systems using OpenNMT-py framework BIBREF24 with the same settings as in BIBREF21 for our baseline systems. Our system are built with two hidden layers in both encoder and decoder, each layer has 512 hidden units. In the encoder, a BiLSTM architecture is used for each layer and in the decoder, each layer are basically an LSTM layer. The size of embedding layers in both source and target sides is also 512. Adam optimizer is used with the initial learning rate of $0.001$ and then we apply learning rate annealing. We train our systems for 16 epochs with the batch size of 32. Other parameters are the same as the default settings of OpenNMT-py.", + "We then modify the baseline architecture with the alternative proposed in Section SECREF5 in comparison to our baseline systems. All settings are the same as the baseline systems." + ], + [ + "In this section, we show the effectiveness of our methods on two low-resource language pairs and compare them to the other works. The empirical results are shown in Table TABREF15 for Japanese-Vietnamese and in Table TABREF20 for English-Vietnamese. Note that, the Multi-BLEU is only measured in the Japanese$\\rightarrow $Vietnamese direction and the standard BLEU points are written in brackets." + ], + [ + "We conduct two out of the three proposed approaches for Japanese-Vietnamese translation systems and the results are given in the Table TABREF15.", + "Baseline Systems. We find that our translation systems which use Sennrich's BPE method for Japanese texts and do not use word segmentation for Vietnamese texts are neither better or insignificant differences compare to those systems used word segmentation in BIBREF21. Particularly, we obtained +0.38 BLEU points between (1) and (4) in the Japanese$\\rightarrow $Vietnamese and -0.18 BLEU points between (1) and (3) in the Vietnamese$\\rightarrow $Japanese.", + "Our Approaches. On the systems trained with the modified architecture mentioned in the section SECREF5, we obtained an improvements of +0.54 BLEU points in the Japanese$\\rightarrow $Vietnamese and +0.42 BLEU points on the Vietnamese$\\rightarrow $Japanese compared to the baseline systems.", + "Due to the fact that Vietnamese WordNet is not available, we only exploit WordNet to tackle unknown words of Japanese texts in our Japanese$\\rightarrow $Vietnamese translation system. After using Kytea, Japanese texts are applied LSW algorithm to replace OOV words by their synonyms. We choose 1-best synonym for each OOV word. Table TABREF18 shows the number of OOV words replaced by their synonyms. The replaced texts are then BPEd and trained on the proposed architecture. The largest improvement is +0.92 between (1) and (3). We observed an improvement of +0.7 BLEU points between (3) and (5) without using data augmentation described in BIBREF21.", + "", + "" + ], + [ + "We examine the effect of all approaches presented in Section SECREF3 for our English-Vietnamese translation systems. Table TABREF20 summarizes those results and the scores from other systems BIBREF3, BIBREF25.", + "Baseline systems. After preprocessing data using Moses scripts, we train the systems of English$\\leftrightarrow $Vietnamese on our baseline architecture. Our translation system obtained +0.82 BLEU points compared to BIBREF3 in the English$\\rightarrow $Vietnamese and this is lower than the system of BIBREF25 with neural phrase-based translation architecture.", + "Our approaches. The datasets from the baseline systems are trained on our modified NMT architecture. The improvements can be found as +0.55 BLEU points between (1) and (2) in the English$\\rightarrow $Vietnamese and +0.45 BLEU points (in tst2012) between (1) and (2) in the Vietnamese$\\rightarrow $English.", + "For comparison purpose, English texts are split into sub-words using Sennrich's BPE methods. We observe that, the achieved BLEU points are lower Therefore, we then apply the SAA algorithm on the English texts from (2) in the English$\\rightarrow $Vietnamese. The number of applied words are listed in Table TABREF21. The improvement in BLEU are +0.74 between (4) and (1).", + "", + "Similarly to the Japanese$\\rightarrow $Vietnamese system, we apply LSW algorithm on the English texts from (4) while selecting 1-best synonym for each OOV word. The number of replaced words on English texts are indicated in the Table TABREF22. Again, we obtained a bigger gain of +0.99 (+1.02) BLEU points in English$\\rightarrow $Vietnamese direction. Compared to the most recent work BIBREF25, our system reports an improvement of +0.47 standard BLEU points on the same dataset.", + "We investigate some examples of translations generated by the English$\\rightarrow $Vietnamese systems with our proposed methods in the Table TABREF23. The bold texts in red color present correct or approximate translations while the italic texts in gray color denote incorrect translations. The first example, we consider two words: presentation and the unknown word applauded. The word presentation is predicted correctly as Vietnamese\"b\u00e0i thuy\u1ebft tr\u00ecnh\" in most cases when we combined source context through embeddings. The unknown word applauded which has not seen in the vocabulary is ignored in the first two cases (baseline and source embedding) but it is roughly translated as Vietnamese\"hoan ngh\u00eanh\" in the SAA because it is separated into applaud and ed. In the second example, we observe the translations of the unknown word tryout, they are mistaken in the first three cases but in the LSW, it is predicted with a closer meaning as Vietnamese\"b\u00e0i ki\u1ec3m tra\" due to the replacement by its synonymous word as test.", + "" + ], + [ + "Addressing unknown words was mentioned early in the Statistical Machine Translation (SMT) systems. Some typical studies as: BIBREF26 proposed four techniques to overcome this situation by extend the morphology and spelling of words or using a bilingual dictionary or transliterating for names. These approaches are difficult when manipulate to different domains. BIBREF27 trained word embedding models to learn word similarity from monolingual data and an unknown word are then replaced by a its similar word. BIBREF28 used a linear model to learn maps between source and target spaces base on a small initial bilingual dictionary to find the translations of source words. However, in NMT, there are not so many works tackling this problem. BIBREF7 use a very large vocabulary to solve unknown words. BIBREF6 generate a dictionary from alignment data based on annotated corpus to decide the hypotheses of unknown words. BIBREF3 have introduced the solutions for dealing with the rare word problem, however, their models require more parameters, thus, decreasing the overall efficiency.", + "In another direction, BIBREF9 exploited the BPE algorithm to reduce number of unknown words in NMT and achieved significant efficiency on many language pairs. The second approach presented in this works follows this direction when instead of using an unsupervised method to split rare words and unknown words into sub-words that are able to translate, we use a supervised method. Our third approach using WordNet can be seen as a smoothing way, when we use the translations of the synonymous words to approximate the translation of an OOV word. Another work followed this direction is worth to mention is BIBREF29, when they use the morphological and semantic information as the factors of the words to help translating rare words." + ], + [ + "In this study, we have proposed three difference strategies to handle rare words in NMT, in which the combination of methods brings significant improvements to the NMT systems on two low-resource language pairs. In future works, we will consider selecting some appropriate synonymous words for the source sentence from n-best synonymous words to further improve the performance of the NMT systems and leverage more unsupervised methods based on monolingual data to address rare word problem." + ], + [ + "This work is supported by the project \"Building a machine translation system to support translation of documents between Vietnamese and Japanese to help managers and businesses in Hanoi approach to Japanese market\", No. TC.02-2016-03." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0654/instruction.md b/qasper-0654/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..504dba93a913fbfe9c07dba0d06ebe684d7d6f5e --- /dev/null +++ b/qasper-0654/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Overcoming the Rare Word Problem for Low-Resource Language Pairs in Neural Machine Translation + +Question: Is the supervised morphological learner tested on Japanese? \ No newline at end of file diff --git a/qasper-0655/instruction.md b/qasper-0655/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..fb860d2f9e42b21300508099ed765f4ec968dcdc --- /dev/null +++ b/qasper-0655/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: A framework for anomaly detection using language modeling, and its applications to finance + +Question: What is the dataset that is used in the paper? \ No newline at end of file diff --git a/qasper-0663/instruction.md b/qasper-0663/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..766e5da8cafe62325da22e4b0f7c8558650fa89e --- /dev/null +++ b/qasper-0663/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: What Gets Echoed? Understanding the"Pointers"in Explanations of Persuasive Arguments + +Question: What metrics are used in evaluation of this task? \ No newline at end of file diff --git a/qasper-0664/instruction.md b/qasper-0664/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..7c6a3ba3c3ca369919d01003150cd1e3578c9be0 --- /dev/null +++ b/qasper-0664/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: What Gets Echoed? Understanding the"Pointers"in Explanations of Persuasive Arguments + +Question: Do authors provide any explanation for intriguing patterns of word being echoed? \ No newline at end of file diff --git a/qasper-0670/instruction.md b/qasper-0670/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..680b8419a4538b1d1a257b711b58fd1e9dfdc849 --- /dev/null +++ b/qasper-0670/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Automatic Reminiscence Therapy for Dementia. + +Question: How big dataset is used for training this system? \ No newline at end of file diff --git a/qasper-0671/instruction.md b/qasper-0671/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..17c9b89400edd4669fe941e99723d1f6f9e103fe --- /dev/null +++ b/qasper-0671/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Lattice CNNs for Matching Based Chinese Question Answering + +Question: How do they obtain word lattices from words? \ No newline at end of file diff --git a/qasper-0676/instruction.md b/qasper-0676/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..dfde8671c68ebb0b656c47053c4a4de3c51579a0 --- /dev/null +++ b/qasper-0676/instruction.md @@ -0,0 +1,66 @@ +Name of Paper: Speaker-independent classification of phonetic segments from raw ultrasound in child speech + +Question: Do they propose any further additions that could be made to improve generalisation to unseen speakers? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Ultrasound Tongue Imaging", + "Related Work", + "Ultrasound Data", + "Data Selection", + "Preprocessing and Model Architectures", + "Training Scenarios and Speaker Means", + "Results and Discussion", + "Future Work", + "Conclusion" + ], + "paragraphs": [ + [ + "Ultrasound tongue imaging (UTI) uses standard medical ultrasound to visualize the tongue surface during speech production. It provides a non-invasive, clinically safe, and increasingly inexpensive method to visualize the vocal tract. Articulatory visual biofeedback of the speech production process, using UTI, can be valuable for speech therapy BIBREF0 , BIBREF1 , BIBREF2 or language learning BIBREF3 , BIBREF4 . Ultrasound visual biofeedback combines auditory information with visual information of the tongue position, allowing users, for example, to correct inaccurate articulations in real-time during therapy or learning. In the context of speech therapy, automatic processing of ultrasound images was used for tongue contour extraction BIBREF5 and the animation of a tongue model BIBREF6 . More broadly, speech recognition and synthesis from articulatory signals BIBREF7 captured using UTI can be used with silent speech interfaces in order to help restore spoken communication for users with speech or motor impairments, or to allow silent spoken communication in situations where audible speech is undesirable BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 . Similarly, ultrasound images of the tongue have been used for direct estimation of acoustic parameters for speech synthesis BIBREF13 , BIBREF14 , BIBREF15 .", + "Speech and language therapists (SLTs) have found UTI to be very useful in speech therapy. In this work we explore the automatic processing of ultrasound tongue images in order to assist SLTs, who currently largely rely on manual processing when using articulatory imaging in speech therapy. One task that could assist SLTs is the automatic classification of tongue shapes from raw ultrasound. This can facilitate the diagnosis and treatment of speech sound disorders, by allowing SLTs to automatically identify incorrect articulations, or by quantifying patient progress in therapy. In addition to being directly useful for speech therapy, the classification of tongue shapes enables further understanding of phonetic variability in ultrasound tongue images. Much of the previous work in this area has focused on speaker-dependent models. In this work we investigate how automatic processing of ultrasound tongue imaging is affected by speaker variation, and how severe degradations in performance can be avoided when applying systems to data from previously unseen speakers through the use of speaker adaptation and speaker normalization approaches.", + "Below, we present the main challenges associated with the automatic processing of ultrasound data, together with a review of speaker-independent models applied to UTI. Following this, we present the experiments that we have performed (Section SECREF2 ), and discuss the results obtained (Section SECREF3 ). Finally we propose some future work and conclude the paper (Sections SECREF4 and SECREF5 )." + ], + [ + "There are several challenges associated with the automatic processing of ultrasound tongue images.", + "Image quality and limitations. UTI output tends to be noisy, with unrelated high-contrast edges, speckle noise, or interruptions of the tongue surface BIBREF16 , BIBREF17 . Additionally, the oral cavity is not entirely visible from the image, missing the lips, the palate, or the pharyngeal wall.", + "Inter-speaker variation. Age and physiology may affect the output, with children imaging better than adults due to more moisture in the mouth and less tissue fat BIBREF16 . However, dry mouths lead to poor imaging, which might occur in speech therapy if a child is nervous during a session. Similarly, the vocal tracts of children across different ages may be more variable than those of adults.", + "Probe placement. Articulators that are orthogonal to the ultrasound beam direction image well, while those at an angle tend to image poorly. Incorrect or variable probe placement during recordings may lead to high variability between otherwise similar tongue shapes. This may be controlled using helmets BIBREF18 , although it is unreasonable to expect the speaker to remain still throughout the recording session, especially if working with children. Therefore, probe displacement should be expected to be a factor in image quality and consistency.", + "Limited data. Although ultrasound imaging is becoming less expensive to acquire, there is still a lack of large publicly available databases to evaluate automatic processing methods. The UltraSuite Repository BIBREF19 , which we use in this work, helps alleviate this issue, but it still does not compare to standard speech recognition or image classification databases, which contain hundreds of hours of speech or millions of images." + ], + [ + "Earlier work concerned with speech recognition from ultrasound data has mostly been focused on speaker-dependent systems BIBREF20 , BIBREF21 , BIBREF22 , BIBREF23 . An exception is the work of Xu et al. BIBREF24 , which investigates the classification of tongue gestures from ultrasound data using convolutional neural networks. Some results are presented for a speaker-independent system, although the investigation is limited to two speakers generalizing to a third. Fabre et al BIBREF5 present a method for automatic tongue contour extraction from ultrasound data. The system is evaluated in a speaker-independent way by training on data from eight speakers and evaluating on a single held out speaker. In both of these studies, a large drop in accuracy was observed when using speaker-independent systems in comparison to speaker-dependent systems. Our investigation differs from previous work in that we focus on child speech while using a larger number of speakers (58 children). Additionally, we use cross-validation to evaluate the performance of speaker-independent systems across all speakers, rather than using a small held out subset." + ], + [ + "We use the Ultrax Typically Developing dataset (UXTD) from the publicly available UltraSuite repository BIBREF19 . This dataset contains synchronized acoustic and ultrasound data from 58 typically developing children, aged 5-12 years old (31 female, 27 male). The data was aligned at the phone-level, according to the methods described in BIBREF19 , BIBREF25 . For this work, we discarded the acoustic data and focused only on the B-Mode ultrasound images capturing a midsaggital view of the tongue. The data was recorded using an Ultrasonix SonixRP machine using Articulate Assistant Advanced (AAA) software at INLINEFORM0 121fps with a 135 field of view. A single ultrasound frame consists of 412 echo returns from each of the 63 scan lines (63x412 raw frames). For this work, we only use UXTD type A (semantically unrelated words, such as pack, tap, peak, tea, oak, toe) and type B (non-words designed to elicit the articulation of target phones, such as apa, eepee, opo) utterances." + ], + [ + "For this investigation, we define a simplified phonetic segment classification task. We determine four classes corresponding to distinct places of articulation. The first consists of bilabial and labiodental phones (e.g. /p, b, v, f, .../). The second class includes dental, alveolar, and postalveolar phones (e.g. /th, d, t, z, s, sh, .../). The third class consists of velar phones (e.g. /k, g, .../). Finally, the fourth class consists of alveolar approximant /r/. Figure FIGREF1 shows examples of the four classes for two speakers.", + "For each speaker, we divide all available utterances into disjoint train, development, and test sets. Using the force-aligned phone boundaries, we extract the mid-phone frame for each example across the four classes, which leads to a data imbalance. Therefore, for all utterances in the training set, we randomly sample additional examples within a window of 5 frames around the center phone, to at least 50 training examples per class per speaker. It is not always possible to reach the target of 50 examples, however, if no more data is available to sample from. This process gives a total of INLINEFORM0 10700 training examples with roughly 2000 to 3000 examples per class, with each speaker having an average of 185 examples. Because the amount of data varies per speaker, we compute a sampling score, which denotes the proportion of sampled examples to the speaker's total training examples. We expect speakers with high sampling scores (less unique data overall) to underperform when compared with speakers with more varied training examples." + ], + [ + "For each system, we normalize the training data to zero mean and unit variance. Due to the high dimensionality of the data (63x412 samples per frame), we have opted to investigate two preprocessing techniques: principal components analysis (PCA, often called eigentongues in this context) and a 2-dimensional discrete cosine transform (DCT). In this paper, Raw input denotes the mean-variance normalized raw ultrasound frame. PCA applies principal components analysis to the normalized training data and preserves the top 1000 components. DCT applies the 2D DCT to the normalized raw ultrasound frame and the upper left 40x40 submatrix (1600 coefficients) is flattened and used as input.", + "The first type of classifier we evaluate in this work are feedforward neural networks (DNNs) consisting of 3 hidden layers, each with 512 rectified linear units (ReLUs) with a softmax activation function. The networks are optimized for 40 epochs with a mini-batch of 32 samples using stochastic gradient descent. Based on preliminary experiments on the validation set, hyperparameters such learning rate, decay rate, and L2 weight vary depending on the input format (Raw, PCA, or DCT). Generally, Raw inputs work better with smaller learning rates and heavier regularization to prevent overfitting to the high-dimensional data. As a second classifier to evaluate, we use convolutional neural networks (CNNs) with 2 convolutional and max pooling layers, followed by 2 fully-connected ReLU layers with 512 nodes. The convolutional layers use 16 filters, 8x8 and 4x4 kernels respectively, and rectified units. The fully-connected layers use dropout with a drop probability of 0.2. Because CNN systems take longer to converge, they are optimized over 200 epochs. For all systems, at the end of every epoch, the model is evaluated on the development set, and the best model across all epochs is kept." + ], + [ + "We train speaker-dependent systems separately for each speaker, using all of their training data (an average of 185 examples per speaker). These systems use less data overall than the remaining systems, although we still expect them to perform well, as the data matches in terms of speaker characteristics. Realistically, such systems would not be viable, as it would be unreasonable to collect large amounts of data for every child who is undergoing speech therapy. We further evaluate all trained systems in a multi-speaker scenario. In this configuration, the speaker sets for training, development, and testing are equal. That is, we evaluate on speakers that we have seen at training time, although on different utterances. A more realistic configuration is a speaker-independent scenario, which assumes that the speaker set available for training and development is disjoint from the speaker set used at test time. This scenario is implemented by leave-one-out cross-validation. Finally, we investigate a speaker adaptation scenario, where training data for the target speaker becomes available. This scenario is realistic, for example, if after a session, the therapist were to annotate a small number of training examples. In this work, we use the held-out training data to finetune a pretrained speaker-independent system for an additional 6 epochs in the DNN systems and 20 epochs for the CNN systems. We use all available training data across all training scenarios, and we investigate the effect of the number of samples on one of the top performing systems.", + "This work is primarily concerned with generalizing to unseen speakers. Therefore, we investigate a method to provide models with speaker-specific inputs. A simple approach is to use the speaker mean, which is the pixel-wise mean of all raw frames associated with a given speaker, illustrated in Figure FIGREF8 . The mean frame might capture an overall area of tongue activity, average out noise, and compensate for probe placement differences across speakers. Speaker means are computed after mean variance normalization. For PCA-based systems, matrix decomposition is applied on the matrix of speaker means for the training data with 50 components being kept, while the 2D DCT is applied normally to each mean frame. In the DNN systems, the speaker mean is appended to the input vector. In the CNN system, the raw speaker mean is given to the network as a second channel. All model configurations are similar to those described earlier, except for the DNN using Raw input. Earlier experiments have shown that a larger number of parameters are needed for good generalization with a large number of inputs, so we use layers of 1024 nodes rather than 512." + ], + [ + "Results for all systems are presented in Table TABREF10 . When comparing preprocessing methods, we observe that PCA underperforms when compared with the 2 dimensional DCT or with the raw input. DCT-based systems achieve good results when compared with similar model architectures, especially when using smaller amounts of data as in the speaker-dependent scenario. When compared with raw input DNNs, the DCT-based systems likely benefit from the reduced dimensionality. In this case, lower dimensional inputs allow the model to generalize better and the truncation of the DCT matrix helps remove noise from the images. Compared with PCA-based systems, it is hypothesized the observed improvements are likely due to the DCT's ability to encode the 2-D structure of the image, which is ignored by PCA. However, the DNN-DCT system does not outperform a CNN with raw input, ranking last across adapted systems.", + "When comparing training scenarios, as expected, speaker-independent systems underperform, which illustrates the difficulty involved in the generalization to unseen speakers. Multi-speaker systems outperform the corresponding speaker-dependent systems, which shows the usefulness of learning from a larger database, even if variable across speakers. Adapted systems improve over the dependent systems, except when using DCT. It is unclear why DCT-based systems underperform when adapting pre-trained models. Figure FIGREF11 shows the effect of the size of the adaptation data when finetuning a pre-trained speaker-independent system. As expected, the more data is available, the better that system performs. It is observed that, for the CNN system, with roughly 50 samples, the model outperforms a similar speaker-dependent system with roughly three times more examples.", + "Speaker means improve results across all scenarios. It is particularly useful for speaker-independent systems. The ability to generalize to unseen speakers is clear in the CNN system. Using the mean as a second channel in the convolutional network has the advantage of relating each pixel to its corresponding speaker mean value, allowing the model to better generalize to unseen speakers.", + "Figure FIGREF12 shows pair-wise scatterplots for the CNN system. Training scenarios are compared in terms of the effect on individual speakers. It is observed, for example, that the performance of a speaker-adapted system is similar to a multi-speaker system, with most speakers clustered around the identity line (bottom left subplot). Figure FIGREF12 also illustrates the variability across speakers for each of the training scenarios. The classification task is easier for some speakers than others. In an attempt to understand this variability, we can look at correlation between accuracy scores and various speaker details. For the CNN systems, we have found some correlation (Pearson's product-moment correlation) between accuracy and age for the dependent ( INLINEFORM0 ), multi-speaker ( INLINEFORM1 ), and adapted ( INLINEFORM2 ) systems. A very small correlation ( INLINEFORM3 ) was found for the independent system. Similarly, some correlation was found between accuracy and sampling score ( INLINEFORM4 ) for the dependent system, but not for the remaining scenarios. No correlation was found between accuracy and gender (point biserial correlation)." + ], + [ + "There are various possible extensions for this work. For example, using all frames assigned to a phone, rather than using only the middle frame. Recurrent architectures are natural candidates for such systems. Additionally, if using these techniques for speech therapy, the audio signal will be available. An extension of these analyses should not be limited to the ultrasound signal, but instead evaluate whether audio and ultrasound can be complementary. Further work should aim to extend the four classes to more a fine-grained place of articulation, possibly based on phonological processes. Similarly, investigating which classes lead to classification errors might help explain some of the observed results. Although we have looked at variables such as age, gender, or amount of data to explain speaker variation, there may be additional factors involved, such as the general quality of the ultrasound image. Image quality could be affected by probe placement, dry mouths, or other factors. Automatically identifying or measuring such cases could be beneficial for speech therapy, for example, by signalling the therapist that the data being collected is sub-optimal." + ], + [ + "In this paper, we have investigated speaker-independent models for the classification of phonetic segments from raw ultrasound data. We have shown that the performance of the models heavily degrades when evaluated on data from unseen speakers. This is a result of the variability in ultrasound images, mostly due to differences across speakers, but also due to shifts in probe placement. Using the mean of all ultrasound frames for a new speaker improves the generalization of the models to unseen data, especially when using convolutional neural networks. We have also shown that adapting a pre-trained speaker-independent system using as few as 50 ultrasound frames can outperform a corresponding speaker-dependent system." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0677/instruction.md b/qasper-0677/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..92bef78d9d5139ec36c6f10c5c68b6a63a440b2f --- /dev/null +++ b/qasper-0677/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Speaker-independent classification of phonetic segments from raw ultrasound in child speech + +Question: What are the characteristics of the dataset? \ No newline at end of file diff --git a/qasper-0678/instruction.md b/qasper-0678/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..5e130d1178c70d31c898ca7abf518aa7ae08f3e9 --- /dev/null +++ b/qasper-0678/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Speaker-independent classification of phonetic segments from raw ultrasound in child speech + +Question: What type of models are used for classification? \ No newline at end of file diff --git a/qasper-0679/instruction.md b/qasper-0679/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..a175360ad52dc9ecc365d82e7a94b4625dc253d7 --- /dev/null +++ b/qasper-0679/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Speaker-independent classification of phonetic segments from raw ultrasound in child speech + +Question: Do they compare to previous work? \ No newline at end of file diff --git a/qasper-0682/instruction.md b/qasper-0682/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..7ea527b2a659e04e4654ee3a9b89a624047db524 --- /dev/null +++ b/qasper-0682/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Speaker-independent classification of phonetic segments from raw ultrasound in child speech + +Question: How many speakers do they have in the dataset? \ No newline at end of file diff --git a/qasper-0683/instruction.md b/qasper-0683/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..9f7558d6a79bb3cba0cd501f5e0938f3cf0ff9a3 --- /dev/null +++ b/qasper-0683/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: A Multi-Turn Emotionally Engaging Dialog Model + +Question: How better is proposed method than baselines perpexity wise? \ No newline at end of file diff --git a/qasper-0684/instruction.md b/qasper-0684/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..c5ed669058f4fb02748ff773aecede0080c5b9ff --- /dev/null +++ b/qasper-0684/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: A Multi-Turn Emotionally Engaging Dialog Model + +Question: How does the multi-turn dialog system learns? \ No newline at end of file diff --git a/qasper-0685/instruction.md b/qasper-0685/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..c853e6bccc662e2e6d16e7e4a531a4cee4e856cd --- /dev/null +++ b/qasper-0685/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: A Multi-Turn Emotionally Engaging Dialog Model + +Question: How is human evaluation performed? \ No newline at end of file diff --git a/qasper-0690/instruction.md b/qasper-0690/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..11d704696e7795847a63218c8e249514e611cded --- /dev/null +++ b/qasper-0690/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Semantic Role Labeling for Learner Chinese: the Importance of Syntactic Parsing and L2-L1 Parallel Data + +Question: Do the authors suggest why syntactic parsing is so important for semantic role labelling for interlanguages? \ No newline at end of file diff --git a/qasper-0696/instruction.md b/qasper-0696/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..a81233e6517077c500ce00210bc2be960dc61aa6 --- /dev/null +++ b/qasper-0696/instruction.md @@ -0,0 +1,76 @@ +Name of Paper: Testing the Generalization Power of Neural Network Models Across NLI Benchmarks + +Question: Which model generalized the best? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Experimental Setup", + "Data", + "Model and Training Details", + "Experimental Results", + "Discussion and Conclusion", + "Acknowledgments" + ], + "paragraphs": [ + [ + "Natural Language Inference (NLI) has attracted considerable interest in the NLP community and, recently, a large number of neural network-based systems have been proposed to deal with the task. One can attempt a rough categorization of these systems into: a) sentence encoding systems, and b) other neural network systems. Both of them have been very successful, with the state of the art on the SNLI and MultiNLI datasets being 90.4%, which is our baseline with BERT BIBREF0 , and 86.7% BIBREF0 respectively. However, a big question with respect to these systems is their ability to generalize outside the specific datasets they are trained and tested on. Recently, BIBREF1 have shown that state-of-the-art NLI systems break considerably easily when, instead of tested on the original SNLI test set, they are tested on a test set which is constructed by taking premises from the training set and creating several hypotheses from them by changing at most one word within the premise. The results show a very significant drop in accuracy for three of the four systems. The system that was more difficult to break and had the least loss in accuracy was the system by BIBREF2 which utilizes external knowledge taken from WordNet BIBREF3 .", + "In this paper we show that NLI systems that have been very successful in specific NLI benchmarks, fail to generalize when trained on a specific NLI dataset and then these trained models are tested across test sets taken from different NLI benchmarks. The results we get are in line with BIBREF1 , showing that the generalization capability of the individual NLI systems is very limited, but, what is more, they further show the only system that was less prone to breaking in BIBREF1 , breaks too in the experiments we have conducted.", + "We train six different state-of-the-art models on three different NLI datasets and test these trained models on an NLI test set taken from another dataset designed for the same NLI task, namely for the task to identify for sentence pairs in the dataset if one sentence entails the other one, if they are in contradiction with each other or if they are neutral with respect to inferential relationship.", + "One would expect that if a model learns to correctly identify inferential relationships in one dataset, then it would also be able to do so in another dataset designed for the same task. Furthermore, two of the datasets, SNLI BIBREF4 and MultiNLI BIBREF5 , have been constructed using the same crowdsourcing approach and annotation instructions BIBREF5 , leading to datasets with the same or at least very similar definition of entailment. It is therefore reasonable to expect that transfer learning between these datasets is possible. As SICK BIBREF6 dataset has been machine-constructed, a bigger difference in performance is expected.", + "In this paper we show that, contrary to our expectations, most models fail to generalize across the different datasets. However, our experiments also show that BERT BIBREF0 performs much better than the other models in experiments between SNLI and MultiNLI. Nevertheless, even BERT fails when testing on SICK. In addition to the negative results, our experiments further highlight the power of pre-trained language models, like BERT, in NLI.", + "The negative results of this paper are significant for the NLP research community as well as to NLP practice as we would like our best models to not only to be able to perform well in a specific benchmark dataset, but rather capture the more general phenomenon this dataset is designed for. The main contribution of this paper is that it shows that most of the best performing neural network models for NLI fail in this regard. The second, and equally important, contribution is that our results highlight that the current NLI datasets do not capture the nuances of NLI extensively enough." + ], + [ + "The ability of NLI systems to generalize and related skepticism has been raised in a number of recent papers. BIBREF1 show that the generalization capabilities of state-of-the-art NLI systems, in cases where some kind of external lexical knowledge is needed, drops dramatically when the SNLI test set is replaced by a test set where the premise and the hypothesis are otherwise identical except for at most one word. The results show a very significant drop in accuracy. BIBREF7 recognize the generalization problem that comes with training on datasets like SNLI, which tend to be homogeneous and with little linguistic variation. In this context, they propose to better train NLI models by making use of adversarial examples.", + "Multiple papers have reported hidden bias and annotation artifacts in the popular NLI datasets SNLI and MultiNLI allowing classification based on the hypothesis sentences alone BIBREF8 , BIBREF9 , BIBREF10 .", + " BIBREF11 evaluate the robustness of NLI models using datasets where label preserving swapping operations have been applied, reporting significant performance drops compared to the results with the original dataset. In these experiments, like in the BreakingNLI experiment, the systems that seem to be performing the better, i.e. less prone to breaking, are the ones where some kind of external knowledge is used by the model (KIM by BIBREF2 is one of those systems).", + "On a theoretical and methodological level, there is discussion on the nature of various NLI datasets, as well as the definition of what counts as NLI and what does not. For example, BIBREF12 , BIBREF13 present an overview of the most standard datasets for NLI and show that the definitions of inference in each of them are actually quite different, capturing only fragments of what seems to be a more general phenomenon.", + " BIBREF4 show that a simple LSTM model trained on the SNLI data fails when tested on SICK. However, their experiment is limited to this single architecture and dataset pair. BIBREF5 show that different models that perform well on SNLI have lower accuracy on MultiNLI. However in their experiments they did not systematically test transfer learning between the two datasets, but instead used separate systems where the training and test data were drawn from the same corpora." + ], + [ + "In this section we describe the datasets and model architectures included in the experiments." + ], + [ + "We chose three different datasets for the experiments: SNLI, MultiNLI and SICK. All of them have been designed for NLI involving three-way classification with the labels entailment, neutral and contradiction. We did not include any datasets with two-way classification, e.g. SciTail BIBREF14 . As SICK is a relatively small dataset with approximately only 10k sentence pairs, we did not use it as training data in any experiment. We also trained the models with a combined SNLI + MultiNLI training set.", + "For all the datasets we report the baseline performance where the training and test data are drawn from the same corpus. We then take these trained models and test them on a test set taken from another NLI corpus. For the case where the models are trained with SNLI + MultiNLI we report the baseline using the SNLI test data. All the experimental combinations are listed in Table 1 . Examples from the selected datasets are provided in Table 2 . To be more precise, we vary three things: training dataset, model and testing dataset. We should qualify this though, since the three datasets we look at, can also be grouped by text domain/genre and type of data collection, with MultiNLI and SNLI using the same data collection style, and SNLI and SICK using roughly the same domain/genre. Hopefully, our set up will let us determine which of these factors matters the most.", + "We describe the source datasets in more detail below.", + "The Stanford Natural Language Inference (SNLI) corpus BIBREF4 is a dataset of 570k human-written sentence pairs manually labeled with the labels entailment, contradiction, and neutral. The source for the premise sentences in SNLI were image captions taken from the Flickr30k corpus BIBREF15 .", + "The Multi-Genre Natural Language Inference (MultiNLI) corpus BIBREF5 consisting of 433k human-written sentence pairs labeled with entailment, contradiction and neutral. MultiNLI contains sentence pairs from ten distinct genres of both written and spoken English. Only five genres are included in the training set. The development and test sets have been divided into matched and mismatched, where the former includes only sentences from the same genres as the training data, and the latter includes sentences from the remaining genres not present in the training data.", + "We used the matched development set (MultiNLI-m) for the experiments. The MultiNLI dataset was annotated using very similar instructions as for the SNLI dataset. Therefore we can assume that the definitions of entailment, contradiction and neutral is the same in these two datasets.", + "SICK BIBREF6 is a dataset that was originally constructed to test compositional distributional semantics (DS) models. The dataset contains 9,840 examples pertaining to logical inference (negation, conjunction, disjunction, apposition, relative clauses, etc.). The dataset was automatically constructed taking pairs of sentences from a random subset of the 8K ImageFlickr data set BIBREF15 and the SemEval 2012 STS MSRVideo Description dataset BIBREF16 ." + ], + [ + "We perform experiments with six high-performing models covering the sentence encoding models, cross-sentence attention models as well as fine-tuned pre-trained language models.", + "For sentence encoding models, we chose a simple one-layer bidirectional LSTM with max pooling (BiLSTM-max) with the hidden size of 600D per direction, used e.g. in InferSent BIBREF17 , and HBMP BIBREF18 . For the other models, we have chosen ESIM BIBREF19 , which includes cross-sentence attention, and KIM BIBREF2 , which has cross-sentence attention and utilizes external knowledge. We also selected two model involving a pre-trained language model, namely ESIM + ELMo BIBREF20 and BERT BIBREF0 . KIM is particularly interesting in this context as it performed significantly better than other models in the Breaking NLI experiment conducted by BIBREF1 . The success of pre-trained language models in multiple NLP tasks make ESIM + ELMo and BERT interesting additions to this experiment. Table 3 lists the different models used in the experiments.", + "For BiLSTM-max we used the Adam optimizer BIBREF21 , a learning rate of 5e-4 and batch size of 64. The learning rate was decreased by the factor of 0.2 after each epoch if the model did not improve. Dropout of 0.1 was used between the layers of the multi-layer perceptron classifier, except before the last layer.The BiLSTM-max models were initialized with pre-trained GloVe 840B word embeddings of size 300 dimensions BIBREF22 , which were fine-tuned during training. Our BiLSMT-max model was implemented in PyTorch.", + "For HBMP, ESIM, KIM and BERT we used the original implementations with the default settings and hyperparameter values as described in BIBREF18 , BIBREF19 , BIBREF2 and BIBREF0 respectively. For BERT we used the uncased 768-dimensional model (BERT-base). For ESIM + ELMo we used the AllenNLP BIBREF23 PyTorch implementation with the default settings and hyperparameter values." + ], + [ + "Table 4 contains all the experimental results.", + "Our experiments show that, while all of the six models perform well when the test set is drawn from the same corpus as the training and development set, accuracy is significantly lower when we test these trained models on a test set drawn from a separate NLI corpus, the average difference in accuracy being 24.9 points across all experiments.", + "Accuracy drops the most when a model is tested on SICK. The difference in this case is between 19.0-29.0 points when trained on MultiNLI, between 31.6-33.7 points when trained on SNLI and between 31.1-33.0 when trained on SNLI + MultiNLI. This was expected, as the method of constructing the sentence pairs was different, and hence there is too much difference in the kind of sentence pairs included in the training and test sets for transfer learning to work. However, the drop was more dramatic than expected.", + "The most surprising result was that the accuracy of all models drops significantly even when the models were trained on MultiNLI and tested on SNLI (3.6-11.1 points). This is surprising as both of these datasets have been constructed with a similar data collection method using the same definition of entailment, contradiction and neutral. The sentences included in SNLI are also much simpler compared to those in MultiNLI, as they are taken from the Flickr image captions. This might also explain why the difference in accuracy for all of the six models is lowest when the models are trained on MultiNLI and tested on SNLI. It is also very surprising that the model with the biggest difference in accuracy was ESIM + ELMo which includes a pre-trained ELMo language model. BERT performed significantly better than the other models in this experiment having an accuracy of 80.4% and only 3.6 point difference in accuracy.", + "The poor performance of most of the models with the MultiNLI-SNLI dataset pair is also very surprising given that neural network models do not seem to suffer a lot from introduction of new genres to the test set which were not included in the training set, as can be seen from the small difference in test accuracies for the matched and mismatched test sets (see e.g BIBREF5 ). In a sense SNLI could be seen as a separate genre not included in MultiNLI. This raises the question if the SNLI and MultiNLI have e.g. different kinds of annotation artifacts, which makes transfer learning between these datasets more difficult.", + "All the models, except BERT, perform almost equally poorly across all the experiments. Both BiLSTM-max and HBMP have an average drop in accuracy of 24.4 points, while the average for KIM is 25.5 and for ESIM + ELMo 25.6. ESIM has the highest average difference of 27.0 points. In contrast to the findings of BIBREF1 , utilizing external knowledge did not improve the model's generalization capability, as KIM performed equally poorly across all dataset combinations.", + "Also including a pretrained ELMo language model did not improve the results significantly. The overall performance of BERT was significantly better than the other models, having the lowest average difference in accuracy of 22.5 points. Our baselines for SNLI (90.4%) and SNLI + MultiNLI (90.6%) outperform the previous state-of-the-art accuracy for SNLI (90.1%) by BIBREF24 .", + "To understand better the types of errors made by neural network models in NLI we looked at some example failure-pairs for selected models. Tables 5 and 6 contain some randomly selected failure-pairs for two models: BERT and HBMP, and for three set-ups: SNLI $\\rightarrow $ SICK, SNLI $\\rightarrow $ MultiNLI and MultiNLI $\\rightarrow $ SICK. We chose BERT as the current the state of the art NLI model. HBMP was selected as a high performing model in the sentence encoding model type. Although the listed sentence pairs represent just a small sample of the errors made by these models, they do include some interesting examples. First, it seems that SICK has a more narrow notion of contradiction \u2013 corresponding more to logical contradiction \u2013 compared to the contradiction in SNLI and MultiNLI, where especially in SNLI the sentences are contradictory if they describe a different state of affairs. This is evident in the sentence pair: A young child is running outside over the fallen leaves and A young child is lying down on a gravel road that is covered with dead leaves, which is predicted by BERT to be contradiction although the gold label is neutral. Another interesting example is the sentence pair: A boat pear with people boarding and disembarking some boats. and people are boarding and disembarking some boats, which is incorrectly predicted by BERT to be contradiction although it has been labeled as entailment. Here the two sentences describe the same event from different points of view: the first one describing a boat pear with some people on it and the second one describing the people directly. Interestingly the added information about the boat pear seems to confuse the model." + ], + [ + "In this paper we have shown that neural network models for NLI fail to generalize across different NLI benchmarks. We experimented with six state-of-the-art models covering sentence encoding approaches, cross-sentence attention models and pre-trained and fine-tuned language models. For all the systems, the accuracy drops between 3.6-33.7 points (the average drop being 24.9 points), when testing with a test set drawn from a separate corpus from that of the training data, as compared to when the test and training data are splits from the same corpus. Our findings, together with the previous negative findings, indicate that the state-of-the-art models fail to capture the semantics of NLI in a way that will enable them to generalize across different NLI situations.", + "The results highlight two issues to be taken into consideration: a) using datasets involving a fraction of what NLI is, will fail when tested in datasets that are testing for a slightly different definition of inference. This is evident when we move from the SNLI to the SICK dataset. b) NLI is to some extent genre/context dependent. Training on SNLI and testing on MultiNLI gives worse results than vice versa. This is particularly evident in the case of BERT. These results highlight that training on multiple genres helps. However, this help is still not enough given that, even in the case of training on MultiNLI (multi genre) and training on SNLI (single genre and same definition of inference with MultiNLI), accuracy drops significantly.", + "We also found that involving a large pre-trained language model helps with transfer learning when the datasets are similar enough, as is the case with SNLI and MultiNLI. Our results further corroborate the power of pre-trained and fine-tuned language models like BERT in NLI. However, not even BERT is able to generalize from SNLI and MultiNLI to SICK, possibly due to the difference between what kind of inference relations are contained in these datasets.", + "Our findings motivate us to look for novel neural network architectures and approaches that better capture the semantics on natural language inference beyond individual datasets. However, there seems to be a need to start with better constructed datasets, i.e. datasets that will not only capture fractions of what NLI is in reality. Better NLI systems need to be able to be more versatile on the types of inference they can recognize. Otherwise, we would be stuck with systems that can cover only some aspects of NLI. On a theoretical level, and in connection to the previous point, we need a better understanding of the range of phenomena NLI must be able to cover and focus our future endeavours for dataset construction towards this direction. In order to do this a more systematic study is needed on the different kinds of entailment relations NLI datasets need to include. Our future work will include a more systematic and broad-coverage analysis of the types of errors the models make and in what kinds of sentence-pairs they make successful predictions." + ], + [ + " The first author is supported by the FoTran project, funded by the European Research Council (ERC) under the European Union\u2019s Horizon 2020 research and innovation programme (grant agreement No 771113). ", + "The first author also gratefully acknowledges the support of the Academy of Finland through project 314062 from the ICT 2023 call on Computation, Machine Learning and Artificial Intelligence.", + "The second author is supported by grant 2014-39 from the Swedish Research Council, which funds the Centre for Linguistic Theory and Studies in Probability (CLASP) in the Department of Philosophy, Linguistics, and Theory of Science at the University of Gothenburg. " + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0697/instruction.md b/qasper-0697/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..e7103c8d5821681af1eef0cc2273fc9c89f50844 --- /dev/null +++ b/qasper-0697/instruction.md @@ -0,0 +1,76 @@ +Name of Paper: Testing the Generalization Power of Neural Network Models Across NLI Benchmarks + +Question: Which models were compared? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Experimental Setup", + "Data", + "Model and Training Details", + "Experimental Results", + "Discussion and Conclusion", + "Acknowledgments" + ], + "paragraphs": [ + [ + "Natural Language Inference (NLI) has attracted considerable interest in the NLP community and, recently, a large number of neural network-based systems have been proposed to deal with the task. One can attempt a rough categorization of these systems into: a) sentence encoding systems, and b) other neural network systems. Both of them have been very successful, with the state of the art on the SNLI and MultiNLI datasets being 90.4%, which is our baseline with BERT BIBREF0 , and 86.7% BIBREF0 respectively. However, a big question with respect to these systems is their ability to generalize outside the specific datasets they are trained and tested on. Recently, BIBREF1 have shown that state-of-the-art NLI systems break considerably easily when, instead of tested on the original SNLI test set, they are tested on a test set which is constructed by taking premises from the training set and creating several hypotheses from them by changing at most one word within the premise. The results show a very significant drop in accuracy for three of the four systems. The system that was more difficult to break and had the least loss in accuracy was the system by BIBREF2 which utilizes external knowledge taken from WordNet BIBREF3 .", + "In this paper we show that NLI systems that have been very successful in specific NLI benchmarks, fail to generalize when trained on a specific NLI dataset and then these trained models are tested across test sets taken from different NLI benchmarks. The results we get are in line with BIBREF1 , showing that the generalization capability of the individual NLI systems is very limited, but, what is more, they further show the only system that was less prone to breaking in BIBREF1 , breaks too in the experiments we have conducted.", + "We train six different state-of-the-art models on three different NLI datasets and test these trained models on an NLI test set taken from another dataset designed for the same NLI task, namely for the task to identify for sentence pairs in the dataset if one sentence entails the other one, if they are in contradiction with each other or if they are neutral with respect to inferential relationship.", + "One would expect that if a model learns to correctly identify inferential relationships in one dataset, then it would also be able to do so in another dataset designed for the same task. Furthermore, two of the datasets, SNLI BIBREF4 and MultiNLI BIBREF5 , have been constructed using the same crowdsourcing approach and annotation instructions BIBREF5 , leading to datasets with the same or at least very similar definition of entailment. It is therefore reasonable to expect that transfer learning between these datasets is possible. As SICK BIBREF6 dataset has been machine-constructed, a bigger difference in performance is expected.", + "In this paper we show that, contrary to our expectations, most models fail to generalize across the different datasets. However, our experiments also show that BERT BIBREF0 performs much better than the other models in experiments between SNLI and MultiNLI. Nevertheless, even BERT fails when testing on SICK. In addition to the negative results, our experiments further highlight the power of pre-trained language models, like BERT, in NLI.", + "The negative results of this paper are significant for the NLP research community as well as to NLP practice as we would like our best models to not only to be able to perform well in a specific benchmark dataset, but rather capture the more general phenomenon this dataset is designed for. The main contribution of this paper is that it shows that most of the best performing neural network models for NLI fail in this regard. The second, and equally important, contribution is that our results highlight that the current NLI datasets do not capture the nuances of NLI extensively enough." + ], + [ + "The ability of NLI systems to generalize and related skepticism has been raised in a number of recent papers. BIBREF1 show that the generalization capabilities of state-of-the-art NLI systems, in cases where some kind of external lexical knowledge is needed, drops dramatically when the SNLI test set is replaced by a test set where the premise and the hypothesis are otherwise identical except for at most one word. The results show a very significant drop in accuracy. BIBREF7 recognize the generalization problem that comes with training on datasets like SNLI, which tend to be homogeneous and with little linguistic variation. In this context, they propose to better train NLI models by making use of adversarial examples.", + "Multiple papers have reported hidden bias and annotation artifacts in the popular NLI datasets SNLI and MultiNLI allowing classification based on the hypothesis sentences alone BIBREF8 , BIBREF9 , BIBREF10 .", + " BIBREF11 evaluate the robustness of NLI models using datasets where label preserving swapping operations have been applied, reporting significant performance drops compared to the results with the original dataset. In these experiments, like in the BreakingNLI experiment, the systems that seem to be performing the better, i.e. less prone to breaking, are the ones where some kind of external knowledge is used by the model (KIM by BIBREF2 is one of those systems).", + "On a theoretical and methodological level, there is discussion on the nature of various NLI datasets, as well as the definition of what counts as NLI and what does not. For example, BIBREF12 , BIBREF13 present an overview of the most standard datasets for NLI and show that the definitions of inference in each of them are actually quite different, capturing only fragments of what seems to be a more general phenomenon.", + " BIBREF4 show that a simple LSTM model trained on the SNLI data fails when tested on SICK. However, their experiment is limited to this single architecture and dataset pair. BIBREF5 show that different models that perform well on SNLI have lower accuracy on MultiNLI. However in their experiments they did not systematically test transfer learning between the two datasets, but instead used separate systems where the training and test data were drawn from the same corpora." + ], + [ + "In this section we describe the datasets and model architectures included in the experiments." + ], + [ + "We chose three different datasets for the experiments: SNLI, MultiNLI and SICK. All of them have been designed for NLI involving three-way classification with the labels entailment, neutral and contradiction. We did not include any datasets with two-way classification, e.g. SciTail BIBREF14 . As SICK is a relatively small dataset with approximately only 10k sentence pairs, we did not use it as training data in any experiment. We also trained the models with a combined SNLI + MultiNLI training set.", + "For all the datasets we report the baseline performance where the training and test data are drawn from the same corpus. We then take these trained models and test them on a test set taken from another NLI corpus. For the case where the models are trained with SNLI + MultiNLI we report the baseline using the SNLI test data. All the experimental combinations are listed in Table 1 . Examples from the selected datasets are provided in Table 2 . To be more precise, we vary three things: training dataset, model and testing dataset. We should qualify this though, since the three datasets we look at, can also be grouped by text domain/genre and type of data collection, with MultiNLI and SNLI using the same data collection style, and SNLI and SICK using roughly the same domain/genre. Hopefully, our set up will let us determine which of these factors matters the most.", + "We describe the source datasets in more detail below.", + "The Stanford Natural Language Inference (SNLI) corpus BIBREF4 is a dataset of 570k human-written sentence pairs manually labeled with the labels entailment, contradiction, and neutral. The source for the premise sentences in SNLI were image captions taken from the Flickr30k corpus BIBREF15 .", + "The Multi-Genre Natural Language Inference (MultiNLI) corpus BIBREF5 consisting of 433k human-written sentence pairs labeled with entailment, contradiction and neutral. MultiNLI contains sentence pairs from ten distinct genres of both written and spoken English. Only five genres are included in the training set. The development and test sets have been divided into matched and mismatched, where the former includes only sentences from the same genres as the training data, and the latter includes sentences from the remaining genres not present in the training data.", + "We used the matched development set (MultiNLI-m) for the experiments. The MultiNLI dataset was annotated using very similar instructions as for the SNLI dataset. Therefore we can assume that the definitions of entailment, contradiction and neutral is the same in these two datasets.", + "SICK BIBREF6 is a dataset that was originally constructed to test compositional distributional semantics (DS) models. The dataset contains 9,840 examples pertaining to logical inference (negation, conjunction, disjunction, apposition, relative clauses, etc.). The dataset was automatically constructed taking pairs of sentences from a random subset of the 8K ImageFlickr data set BIBREF15 and the SemEval 2012 STS MSRVideo Description dataset BIBREF16 ." + ], + [ + "We perform experiments with six high-performing models covering the sentence encoding models, cross-sentence attention models as well as fine-tuned pre-trained language models.", + "For sentence encoding models, we chose a simple one-layer bidirectional LSTM with max pooling (BiLSTM-max) with the hidden size of 600D per direction, used e.g. in InferSent BIBREF17 , and HBMP BIBREF18 . For the other models, we have chosen ESIM BIBREF19 , which includes cross-sentence attention, and KIM BIBREF2 , which has cross-sentence attention and utilizes external knowledge. We also selected two model involving a pre-trained language model, namely ESIM + ELMo BIBREF20 and BERT BIBREF0 . KIM is particularly interesting in this context as it performed significantly better than other models in the Breaking NLI experiment conducted by BIBREF1 . The success of pre-trained language models in multiple NLP tasks make ESIM + ELMo and BERT interesting additions to this experiment. Table 3 lists the different models used in the experiments.", + "For BiLSTM-max we used the Adam optimizer BIBREF21 , a learning rate of 5e-4 and batch size of 64. The learning rate was decreased by the factor of 0.2 after each epoch if the model did not improve. Dropout of 0.1 was used between the layers of the multi-layer perceptron classifier, except before the last layer.The BiLSTM-max models were initialized with pre-trained GloVe 840B word embeddings of size 300 dimensions BIBREF22 , which were fine-tuned during training. Our BiLSMT-max model was implemented in PyTorch.", + "For HBMP, ESIM, KIM and BERT we used the original implementations with the default settings and hyperparameter values as described in BIBREF18 , BIBREF19 , BIBREF2 and BIBREF0 respectively. For BERT we used the uncased 768-dimensional model (BERT-base). For ESIM + ELMo we used the AllenNLP BIBREF23 PyTorch implementation with the default settings and hyperparameter values." + ], + [ + "Table 4 contains all the experimental results.", + "Our experiments show that, while all of the six models perform well when the test set is drawn from the same corpus as the training and development set, accuracy is significantly lower when we test these trained models on a test set drawn from a separate NLI corpus, the average difference in accuracy being 24.9 points across all experiments.", + "Accuracy drops the most when a model is tested on SICK. The difference in this case is between 19.0-29.0 points when trained on MultiNLI, between 31.6-33.7 points when trained on SNLI and between 31.1-33.0 when trained on SNLI + MultiNLI. This was expected, as the method of constructing the sentence pairs was different, and hence there is too much difference in the kind of sentence pairs included in the training and test sets for transfer learning to work. However, the drop was more dramatic than expected.", + "The most surprising result was that the accuracy of all models drops significantly even when the models were trained on MultiNLI and tested on SNLI (3.6-11.1 points). This is surprising as both of these datasets have been constructed with a similar data collection method using the same definition of entailment, contradiction and neutral. The sentences included in SNLI are also much simpler compared to those in MultiNLI, as they are taken from the Flickr image captions. This might also explain why the difference in accuracy for all of the six models is lowest when the models are trained on MultiNLI and tested on SNLI. It is also very surprising that the model with the biggest difference in accuracy was ESIM + ELMo which includes a pre-trained ELMo language model. BERT performed significantly better than the other models in this experiment having an accuracy of 80.4% and only 3.6 point difference in accuracy.", + "The poor performance of most of the models with the MultiNLI-SNLI dataset pair is also very surprising given that neural network models do not seem to suffer a lot from introduction of new genres to the test set which were not included in the training set, as can be seen from the small difference in test accuracies for the matched and mismatched test sets (see e.g BIBREF5 ). In a sense SNLI could be seen as a separate genre not included in MultiNLI. This raises the question if the SNLI and MultiNLI have e.g. different kinds of annotation artifacts, which makes transfer learning between these datasets more difficult.", + "All the models, except BERT, perform almost equally poorly across all the experiments. Both BiLSTM-max and HBMP have an average drop in accuracy of 24.4 points, while the average for KIM is 25.5 and for ESIM + ELMo 25.6. ESIM has the highest average difference of 27.0 points. In contrast to the findings of BIBREF1 , utilizing external knowledge did not improve the model's generalization capability, as KIM performed equally poorly across all dataset combinations.", + "Also including a pretrained ELMo language model did not improve the results significantly. The overall performance of BERT was significantly better than the other models, having the lowest average difference in accuracy of 22.5 points. Our baselines for SNLI (90.4%) and SNLI + MultiNLI (90.6%) outperform the previous state-of-the-art accuracy for SNLI (90.1%) by BIBREF24 .", + "To understand better the types of errors made by neural network models in NLI we looked at some example failure-pairs for selected models. Tables 5 and 6 contain some randomly selected failure-pairs for two models: BERT and HBMP, and for three set-ups: SNLI $\\rightarrow $ SICK, SNLI $\\rightarrow $ MultiNLI and MultiNLI $\\rightarrow $ SICK. We chose BERT as the current the state of the art NLI model. HBMP was selected as a high performing model in the sentence encoding model type. Although the listed sentence pairs represent just a small sample of the errors made by these models, they do include some interesting examples. First, it seems that SICK has a more narrow notion of contradiction \u2013 corresponding more to logical contradiction \u2013 compared to the contradiction in SNLI and MultiNLI, where especially in SNLI the sentences are contradictory if they describe a different state of affairs. This is evident in the sentence pair: A young child is running outside over the fallen leaves and A young child is lying down on a gravel road that is covered with dead leaves, which is predicted by BERT to be contradiction although the gold label is neutral. Another interesting example is the sentence pair: A boat pear with people boarding and disembarking some boats. and people are boarding and disembarking some boats, which is incorrectly predicted by BERT to be contradiction although it has been labeled as entailment. Here the two sentences describe the same event from different points of view: the first one describing a boat pear with some people on it and the second one describing the people directly. Interestingly the added information about the boat pear seems to confuse the model." + ], + [ + "In this paper we have shown that neural network models for NLI fail to generalize across different NLI benchmarks. We experimented with six state-of-the-art models covering sentence encoding approaches, cross-sentence attention models and pre-trained and fine-tuned language models. For all the systems, the accuracy drops between 3.6-33.7 points (the average drop being 24.9 points), when testing with a test set drawn from a separate corpus from that of the training data, as compared to when the test and training data are splits from the same corpus. Our findings, together with the previous negative findings, indicate that the state-of-the-art models fail to capture the semantics of NLI in a way that will enable them to generalize across different NLI situations.", + "The results highlight two issues to be taken into consideration: a) using datasets involving a fraction of what NLI is, will fail when tested in datasets that are testing for a slightly different definition of inference. This is evident when we move from the SNLI to the SICK dataset. b) NLI is to some extent genre/context dependent. Training on SNLI and testing on MultiNLI gives worse results than vice versa. This is particularly evident in the case of BERT. These results highlight that training on multiple genres helps. However, this help is still not enough given that, even in the case of training on MultiNLI (multi genre) and training on SNLI (single genre and same definition of inference with MultiNLI), accuracy drops significantly.", + "We also found that involving a large pre-trained language model helps with transfer learning when the datasets are similar enough, as is the case with SNLI and MultiNLI. Our results further corroborate the power of pre-trained and fine-tuned language models like BERT in NLI. However, not even BERT is able to generalize from SNLI and MultiNLI to SICK, possibly due to the difference between what kind of inference relations are contained in these datasets.", + "Our findings motivate us to look for novel neural network architectures and approaches that better capture the semantics on natural language inference beyond individual datasets. However, there seems to be a need to start with better constructed datasets, i.e. datasets that will not only capture fractions of what NLI is in reality. Better NLI systems need to be able to be more versatile on the types of inference they can recognize. Otherwise, we would be stuck with systems that can cover only some aspects of NLI. On a theoretical level, and in connection to the previous point, we need a better understanding of the range of phenomena NLI must be able to cover and focus our future endeavours for dataset construction towards this direction. In order to do this a more systematic study is needed on the different kinds of entailment relations NLI datasets need to include. Our future work will include a more systematic and broad-coverage analysis of the types of errors the models make and in what kinds of sentence-pairs they make successful predictions." + ], + [ + " The first author is supported by the FoTran project, funded by the European Research Council (ERC) under the European Union\u2019s Horizon 2020 research and innovation programme (grant agreement No 771113). ", + "The first author also gratefully acknowledges the support of the Academy of Finland through project 314062 from the ICT 2023 call on Computation, Machine Learning and Artificial Intelligence.", + "The second author is supported by grant 2014-39 from the Swedish Research Council, which funds the Centre for Linguistic Theory and Studies in Probability (CLASP) in the Department of Philosophy, Linguistics, and Theory of Science at the University of Gothenburg. " + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0698/instruction.md b/qasper-0698/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..3f00fa99b1e54dc8ce113fb86dd723b298a42247 --- /dev/null +++ b/qasper-0698/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Testing the Generalization Power of Neural Network Models Across NLI Benchmarks + +Question: Which datasets were used? \ No newline at end of file diff --git a/qasper-0699/instruction.md b/qasper-0699/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..ddcbb05b8e486b709bf80a82cc3f31e33c5b37db --- /dev/null +++ b/qasper-0699/instruction.md @@ -0,0 +1,73 @@ +Name of Paper: VAIS Hate Speech Detection System: A Deep Learning based Approach for System Combination + +Question: What was the baseline? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "System description", + "System description ::: System overview", + "System description ::: Data pre-processing", + "System description ::: Models architecture", + "System description ::: Ensemble method", + "Experiment", + "Conclusion" + ], + "paragraphs": [ + [ + "Currently, social networks are so popular. Some of the biggest ones include Facebook, Twitter, Youtube,... with extremely number of users. Thus, controlling content of those platforms is essential. For years, social media companies such as Twitter, Facebook, and YouTube have been investing hundreds of millions euros on this task BIBREF0, BIBREF1. However, their effort is not enough since such efforts are primarily based on manual moderation to identify and delete offensive materials. The process is labour intensive, time consuming, and not sustainable or scalable in reality BIBREF2, BIBREF0, BIBREF3.", + "In the sixth international workshop on Vietnamese Language and Speech Processing (VLSP 2019), the Hate Speech Detection (HSD) task is proposed as one of the shared-tasks to handle the problem related to controlling content in SNSs. HSD is required to build a multi-class classification model that is capable of classifying an item to one of 3 classes (hate, offensive, clean). Hate speech (hate): an item is identified as hate speech if it (1) targets individuals or groups on the basis of their characteristics; (2) demonstrates a clear intention to incite harm, or to promote hatred; (3) may or may not use offensive or profane words. Offensive but not hate speech (offensive): an item (posts/comments) may contain offensive words but it does not target individuals or groups on the basis of their characteristics. Neither offensive nor hate speech (clean): normal item, it does not contain offensive language or hate speech.", + "The term `hate speech' was formally defined as `any communication that disparages a person or a group on the basis of some characteristics (to be referred to as types of hate or hate classes) such as race, colour, ethnicity, gender, sexual orientation, nationality, religion, or other characteristics' BIBREF4. Many researches have been conducted in recent years to develop automatic methods for hate speech detection in the social media domain. These typically employ semantic content analysis techniques built on Natural Language Processing (NLP) and Machine Learning (ML) methods. The task typically involves classifying textual content into non-hate or hateful. This HSD task is much more difficult when it requires classify text in three classes, with hate and offensive class quite hard to classify even with humans.", + "In this paper, we propose a method to handle this HSD problem. Our system combines multiple text representations and models architecture in order to make diverse predictions. The system is heavily based on the ensemble method. The next section will present detail of our system including data preparation (how we clean text and build text representation), architecture of the model using in the system, and how we combine them together. The third section is our experiment and result report in HSD shared-task VLSP 2019. The final section is our conclusion with advantages and disadvantages of the system following by our perspective." + ], + [ + "In this section, we present the system architecture. It includes how we pre-process text, what types of text representation we use and models used in our system. In the end, we combine model results by using an ensemble technique." + ], + [ + "The fundamental idea of this system is how to make a system that has the diversity of viewing an input. That because of the variety of the meaning in Vietnamese language especially with the acronym, teen code type. To make this diversity, after cleaning raw text input, we use multiple types of word tokenizers. Each one of these tokenizers, we combine with some types of representation methods, including word to vector methods such as continuous bag of words BIBREF5, pre-trained embedding as fasttext (trained on Wiki Vietnamese language) BIBREF6 and sonvx (trained on Vietnamese newspaper) BIBREF7. Each sentence has a set of words corresponding to a set of word vectors, and that set of word vectors is a representation of a sentence. We also make a sentence embedding by using RoBERTa architecture BIBREF8. CBOW and RoBERTa models trained on text from some resources including VLSP 2016 Sentiment Analysis, VLSP 2018 Sentiment Analysis, VLSP 2019 HSD and text crawled from Facebook. After having sentence representation, we use some classification models to classify input sentences. Those models will be described in detail in the section SECREF13. With the multiply output results, we will use an ensemble method to combine them and output the final result. Ensemble method we use here is Stacking method will be introduced in the section SECREF16." + ], + [ + "Content in the dataset that provided in this HSD task is very diverse. Words having the same meaning were written in various types (teen code, non tone, emojis,..) depending on the style of users. Dataset was crawled from various sources with multiple text encodes. In order to make it easy for training, all types of encoding need to be unified. This cleaning module will be used in two processes: cleaning data before training and cleaning input in inferring phase. Following is the data processing steps that we use:", + "Step 1: Format encoding. Vietnamese has many accents, intonations with different Unicode typing programs which may have different outputs with the same typing type. To make it unified, we build a library named visen. For example, the input \"th\u00ed\u00eat k\u00ea will be normalized to \"thi\u1ebft k\u1ebf\" as the output.", + "Step 2: In social networks, people show their feelings a lot by emojis. Emoticon is often a special Unicode character, but sometimes, it is combined by multiple normal characters like `: ( = ]'. We make a dictionary mapping this emoji (combined by some characters) to a single Unicode character like other emojis to make it unified.", + "Step 3: Remove unseen characters. For human, unseen character is invisible but for a computer, it makes the model harder to process and inserts space between words, punctuation and emoji. This step aims at reducing the number of words in the dictionary which is important task, especially with low dataset resources like this HSD task.", + "Step 4: With model requiring Vietnamese word segmentation as the input, we use BIBREF9, BIBREF10 to tokenize the input text.", + "Step 5: Make all string lower. We experimented and found that lower-case or upper-case are not a significant impact on the result, but with lower characters, the number of words in the dictionary is reduced.", + "RoBERTa proposed in BIBREF8 an optimized method for pretraining self-supervised NLP systems. In our system, we use RoBERTa not only to make sentence representation but also to augment data. With mask mechanism, we replace a word in the input sentence with another word that RoBERTa model proposes. To reduce the impact of replacement word, the chosen words are all common words that appear in almost three classes of the dataset. For example, with input `nh\u1ed5n l\u00e0m g\u1eaft vl', we can augment to other outputs: `vl l\u00e0m g\u1eaft q\u00e1', `c\u00f2n l\u00e0m vl v\u1eady', `vl l\u00e0m \u0111\u1ec9nh vl' or `thanh ch\u00fat g\u1eaft vl'.", + "british" + ], + [ + "Social comment dataset has high variety, the core idea is using multiple model architectures to handle data in many viewpoints. In our system, we use five different model architectures combining many types of CNN, and RNN. Each model will use some types of word embedding or handle directly sentence embedding to achieve the best general result. Source code of five models is extended from the GitHub repository", + "The first model is TextCNN (figure FIGREF2) proposed in BIBREF11. It only contains CNN blocks following by some Dense layers. The output of multiple CNN blocks with different kernel sizes is connected to each other.", + "The second model is VDCNN (figure FIGREF5) inspired by the research in BIBREF12. Like the TextCNN model, it contains multiple CNN blocks. The addition in this model is its residual connection.", + "The third model is a simple LSTM bidirectional model (figure FIGREF15). It contains multiple LSTM bidirectional blocks stacked to each other.", + "The fourth model is LSTMCNN (figure FIGREF24). Before going through CNN blocks, series of word embedding will be transformed by LSTM bidirectional block.", + "The final model is the system named SARNN (figure FIGREF25). It adds an attention block between LTSM blocks." + ], + [ + "Ensemble methods is a machine learning technique that combines several base models in order to produce one optimal predictive model. Have the main three types of ensemble methods including Bagging, Boosting and Stacking. In this system, we use the Stacking method. In this method, the output of each model is not only class id but also the probability of each class in the set of three classes. This probability will become a feature for the ensemble model. The stacking ensemble model here is a simple full-connection model with input is all of probability that output from sub-model. The output is the probability of each class." + ], + [ + "The dataset in this HSD task is really imbalance. Clean class dominates with 91.5%, offensive class takes 5% and the rest belongs to hate class with 3.5%. To make model being able to learn with this imbalance data, we inject class weight to the loss function with the corresponding ratio (clean, offensive, hate) is $(0.09, 0.95, 0.96)$. Formular DISPLAY_FORM17 is the loss function apply for all models in our system. $w_i$ is the class weight, $y_i$ is the ground truth and $\\hat{y}_i$ is the output of the model. If the class weight is not set, we find that model cannot adjust parameters. The model tends to output all clean classes.", + "We experiment 8 types of embedding in total:", + "comment: CBOW embedding training in all dataset comment, each word is splited by space. Embedding size is 200.", + "comment_bpe: CBOW embedding training in all dataset comment, each word is splited by subword bpe. Embedding size is 200.", + "comment_tokenize: CBOW embedding training in all dataset comment, each word is splited by space. Before split by space, word is concatenated by using BIBREF9, BIBREF13, BIBREF10. Embedding size is 200.", + "roberta: sentence embedding training in all dataset comment, training by using RoBERTa architecture. Embedding size is 256.", + "fasttext, sonvx* is all pre-trained word embedding in general domain. Before mapping word to vector, word is concatenated by using BIBREF9, BIBREF13, BIBREF10. Embedding size of fasttext is 300. (sonvx_wiki, sonvx_baomoi_w2, sonvx_baomoi_w5) have embedding size corresponding is (400, 300, 400).", + "In our experiment, the dataset is split into two-part: train set and dev set with the corresponding ratio $(0.9, 0.1)$. Two subsets have the same imbalance ratio like the root set. For each combination of model and word embedding, we train model in train set until it achieve the best result of loss score in the dev set. The table TABREF12 shows the best result of each combination on the f1_macro score.", + "For each model having the best fit on the dev set, we export the probability distribution of classes for each sample in the dev set. In this case, we only use the result of model that has f1_macro score that larger than 0.67. The probability distribution of classes is then used as feature to input into a dense model with only one hidden layer (size 128). The training process of the ensemble model is done on samples of the dev set. The best fit result is 0.7356. The final result submitted in public leaderboard is 0.73019 and in private leaderboard is 0.58455. It is quite different in bad way. That maybe is the result of the model too overfit on train set tuning on public test set.", + "Statistics of the final result on the dev set shows that almost cases have wrong prediction from offensive and hate class to clean class belong to samples containing the word `vl'. (62% in the offensive class and 48% in the hate class). It means that model overfit the word `vl' to the clean class. This makes sense because `vl' appears too much in the clean class dataset.", + "In case the model predicts wrong from the clean class to the offensive class and the hate class, the model tends to decide case having sensitive words to be wrong class. The class offensive and the hate are quite difficult to distinguish even with human." + ], + [ + "In this study, we experiment the combination of multiple embedding types and multiple model architecture to solve a part of the problem Hate Speech Detection with a signification good classification results. Our system heavily based on the ensemble technique so the weakness of the system is slow processing speed. But in fact, it is not big trouble with this HSD problem when human usually involve handling directly in the before.", + "HSD is a hard problem even with human. In order to improve classification quality, in the future, we need to collect more data especially social networks content. This will make building text representation more correct and help model easier to classify.", + "british" + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0702/instruction.md b/qasper-0702/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..41df750490e6153a0b2f94de50f9734d31606a0e --- /dev/null +++ b/qasper-0702/instruction.md @@ -0,0 +1,73 @@ +Name of Paper: VAIS Hate Speech Detection System: A Deep Learning based Approach for System Combination + +Question: What is private dashboard? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "System description", + "System description ::: System overview", + "System description ::: Data pre-processing", + "System description ::: Models architecture", + "System description ::: Ensemble method", + "Experiment", + "Conclusion" + ], + "paragraphs": [ + [ + "Currently, social networks are so popular. Some of the biggest ones include Facebook, Twitter, Youtube,... with extremely number of users. Thus, controlling content of those platforms is essential. For years, social media companies such as Twitter, Facebook, and YouTube have been investing hundreds of millions euros on this task BIBREF0, BIBREF1. However, their effort is not enough since such efforts are primarily based on manual moderation to identify and delete offensive materials. The process is labour intensive, time consuming, and not sustainable or scalable in reality BIBREF2, BIBREF0, BIBREF3.", + "In the sixth international workshop on Vietnamese Language and Speech Processing (VLSP 2019), the Hate Speech Detection (HSD) task is proposed as one of the shared-tasks to handle the problem related to controlling content in SNSs. HSD is required to build a multi-class classification model that is capable of classifying an item to one of 3 classes (hate, offensive, clean). Hate speech (hate): an item is identified as hate speech if it (1) targets individuals or groups on the basis of their characteristics; (2) demonstrates a clear intention to incite harm, or to promote hatred; (3) may or may not use offensive or profane words. Offensive but not hate speech (offensive): an item (posts/comments) may contain offensive words but it does not target individuals or groups on the basis of their characteristics. Neither offensive nor hate speech (clean): normal item, it does not contain offensive language or hate speech.", + "The term `hate speech' was formally defined as `any communication that disparages a person or a group on the basis of some characteristics (to be referred to as types of hate or hate classes) such as race, colour, ethnicity, gender, sexual orientation, nationality, religion, or other characteristics' BIBREF4. Many researches have been conducted in recent years to develop automatic methods for hate speech detection in the social media domain. These typically employ semantic content analysis techniques built on Natural Language Processing (NLP) and Machine Learning (ML) methods. The task typically involves classifying textual content into non-hate or hateful. This HSD task is much more difficult when it requires classify text in three classes, with hate and offensive class quite hard to classify even with humans.", + "In this paper, we propose a method to handle this HSD problem. Our system combines multiple text representations and models architecture in order to make diverse predictions. The system is heavily based on the ensemble method. The next section will present detail of our system including data preparation (how we clean text and build text representation), architecture of the model using in the system, and how we combine them together. The third section is our experiment and result report in HSD shared-task VLSP 2019. The final section is our conclusion with advantages and disadvantages of the system following by our perspective." + ], + [ + "In this section, we present the system architecture. It includes how we pre-process text, what types of text representation we use and models used in our system. In the end, we combine model results by using an ensemble technique." + ], + [ + "The fundamental idea of this system is how to make a system that has the diversity of viewing an input. That because of the variety of the meaning in Vietnamese language especially with the acronym, teen code type. To make this diversity, after cleaning raw text input, we use multiple types of word tokenizers. Each one of these tokenizers, we combine with some types of representation methods, including word to vector methods such as continuous bag of words BIBREF5, pre-trained embedding as fasttext (trained on Wiki Vietnamese language) BIBREF6 and sonvx (trained on Vietnamese newspaper) BIBREF7. Each sentence has a set of words corresponding to a set of word vectors, and that set of word vectors is a representation of a sentence. We also make a sentence embedding by using RoBERTa architecture BIBREF8. CBOW and RoBERTa models trained on text from some resources including VLSP 2016 Sentiment Analysis, VLSP 2018 Sentiment Analysis, VLSP 2019 HSD and text crawled from Facebook. After having sentence representation, we use some classification models to classify input sentences. Those models will be described in detail in the section SECREF13. With the multiply output results, we will use an ensemble method to combine them and output the final result. Ensemble method we use here is Stacking method will be introduced in the section SECREF16." + ], + [ + "Content in the dataset that provided in this HSD task is very diverse. Words having the same meaning were written in various types (teen code, non tone, emojis,..) depending on the style of users. Dataset was crawled from various sources with multiple text encodes. In order to make it easy for training, all types of encoding need to be unified. This cleaning module will be used in two processes: cleaning data before training and cleaning input in inferring phase. Following is the data processing steps that we use:", + "Step 1: Format encoding. Vietnamese has many accents, intonations with different Unicode typing programs which may have different outputs with the same typing type. To make it unified, we build a library named visen. For example, the input \"th\u00ed\u00eat k\u00ea will be normalized to \"thi\u1ebft k\u1ebf\" as the output.", + "Step 2: In social networks, people show their feelings a lot by emojis. Emoticon is often a special Unicode character, but sometimes, it is combined by multiple normal characters like `: ( = ]'. We make a dictionary mapping this emoji (combined by some characters) to a single Unicode character like other emojis to make it unified.", + "Step 3: Remove unseen characters. For human, unseen character is invisible but for a computer, it makes the model harder to process and inserts space between words, punctuation and emoji. This step aims at reducing the number of words in the dictionary which is important task, especially with low dataset resources like this HSD task.", + "Step 4: With model requiring Vietnamese word segmentation as the input, we use BIBREF9, BIBREF10 to tokenize the input text.", + "Step 5: Make all string lower. We experimented and found that lower-case or upper-case are not a significant impact on the result, but with lower characters, the number of words in the dictionary is reduced.", + "RoBERTa proposed in BIBREF8 an optimized method for pretraining self-supervised NLP systems. In our system, we use RoBERTa not only to make sentence representation but also to augment data. With mask mechanism, we replace a word in the input sentence with another word that RoBERTa model proposes. To reduce the impact of replacement word, the chosen words are all common words that appear in almost three classes of the dataset. For example, with input `nh\u1ed5n l\u00e0m g\u1eaft vl', we can augment to other outputs: `vl l\u00e0m g\u1eaft q\u00e1', `c\u00f2n l\u00e0m vl v\u1eady', `vl l\u00e0m \u0111\u1ec9nh vl' or `thanh ch\u00fat g\u1eaft vl'.", + "british" + ], + [ + "Social comment dataset has high variety, the core idea is using multiple model architectures to handle data in many viewpoints. In our system, we use five different model architectures combining many types of CNN, and RNN. Each model will use some types of word embedding or handle directly sentence embedding to achieve the best general result. Source code of five models is extended from the GitHub repository", + "The first model is TextCNN (figure FIGREF2) proposed in BIBREF11. It only contains CNN blocks following by some Dense layers. The output of multiple CNN blocks with different kernel sizes is connected to each other.", + "The second model is VDCNN (figure FIGREF5) inspired by the research in BIBREF12. Like the TextCNN model, it contains multiple CNN blocks. The addition in this model is its residual connection.", + "The third model is a simple LSTM bidirectional model (figure FIGREF15). It contains multiple LSTM bidirectional blocks stacked to each other.", + "The fourth model is LSTMCNN (figure FIGREF24). Before going through CNN blocks, series of word embedding will be transformed by LSTM bidirectional block.", + "The final model is the system named SARNN (figure FIGREF25). It adds an attention block between LTSM blocks." + ], + [ + "Ensemble methods is a machine learning technique that combines several base models in order to produce one optimal predictive model. Have the main three types of ensemble methods including Bagging, Boosting and Stacking. In this system, we use the Stacking method. In this method, the output of each model is not only class id but also the probability of each class in the set of three classes. This probability will become a feature for the ensemble model. The stacking ensemble model here is a simple full-connection model with input is all of probability that output from sub-model. The output is the probability of each class." + ], + [ + "The dataset in this HSD task is really imbalance. Clean class dominates with 91.5%, offensive class takes 5% and the rest belongs to hate class with 3.5%. To make model being able to learn with this imbalance data, we inject class weight to the loss function with the corresponding ratio (clean, offensive, hate) is $(0.09, 0.95, 0.96)$. Formular DISPLAY_FORM17 is the loss function apply for all models in our system. $w_i$ is the class weight, $y_i$ is the ground truth and $\\hat{y}_i$ is the output of the model. If the class weight is not set, we find that model cannot adjust parameters. The model tends to output all clean classes.", + "We experiment 8 types of embedding in total:", + "comment: CBOW embedding training in all dataset comment, each word is splited by space. Embedding size is 200.", + "comment_bpe: CBOW embedding training in all dataset comment, each word is splited by subword bpe. Embedding size is 200.", + "comment_tokenize: CBOW embedding training in all dataset comment, each word is splited by space. Before split by space, word is concatenated by using BIBREF9, BIBREF13, BIBREF10. Embedding size is 200.", + "roberta: sentence embedding training in all dataset comment, training by using RoBERTa architecture. Embedding size is 256.", + "fasttext, sonvx* is all pre-trained word embedding in general domain. Before mapping word to vector, word is concatenated by using BIBREF9, BIBREF13, BIBREF10. Embedding size of fasttext is 300. (sonvx_wiki, sonvx_baomoi_w2, sonvx_baomoi_w5) have embedding size corresponding is (400, 300, 400).", + "In our experiment, the dataset is split into two-part: train set and dev set with the corresponding ratio $(0.9, 0.1)$. Two subsets have the same imbalance ratio like the root set. For each combination of model and word embedding, we train model in train set until it achieve the best result of loss score in the dev set. The table TABREF12 shows the best result of each combination on the f1_macro score.", + "For each model having the best fit on the dev set, we export the probability distribution of classes for each sample in the dev set. In this case, we only use the result of model that has f1_macro score that larger than 0.67. The probability distribution of classes is then used as feature to input into a dense model with only one hidden layer (size 128). The training process of the ensemble model is done on samples of the dev set. The best fit result is 0.7356. The final result submitted in public leaderboard is 0.73019 and in private leaderboard is 0.58455. It is quite different in bad way. That maybe is the result of the model too overfit on train set tuning on public test set.", + "Statistics of the final result on the dev set shows that almost cases have wrong prediction from offensive and hate class to clean class belong to samples containing the word `vl'. (62% in the offensive class and 48% in the hate class). It means that model overfit the word `vl' to the clean class. This makes sense because `vl' appears too much in the clean class dataset.", + "In case the model predicts wrong from the clean class to the offensive class and the hate class, the model tends to decide case having sensitive words to be wrong class. The class offensive and the hate are quite difficult to distinguish even with human." + ], + [ + "In this study, we experiment the combination of multiple embedding types and multiple model architecture to solve a part of the problem Hate Speech Detection with a signification good classification results. Our system heavily based on the ensemble technique so the weakness of the system is slow processing speed. But in fact, it is not big trouble with this HSD problem when human usually involve handling directly in the before.", + "HSD is a hard problem even with human. In order to improve classification quality, in the future, we need to collect more data especially social networks content. This will make building text representation more correct and help model easier to classify.", + "british" + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0703/environment/Dockerfile b/qasper-0703/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0703/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/* diff --git a/qasper-0703/instruction.md b/qasper-0703/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..c2a227fd6a977943a5adba1a98a2b01b91729879 --- /dev/null +++ b/qasper-0703/instruction.md @@ -0,0 +1,73 @@ +Name of Paper: VAIS Hate Speech Detection System: A Deep Learning based Approach for System Combination + +Question: What is public dashboard? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "System description", + "System description ::: System overview", + "System description ::: Data pre-processing", + "System description ::: Models architecture", + "System description ::: Ensemble method", + "Experiment", + "Conclusion" + ], + "paragraphs": [ + [ + "Currently, social networks are so popular. Some of the biggest ones include Facebook, Twitter, Youtube,... with extremely number of users. Thus, controlling content of those platforms is essential. For years, social media companies such as Twitter, Facebook, and YouTube have been investing hundreds of millions euros on this task BIBREF0, BIBREF1. However, their effort is not enough since such efforts are primarily based on manual moderation to identify and delete offensive materials. The process is labour intensive, time consuming, and not sustainable or scalable in reality BIBREF2, BIBREF0, BIBREF3.", + "In the sixth international workshop on Vietnamese Language and Speech Processing (VLSP 2019), the Hate Speech Detection (HSD) task is proposed as one of the shared-tasks to handle the problem related to controlling content in SNSs. HSD is required to build a multi-class classification model that is capable of classifying an item to one of 3 classes (hate, offensive, clean). Hate speech (hate): an item is identified as hate speech if it (1) targets individuals or groups on the basis of their characteristics; (2) demonstrates a clear intention to incite harm, or to promote hatred; (3) may or may not use offensive or profane words. Offensive but not hate speech (offensive): an item (posts/comments) may contain offensive words but it does not target individuals or groups on the basis of their characteristics. Neither offensive nor hate speech (clean): normal item, it does not contain offensive language or hate speech.", + "The term `hate speech' was formally defined as `any communication that disparages a person or a group on the basis of some characteristics (to be referred to as types of hate or hate classes) such as race, colour, ethnicity, gender, sexual orientation, nationality, religion, or other characteristics' BIBREF4. Many researches have been conducted in recent years to develop automatic methods for hate speech detection in the social media domain. These typically employ semantic content analysis techniques built on Natural Language Processing (NLP) and Machine Learning (ML) methods. The task typically involves classifying textual content into non-hate or hateful. This HSD task is much more difficult when it requires classify text in three classes, with hate and offensive class quite hard to classify even with humans.", + "In this paper, we propose a method to handle this HSD problem. Our system combines multiple text representations and models architecture in order to make diverse predictions. The system is heavily based on the ensemble method. The next section will present detail of our system including data preparation (how we clean text and build text representation), architecture of the model using in the system, and how we combine them together. The third section is our experiment and result report in HSD shared-task VLSP 2019. The final section is our conclusion with advantages and disadvantages of the system following by our perspective." + ], + [ + "In this section, we present the system architecture. It includes how we pre-process text, what types of text representation we use and models used in our system. In the end, we combine model results by using an ensemble technique." + ], + [ + "The fundamental idea of this system is how to make a system that has the diversity of viewing an input. That because of the variety of the meaning in Vietnamese language especially with the acronym, teen code type. To make this diversity, after cleaning raw text input, we use multiple types of word tokenizers. Each one of these tokenizers, we combine with some types of representation methods, including word to vector methods such as continuous bag of words BIBREF5, pre-trained embedding as fasttext (trained on Wiki Vietnamese language) BIBREF6 and sonvx (trained on Vietnamese newspaper) BIBREF7. Each sentence has a set of words corresponding to a set of word vectors, and that set of word vectors is a representation of a sentence. We also make a sentence embedding by using RoBERTa architecture BIBREF8. CBOW and RoBERTa models trained on text from some resources including VLSP 2016 Sentiment Analysis, VLSP 2018 Sentiment Analysis, VLSP 2019 HSD and text crawled from Facebook. After having sentence representation, we use some classification models to classify input sentences. Those models will be described in detail in the section SECREF13. With the multiply output results, we will use an ensemble method to combine them and output the final result. Ensemble method we use here is Stacking method will be introduced in the section SECREF16." + ], + [ + "Content in the dataset that provided in this HSD task is very diverse. Words having the same meaning were written in various types (teen code, non tone, emojis,..) depending on the style of users. Dataset was crawled from various sources with multiple text encodes. In order to make it easy for training, all types of encoding need to be unified. This cleaning module will be used in two processes: cleaning data before training and cleaning input in inferring phase. Following is the data processing steps that we use:", + "Step 1: Format encoding. Vietnamese has many accents, intonations with different Unicode typing programs which may have different outputs with the same typing type. To make it unified, we build a library named visen. For example, the input \"th\u00ed\u00eat k\u00ea will be normalized to \"thi\u1ebft k\u1ebf\" as the output.", + "Step 2: In social networks, people show their feelings a lot by emojis. Emoticon is often a special Unicode character, but sometimes, it is combined by multiple normal characters like `: ( = ]'. We make a dictionary mapping this emoji (combined by some characters) to a single Unicode character like other emojis to make it unified.", + "Step 3: Remove unseen characters. For human, unseen character is invisible but for a computer, it makes the model harder to process and inserts space between words, punctuation and emoji. This step aims at reducing the number of words in the dictionary which is important task, especially with low dataset resources like this HSD task.", + "Step 4: With model requiring Vietnamese word segmentation as the input, we use BIBREF9, BIBREF10 to tokenize the input text.", + "Step 5: Make all string lower. We experimented and found that lower-case or upper-case are not a significant impact on the result, but with lower characters, the number of words in the dictionary is reduced.", + "RoBERTa proposed in BIBREF8 an optimized method for pretraining self-supervised NLP systems. In our system, we use RoBERTa not only to make sentence representation but also to augment data. With mask mechanism, we replace a word in the input sentence with another word that RoBERTa model proposes. To reduce the impact of replacement word, the chosen words are all common words that appear in almost three classes of the dataset. For example, with input `nh\u1ed5n l\u00e0m g\u1eaft vl', we can augment to other outputs: `vl l\u00e0m g\u1eaft q\u00e1', `c\u00f2n l\u00e0m vl v\u1eady', `vl l\u00e0m \u0111\u1ec9nh vl' or `thanh ch\u00fat g\u1eaft vl'.", + "british" + ], + [ + "Social comment dataset has high variety, the core idea is using multiple model architectures to handle data in many viewpoints. In our system, we use five different model architectures combining many types of CNN, and RNN. Each model will use some types of word embedding or handle directly sentence embedding to achieve the best general result. Source code of five models is extended from the GitHub repository", + "The first model is TextCNN (figure FIGREF2) proposed in BIBREF11. It only contains CNN blocks following by some Dense layers. The output of multiple CNN blocks with different kernel sizes is connected to each other.", + "The second model is VDCNN (figure FIGREF5) inspired by the research in BIBREF12. Like the TextCNN model, it contains multiple CNN blocks. The addition in this model is its residual connection.", + "The third model is a simple LSTM bidirectional model (figure FIGREF15). It contains multiple LSTM bidirectional blocks stacked to each other.", + "The fourth model is LSTMCNN (figure FIGREF24). Before going through CNN blocks, series of word embedding will be transformed by LSTM bidirectional block.", + "The final model is the system named SARNN (figure FIGREF25). It adds an attention block between LTSM blocks." + ], + [ + "Ensemble methods is a machine learning technique that combines several base models in order to produce one optimal predictive model. Have the main three types of ensemble methods including Bagging, Boosting and Stacking. In this system, we use the Stacking method. In this method, the output of each model is not only class id but also the probability of each class in the set of three classes. This probability will become a feature for the ensemble model. The stacking ensemble model here is a simple full-connection model with input is all of probability that output from sub-model. The output is the probability of each class." + ], + [ + "The dataset in this HSD task is really imbalance. Clean class dominates with 91.5%, offensive class takes 5% and the rest belongs to hate class with 3.5%. To make model being able to learn with this imbalance data, we inject class weight to the loss function with the corresponding ratio (clean, offensive, hate) is $(0.09, 0.95, 0.96)$. Formular DISPLAY_FORM17 is the loss function apply for all models in our system. $w_i$ is the class weight, $y_i$ is the ground truth and $\\hat{y}_i$ is the output of the model. If the class weight is not set, we find that model cannot adjust parameters. The model tends to output all clean classes.", + "We experiment 8 types of embedding in total:", + "comment: CBOW embedding training in all dataset comment, each word is splited by space. Embedding size is 200.", + "comment_bpe: CBOW embedding training in all dataset comment, each word is splited by subword bpe. Embedding size is 200.", + "comment_tokenize: CBOW embedding training in all dataset comment, each word is splited by space. Before split by space, word is concatenated by using BIBREF9, BIBREF13, BIBREF10. Embedding size is 200.", + "roberta: sentence embedding training in all dataset comment, training by using RoBERTa architecture. Embedding size is 256.", + "fasttext, sonvx* is all pre-trained word embedding in general domain. Before mapping word to vector, word is concatenated by using BIBREF9, BIBREF13, BIBREF10. Embedding size of fasttext is 300. (sonvx_wiki, sonvx_baomoi_w2, sonvx_baomoi_w5) have embedding size corresponding is (400, 300, 400).", + "In our experiment, the dataset is split into two-part: train set and dev set with the corresponding ratio $(0.9, 0.1)$. Two subsets have the same imbalance ratio like the root set. For each combination of model and word embedding, we train model in train set until it achieve the best result of loss score in the dev set. The table TABREF12 shows the best result of each combination on the f1_macro score.", + "For each model having the best fit on the dev set, we export the probability distribution of classes for each sample in the dev set. In this case, we only use the result of model that has f1_macro score that larger than 0.67. The probability distribution of classes is then used as feature to input into a dense model with only one hidden layer (size 128). The training process of the ensemble model is done on samples of the dev set. The best fit result is 0.7356. The final result submitted in public leaderboard is 0.73019 and in private leaderboard is 0.58455. It is quite different in bad way. That maybe is the result of the model too overfit on train set tuning on public test set.", + "Statistics of the final result on the dev set shows that almost cases have wrong prediction from offensive and hate class to clean class belong to samples containing the word `vl'. (62% in the offensive class and 48% in the hate class). It means that model overfit the word `vl' to the clean class. This makes sense because `vl' appears too much in the clean class dataset.", + "In case the model predicts wrong from the clean class to the offensive class and the hate class, the model tends to decide case having sensitive words to be wrong class. The class offensive and the hate are quite difficult to distinguish even with human." + ], + [ + "In this study, we experiment the combination of multiple embedding types and multiple model architecture to solve a part of the problem Hate Speech Detection with a signification good classification results. Our system heavily based on the ensemble technique so the weakness of the system is slow processing speed. But in fact, it is not big trouble with this HSD problem when human usually involve handling directly in the before.", + "HSD is a hard problem even with human. In order to improve classification quality, in the future, we need to collect more data especially social networks content. This will make building text representation more correct and help model easier to classify.", + "british" + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0704/environment/Dockerfile b/qasper-0704/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0704/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/* diff --git a/qasper-0704/instruction.md b/qasper-0704/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..49626ccfa8a4ba53e5d73ea44908c9d819306932 --- /dev/null +++ b/qasper-0704/instruction.md @@ -0,0 +1,73 @@ +Name of Paper: VAIS Hate Speech Detection System: A Deep Learning based Approach for System Combination + +Question: What dataset do they use? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "System description", + "System description ::: System overview", + "System description ::: Data pre-processing", + "System description ::: Models architecture", + "System description ::: Ensemble method", + "Experiment", + "Conclusion" + ], + "paragraphs": [ + [ + "Currently, social networks are so popular. Some of the biggest ones include Facebook, Twitter, Youtube,... with extremely number of users. Thus, controlling content of those platforms is essential. For years, social media companies such as Twitter, Facebook, and YouTube have been investing hundreds of millions euros on this task BIBREF0, BIBREF1. However, their effort is not enough since such efforts are primarily based on manual moderation to identify and delete offensive materials. The process is labour intensive, time consuming, and not sustainable or scalable in reality BIBREF2, BIBREF0, BIBREF3.", + "In the sixth international workshop on Vietnamese Language and Speech Processing (VLSP 2019), the Hate Speech Detection (HSD) task is proposed as one of the shared-tasks to handle the problem related to controlling content in SNSs. HSD is required to build a multi-class classification model that is capable of classifying an item to one of 3 classes (hate, offensive, clean). Hate speech (hate): an item is identified as hate speech if it (1) targets individuals or groups on the basis of their characteristics; (2) demonstrates a clear intention to incite harm, or to promote hatred; (3) may or may not use offensive or profane words. Offensive but not hate speech (offensive): an item (posts/comments) may contain offensive words but it does not target individuals or groups on the basis of their characteristics. Neither offensive nor hate speech (clean): normal item, it does not contain offensive language or hate speech.", + "The term `hate speech' was formally defined as `any communication that disparages a person or a group on the basis of some characteristics (to be referred to as types of hate or hate classes) such as race, colour, ethnicity, gender, sexual orientation, nationality, religion, or other characteristics' BIBREF4. Many researches have been conducted in recent years to develop automatic methods for hate speech detection in the social media domain. These typically employ semantic content analysis techniques built on Natural Language Processing (NLP) and Machine Learning (ML) methods. The task typically involves classifying textual content into non-hate or hateful. This HSD task is much more difficult when it requires classify text in three classes, with hate and offensive class quite hard to classify even with humans.", + "In this paper, we propose a method to handle this HSD problem. Our system combines multiple text representations and models architecture in order to make diverse predictions. The system is heavily based on the ensemble method. The next section will present detail of our system including data preparation (how we clean text and build text representation), architecture of the model using in the system, and how we combine them together. The third section is our experiment and result report in HSD shared-task VLSP 2019. The final section is our conclusion with advantages and disadvantages of the system following by our perspective." + ], + [ + "In this section, we present the system architecture. It includes how we pre-process text, what types of text representation we use and models used in our system. In the end, we combine model results by using an ensemble technique." + ], + [ + "The fundamental idea of this system is how to make a system that has the diversity of viewing an input. That because of the variety of the meaning in Vietnamese language especially with the acronym, teen code type. To make this diversity, after cleaning raw text input, we use multiple types of word tokenizers. Each one of these tokenizers, we combine with some types of representation methods, including word to vector methods such as continuous bag of words BIBREF5, pre-trained embedding as fasttext (trained on Wiki Vietnamese language) BIBREF6 and sonvx (trained on Vietnamese newspaper) BIBREF7. Each sentence has a set of words corresponding to a set of word vectors, and that set of word vectors is a representation of a sentence. We also make a sentence embedding by using RoBERTa architecture BIBREF8. CBOW and RoBERTa models trained on text from some resources including VLSP 2016 Sentiment Analysis, VLSP 2018 Sentiment Analysis, VLSP 2019 HSD and text crawled from Facebook. After having sentence representation, we use some classification models to classify input sentences. Those models will be described in detail in the section SECREF13. With the multiply output results, we will use an ensemble method to combine them and output the final result. Ensemble method we use here is Stacking method will be introduced in the section SECREF16." + ], + [ + "Content in the dataset that provided in this HSD task is very diverse. Words having the same meaning were written in various types (teen code, non tone, emojis,..) depending on the style of users. Dataset was crawled from various sources with multiple text encodes. In order to make it easy for training, all types of encoding need to be unified. This cleaning module will be used in two processes: cleaning data before training and cleaning input in inferring phase. Following is the data processing steps that we use:", + "Step 1: Format encoding. Vietnamese has many accents, intonations with different Unicode typing programs which may have different outputs with the same typing type. To make it unified, we build a library named visen. For example, the input \"th\u00ed\u00eat k\u00ea will be normalized to \"thi\u1ebft k\u1ebf\" as the output.", + "Step 2: In social networks, people show their feelings a lot by emojis. Emoticon is often a special Unicode character, but sometimes, it is combined by multiple normal characters like `: ( = ]'. We make a dictionary mapping this emoji (combined by some characters) to a single Unicode character like other emojis to make it unified.", + "Step 3: Remove unseen characters. For human, unseen character is invisible but for a computer, it makes the model harder to process and inserts space between words, punctuation and emoji. This step aims at reducing the number of words in the dictionary which is important task, especially with low dataset resources like this HSD task.", + "Step 4: With model requiring Vietnamese word segmentation as the input, we use BIBREF9, BIBREF10 to tokenize the input text.", + "Step 5: Make all string lower. We experimented and found that lower-case or upper-case are not a significant impact on the result, but with lower characters, the number of words in the dictionary is reduced.", + "RoBERTa proposed in BIBREF8 an optimized method for pretraining self-supervised NLP systems. In our system, we use RoBERTa not only to make sentence representation but also to augment data. With mask mechanism, we replace a word in the input sentence with another word that RoBERTa model proposes. To reduce the impact of replacement word, the chosen words are all common words that appear in almost three classes of the dataset. For example, with input `nh\u1ed5n l\u00e0m g\u1eaft vl', we can augment to other outputs: `vl l\u00e0m g\u1eaft q\u00e1', `c\u00f2n l\u00e0m vl v\u1eady', `vl l\u00e0m \u0111\u1ec9nh vl' or `thanh ch\u00fat g\u1eaft vl'.", + "british" + ], + [ + "Social comment dataset has high variety, the core idea is using multiple model architectures to handle data in many viewpoints. In our system, we use five different model architectures combining many types of CNN, and RNN. Each model will use some types of word embedding or handle directly sentence embedding to achieve the best general result. Source code of five models is extended from the GitHub repository", + "The first model is TextCNN (figure FIGREF2) proposed in BIBREF11. It only contains CNN blocks following by some Dense layers. The output of multiple CNN blocks with different kernel sizes is connected to each other.", + "The second model is VDCNN (figure FIGREF5) inspired by the research in BIBREF12. Like the TextCNN model, it contains multiple CNN blocks. The addition in this model is its residual connection.", + "The third model is a simple LSTM bidirectional model (figure FIGREF15). It contains multiple LSTM bidirectional blocks stacked to each other.", + "The fourth model is LSTMCNN (figure FIGREF24). Before going through CNN blocks, series of word embedding will be transformed by LSTM bidirectional block.", + "The final model is the system named SARNN (figure FIGREF25). It adds an attention block between LTSM blocks." + ], + [ + "Ensemble methods is a machine learning technique that combines several base models in order to produce one optimal predictive model. Have the main three types of ensemble methods including Bagging, Boosting and Stacking. In this system, we use the Stacking method. In this method, the output of each model is not only class id but also the probability of each class in the set of three classes. This probability will become a feature for the ensemble model. The stacking ensemble model here is a simple full-connection model with input is all of probability that output from sub-model. The output is the probability of each class." + ], + [ + "The dataset in this HSD task is really imbalance. Clean class dominates with 91.5%, offensive class takes 5% and the rest belongs to hate class with 3.5%. To make model being able to learn with this imbalance data, we inject class weight to the loss function with the corresponding ratio (clean, offensive, hate) is $(0.09, 0.95, 0.96)$. Formular DISPLAY_FORM17 is the loss function apply for all models in our system. $w_i$ is the class weight, $y_i$ is the ground truth and $\\hat{y}_i$ is the output of the model. If the class weight is not set, we find that model cannot adjust parameters. The model tends to output all clean classes.", + "We experiment 8 types of embedding in total:", + "comment: CBOW embedding training in all dataset comment, each word is splited by space. Embedding size is 200.", + "comment_bpe: CBOW embedding training in all dataset comment, each word is splited by subword bpe. Embedding size is 200.", + "comment_tokenize: CBOW embedding training in all dataset comment, each word is splited by space. Before split by space, word is concatenated by using BIBREF9, BIBREF13, BIBREF10. Embedding size is 200.", + "roberta: sentence embedding training in all dataset comment, training by using RoBERTa architecture. Embedding size is 256.", + "fasttext, sonvx* is all pre-trained word embedding in general domain. Before mapping word to vector, word is concatenated by using BIBREF9, BIBREF13, BIBREF10. Embedding size of fasttext is 300. (sonvx_wiki, sonvx_baomoi_w2, sonvx_baomoi_w5) have embedding size corresponding is (400, 300, 400).", + "In our experiment, the dataset is split into two-part: train set and dev set with the corresponding ratio $(0.9, 0.1)$. Two subsets have the same imbalance ratio like the root set. For each combination of model and word embedding, we train model in train set until it achieve the best result of loss score in the dev set. The table TABREF12 shows the best result of each combination on the f1_macro score.", + "For each model having the best fit on the dev set, we export the probability distribution of classes for each sample in the dev set. In this case, we only use the result of model that has f1_macro score that larger than 0.67. The probability distribution of classes is then used as feature to input into a dense model with only one hidden layer (size 128). The training process of the ensemble model is done on samples of the dev set. The best fit result is 0.7356. The final result submitted in public leaderboard is 0.73019 and in private leaderboard is 0.58455. It is quite different in bad way. That maybe is the result of the model too overfit on train set tuning on public test set.", + "Statistics of the final result on the dev set shows that almost cases have wrong prediction from offensive and hate class to clean class belong to samples containing the word `vl'. (62% in the offensive class and 48% in the hate class). It means that model overfit the word `vl' to the clean class. This makes sense because `vl' appears too much in the clean class dataset.", + "In case the model predicts wrong from the clean class to the offensive class and the hate class, the model tends to decide case having sensitive words to be wrong class. The class offensive and the hate are quite difficult to distinguish even with human." + ], + [ + "In this study, we experiment the combination of multiple embedding types and multiple model architecture to solve a part of the problem Hate Speech Detection with a signification good classification results. Our system heavily based on the ensemble technique so the weakness of the system is slow processing speed. But in fact, it is not big trouble with this HSD problem when human usually involve handling directly in the before.", + "HSD is a hard problem even with human. In order to improve classification quality, in the future, we need to collect more data especially social networks content. This will make building text representation more correct and help model easier to classify.", + "british" + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0705/instruction.md b/qasper-0705/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..f6d97becc4b242ae981a9fa684f763176e6527b5 --- /dev/null +++ b/qasper-0705/instruction.md @@ -0,0 +1,109 @@ +Name of Paper: Yoga-Veganism: Correlation Mining of Twitter Health Data + +Question: Do the authors report results only on English data? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Data Collection", + "Apache Kafka", + "Apache Zookeeper", + "Data Extraction using Tweepy", + "Data Pre-processing", + "Methodology", + "Construct document-term matrix", + "Topic Modeling", + "Optimal number of Topics", + "Topic Inference", + "Manual Annotation", + "Visualization", + "Topic Frequency Distribution", + "Comparison with Ground Truth", + "Observation and Future Work", + "Conclusions" + ], + "paragraphs": [ + [ + "The main motivation of this work has been started with a question \"What do people do to maintain their health?\"\u2013 some people do balanced diet, some do exercise. Among diet plans some people maintain vegetarian diet/vegan diet, among exercises some people do swimming, cycling or yoga. There are people who do both. If we want to know the answers of the following questions\u2013 \"How many people follow diet?\", \"How many people do yoga?\", \"Does yogi follow vegetarian/vegan diet?\", may be we could ask our acquainted person but this will provide very few intuition about the data. Nowadays people usually share their interests, thoughts via discussions, tweets, status in social media (i.e. Facebook, Twitter, Instagram etc.). It's huge amount of data and it's not possible to go through all the data manually. We need to mine the data to get overall statistics and then we will also be able to find some interesting correlation of data.", + "Several works have been done on prediction of social media content BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 . Prieto et al. proposed a method to extract a set of tweets to estimate and track the incidence of health conditions in society BIBREF5 . Discovering public health topics and themes in tweets had been examined by Prier et al. BIBREF6 . Yoon et al. described a practical approach of content mining to analyze tweet contents and illustrate an application of the approach to the topic of physical activity BIBREF7 .", + "Twitter data constitutes a rich source that can be used for capturing information about any topic imaginable. In this work, we use text mining to mine the Twitter health-related data. Text mining is the application of natural language processing techniques to derive relevant information BIBREF8 . Millions of tweets are generated each day on multifarious issues BIBREF9 . Twitter mining in large scale has been getting a lot of attention last few years. Lin and Ryaboy discussed the evolution of Twitter infrastructure and the development of capabilities for data mining on \"big data\" BIBREF10 . Pandarachalil et al. provided a scalable and distributed solution using Parallel python framework for Twitter sentiment analysis BIBREF9 . Large-scale Twitter Mining for drug-related adverse events was developed by Bian et al. BIBREF11 .", + "In this paper, we use parallel and distributed technology Apache Kafka BIBREF12 to handle the large streaming twitter data. The data processing is conducted in parallel with data extraction by integration of Apache Kafka and Spark Streaming. Then we use Topic Modeling to infer semantic structure of the unstructured data (i.e Tweets). Topic Modeling is a text mining technique which automatically discovers the hidden themes from given documents. It is an unsupervised text analytic algorithm that is used for finding the group of words from the given document. We build the model using three different algorithms Latent Semantic Analysis (LSA) BIBREF13 , Non-negative Matrix Factorization (NMF) BIBREF14 , and Latent Dirichlet Allocation (LDA) BIBREF15 and infer the topic of tweets. To observe the model behavior, we test the model to infer new tweets. The implication of our work is to annotate unlabeled data using the model and find interesting correlation." + ], + [ + "Tweet messages are retrieved from the Twitter source by utilizing the Twitter API and stored in Kafka topics. The Producer API is used to connect the source (i.e. Twitter) to any Kafka topic as a stream of records for a specific category. We fetch data from a source (Twitter), push it to a message queue, and consume it for further analysis. Fig. FIGREF2 shows the overview of Twitter data collection using Kafka." + ], + [ + "In order to handle the large streaming twitter data, we use parallel and distributed technology for big data framework. In this case, the output of the twitter crawling is queued in messaging system called Apache Kafka. This is a distributed streaming platform created and open sourced by LinkedIn in 2011 BIBREF12 . We write a Producer Client which fetches latest tweets continuously using Twitter API and push them to single node Kafka Broker. There is a Consumer that reads data from Kafka (Fig. FIGREF2 )." + ], + [ + "Apache Zookeeper is a distributed, open-source configuration, synchronization service along with naming registry for distributed applications. Kafka uses Zookeeper to store metadata about the Kafka cluster, as well as consumer client details." + ], + [ + "The twitter data has been crawled using Tweepy which is a Python library for accessing the Twitter API. We use Twitter streaming API to extract 40k tweets (April 17-19, 2019). For the crawling, we focus on several keywords that are related to health. The keywords are processed in a non-case-sensitive way. We use filter to stream all tweets containing the word `yoga', `healthylife', `healthydiet', `diet',`hiking', `swimming', `cycling', `yogi', `fatburn', `weightloss', `pilates', `zumba', `nutritiousfood', `wellness', `fitness', `workout', `vegetarian', `vegan', `lowcarb', `glutenfree', `calorieburn'.", + "The streaming API returns tweets, as well as several other types of messages (e.g. a tweet deletion notice, user update profile notice, etc), all in JSON format. We use Python libraries json for parsing the data, pandas for data manipulation." + ], + [ + "Data pre-processing is one of the key components in many text mining algorithms BIBREF8 . Data cleaning is crucial for generating a useful topic model. We have some prerequisites i.e. we download the stopwords from NLTK (Natural Language Toolkit) and spacy's en model for text pre-processing.", + "It is noticeable that the parsed full-text tweets have many emails, `RT', newline and extra spaces that is quite distracting. We use Python Regular Expressions (re module) to get rid of them. Then we tokenize each text into a list of words, remove punctuation and unnecessary characters. We use Python Gensim package for further processing. Gensim's simple_preprocess() is used for tokenization and removing punctuation. We use Gensim's Phrases model to build bigrams. Certain parts of English speech, like conjunctions (\"for\", \"or\") or the word \"the\" are meaningless to a topic model. These terms are called stopwords and we remove them from the token list. We use spacy model for lemmatization to keep only noun, adjective, verb, adverb. Stemming words is another common NLP technique to reduce topically similar words to their root. For example, \"connect\", \"connecting\", \"connected\", \"connection\", \"connections\" all have similar meanings; stemming reduces those terms to \"connect\". The Porter stemming algorithm BIBREF16 is the most widely used method." + ], + [ + "We use Twitter health-related data for this analysis. In subsections [subsec:3.1]3.1, [subsec:3.2]3.2, [subsec:3.3]3.3, and [subsec:3.4]3.4 elaborately present how we can infer the meaning of unstructured data. Subsection [subsec:3.5]3.5 shows how we do manual annotation for ground truth comparison. Fig. FIGREF6 shows the overall pipeline of correlation mining." + ], + [ + "The result of the data cleaning stage is texts, a tokenized, stopped, stemmed and lemmatized list of words from a single tweet. To understand how frequently each term occurs within each tweet, we construct a document-term matrix using Gensim's Dictionary() function. Gensim's doc2bow() function converts dictionary into a bag-of-words. In the bag-of-words model, each tweet is represented by a vector in a m-dimensional coordinate space, where m is number of unique terms across all tweets. This set of terms is called the corpus vocabulary." + ], + [ + "Topic modeling is a text mining technique which provides methods for identifying co-occurring keywords to summarize collections of textual information. This is used to analyze collections of documents, each of which is represented as a mixture of topics, where each topic is a probability distribution over words BIBREF17 . Applying these models to a document collection involves estimating the topic distributions and the weight each topic receives in each document. A number of algorithms exist for solving this problem. We use three unsupervised machine learning algorithms to explore the topics of the tweets: Latent Semantic Analysis (LSA) BIBREF13 , Non-negative Matrix Factorization (NMF) BIBREF14 , and Latent Dirichlet Allocation (LDA) BIBREF15 . Fig. FIGREF7 shows the general idea of topic modeling methodology. Each tweet is considered as a document. LSA, NMF, and LDA use Bag of Words (BoW) model, which results in a term-document matrix (occurrence of terms in a document). Rows represent terms (words) and columns represent documents (tweets). After completing topic modeling, we identify the groups of co-occurring words in tweets. These group co-occurring related words makes \"topics\".", + "LSA (Latent Semantic Analysis) BIBREF13 is also known as LSI (Latent Semantic Index). It learns latent topics by performing a matrix decomposition on the document-term matrix using Singular Value Decomposition (SVD) BIBREF18 . After corpus creation in [subsec:3.1]Subsection 3.1, we generate an LSA model using Gensim.", + "Non-negative Matrix Factorization (NMF) BIBREF14 is a widely used tool for the analysis of high-dimensional data as it automatically extracts sparse and meaningful features from a set of non-negative data vectors. It is a matrix factorization method where we constrain the matrices to be non-negative.", + "We apply Term Weighting with term frequency-inverse document frequency (TF-IDF) BIBREF19 to improve the usefulness of the document-term matrix (created in [subsec:3.1]Subsection 3.1) by giving more weight to the more \"important\" terms. In Scikit-learn, we can generate at TF-IDF weighted document-term matrix by using TfidfVectorizer. We import the NMF model class from sklearn.decomposition and fit the topic model to tweets.", + "Latent Dirichlet Allocation (LDA) BIBREF15 is widely used for identifying the topics in a set of documents, building on Probabilistic Latent Semantic Analysis (PLSI) BIBREF20 . LDA considers each document as a collection of topics in a certain proportion and each topic as a collection of keywords in a certain proportion. We provide LDA the optimal number of topics, it rearranges the topics' distribution within the documents and keywords' distribution within the topics to obtain a good composition of topic-keywords distribution.", + "We have corpus generated in [subsec:3.1]Subsection 3.1 to train the LDA model. In addition to the corpus and dictionary, we provide the number of topics as well." + ], + [ + "Topic modeling is an unsupervised learning, so the set of possible topics are unknown. To find out the optimal number of topic, we build many LSA, NMF, LDA models with different values of number of topics (k) and pick the one that gives the highest coherence score. Choosing a `k' that marks the end of a rapid growth of topic coherence usually offers meaningful and interpretable topics.", + "We use Gensim's coherencemodel to calculate topic coherence for topic models (LSA and LDA). For NMF, we use a topic coherence measure called TC-W2V. This measure relies on the use of a word embedding model constructed from the corpus. So in this step, we use the Gensim implementation of Word2Vec BIBREF21 to build a Word2Vec model based on the collection of tweets.", + "We achieve the highest coherence score = 0.4495 when the number of topics is 2 for LSA, for NMF the highest coherence value is 0.6433 for K = 4, and for LDA we also get number of topics is 4 with the highest coherence score which is 0.3871 (see Fig. FIGREF8 ).", + "For our dataset, we picked k = 2, 4, and 4 with the highest coherence value for LSA, NMF, and LDA correspondingly (Fig. FIGREF8 ). Table TABREF13 shows the topics and top-10 keywords of the corresponding topic. We get more informative and understandable topics using LDA model than LSA. LSA decomposed matrix is a highly dense matrix, so it is difficult to index individual dimension. LSA is unable to capture the multiple meanings of words. It offers lower accuracy than LDA.", + "In case of NMF, we observe same keywords are repeated in multiple topics. Keywords \"go\", \"day\" both are repeated in Topic 2, Topic 3, and Topic 4 (Table TABREF13 ). In Table TABREF13 keyword \"yoga\" has been found both in Topic 1 and Topic 4. We also notice that keyword \"eat\" is in Topic 2 and Topic 3 (Table TABREF13 ). If the same keywords being repeated in multiple topics, it is probably a sign that the `k' is large though we achieve the highest coherence score in NMF for k=4.", + "We use LDA model for our further analysis. Because LDA is good in identifying coherent topics where as NMF usually gives incoherent topics. However, in the average case NMF and LDA are similar but LDA is more consistent." + ], + [ + "After doing topic modeling using three different method LSA, NMF, and LDA, we use LDA for further analysis i.e. to observe the dominant topic, 2nd dominant topic and percentage of contribution of the topics in each tweet of training data. To observe the model behavior on new tweets those are not included in training set, we follow the same procedure to observe the dominant topic, 2nd dominant topic and percentage of contribution of the topics in each tweet on testing data. Table TABREF30 shows some tweets and corresponding dominant topic, 2nd dominant topic and percentage of contribution of the topics in each tweet." + ], + [ + "To calculate the accuracy of model in comparison with ground truth label, we selected top 500 tweets from train dataset (40k tweets). We extracted 500 new tweets (22 April, 2019) as a test dataset. We did manual annotation both for train and test data by choosing one topic among the 4 topics generated from LDA model (7th, 8th, 9th, and 10th columns of Table TABREF13 ) for each tweet based on the intent of the tweet. Consider the following two tweets:", + "Tweet 1: Learning some traditional yoga with my good friend.", + "Tweet 2: Why You Should #LiftWeights to Lose #BellyFat #Fitness #core #abs #diet #gym #bodybuilding #workout #yoga", + "The intention of Tweet 1 is yoga activity (i.e. learning yoga). Tweet 2 is more about weight lifting to reduce belly fat. This tweet is related to workout. When we do manual annotation, we assign Topic 2 in Tweet 1, and Topic 1 in Tweet 2. It's not wise to assign Topic 2 for both tweets based on the keyword \"yoga\". During annotation, we focus on functionality of tweets." + ], + [ + "We use LDAvis BIBREF22 , a web-based interactive visualization of topics estimated using LDA. Gensim's pyLDAVis is the most commonly used visualization tool to visualize the information contained in a topic model. In Fig. FIGREF21 , each bubble on the left-hand side plot represents a topic. The larger the bubble, the more prevalent is that topic. A good topic model has fairly big, non-overlapping bubbles scattered throughout the chart instead of being clustered in one quadrant. A model with too many topics, is typically have many overlaps, small sized bubbles clustered in one region of the chart. In right hand side, the words represent the salient keywords.", + "If we move the cursor over one of the bubbles (Fig. FIGREF21 ), the words and bars on the right-hand side have been updated and top-30 salient keywords that form the selected topic and their estimated term frequencies are shown.", + "We observe interesting hidden correlation in data. Fig. FIGREF24 has Topic 2 as selected topic. Topic 2 contains top-4 co-occurring keywords \"vegan\", \"yoga\", \"job\", \"every_woman\" having the highest term frequency. We can infer different things from the topic that \"women usually practice yoga more than men\", \"women teach yoga and take it as a job\", \"Yogi follow vegan diet\". We would say there are noticeable correlation in data i.e. `Yoga-Veganism', `Women-Yoga'." + ], + [ + "Each tweet is composed of multiple topics. But, typically only one of the topics is dominant. We extract the dominant and 2nd dominant topic for each tweet and show the weight of the topic (percentage of contribution in each tweet) and the corresponding keywords.", + "We plot the frequency of each topic's distribution on tweets in histogram. Fig. FIGREF25 shows the dominant topics' frequency and Fig. FIGREF25 shows the 2nd dominant topics' frequency on tweets. From Fig. FIGREF25 we observe that Topic 1 became either the dominant topic or the 2nd dominant topic for most of the tweets. 7th column of Table TABREF13 shows the corresponding top-10 keywords of Topic 1." + ], + [ + "To compare with ground truth, we gradually increased the size of dataset 100, 200, 300, 400, and 500 tweets from train data and test data (new tweets) and did manual annotation both for train/test data based on functionality of tweets (described in [subsec:3.5]Subsection 3.5).", + "For accuracy calculation, we consider the dominant topic only. We achieved 66% train accuracy and 51% test accuracy when the size of dataset is 500 (Fig. FIGREF28 ). We did baseline implementation with random inference by running multiple times with different seeds and took the average accuracy. For dataset 500, the accuracy converged towards 25% which is reasonable as we have 4 topics." + ], + [ + "In Table TABREF30 , we show some observations. For the tweets in 1st and 2nd row (Table TABREF30 ), we observed understandable topic. We also noticed misleading topic and unrelated topic for few tweets (3rd and 4th row of Table TABREF30 ).", + "In the 1st row of Table TABREF30 , we show a tweet from train data and we got Topic 2 as a dominant topic which has 61% of contribution in this tweet. Topic 1 is 2nd dominant topic and 18% contribution here.", + "2nd row of Table TABREF30 shows a tweet from test set. We found Topic 2 as a dominant topic with 33% of contribution and Topic 4 as 2nd dominant topic with 32% contribution in this tweet.", + "In the 3rd (Table TABREF30 ), we have a tweet from test data and we got Topic 2 as a dominant topic which has 43% of contribution in this tweet. Topic 3 is 2nd dominant with 23% contribution which is misleading topic. The model misinterprets the words `water in hand' and infers topic which has keywords \"swimming, swim, pool\". But the model should infer more reasonable topic (Topic 1 which has keywords \"diet, workout\") here.", + "We got Topic 2 as dominant topic for the tweet in 4th row (Table TABREF30 ) which is unrelated topic for this tweet and most relevant topic of this tweet (Topic 2) as 2nd dominant topic. We think during accuracy comparison with ground truth 2nd dominant topic might be considered.", + "In future, we will extract more tweets and train the model and observe the model behavior on test data. As we found misleading and unrelated topic in test cases, it is important to understand the reasons behind the predictions. We will incorporate Local Interpretable model-agnostic Explanation (LIME) BIBREF23 method for the explanation of model predictions. We will also do predictive causality analysis on tweets." + ], + [ + "It is challenging to analyze social media data for different application purpose. In this work, we explored Twitter health-related data, inferred topic using topic modeling (i.e. LSA, NMF, LDA), observed model behavior on new tweets, compared train/test accuracy with ground truth, employed different visualizations after information integration and discovered interesting correlation (Yoga-Veganism) in data. In future, we will incorporate Local Interpretable model-agnostic Explanation (LIME) method to understand model interpretability." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0732/environment/Dockerfile b/qasper-0732/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0732/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/* diff --git a/qasper-0732/instruction.md b/qasper-0732/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..66a5cc4aebe76a84878a1835d09bbaf5d728fdca --- /dev/null +++ b/qasper-0732/instruction.md @@ -0,0 +1,72 @@ +Name of Paper: An Analysis of Word2Vec for the Italian Language + +Question: Are the word embeddings tested on a NLP task? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Word2Vec", + "Word2Vec ::: Sampling rate", + "Word2Vec ::: Negative sampling", + "Implementation details", + "Results", + "Results ::: Analysis of the various models", + "Results ::: Comparison with other models", + "Conclusion" + ], + "paragraphs": [ + [ + "In order to make human language comprehensible to a computer, it is obviously essential to provide some word encoding. The simplest approach is the one-hot encoding, where each word is represented by a sparse vector with dimension equal to the vocabulary size. In addition to the storage need, the main problem of this representation is that any concept of word similarity is completely ignored (each vector is orthogonal and equidistant from each other). On the contrary, the understanding of natural language cannot be separated from the semantic knowledge of words, which conditions a different closeness between them. Indeed, the semantic representation of words is the basic problem of Natural Language Processing (NLP). Therefore, there is a necessary need to code words in a space that is linked to their meaning, in order to facilitate a machine in potential task of \u201cunderstanding\" it. In particular, starting from the seminal work BIBREF0, words are usually represented as dense distributed vectors that preserve their uniqueness but, at the same time, are able to encode the similarities.", + "These word representations are called Word Embeddings since the words (points in a space of vocabulary size) are mapped in an embedding space of lower dimension. Supported by the distributional hypothesis BIBREF1 BIBREF2, which states that a word can be semantically characterized based on its context (i.e. the words that surround it in the sentence), in recent years many word embedding representations have been proposed (a fairly complete and updated review can be found in BIBREF3 and BIBREF4). These methods can be roughly categorized into two main classes: prediction-based models and count-based models. The former is generally linked to work on Neural Network Language Models (NNLM) and use a training algorithm that predicts the word given its local context, the latter leverage word-context statistics and co-occurrence counts in an entire corpus. The main prediction-based and count-based models are respectively Word2Vec BIBREF5 (W2V) and GloVe BIBREF6.", + "Despite the widespread use of these concepts BIBREF7 BIBREF8, few contributions exist regarding the development of a W2V that is not in English. In particular, no detailed analysis on an Italian W2V seems to be present in the literature, except for BIBREF9 and BIBREF10. However, both seem to leave out some elements of fundamental interest in the learning of the neural network, in particular relating to the number of epochs performed during learning, reducing the importance that it may have on the final result. In BIBREF9, this for example leads to the simplistic conclusion that (being able to organize with more freedom in space) the more space is given to the vectors, the better the results may be. However, the problem in complex structures is that large embedding spaces can make training too difficult.", + "In this work, by setting the size of the embedding to a commonly used average value, various parameters are analysed as the number of learning epochs changes, depending on the window sizes and the negatively backpropagated samples." + ], + [ + "The W2V structure consists of a simple two-level neural network (Figure FIGREF1) with one-hot vectors representing words at the input. It can be trained in two different modes, algorithmically similar, but different in concept: Continuous Bag-of-Words (CBOW) model and Skip-Gram model. While CBOW tries to predict the target words from the context, Skip-Gram instead aims to determine the context for a given target word. The two different approaches therefore modify only the way in which the inputs and outputs are to be managed, but in any case, the network does not change, and the training always takes place between single pairs of words (placed as one-hot in input and output).", + "The text is in fact divided into sentences, and for each word of a given sentence a window of words is taken from the right and from the left to define the context. The central word is coupled with each of the words forming the set of pairs for training. Depending on the fact that the central word represents the output or the input in training pairs, the CBOW and Skip-gram models are obtained respectively.", + "Regardless of whether W2V is trained to predict the context or the target word, it is used as a word embedding in a substantially different manner from the one for which it has been trained. In particular, the second matrix is totally discarded during use, since the only thing relevant to the representation is the space of the vectors generated in the intermediate level (embedding space)." + ], + [ + "The common words (such as \u201cthe\", \u201cof\", etc.) carry very little information on the target word with which they are coupled, and through backpropagation they tend to have extremely small representative vectors in the embedding space. To solve both these problems the W2V algorithm implements a particular \u201csubsampling\" BIBREF11, which acts by eliminating some words from certain sentences. Note that the elimination of a word directly from the text means that it no longer appears in the context of any of the words of the sentence and, at the same time, a number of pairs equal to (at most) twice the size of the window relating to the deleted word will also disappear from the training set.", + "In practice, each word is associated with a sort of \u201ckeeping probability\" and, when you meet that word, if this value is greater than a randomly generated value then the word will not be discarded from the text. The W2V implementation assigns this \u201cprobability\" to the generic word $w_i$ through the formula:", + "where $f(w_i)$ is the relative frequency of the word $w_i$ (namely $count(w_i)/total$), while $s$ is a sample value, typically set between $10^{-3}$ and $10^{-5}$." + ], + [ + "Working with one-hot pairs of words means that the size of the network must be the same at input and output, and must be equal to the size of the vocabulary. So, although very simple, the network has a considerable number of parameters to train, which lead to an excessive computational cost if we are supposed to backpropagate all the elements of the one-hot vector in output.", + "The \u201cnegative sampling\" technique BIBREF11 tries to solve this problem by modifying only a small percentage of the net weights every time. In practice, for each pair of words in the training set, the loss function is calculated only for the value 1 and for a few values 0 of the one-hot vector of the desired output. The computational cost is therefore reduced by choosing to backpropagate only $K$ words \u201cnegative\" and one positive, instead of the entire vocabulary. Typical values for negative sampling (the number of negative samples that will be backpropagated and to which therefore the only positive value will always be added), range from 2 to 20, depending on the size of the dataset.", + "The probability of selecting a negative word to backpropagate depends on its frequency, in particular through the formula:", + "Negative samples are then selected by choosing a sort of \u201cunigram distribution\", so that the most frequent words are also the most often backpropated ones." + ], + [ + "The dataset needed to train the W2V was obtained using the information extracted from a dump of the Italian Wikipedia (dated 2019.04.01), from the main categories of Italian Google News (WORLD, NATION, BUSINESS, TECHNOLOGY, ENTERTAINMENT, SPORTS, SCIENCE, HEALTH) and from some anonymized chats between users and a customer care chatbot (Laila). The dataset (composed of 2.6 GB of raw text) includes $421\\,829\\,960$ words divided into $17\\,305\\,401$ sentences.", + "The text was previously preprocessed by removing the words whose absolute frequency was less than 5 and eliminating all special characters. Since it is impossible to represent every imaginable numerical value, but not wanting to eliminate the concept of \u201cnumerical representation\" linked to certain words, it was also decided to replace every number present in the text with the particular $\\langle NUM \\rangle $ token; which probably also assumes a better representation in the embedding space (not separating into the various possible values). All the words were then transformed to lowercase (to avoid a double presence) finally producing a vocabulary of $618\\,224$ words.", + "Note that among the special characters are also included punctuation marks, which therefore do not appear within the vocabulary. However, some of them (`.', `?' and `!') are later removed, as they are used to separate the sentences.", + "The Python implementation provided by Gensim was used for training the various embeddings all with size 300 and sampling parameter ($s$ in Equation DISPLAY_FORM3) set at $0.001$." + ], + [ + "To analyse the results we chose to use the test provided by BIBREF10, which consists of $19\\,791$ analogies divided into 19 different categories: 6 related to the \u201csemantic\" macro-area (8915 analogies) and 13 to the \u201csyntactic\" one (10876 analogies). All the analogies are composed by two pairs of words that share a relation, schematized with the equation: $a:a^{*}=b:b^{*}$ (e.g. \u201cman : woman = king : queen\"); where $b^{*}$ is the word to be guessed (\u201cqueen\"), $b$ is the word coupled to it (\u201cking\"), $a$ is the word for the components to be eliminated (\u201cman\"), and $a^{*}$ is the word for the components to be added (\u201cwoman\").", + "The determination of the correct response was obtained both through the classical additive cosine distance (3COSADD) BIBREF5:", + "and through the multiplicative cosine distance (3COSMUL) BIBREF12:", + "where $\\epsilon =10^{-6}$ and $\\cos (x, y) = \\frac{x \\cdot y}{\\left\\Vert x\\right\\Vert \\left\\Vert y\\right\\Vert }$. The extremely low value chosen for the $\\epsilon $ is due to the desire to minimize as much as possible its impact on performance, as during the various testing phases we noticed a strange bound that is still being investigated. As usual, moreover, the representative vectors of the embedding space are previously normalized for the execution of the various tests." + ], + [ + "We first analysed 6 different implementations of the Skip-gram model each one trained for 20 epochs. Table TABREF10 shows the accuracy values (only on possible analogies) at the 20th epoch for the six models both using 3COSADD and 3COSMUL. It is interesting to note that the 3COSADD total metric, respect to 3COSMUL, seems to have slightly better results in the two extreme cases of limited learning (W5N5 and W10N20) and under the semantic profile. However, we should keep in mind that the semantic profile is the one best captured by the network in both cases, which is probably due to the nature of the database (mainly composed of articles and news that principally use an impersonal language). In any case, the improvements that are obtained under the syntactic profile lead to the 3COSMUL metric obtaining better overall results.", + "Figure FIGREF11 shows the trends of the total accuracy at different epochs for the various models using 3COSMUL (the trend obtained with 3COSADD is very similar). Here we can see how the use of high negative sampling can worsen performance, even causing the network to oscillate (W5N20) in order to better adapt to all the data. The choice of the negative sampling to be used should therefore be strongly linked to the choice of the window size as well as to the number of training epochs.", + "Continuing the training of the two worst models up to the 50th epoch, it is observed (Table TABREF12) that they are still able to reach the performances of the other models. The W10N20 model at the 50th epoch even proves to be better than all the other previous models, becoming the reference model for subsequent comparisons. As the various epochs change (Figure FIGREF13.a) it appears to have the same oscillatory pattern observed previously, albeit with only one oscillation given the greater window size. This model is available at: https://mlunicampania.gitlab.io/italian-word2vec/.", + "Various tests were also conducted on CBOW models, which however proved to be in general significantly lower than Skip-gram models. Figure FIGREF13.b shows, for example, the accuracy trend for a CBOW model with a window equal to 10 and negative sampling equal to 20, which on 50 epochs reaches only $37.20\\%$ of total accuracy (with 3COSMUL metric)." + ], + [ + "Finally, a comparison was made between the Skip-gram model W10N20 obtained at the 50th epoch and the other two W2V in Italian present in the literature (BIBREF9 and BIBREF10). The first test (Table TABREF15) was performed considering all the analogies present, and therefore evaluating as an error any analogy that was not executable (as it related to one or more words absent from the vocabulary).", + "As it can be seen, regardless of the metric used, our model has significantly better results than the other two models, both overall and within the two macro-areas. Furthermore, the other two models seem to be more subject to the metric used, perhaps due to a stabilization not yet reached for the few training epochs.", + "For a complete comparison, both models were also tested considering only the subset of the analogies in common with our model (i.e. eliminating from the test all those analogies that were not executable by one or the other model). Tables TABREF16 and TABREF17 again highlight the marked increase in performance of our model compared to both." + ], + [ + "In this work we have analysed the Word2Vec model for Italian Language obtaining a substantial increase in performance respect to other two models in the literature (and despite the fixed size of the embedding). These results, in addition to the number of learning epochs, are probably also due to the different phase of data pre-processing, very carefully excuted in performing a complete cleaning of the text and above all in substituting the numerical values with a single particular token. We have observed that the number of epochs is an important parameter and its increase leads to results that rank our two worst models almost equal, or even better than others.", + "Changing the number of epochs, in some configurations, creates an oscillatory trend, which seems to be linked to a particular interaction between the window size and the negative sampling value. In the future, thanks to the collaboration in the Laila project, we intend to expand the dataset by adding more user chats. The objective will be to verify if the use of a less formal language can improves accuracy in the syntactic macro-area." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0733/environment/Dockerfile b/qasper-0733/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0733/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/* diff --git a/qasper-0733/instruction.md b/qasper-0733/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..055f85fb32e0a9496c3740f80d5d2ac2d51bc4a5 --- /dev/null +++ b/qasper-0733/instruction.md @@ -0,0 +1,72 @@ +Name of Paper: An Analysis of Word2Vec for the Italian Language + +Question: Are the word embeddings evaluated? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Word2Vec", + "Word2Vec ::: Sampling rate", + "Word2Vec ::: Negative sampling", + "Implementation details", + "Results", + "Results ::: Analysis of the various models", + "Results ::: Comparison with other models", + "Conclusion" + ], + "paragraphs": [ + [ + "In order to make human language comprehensible to a computer, it is obviously essential to provide some word encoding. The simplest approach is the one-hot encoding, where each word is represented by a sparse vector with dimension equal to the vocabulary size. In addition to the storage need, the main problem of this representation is that any concept of word similarity is completely ignored (each vector is orthogonal and equidistant from each other). On the contrary, the understanding of natural language cannot be separated from the semantic knowledge of words, which conditions a different closeness between them. Indeed, the semantic representation of words is the basic problem of Natural Language Processing (NLP). Therefore, there is a necessary need to code words in a space that is linked to their meaning, in order to facilitate a machine in potential task of \u201cunderstanding\" it. In particular, starting from the seminal work BIBREF0, words are usually represented as dense distributed vectors that preserve their uniqueness but, at the same time, are able to encode the similarities.", + "These word representations are called Word Embeddings since the words (points in a space of vocabulary size) are mapped in an embedding space of lower dimension. Supported by the distributional hypothesis BIBREF1 BIBREF2, which states that a word can be semantically characterized based on its context (i.e. the words that surround it in the sentence), in recent years many word embedding representations have been proposed (a fairly complete and updated review can be found in BIBREF3 and BIBREF4). These methods can be roughly categorized into two main classes: prediction-based models and count-based models. The former is generally linked to work on Neural Network Language Models (NNLM) and use a training algorithm that predicts the word given its local context, the latter leverage word-context statistics and co-occurrence counts in an entire corpus. The main prediction-based and count-based models are respectively Word2Vec BIBREF5 (W2V) and GloVe BIBREF6.", + "Despite the widespread use of these concepts BIBREF7 BIBREF8, few contributions exist regarding the development of a W2V that is not in English. In particular, no detailed analysis on an Italian W2V seems to be present in the literature, except for BIBREF9 and BIBREF10. However, both seem to leave out some elements of fundamental interest in the learning of the neural network, in particular relating to the number of epochs performed during learning, reducing the importance that it may have on the final result. In BIBREF9, this for example leads to the simplistic conclusion that (being able to organize with more freedom in space) the more space is given to the vectors, the better the results may be. However, the problem in complex structures is that large embedding spaces can make training too difficult.", + "In this work, by setting the size of the embedding to a commonly used average value, various parameters are analysed as the number of learning epochs changes, depending on the window sizes and the negatively backpropagated samples." + ], + [ + "The W2V structure consists of a simple two-level neural network (Figure FIGREF1) with one-hot vectors representing words at the input. It can be trained in two different modes, algorithmically similar, but different in concept: Continuous Bag-of-Words (CBOW) model and Skip-Gram model. While CBOW tries to predict the target words from the context, Skip-Gram instead aims to determine the context for a given target word. The two different approaches therefore modify only the way in which the inputs and outputs are to be managed, but in any case, the network does not change, and the training always takes place between single pairs of words (placed as one-hot in input and output).", + "The text is in fact divided into sentences, and for each word of a given sentence a window of words is taken from the right and from the left to define the context. The central word is coupled with each of the words forming the set of pairs for training. Depending on the fact that the central word represents the output or the input in training pairs, the CBOW and Skip-gram models are obtained respectively.", + "Regardless of whether W2V is trained to predict the context or the target word, it is used as a word embedding in a substantially different manner from the one for which it has been trained. In particular, the second matrix is totally discarded during use, since the only thing relevant to the representation is the space of the vectors generated in the intermediate level (embedding space)." + ], + [ + "The common words (such as \u201cthe\", \u201cof\", etc.) carry very little information on the target word with which they are coupled, and through backpropagation they tend to have extremely small representative vectors in the embedding space. To solve both these problems the W2V algorithm implements a particular \u201csubsampling\" BIBREF11, which acts by eliminating some words from certain sentences. Note that the elimination of a word directly from the text means that it no longer appears in the context of any of the words of the sentence and, at the same time, a number of pairs equal to (at most) twice the size of the window relating to the deleted word will also disappear from the training set.", + "In practice, each word is associated with a sort of \u201ckeeping probability\" and, when you meet that word, if this value is greater than a randomly generated value then the word will not be discarded from the text. The W2V implementation assigns this \u201cprobability\" to the generic word $w_i$ through the formula:", + "where $f(w_i)$ is the relative frequency of the word $w_i$ (namely $count(w_i)/total$), while $s$ is a sample value, typically set between $10^{-3}$ and $10^{-5}$." + ], + [ + "Working with one-hot pairs of words means that the size of the network must be the same at input and output, and must be equal to the size of the vocabulary. So, although very simple, the network has a considerable number of parameters to train, which lead to an excessive computational cost if we are supposed to backpropagate all the elements of the one-hot vector in output.", + "The \u201cnegative sampling\" technique BIBREF11 tries to solve this problem by modifying only a small percentage of the net weights every time. In practice, for each pair of words in the training set, the loss function is calculated only for the value 1 and for a few values 0 of the one-hot vector of the desired output. The computational cost is therefore reduced by choosing to backpropagate only $K$ words \u201cnegative\" and one positive, instead of the entire vocabulary. Typical values for negative sampling (the number of negative samples that will be backpropagated and to which therefore the only positive value will always be added), range from 2 to 20, depending on the size of the dataset.", + "The probability of selecting a negative word to backpropagate depends on its frequency, in particular through the formula:", + "Negative samples are then selected by choosing a sort of \u201cunigram distribution\", so that the most frequent words are also the most often backpropated ones." + ], + [ + "The dataset needed to train the W2V was obtained using the information extracted from a dump of the Italian Wikipedia (dated 2019.04.01), from the main categories of Italian Google News (WORLD, NATION, BUSINESS, TECHNOLOGY, ENTERTAINMENT, SPORTS, SCIENCE, HEALTH) and from some anonymized chats between users and a customer care chatbot (Laila). The dataset (composed of 2.6 GB of raw text) includes $421\\,829\\,960$ words divided into $17\\,305\\,401$ sentences.", + "The text was previously preprocessed by removing the words whose absolute frequency was less than 5 and eliminating all special characters. Since it is impossible to represent every imaginable numerical value, but not wanting to eliminate the concept of \u201cnumerical representation\" linked to certain words, it was also decided to replace every number present in the text with the particular $\\langle NUM \\rangle $ token; which probably also assumes a better representation in the embedding space (not separating into the various possible values). All the words were then transformed to lowercase (to avoid a double presence) finally producing a vocabulary of $618\\,224$ words.", + "Note that among the special characters are also included punctuation marks, which therefore do not appear within the vocabulary. However, some of them (`.', `?' and `!') are later removed, as they are used to separate the sentences.", + "The Python implementation provided by Gensim was used for training the various embeddings all with size 300 and sampling parameter ($s$ in Equation DISPLAY_FORM3) set at $0.001$." + ], + [ + "To analyse the results we chose to use the test provided by BIBREF10, which consists of $19\\,791$ analogies divided into 19 different categories: 6 related to the \u201csemantic\" macro-area (8915 analogies) and 13 to the \u201csyntactic\" one (10876 analogies). All the analogies are composed by two pairs of words that share a relation, schematized with the equation: $a:a^{*}=b:b^{*}$ (e.g. \u201cman : woman = king : queen\"); where $b^{*}$ is the word to be guessed (\u201cqueen\"), $b$ is the word coupled to it (\u201cking\"), $a$ is the word for the components to be eliminated (\u201cman\"), and $a^{*}$ is the word for the components to be added (\u201cwoman\").", + "The determination of the correct response was obtained both through the classical additive cosine distance (3COSADD) BIBREF5:", + "and through the multiplicative cosine distance (3COSMUL) BIBREF12:", + "where $\\epsilon =10^{-6}$ and $\\cos (x, y) = \\frac{x \\cdot y}{\\left\\Vert x\\right\\Vert \\left\\Vert y\\right\\Vert }$. The extremely low value chosen for the $\\epsilon $ is due to the desire to minimize as much as possible its impact on performance, as during the various testing phases we noticed a strange bound that is still being investigated. As usual, moreover, the representative vectors of the embedding space are previously normalized for the execution of the various tests." + ], + [ + "We first analysed 6 different implementations of the Skip-gram model each one trained for 20 epochs. Table TABREF10 shows the accuracy values (only on possible analogies) at the 20th epoch for the six models both using 3COSADD and 3COSMUL. It is interesting to note that the 3COSADD total metric, respect to 3COSMUL, seems to have slightly better results in the two extreme cases of limited learning (W5N5 and W10N20) and under the semantic profile. However, we should keep in mind that the semantic profile is the one best captured by the network in both cases, which is probably due to the nature of the database (mainly composed of articles and news that principally use an impersonal language). In any case, the improvements that are obtained under the syntactic profile lead to the 3COSMUL metric obtaining better overall results.", + "Figure FIGREF11 shows the trends of the total accuracy at different epochs for the various models using 3COSMUL (the trend obtained with 3COSADD is very similar). Here we can see how the use of high negative sampling can worsen performance, even causing the network to oscillate (W5N20) in order to better adapt to all the data. The choice of the negative sampling to be used should therefore be strongly linked to the choice of the window size as well as to the number of training epochs.", + "Continuing the training of the two worst models up to the 50th epoch, it is observed (Table TABREF12) that they are still able to reach the performances of the other models. The W10N20 model at the 50th epoch even proves to be better than all the other previous models, becoming the reference model for subsequent comparisons. As the various epochs change (Figure FIGREF13.a) it appears to have the same oscillatory pattern observed previously, albeit with only one oscillation given the greater window size. This model is available at: https://mlunicampania.gitlab.io/italian-word2vec/.", + "Various tests were also conducted on CBOW models, which however proved to be in general significantly lower than Skip-gram models. Figure FIGREF13.b shows, for example, the accuracy trend for a CBOW model with a window equal to 10 and negative sampling equal to 20, which on 50 epochs reaches only $37.20\\%$ of total accuracy (with 3COSMUL metric)." + ], + [ + "Finally, a comparison was made between the Skip-gram model W10N20 obtained at the 50th epoch and the other two W2V in Italian present in the literature (BIBREF9 and BIBREF10). The first test (Table TABREF15) was performed considering all the analogies present, and therefore evaluating as an error any analogy that was not executable (as it related to one or more words absent from the vocabulary).", + "As it can be seen, regardless of the metric used, our model has significantly better results than the other two models, both overall and within the two macro-areas. Furthermore, the other two models seem to be more subject to the metric used, perhaps due to a stabilization not yet reached for the few training epochs.", + "For a complete comparison, both models were also tested considering only the subset of the analogies in common with our model (i.e. eliminating from the test all those analogies that were not executable by one or the other model). Tables TABREF16 and TABREF17 again highlight the marked increase in performance of our model compared to both." + ], + [ + "In this work we have analysed the Word2Vec model for Italian Language obtaining a substantial increase in performance respect to other two models in the literature (and despite the fixed size of the embedding). These results, in addition to the number of learning epochs, are probably also due to the different phase of data pre-processing, very carefully excuted in performing a complete cleaning of the text and above all in substituting the numerical values with a single particular token. We have observed that the number of epochs is an important parameter and its increase leads to results that rank our two worst models almost equal, or even better than others.", + "Changing the number of epochs, in some configurations, creates an oscillatory trend, which seems to be linked to a particular interaction between the window size and the negative sampling value. In the future, thanks to the collaboration in the Laila project, we intend to expand the dataset by adding more user chats. The objective will be to verify if the use of a less formal language can improves accuracy in the syntactic macro-area." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0734/environment/Dockerfile b/qasper-0734/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0734/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/* diff --git a/qasper-0734/instruction.md b/qasper-0734/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..0c085d4d7423308c374e35848daacdedd63bef95 --- /dev/null +++ b/qasper-0734/instruction.md @@ -0,0 +1,72 @@ +Name of Paper: An Analysis of Word2Vec for the Italian Language + +Question: How big is dataset used to train Word2Vec for the Italian Language? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Word2Vec", + "Word2Vec ::: Sampling rate", + "Word2Vec ::: Negative sampling", + "Implementation details", + "Results", + "Results ::: Analysis of the various models", + "Results ::: Comparison with other models", + "Conclusion" + ], + "paragraphs": [ + [ + "In order to make human language comprehensible to a computer, it is obviously essential to provide some word encoding. The simplest approach is the one-hot encoding, where each word is represented by a sparse vector with dimension equal to the vocabulary size. In addition to the storage need, the main problem of this representation is that any concept of word similarity is completely ignored (each vector is orthogonal and equidistant from each other). On the contrary, the understanding of natural language cannot be separated from the semantic knowledge of words, which conditions a different closeness between them. Indeed, the semantic representation of words is the basic problem of Natural Language Processing (NLP). Therefore, there is a necessary need to code words in a space that is linked to their meaning, in order to facilitate a machine in potential task of \u201cunderstanding\" it. In particular, starting from the seminal work BIBREF0, words are usually represented as dense distributed vectors that preserve their uniqueness but, at the same time, are able to encode the similarities.", + "These word representations are called Word Embeddings since the words (points in a space of vocabulary size) are mapped in an embedding space of lower dimension. Supported by the distributional hypothesis BIBREF1 BIBREF2, which states that a word can be semantically characterized based on its context (i.e. the words that surround it in the sentence), in recent years many word embedding representations have been proposed (a fairly complete and updated review can be found in BIBREF3 and BIBREF4). These methods can be roughly categorized into two main classes: prediction-based models and count-based models. The former is generally linked to work on Neural Network Language Models (NNLM) and use a training algorithm that predicts the word given its local context, the latter leverage word-context statistics and co-occurrence counts in an entire corpus. The main prediction-based and count-based models are respectively Word2Vec BIBREF5 (W2V) and GloVe BIBREF6.", + "Despite the widespread use of these concepts BIBREF7 BIBREF8, few contributions exist regarding the development of a W2V that is not in English. In particular, no detailed analysis on an Italian W2V seems to be present in the literature, except for BIBREF9 and BIBREF10. However, both seem to leave out some elements of fundamental interest in the learning of the neural network, in particular relating to the number of epochs performed during learning, reducing the importance that it may have on the final result. In BIBREF9, this for example leads to the simplistic conclusion that (being able to organize with more freedom in space) the more space is given to the vectors, the better the results may be. However, the problem in complex structures is that large embedding spaces can make training too difficult.", + "In this work, by setting the size of the embedding to a commonly used average value, various parameters are analysed as the number of learning epochs changes, depending on the window sizes and the negatively backpropagated samples." + ], + [ + "The W2V structure consists of a simple two-level neural network (Figure FIGREF1) with one-hot vectors representing words at the input. It can be trained in two different modes, algorithmically similar, but different in concept: Continuous Bag-of-Words (CBOW) model and Skip-Gram model. While CBOW tries to predict the target words from the context, Skip-Gram instead aims to determine the context for a given target word. The two different approaches therefore modify only the way in which the inputs and outputs are to be managed, but in any case, the network does not change, and the training always takes place between single pairs of words (placed as one-hot in input and output).", + "The text is in fact divided into sentences, and for each word of a given sentence a window of words is taken from the right and from the left to define the context. The central word is coupled with each of the words forming the set of pairs for training. Depending on the fact that the central word represents the output or the input in training pairs, the CBOW and Skip-gram models are obtained respectively.", + "Regardless of whether W2V is trained to predict the context or the target word, it is used as a word embedding in a substantially different manner from the one for which it has been trained. In particular, the second matrix is totally discarded during use, since the only thing relevant to the representation is the space of the vectors generated in the intermediate level (embedding space)." + ], + [ + "The common words (such as \u201cthe\", \u201cof\", etc.) carry very little information on the target word with which they are coupled, and through backpropagation they tend to have extremely small representative vectors in the embedding space. To solve both these problems the W2V algorithm implements a particular \u201csubsampling\" BIBREF11, which acts by eliminating some words from certain sentences. Note that the elimination of a word directly from the text means that it no longer appears in the context of any of the words of the sentence and, at the same time, a number of pairs equal to (at most) twice the size of the window relating to the deleted word will also disappear from the training set.", + "In practice, each word is associated with a sort of \u201ckeeping probability\" and, when you meet that word, if this value is greater than a randomly generated value then the word will not be discarded from the text. The W2V implementation assigns this \u201cprobability\" to the generic word $w_i$ through the formula:", + "where $f(w_i)$ is the relative frequency of the word $w_i$ (namely $count(w_i)/total$), while $s$ is a sample value, typically set between $10^{-3}$ and $10^{-5}$." + ], + [ + "Working with one-hot pairs of words means that the size of the network must be the same at input and output, and must be equal to the size of the vocabulary. So, although very simple, the network has a considerable number of parameters to train, which lead to an excessive computational cost if we are supposed to backpropagate all the elements of the one-hot vector in output.", + "The \u201cnegative sampling\" technique BIBREF11 tries to solve this problem by modifying only a small percentage of the net weights every time. In practice, for each pair of words in the training set, the loss function is calculated only for the value 1 and for a few values 0 of the one-hot vector of the desired output. The computational cost is therefore reduced by choosing to backpropagate only $K$ words \u201cnegative\" and one positive, instead of the entire vocabulary. Typical values for negative sampling (the number of negative samples that will be backpropagated and to which therefore the only positive value will always be added), range from 2 to 20, depending on the size of the dataset.", + "The probability of selecting a negative word to backpropagate depends on its frequency, in particular through the formula:", + "Negative samples are then selected by choosing a sort of \u201cunigram distribution\", so that the most frequent words are also the most often backpropated ones." + ], + [ + "The dataset needed to train the W2V was obtained using the information extracted from a dump of the Italian Wikipedia (dated 2019.04.01), from the main categories of Italian Google News (WORLD, NATION, BUSINESS, TECHNOLOGY, ENTERTAINMENT, SPORTS, SCIENCE, HEALTH) and from some anonymized chats between users and a customer care chatbot (Laila). The dataset (composed of 2.6 GB of raw text) includes $421\\,829\\,960$ words divided into $17\\,305\\,401$ sentences.", + "The text was previously preprocessed by removing the words whose absolute frequency was less than 5 and eliminating all special characters. Since it is impossible to represent every imaginable numerical value, but not wanting to eliminate the concept of \u201cnumerical representation\" linked to certain words, it was also decided to replace every number present in the text with the particular $\\langle NUM \\rangle $ token; which probably also assumes a better representation in the embedding space (not separating into the various possible values). All the words were then transformed to lowercase (to avoid a double presence) finally producing a vocabulary of $618\\,224$ words.", + "Note that among the special characters are also included punctuation marks, which therefore do not appear within the vocabulary. However, some of them (`.', `?' and `!') are later removed, as they are used to separate the sentences.", + "The Python implementation provided by Gensim was used for training the various embeddings all with size 300 and sampling parameter ($s$ in Equation DISPLAY_FORM3) set at $0.001$." + ], + [ + "To analyse the results we chose to use the test provided by BIBREF10, which consists of $19\\,791$ analogies divided into 19 different categories: 6 related to the \u201csemantic\" macro-area (8915 analogies) and 13 to the \u201csyntactic\" one (10876 analogies). All the analogies are composed by two pairs of words that share a relation, schematized with the equation: $a:a^{*}=b:b^{*}$ (e.g. \u201cman : woman = king : queen\"); where $b^{*}$ is the word to be guessed (\u201cqueen\"), $b$ is the word coupled to it (\u201cking\"), $a$ is the word for the components to be eliminated (\u201cman\"), and $a^{*}$ is the word for the components to be added (\u201cwoman\").", + "The determination of the correct response was obtained both through the classical additive cosine distance (3COSADD) BIBREF5:", + "and through the multiplicative cosine distance (3COSMUL) BIBREF12:", + "where $\\epsilon =10^{-6}$ and $\\cos (x, y) = \\frac{x \\cdot y}{\\left\\Vert x\\right\\Vert \\left\\Vert y\\right\\Vert }$. The extremely low value chosen for the $\\epsilon $ is due to the desire to minimize as much as possible its impact on performance, as during the various testing phases we noticed a strange bound that is still being investigated. As usual, moreover, the representative vectors of the embedding space are previously normalized for the execution of the various tests." + ], + [ + "We first analysed 6 different implementations of the Skip-gram model each one trained for 20 epochs. Table TABREF10 shows the accuracy values (only on possible analogies) at the 20th epoch for the six models both using 3COSADD and 3COSMUL. It is interesting to note that the 3COSADD total metric, respect to 3COSMUL, seems to have slightly better results in the two extreme cases of limited learning (W5N5 and W10N20) and under the semantic profile. However, we should keep in mind that the semantic profile is the one best captured by the network in both cases, which is probably due to the nature of the database (mainly composed of articles and news that principally use an impersonal language). In any case, the improvements that are obtained under the syntactic profile lead to the 3COSMUL metric obtaining better overall results.", + "Figure FIGREF11 shows the trends of the total accuracy at different epochs for the various models using 3COSMUL (the trend obtained with 3COSADD is very similar). Here we can see how the use of high negative sampling can worsen performance, even causing the network to oscillate (W5N20) in order to better adapt to all the data. The choice of the negative sampling to be used should therefore be strongly linked to the choice of the window size as well as to the number of training epochs.", + "Continuing the training of the two worst models up to the 50th epoch, it is observed (Table TABREF12) that they are still able to reach the performances of the other models. The W10N20 model at the 50th epoch even proves to be better than all the other previous models, becoming the reference model for subsequent comparisons. As the various epochs change (Figure FIGREF13.a) it appears to have the same oscillatory pattern observed previously, albeit with only one oscillation given the greater window size. This model is available at: https://mlunicampania.gitlab.io/italian-word2vec/.", + "Various tests were also conducted on CBOW models, which however proved to be in general significantly lower than Skip-gram models. Figure FIGREF13.b shows, for example, the accuracy trend for a CBOW model with a window equal to 10 and negative sampling equal to 20, which on 50 epochs reaches only $37.20\\%$ of total accuracy (with 3COSMUL metric)." + ], + [ + "Finally, a comparison was made between the Skip-gram model W10N20 obtained at the 50th epoch and the other two W2V in Italian present in the literature (BIBREF9 and BIBREF10). The first test (Table TABREF15) was performed considering all the analogies present, and therefore evaluating as an error any analogy that was not executable (as it related to one or more words absent from the vocabulary).", + "As it can be seen, regardless of the metric used, our model has significantly better results than the other two models, both overall and within the two macro-areas. Furthermore, the other two models seem to be more subject to the metric used, perhaps due to a stabilization not yet reached for the few training epochs.", + "For a complete comparison, both models were also tested considering only the subset of the analogies in common with our model (i.e. eliminating from the test all those analogies that were not executable by one or the other model). Tables TABREF16 and TABREF17 again highlight the marked increase in performance of our model compared to both." + ], + [ + "In this work we have analysed the Word2Vec model for Italian Language obtaining a substantial increase in performance respect to other two models in the literature (and despite the fixed size of the embedding). These results, in addition to the number of learning epochs, are probably also due to the different phase of data pre-processing, very carefully excuted in performing a complete cleaning of the text and above all in substituting the numerical values with a single particular token. We have observed that the number of epochs is an important parameter and its increase leads to results that rank our two worst models almost equal, or even better than others.", + "Changing the number of epochs, in some configurations, creates an oscillatory trend, which seems to be linked to a particular interaction between the window size and the negative sampling value. In the future, thanks to the collaboration in the Laila project, we intend to expand the dataset by adding more user chats. The objective will be to verify if the use of a less formal language can improves accuracy in the syntactic macro-area." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0735/environment/Dockerfile b/qasper-0735/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0735/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/* diff --git a/qasper-0735/instruction.md b/qasper-0735/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..742c3f234f6243358ead35e39e6a4308a5d4ce29 --- /dev/null +++ b/qasper-0735/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: An Analysis of Word2Vec for the Italian Language + +Question: How does different parameter settings impact the performance and semantic capacity of resulting model? \ No newline at end of file diff --git a/qasper-0750/environment/Dockerfile b/qasper-0750/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0750/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/* diff --git a/qasper-0750/instruction.md b/qasper-0750/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..7d9557c5871525abd1b432fe4ed52c6aef8910bc --- /dev/null +++ b/qasper-0750/instruction.md @@ -0,0 +1,95 @@ +Name of Paper: Uncover Sexual Harassment Patterns from Personal Stories by Joint Key Element Extraction and Categorization + +Question: What patterns were discovered from the stories? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Data Collection and Annotation", + "Proposed Models", + "Proposed Models ::: CNN Based Joint Learning Models", + "Proposed Models ::: BiLSTM Based Joint Learning Models", + "Experiments and Results ::: Experimental Settings", + "Experiments and Results ::: Results and Discussions", + "Patterns of Sexual Harassment", + "Conclusions", + "Acknowledgments" + ], + "paragraphs": [ + [ + "Sexual violence, including harassment, is a pervasive, worldwide problem with a long history. This global problem has finally become a mainstream issue thanks to the efforts of survivors and advocates. Statistics show that girls and women are put at high risk of experiencing harassment. Women have about a 3 in 5 chance of experiencing sexual harassment, whereas men have slightly less than 1 in 5 chance BIBREF0, BIBREF1, BIBREF2. While women in developing countries are facing distinct challenges with sexual violence BIBREF3, however sexual violence is ubiquitous. In the United States, for example, there are on average >300,000 people who are sexually assaulted every year BIBREF4. Additionally, these numbers could be underestimated, due to reasons like guilt, blame, doubt and fear, which stopped many survivors from reporting BIBREF5. Social media can be a more open and accessible channel for those who have experienced harassment to be empowered to freely share their traumatic experiences and to raise awareness of the vast scale of sexual harassment, which then allows us to understand and actively address abusive behavior as part of larger efforts to prevent future sexual harassment. The deadly gang rape of a medical student on a Delhi bus in 2012 was a catalyst for protest and action, including the development of Safecity, which uses online and mobile technology to work towards ending sexual harassment and assault. More recently, the #MeToo and #TimesUp movements, further demonstrate how reporting personal stories on social media can raise awareness and empower women. Millions of people around the world have come forward and shared their stories. Instead of being bystanders, more and more people become up-standers, who take action to protest against sexual harassment online. The stories of people who experienced harassment can be studied to identify different patterns of sexual harassment, which can enable solutions to be developed to make streets safer and to keep women and girls more secure when navigating city spaces BIBREF6. In this paper, we demonstrated the application of natural language processing (NLP) technologies to uncover harassment patterns from social media data. We made three key contributions:", + "1. Safecity is the largest publicly-available online forum for reporting sexual harassment BIBREF6. We annotated about 10,000 personal stories from Safecity with the key elements, including information of harasser (i.e. the words describing the harasser), time, location and the trigger words (i.e. the phrases indicate the harassment that occurred). The key elements are important for studying the patterns of harassment and victimology BIBREF5, BIBREF7. Furthermore, we also associated each story with five labels that characterize the story in multiple dimensions (i.e. age of harasser, single/multiple harasser(s), type of harasser, type of location and time of day). The annotation data are available online.", + "2. We proposed joint learning NLP models that use convolutional neural network (CNN) BIBREF8 and bi-directional long short-term memory (BiLSTM) BIBREF9, BIBREF10 as basic units. Our models can automatically extract the key elements from the sexual harassment stories and at the same time categorize the stories in different dimensions. The proposed models outperformed the single task models, and achieved higher than previously reported accuracy in classifications of harassment forms BIBREF6.", + "3. We uncovered significant patterns from the categorized sexual harassment stories." + ], + [ + "Conventional surveys and reports are often used to study sexual harassment, but harassment on these is usually under-reported BIBREF2, BIBREF5. The high volume of social media data available online can provide us a much larger collection of firsthand stories of sexual harassment. Social media data has already been used to analyze and predict distinct societal and health issues, in order to improve the understanding of wide-reaching societal concerns, including mental health, detecting domestic abuse, and cyberbullying BIBREF11, BIBREF12, BIBREF13, BIBREF14.", + "There are a very limited number of studies on sexual harassment stories shared online. Karlekar and Bansal karlekar2018safecity were the first group to our knowledge that applied NLP to analyze large amount ( $\\sim $10,000) of sexual harassment stories. Although their CNN-RNN classification models demonstrated high performance on classifying the forms of harassment, only the top 3 majority forms were studied. In order to study the details of the sexual harassment, the trigger words are crucial. Additionally, research indicated that both situational factors and person (or individual difference) factors contribute to sexual harassment BIBREF15. Therefore, the information about perpetrators needs to be extracted as well as the location and time of events. Karlekar and Bansal karlekar2018safecity applied several visualization techniques in order to capture such information, but it was not obtained explicitly. Our preliminary research demonstrated automatic extraction of key element and story classification in separate steps BIBREF16. In this paper, we proposed joint learning NLP models to directly extract the information of the harasser, time, location and trigger word as key elements and categorize the harassment stories in five dimensions as well. Our approach can provide an avenue to automatically uncover nuanced circumstances informing sexual harassment from online stories." + ], + [ + "We obtained 9,892 stories of sexual harassment incidents that was reported on Safecity. Those stories include a text description, along with tags of the forms of harassment, e.g. commenting, ogling and groping. A dataset of these stories was published by Karlekar and Bansal karlekar2018safecity. In addition to the forms of harassment, we manually annotated each story with the key elements (i.e. \u201charasser\", \u201ctime\", \u201clocation\", \u201ctrigger\"), because they are essential to uncover the harassment patterns. An example is shown in Figure FIGREF3. Furthermore, we also assigned each story classification labels in five dimensions (Table TABREF4). The detailed definitions of classifications in all dimensions are explained below.", + "Age of Harasser: Individual difference such as age can affect harassment behaviors. Therefore, we studied the harassers in two age groups, young and adult. Young people in this paper refer to people in the early 20s or younger.", + "Single/Multiple Harasser(s): Harassers may behave differently in groups than they do alone.", + "Type of Harasser: Person factors in harassment include the common relationships or titles of the harassers. Additionally, the reactions of people who experience harassment may vary with the harassers' relations to themselves BIBREF5. We defined 10 groups with respects to the harassers' relationships or titles. We put conductors and drivers in one group, as they both work on the public transportation. Police and guards are put in the same category, because they are employed to provide security. Manager, supervisors, and colleagues are in the work-related group. The others are described by their names.", + "Type of Location: It will be helpful to reveal the places where harassment most frequently occurs BIBREF7, BIBREF6. We defined 14 types of locations. \u201cStation/stop\u201d refers to places where people wait for public transportation or buy tickets. Private places include survivors' or harassers' home, places of parties and etc. The others are described by their names.", + "Time of Day: The time of an incident may be reported as \u201cin evening\u201d or at a specific time, e.g. \u201c10 pm\u201d. We considered that 5 am to 6 pm as day time, and the rest of the day as the night.", + "Because many of the stories collected are short, many do not contain all of the key elements. For example, \u201cA man came near to her tried to be physical with her .\u201d. The time and location are unknown from the story. In addition, the harassers were strangers to those they harassed in many cases. For instance, \u201cMy friend was standing in the queue to pay bill and was ogled by a group of boys.\u201d, we can only learn that there were multiple young harassers, but the type of harasser is unclear. The missing information is hence marked as \u201cunspecified\u201d. It is different from the label \u201cother\", which means the information is provided but the number of them is too small to be represented by a group, for example, a \u201ctrader\u201d.", + "All the data were labeled by two annotators with training. Inter-rater agreement was measured by Cohen's kappa coefficient, ranging from 0.71 to 0.91 for classifications in different dimensions and 0.75 for key element extraction (details can refer to Table 1 in supplementary file). The disagreements were reviewed by a third annotator and a final decision was made." + ], + [ + "The key elements can be very informative when categorizing the incidents. For instance, in Figure 1, with identified key elements, one can easily categorize the incident in dimensions of \u201cage of harasser\u201d (adult), \u201csingle/multiple harasser(s)\u201d (single), \u201ctype of harasser\u201d (unspecified), \u201ctype of location\u201d (park) , \u201ctime of day\u201d (day time). Therefore, we proposed two joint learning schemes to extract the key elements and categorize the incidents together. In the models' names, \u201cJ\u201d, \u201cA\u201d, \u201cSA\u201d stand for joint learning, attention, and supervised attention, respectively." + ], + [ + "In Figure FIGREF6, the first proposed structure consists of two layers of CNN modules.", + "J-CNN: To predict the type of key element, it is essential for the CNN model to capture the context information around each word. Therefore, the word along with its surrounding context of a fixed window size was converted into a context sequence. Assuming a window size of $2l + 1$ around the target word $w_0$, the context sequence is $[(w_{-l}, w_{-l+1},...w_0, ...w_{l-1},w_l)]$, where $w_i (i \\in [-l,l])$ stands for the $ith$ word from $w_0$.", + "Because the context of the two consecutive words in the original text are only off by one position, it will be difficult for the CNN model to detect the difference. Therefore, the position of each word in this context sequence is crucial information for the CNN model to make the correct predictions BIBREF17. That position was embedded as a $p$ dimensional vector, where $p$ is a hyperparameter. The position embeddings were learned at the training stage. Each word in the original text was then converted into a sequence of the concatenation of word and position embeddings. Such sequence was fed into the CNN modules in the first layer of the model, which output the high level word representation ($h_i, i\\in [0,n-1]$, where n is the number of input words). The high level word representation was then passed into a fully connected layer, to predict the key element type for the word. The CNN modules in this layer share the same parameters.", + "We input the sequence of high level word representations ($h_i$) from the first layer into another layer of multiple CNN modules to categorize the harassment incident in each dimension (Figure FIGREF6). Inside each CNN module, the sequence of word representations were first passed through a convolution layer to generate a sequence of new feature vectors ($C =[c_0,c_1,...c_q]$). This vector sequence ($C$) was then fed into a max pooling layer. This is followed by a fully connected layer. Modules in this layer do not share parameters across classification tasks.", + "J-ACNN: We also experimented with attentive pooling, by replacing the max pooling layer. The attention layer aggregates the sequence of feature vectors ($C$) by measuring the contribution of each vector to form the high level representation of the harassment story. Specifically,", + "That is, a fully connected layer with non-linear activation was applied to each vector $c_{i}$ to get its hidden representation $u_{i}$. The similarity of $u_{i}$ with a context vector $u_{w}$ was measured and get normalized through a softmax function, as the importance weight $\\alpha _{i}$. The final representation of the incident story $v$ was an aggregation of all the feature vectors weighted by $\\alpha _{i}$. $W_{\\omega }$, $b_{\\omega }$ and $u_{w}$ were learned during training.", + "The final representation ($v$) was passed into one fully connected layer for each classification task. We also applied different attention layers for different classifications, because the classification modules categorize the incident in different dimensions, their focuses vary. For example, to classify \u201ctime of day\u201d, one needs to focus on the time phrases, but pays more attention to harassers when classifying \u201cage of harasser\u201d.", + "J-SACNN: To further exploit the information of the key elements, we applied supervision BIBREF18 to the attentive pooling layer, with the annotated key element types of the words as ground truth. For instance, in classification of \u201cage of harasser\u201d, the ground truth attention labels for words with key element types of \u201charasser\u201d are 1 and others are 0. To conform to the CNN structure, we applied convolution to the sequence of ground truth attention labels, with the same window size ($w$) that was applied to the word sequence (Eq. DISPLAY_FORM11).", + "where $\\circ $ is element-wise multiplication, $e_t$ is the ground truth attention label, and the $W \\in R^{w\\times 1}$ is a constant matrix with all elements equal to 1. $\\alpha ^{*}$ was normalized through a softmax function and used as ground truth weight values of the vector sequence ($C$) output from the convolution layer. The loss was calculated between learned attention $\\alpha $ and $\\alpha ^{*}$ (Eq. DISPLAY_FORM12), and added to the total loss." + ], + [ + "J-BiLSTM: The model input the sequence of word embeddings to the BiLSTM layer. To extract key elements, the hidden states from the forward and backward LSTM cells were concatenated and used as word representations to predict the key element types.", + "To classify the harassment story in different dimensions, concatenation of the forward and backward final states of BiLSTM layer was used as document level representation of the story.", + "J-ABiLSTM: We also experimented on BiLSTM model with the attention layer to aggregate the outputs from BiLSTM layer (Figure FIGREF7). The aggregation of the outputs was used as document level representation.", + "J-SABiLSTM: Similarly, we experimented with the supervised attention.", + "In all the models, softmax function was used to calculate the probabilities at the prediction step, and the cross entropy losses from extraction and classification tasks were added together. In case of supervised attention, the loss defined in Eq. DISPLAY_FORM12 was added to the total loss as well. We applied the stochastic gradient descent algorithm with mini-batches and the AdaDelta update Rule (rho=0.95 and epsilon=1e-6) BIBREF19, BIBREF20. The gradients were computed using back-propagation. During training, we also optimized the word and position embeddings." + ], + [ + "Data Splits: We used the same splits of train, develop, and test sets used by Karlekar and Bansal BIBREF6, with 7201, 990 and 1701 stories, respectively. In this study, we only considered single label classifications.", + "Baseline Models: CNN and BiLSTM models that perform classification and extraction separately were used as baseline models. In classification, we also experimented with BiLSTM with the attention layer. To demonstrate that the improvement came from joint learning structure rather the two layer structure in J-CNN, we investigated the same model structure without training on key element extraction. We use J-CNN* to denote it.", + "Preprocess: All the texts were converted to lowercase and preprocessed by removing non-alphanumeric characters, excluding \u201c. ! ? \u201d . The word embeddings were pre-trained using fastText BIBREF21 with dimension equaling 100.", + "Hyperparameters: For the CNN model, the filter size was chosen to be (1,2,3,4), with 50 filters per filter size. Batch size was set to 50 and the dropout rate was 0.5. The BiLSTM model comprises two layers of one directional LSTM. Every LSTM cell has 50 hidden units. The dropout rate was 0.25. Attention size was 50." + ], + [ + "We compared joint learning models with the single task models. Results are averages from five experiments. Although not much improvement was achieved in key element extraction (Figure TABREF16), classification performance improved significantly with joint learning schemes (Table TABREF17). Significance t-test results are shown in Table 2 in the supplementary file.", + "BiLSTM Based Models: Joint learning BiLSTM with attention outperformed single task BiLSTM models. One reason is that it directed the attention of the model to the correct part of the text. For example,", + "S1: \u201c foogreen!1.7003483371809125 foowhen foogreen!3.4324652515351772 fooi foogreen!10.76661329716444 foowas foogreen!20.388443022966385 fooreturning foogreen!9.704475291073322 foomy foogreen!6.052316632121801 foohome foogreen!2.477810252457857 fooafter foogreen!3.5612427163869143 foofinishing foogreen!4.7736018896102905 foomy foogreen!4.634172189980745 fooclass foogreen!0.6899426807649434 foo. foogreen!0.35572052001953125 fooi foogreen!0.3427551419008523 foowas foogreen!0.293194578262046 fooin foogreen!0.2028885210165754 fooqueue foogreen!0.10553237370913848 footo foogreen!0.19472737039905041 fooget foogreen!0.44946340494789183 fooon foogreen!0.5511227645911276 foothe foogreen!2.056689700111747 foomicro foogreen!2.597035141661763 foobus foogreen!2.5683704297989607 fooand foogreen!4.6382867731153965 foothere foogreen!9.827975183725357 foowas foogreen!21.346069872379303 fooa foogreen!22.295180708169937 foogirl foogreen!11.672522872686386 fooopposite foogreen!8.892465382814407 footo foogreen!18.20233091711998 foome foogreen!13.192926533520222 foojust foogreen!26.24184638261795 foothen foogreen!40.2555949985981 fooa foogreen!30.108729377388954 fooyoung foogreen!115.02625793218613 fooman foogreen!93.40204298496246 footried foogreen!58.68498980998993 footo foogreen!144.01434361934662 footouch foogreen!108.82275551557541 fooher foogreen!80.9452086687088 fooon foogreen!47.26015031337738 foothe foogreen!47.71501570940018 foobreast foogreen!19.392695277929306 foo.\u201d", + "S2: \u201c foogreen!0.2212507533840835 foowhen foogreen!0.26129744946956635 fooi foogreen!0.3014186804648489 foowas foogreen!0.314583390718326 fooreturning foogreen!0.23829322890378535 foomy foogreen!0.018542312318459153 foohome foogreen!0.06052045864635147 fooafter foogreen!0.3865368489641696 foofinishing foogreen!0.5127551266923547 foomy foogreen!0.569560332223773 fooclass foogreen!0.037081812479300424 foo. foogreen!0.061129467212595046 fooi foogreen!0.12043083552271128 foowas foogreen!0.2053432835964486 fooin foogreen!0.038308095099637285 fooqueue foogreen!0.05270353358355351 footo foogreen!0.07939991337480024 fooget foogreen!0.14962266141083091 fooon foogreen!0.11444976553320885 foothe foogreen!0.013002995729038958 foomicro foogreen!0.016201976904994808 foobus foogreen!0.14046543219592422 fooand foogreen!0.12413455988280475 foothere foogreen!0.18423641449771821 foowas foogreen!0.3394613158889115 fooa foogreen!1.0372470133006573 foogirl foogreen!0.20553644571918994 fooopposite foogreen!0.2821453963406384 footo foogreen!0.5574009846895933 foome foogreen!0.2709480468183756 foojust foogreen!0.2582515007816255 foothen foogreen!0.9223996312357485 fooa foogreen!788.9420390129089 fooyoung foogreen!199.1765946149826 fooman foogreen!0.39259070763364434 footried foogreen!0.27069455245509744 footo foogreen!0.5092779756523669 footouch foogreen!0.7033208385109901 fooher foogreen!0.6793316570110619 fooon foogreen!0.5892394692637026 foothe foogreen!0.4084075626451522 foobreast foogreen!0.14951340563129634 foo.\u201d", + "S3: \u201c foogreen!0.23944019631017 foowhen foogreen!0.16698541003279388 fooi foogreen!0.3381385176908225 foowas foogreen!0.21315943740773946 fooreturning foogreen!0.3222442464902997 foomy foogreen!0.8483575657010078 foohome foogreen!0.10339960863348097 fooafter foogreen!0.2440519310766831 foofinishing foogreen!0.39699181797914207 foomy foogreen!1.2218113988637924 fooclass foogreen!0.1232976937899366 foo. foogreen!0.10928708070423454 fooi foogreen!0.2562549489084631 foowas foogreen!0.8099888218566775 fooin foogreen!2.9650430660694838 fooqueue foogreen!0.507337914314121 footo foogreen!0.727736041881144 fooget foogreen!0.7367140497080982 fooon foogreen!0.711284636054188 foothe foogreen!194.2763775587082 foomicro foogreen!786.8869304656982 foobus foogreen!0.4422159108798951 fooand foogreen!0.43104542419314384 foothere foogreen!0.4694198723882437 foowas foogreen!0.5085613229312003 fooa foogreen!0.4430979897733778 foogirl foogreen!0.36199347232468426 fooopposite foogreen!0.31067250529304147 footo foogreen!0.2927705936599523 foome foogreen!0.24646619567647576 foojust foogreen!0.23911069729365408 foothen foogreen!0.11775700113503262 fooa foogreen!0.002219072712250636 fooyoung foogreen!0.0019248132048232947 fooman foogreen!0.32698659924790263 footried foogreen!0.3118939639534801 footo foogreen!0.5727249081246555 footouch foogreen!0.5670131067745388 fooher foogreen!0.7104063988663256 fooon foogreen!0.6698771030642092 foothe foogreen!0.4756081907544285 foobreast foogreen!0.26600153069011867 foo.\u201d", + "In S1, the regular BiLSTM with attention model for classification on \u201cage of harasser\u201d put some attention on phrases other than the harasser, and hence aggregated noise. This could explain why the regular BiLSTM model got lower performance than the CNN model. However, when training with key element extractions, it put almost all attention on the harasser \u201cyoung man\u201d (S2), which helped the model make correct prediction of \u201cyoung harasser\u201d. When predicting the \u201ctype of location\u201d (S3), the joint learning model directed its attention to \u201cmicro bus\u201d.", + "CNN Based Models: Since CNN is efficient for capturing the most useful information BIBREF22, it is quite suitable for the classification tasks in this study. It achieved better performance than the BiLSTM model. The joint learning method boosted the performance even higher. This is because the classifications are related to the extracted key elements, and the word representation learned by the first layer of CNNs (Figure FIGREF6) is more informative than word embedding. By plotting of t-SNEs BIBREF23 of the two kinds of word vectors, we can see the word representations in the joint learning model made the words more separable (Figure 1 in supplementary file). In addition, no improvement was found with the J-CNN* model, which demonstrated the joint learning with extraction is essential for the improvement.", + "With supervised attentive pooling, the model can get additional knowledge from key element labels. It helped the model in cases when certain location phrases were mentioned but the incidents did not happen at those locations. For instance, \u201cI was followed on my way home .\u201d, max pooling will very likely to predict it as \u201cprivate places\u201d. But, it is actually unknown. In other cases, with supervised attentive pooling, the model can distinguish \u201cmetro\u201d and \u201cmetro station\u201d, which are \u201ctransportation\u201d and \u201cstop/station\u201d respectively. Therefore, the model further improved on classifications on \u201ctype of location\u201d with supervised attention in terms of macro F1. For some tasks, like \u201ctime of day\u201d, there are fewer cases with such disambiguation and hence max pooling worked well. Supervised attention improved macro F1 in location and harasser classifications, because it made more correct predictions in cases that mentioned location and harasser. But the majority did not mention them. Therefore, the accuracy of J-SACNN did not increase, compared with the other models.", + "Classification on Harassment Forms: In Table TABREF18, we also compared the performance of binary classifications on harassment forms with the results reported by Karlekar and Bansal karlekar2018safecity. Joint learning models achieved higher accuracy. In some harassment stories, the whole text or a span of the text consists of trigger words of multiple forms, such as \u201cstare, whistles, start to sing, commenting\u201d. The supervised attention mechanism will force the model to look at all such words rather than just the one related to the harassment form for classification and hence it can introduce noise. This can explain why J-SACNN got lower accuracy in two of the harassment form classifications, compared to J-ACNN. In addition, J-CNN model did best in \u201cogling\u201d classification." + ], + [ + "We plotted the distribution of harassment incidents in each categorization dimension (Figure FIGREF19). It displays statistics that provide important evidence as to the scale of harassment and that can serve as the basis for more effective interventions to be developed by authorities ranging from advocacy organizations to policy makers. It provides evidence to support some commonly assumed factors about harassment: First, we demonstrate that harassment occurred more frequently during the night time than the day time. Second, it shows that besides unspecified strangers (not shown in the figure), conductors and drivers are top the list of identified types of harassers, followed by friends and relatives.", + "Furthermore, we uncovered that there exist strong correlations between the age of perpetrators and the location of harassment, between the single/multiple harasser(s) and location, and between age and single/multiple harasser(s) (Figure FIGREF20). The significance of the correlation is tested by chi-square independence with p value less than 0.05. Identifying these patterns will enable interventions to be differentiated for and targeted at specific populations. For instance, the young harassers often engage in harassment activities as groups. This points to the influence of peer pressure and masculine behavioral norms for men and boys on these activities. We also found that the majority of young perpetrators engaged in harassment behaviors on the streets. These findings suggest that interventions with young men and boys, who are readily influenced by peers, might be most effective when education is done peer-to-peer. It also points to the locations where such efforts could be made, including both in schools and on the streets. In contrast, we found that adult perpetrators of sexual harassment are more likely to act alone. Most of the adult harassers engaged in harassment on public transportation. These differences in adult harassment activities and locations, mean that interventions should be responsive to these factors. For example, increasing the security measures on transit at key times and locations.", + "In addition, we also found that the correlations between the forms of harassment with the age, single/multiple harasser, type of harasser, and location (Figure FIGREF21). For example, young harassers are more likely to engage in behaviors of verbal harassment, rather than physical harassment as compared to adults. It was a single perpetrator that engaged in touching or groping more often, rather than groups of perpetrators. In contrast, commenting happened more frequently when harassers were in groups. Last but not least, public transportation is where people got indecently touched most frequently both by fellow passengers and by conductors and drivers. The nature and location of the harassment are particularly significant in developing strategies for those who are harassed or who witness the harassment to respond and manage the everyday threat of harassment. For example, some strategies will work best on public transport, a particular closed, shared space setting, while other strategies might be more effective on the open space of the street.", + "These results can provide valuable information for all members of the public. Sharing stories of harassment has been found by researchers to shift people\u2019s cognitive and emotional orientation towards their traumatic experiences BIBREF24. Greater awareness of patterns and scale of harassment experiences promises to ensure those who have been subjected to this violence that they are not alone, empowering others to report incidents, and ensuring them that efforts are being made to prevent others from experiencing the same harassment. These results also provide various authorities tools to identify potential harassment patterns and to make more effective interventions to prevent further harassment incidents. For instance, the authorities can increase targeted educational efforts at youth and adults, and be guided in utilizing limited resources the most effectively to offer more safety measures, including policing and community-based responses. For example, focusing efforts on highly populated public transportation during the nighttime, when harassment is found to be most likely to occur." + ], + [ + "We provided a large number of annotated personal stories of sexual harassment. Analyzing and identifying the social patterns of harassment behavior is essential to changing these patterns and social tolerance for them. We demonstrated the joint learning NLP models with strong performances to automatically extract key elements and categorize the stories. Potentiality, the approaches and models proposed in this study can be applied to sexual harassment stories from other sources, which can process and summarize the harassment stories and help those who have experienced harassment and authorities to work faster, such as by automatically filing reports BIBREF6. Furthermore, we discovered meaningful patterns in the situations where harassment commonly occurred. The volume of social media data is huge, and the more we can extract from these data, the more powerful we can be as part of the efforts to build a safer and more inclusive communities. Our work can increase the understanding of sexual harassment in society, ease the processing of such incidents by advocates and officials, and most importantly, raise awareness of this urgent problem." + ], + [ + "We thank the Safecity for granting the permission of using the data." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0751/instruction.md b/qasper-0751/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..3c52664895676ec44fd26aeb63c7dc14ff10f927 --- /dev/null +++ b/qasper-0751/instruction.md @@ -0,0 +1,95 @@ +Name of Paper: Uncover Sexual Harassment Patterns from Personal Stories by Joint Key Element Extraction and Categorization + +Question: Did they use a crowdsourcing platform? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Data Collection and Annotation", + "Proposed Models", + "Proposed Models ::: CNN Based Joint Learning Models", + "Proposed Models ::: BiLSTM Based Joint Learning Models", + "Experiments and Results ::: Experimental Settings", + "Experiments and Results ::: Results and Discussions", + "Patterns of Sexual Harassment", + "Conclusions", + "Acknowledgments" + ], + "paragraphs": [ + [ + "Sexual violence, including harassment, is a pervasive, worldwide problem with a long history. This global problem has finally become a mainstream issue thanks to the efforts of survivors and advocates. Statistics show that girls and women are put at high risk of experiencing harassment. Women have about a 3 in 5 chance of experiencing sexual harassment, whereas men have slightly less than 1 in 5 chance BIBREF0, BIBREF1, BIBREF2. While women in developing countries are facing distinct challenges with sexual violence BIBREF3, however sexual violence is ubiquitous. In the United States, for example, there are on average >300,000 people who are sexually assaulted every year BIBREF4. Additionally, these numbers could be underestimated, due to reasons like guilt, blame, doubt and fear, which stopped many survivors from reporting BIBREF5. Social media can be a more open and accessible channel for those who have experienced harassment to be empowered to freely share their traumatic experiences and to raise awareness of the vast scale of sexual harassment, which then allows us to understand and actively address abusive behavior as part of larger efforts to prevent future sexual harassment. The deadly gang rape of a medical student on a Delhi bus in 2012 was a catalyst for protest and action, including the development of Safecity, which uses online and mobile technology to work towards ending sexual harassment and assault. More recently, the #MeToo and #TimesUp movements, further demonstrate how reporting personal stories on social media can raise awareness and empower women. Millions of people around the world have come forward and shared their stories. Instead of being bystanders, more and more people become up-standers, who take action to protest against sexual harassment online. The stories of people who experienced harassment can be studied to identify different patterns of sexual harassment, which can enable solutions to be developed to make streets safer and to keep women and girls more secure when navigating city spaces BIBREF6. In this paper, we demonstrated the application of natural language processing (NLP) technologies to uncover harassment patterns from social media data. We made three key contributions:", + "1. Safecity is the largest publicly-available online forum for reporting sexual harassment BIBREF6. We annotated about 10,000 personal stories from Safecity with the key elements, including information of harasser (i.e. the words describing the harasser), time, location and the trigger words (i.e. the phrases indicate the harassment that occurred). The key elements are important for studying the patterns of harassment and victimology BIBREF5, BIBREF7. Furthermore, we also associated each story with five labels that characterize the story in multiple dimensions (i.e. age of harasser, single/multiple harasser(s), type of harasser, type of location and time of day). The annotation data are available online.", + "2. We proposed joint learning NLP models that use convolutional neural network (CNN) BIBREF8 and bi-directional long short-term memory (BiLSTM) BIBREF9, BIBREF10 as basic units. Our models can automatically extract the key elements from the sexual harassment stories and at the same time categorize the stories in different dimensions. The proposed models outperformed the single task models, and achieved higher than previously reported accuracy in classifications of harassment forms BIBREF6.", + "3. We uncovered significant patterns from the categorized sexual harassment stories." + ], + [ + "Conventional surveys and reports are often used to study sexual harassment, but harassment on these is usually under-reported BIBREF2, BIBREF5. The high volume of social media data available online can provide us a much larger collection of firsthand stories of sexual harassment. Social media data has already been used to analyze and predict distinct societal and health issues, in order to improve the understanding of wide-reaching societal concerns, including mental health, detecting domestic abuse, and cyberbullying BIBREF11, BIBREF12, BIBREF13, BIBREF14.", + "There are a very limited number of studies on sexual harassment stories shared online. Karlekar and Bansal karlekar2018safecity were the first group to our knowledge that applied NLP to analyze large amount ( $\\sim $10,000) of sexual harassment stories. Although their CNN-RNN classification models demonstrated high performance on classifying the forms of harassment, only the top 3 majority forms were studied. In order to study the details of the sexual harassment, the trigger words are crucial. Additionally, research indicated that both situational factors and person (or individual difference) factors contribute to sexual harassment BIBREF15. Therefore, the information about perpetrators needs to be extracted as well as the location and time of events. Karlekar and Bansal karlekar2018safecity applied several visualization techniques in order to capture such information, but it was not obtained explicitly. Our preliminary research demonstrated automatic extraction of key element and story classification in separate steps BIBREF16. In this paper, we proposed joint learning NLP models to directly extract the information of the harasser, time, location and trigger word as key elements and categorize the harassment stories in five dimensions as well. Our approach can provide an avenue to automatically uncover nuanced circumstances informing sexual harassment from online stories." + ], + [ + "We obtained 9,892 stories of sexual harassment incidents that was reported on Safecity. Those stories include a text description, along with tags of the forms of harassment, e.g. commenting, ogling and groping. A dataset of these stories was published by Karlekar and Bansal karlekar2018safecity. In addition to the forms of harassment, we manually annotated each story with the key elements (i.e. \u201charasser\", \u201ctime\", \u201clocation\", \u201ctrigger\"), because they are essential to uncover the harassment patterns. An example is shown in Figure FIGREF3. Furthermore, we also assigned each story classification labels in five dimensions (Table TABREF4). The detailed definitions of classifications in all dimensions are explained below.", + "Age of Harasser: Individual difference such as age can affect harassment behaviors. Therefore, we studied the harassers in two age groups, young and adult. Young people in this paper refer to people in the early 20s or younger.", + "Single/Multiple Harasser(s): Harassers may behave differently in groups than they do alone.", + "Type of Harasser: Person factors in harassment include the common relationships or titles of the harassers. Additionally, the reactions of people who experience harassment may vary with the harassers' relations to themselves BIBREF5. We defined 10 groups with respects to the harassers' relationships or titles. We put conductors and drivers in one group, as they both work on the public transportation. Police and guards are put in the same category, because they are employed to provide security. Manager, supervisors, and colleagues are in the work-related group. The others are described by their names.", + "Type of Location: It will be helpful to reveal the places where harassment most frequently occurs BIBREF7, BIBREF6. We defined 14 types of locations. \u201cStation/stop\u201d refers to places where people wait for public transportation or buy tickets. Private places include survivors' or harassers' home, places of parties and etc. The others are described by their names.", + "Time of Day: The time of an incident may be reported as \u201cin evening\u201d or at a specific time, e.g. \u201c10 pm\u201d. We considered that 5 am to 6 pm as day time, and the rest of the day as the night.", + "Because many of the stories collected are short, many do not contain all of the key elements. For example, \u201cA man came near to her tried to be physical with her .\u201d. The time and location are unknown from the story. In addition, the harassers were strangers to those they harassed in many cases. For instance, \u201cMy friend was standing in the queue to pay bill and was ogled by a group of boys.\u201d, we can only learn that there were multiple young harassers, but the type of harasser is unclear. The missing information is hence marked as \u201cunspecified\u201d. It is different from the label \u201cother\", which means the information is provided but the number of them is too small to be represented by a group, for example, a \u201ctrader\u201d.", + "All the data were labeled by two annotators with training. Inter-rater agreement was measured by Cohen's kappa coefficient, ranging from 0.71 to 0.91 for classifications in different dimensions and 0.75 for key element extraction (details can refer to Table 1 in supplementary file). The disagreements were reviewed by a third annotator and a final decision was made." + ], + [ + "The key elements can be very informative when categorizing the incidents. For instance, in Figure 1, with identified key elements, one can easily categorize the incident in dimensions of \u201cage of harasser\u201d (adult), \u201csingle/multiple harasser(s)\u201d (single), \u201ctype of harasser\u201d (unspecified), \u201ctype of location\u201d (park) , \u201ctime of day\u201d (day time). Therefore, we proposed two joint learning schemes to extract the key elements and categorize the incidents together. In the models' names, \u201cJ\u201d, \u201cA\u201d, \u201cSA\u201d stand for joint learning, attention, and supervised attention, respectively." + ], + [ + "In Figure FIGREF6, the first proposed structure consists of two layers of CNN modules.", + "J-CNN: To predict the type of key element, it is essential for the CNN model to capture the context information around each word. Therefore, the word along with its surrounding context of a fixed window size was converted into a context sequence. Assuming a window size of $2l + 1$ around the target word $w_0$, the context sequence is $[(w_{-l}, w_{-l+1},...w_0, ...w_{l-1},w_l)]$, where $w_i (i \\in [-l,l])$ stands for the $ith$ word from $w_0$.", + "Because the context of the two consecutive words in the original text are only off by one position, it will be difficult for the CNN model to detect the difference. Therefore, the position of each word in this context sequence is crucial information for the CNN model to make the correct predictions BIBREF17. That position was embedded as a $p$ dimensional vector, where $p$ is a hyperparameter. The position embeddings were learned at the training stage. Each word in the original text was then converted into a sequence of the concatenation of word and position embeddings. Such sequence was fed into the CNN modules in the first layer of the model, which output the high level word representation ($h_i, i\\in [0,n-1]$, where n is the number of input words). The high level word representation was then passed into a fully connected layer, to predict the key element type for the word. The CNN modules in this layer share the same parameters.", + "We input the sequence of high level word representations ($h_i$) from the first layer into another layer of multiple CNN modules to categorize the harassment incident in each dimension (Figure FIGREF6). Inside each CNN module, the sequence of word representations were first passed through a convolution layer to generate a sequence of new feature vectors ($C =[c_0,c_1,...c_q]$). This vector sequence ($C$) was then fed into a max pooling layer. This is followed by a fully connected layer. Modules in this layer do not share parameters across classification tasks.", + "J-ACNN: We also experimented with attentive pooling, by replacing the max pooling layer. The attention layer aggregates the sequence of feature vectors ($C$) by measuring the contribution of each vector to form the high level representation of the harassment story. Specifically,", + "That is, a fully connected layer with non-linear activation was applied to each vector $c_{i}$ to get its hidden representation $u_{i}$. The similarity of $u_{i}$ with a context vector $u_{w}$ was measured and get normalized through a softmax function, as the importance weight $\\alpha _{i}$. The final representation of the incident story $v$ was an aggregation of all the feature vectors weighted by $\\alpha _{i}$. $W_{\\omega }$, $b_{\\omega }$ and $u_{w}$ were learned during training.", + "The final representation ($v$) was passed into one fully connected layer for each classification task. We also applied different attention layers for different classifications, because the classification modules categorize the incident in different dimensions, their focuses vary. For example, to classify \u201ctime of day\u201d, one needs to focus on the time phrases, but pays more attention to harassers when classifying \u201cage of harasser\u201d.", + "J-SACNN: To further exploit the information of the key elements, we applied supervision BIBREF18 to the attentive pooling layer, with the annotated key element types of the words as ground truth. For instance, in classification of \u201cage of harasser\u201d, the ground truth attention labels for words with key element types of \u201charasser\u201d are 1 and others are 0. To conform to the CNN structure, we applied convolution to the sequence of ground truth attention labels, with the same window size ($w$) that was applied to the word sequence (Eq. DISPLAY_FORM11).", + "where $\\circ $ is element-wise multiplication, $e_t$ is the ground truth attention label, and the $W \\in R^{w\\times 1}$ is a constant matrix with all elements equal to 1. $\\alpha ^{*}$ was normalized through a softmax function and used as ground truth weight values of the vector sequence ($C$) output from the convolution layer. The loss was calculated between learned attention $\\alpha $ and $\\alpha ^{*}$ (Eq. DISPLAY_FORM12), and added to the total loss." + ], + [ + "J-BiLSTM: The model input the sequence of word embeddings to the BiLSTM layer. To extract key elements, the hidden states from the forward and backward LSTM cells were concatenated and used as word representations to predict the key element types.", + "To classify the harassment story in different dimensions, concatenation of the forward and backward final states of BiLSTM layer was used as document level representation of the story.", + "J-ABiLSTM: We also experimented on BiLSTM model with the attention layer to aggregate the outputs from BiLSTM layer (Figure FIGREF7). The aggregation of the outputs was used as document level representation.", + "J-SABiLSTM: Similarly, we experimented with the supervised attention.", + "In all the models, softmax function was used to calculate the probabilities at the prediction step, and the cross entropy losses from extraction and classification tasks were added together. In case of supervised attention, the loss defined in Eq. DISPLAY_FORM12 was added to the total loss as well. We applied the stochastic gradient descent algorithm with mini-batches and the AdaDelta update Rule (rho=0.95 and epsilon=1e-6) BIBREF19, BIBREF20. The gradients were computed using back-propagation. During training, we also optimized the word and position embeddings." + ], + [ + "Data Splits: We used the same splits of train, develop, and test sets used by Karlekar and Bansal BIBREF6, with 7201, 990 and 1701 stories, respectively. In this study, we only considered single label classifications.", + "Baseline Models: CNN and BiLSTM models that perform classification and extraction separately were used as baseline models. In classification, we also experimented with BiLSTM with the attention layer. To demonstrate that the improvement came from joint learning structure rather the two layer structure in J-CNN, we investigated the same model structure without training on key element extraction. We use J-CNN* to denote it.", + "Preprocess: All the texts were converted to lowercase and preprocessed by removing non-alphanumeric characters, excluding \u201c. ! ? \u201d . The word embeddings were pre-trained using fastText BIBREF21 with dimension equaling 100.", + "Hyperparameters: For the CNN model, the filter size was chosen to be (1,2,3,4), with 50 filters per filter size. Batch size was set to 50 and the dropout rate was 0.5. The BiLSTM model comprises two layers of one directional LSTM. Every LSTM cell has 50 hidden units. The dropout rate was 0.25. Attention size was 50." + ], + [ + "We compared joint learning models with the single task models. Results are averages from five experiments. Although not much improvement was achieved in key element extraction (Figure TABREF16), classification performance improved significantly with joint learning schemes (Table TABREF17). Significance t-test results are shown in Table 2 in the supplementary file.", + "BiLSTM Based Models: Joint learning BiLSTM with attention outperformed single task BiLSTM models. One reason is that it directed the attention of the model to the correct part of the text. For example,", + "S1: \u201c foogreen!1.7003483371809125 foowhen foogreen!3.4324652515351772 fooi foogreen!10.76661329716444 foowas foogreen!20.388443022966385 fooreturning foogreen!9.704475291073322 foomy foogreen!6.052316632121801 foohome foogreen!2.477810252457857 fooafter foogreen!3.5612427163869143 foofinishing foogreen!4.7736018896102905 foomy foogreen!4.634172189980745 fooclass foogreen!0.6899426807649434 foo. foogreen!0.35572052001953125 fooi foogreen!0.3427551419008523 foowas foogreen!0.293194578262046 fooin foogreen!0.2028885210165754 fooqueue foogreen!0.10553237370913848 footo foogreen!0.19472737039905041 fooget foogreen!0.44946340494789183 fooon foogreen!0.5511227645911276 foothe foogreen!2.056689700111747 foomicro foogreen!2.597035141661763 foobus foogreen!2.5683704297989607 fooand foogreen!4.6382867731153965 foothere foogreen!9.827975183725357 foowas foogreen!21.346069872379303 fooa foogreen!22.295180708169937 foogirl foogreen!11.672522872686386 fooopposite foogreen!8.892465382814407 footo foogreen!18.20233091711998 foome foogreen!13.192926533520222 foojust foogreen!26.24184638261795 foothen foogreen!40.2555949985981 fooa foogreen!30.108729377388954 fooyoung foogreen!115.02625793218613 fooman foogreen!93.40204298496246 footried foogreen!58.68498980998993 footo foogreen!144.01434361934662 footouch foogreen!108.82275551557541 fooher foogreen!80.9452086687088 fooon foogreen!47.26015031337738 foothe foogreen!47.71501570940018 foobreast foogreen!19.392695277929306 foo.\u201d", + "S2: \u201c foogreen!0.2212507533840835 foowhen foogreen!0.26129744946956635 fooi foogreen!0.3014186804648489 foowas foogreen!0.314583390718326 fooreturning foogreen!0.23829322890378535 foomy foogreen!0.018542312318459153 foohome foogreen!0.06052045864635147 fooafter foogreen!0.3865368489641696 foofinishing foogreen!0.5127551266923547 foomy foogreen!0.569560332223773 fooclass foogreen!0.037081812479300424 foo. foogreen!0.061129467212595046 fooi foogreen!0.12043083552271128 foowas foogreen!0.2053432835964486 fooin foogreen!0.038308095099637285 fooqueue foogreen!0.05270353358355351 footo foogreen!0.07939991337480024 fooget foogreen!0.14962266141083091 fooon foogreen!0.11444976553320885 foothe foogreen!0.013002995729038958 foomicro foogreen!0.016201976904994808 foobus foogreen!0.14046543219592422 fooand foogreen!0.12413455988280475 foothere foogreen!0.18423641449771821 foowas foogreen!0.3394613158889115 fooa foogreen!1.0372470133006573 foogirl foogreen!0.20553644571918994 fooopposite foogreen!0.2821453963406384 footo foogreen!0.5574009846895933 foome foogreen!0.2709480468183756 foojust foogreen!0.2582515007816255 foothen foogreen!0.9223996312357485 fooa foogreen!788.9420390129089 fooyoung foogreen!199.1765946149826 fooman foogreen!0.39259070763364434 footried foogreen!0.27069455245509744 footo foogreen!0.5092779756523669 footouch foogreen!0.7033208385109901 fooher foogreen!0.6793316570110619 fooon foogreen!0.5892394692637026 foothe foogreen!0.4084075626451522 foobreast foogreen!0.14951340563129634 foo.\u201d", + "S3: \u201c foogreen!0.23944019631017 foowhen foogreen!0.16698541003279388 fooi foogreen!0.3381385176908225 foowas foogreen!0.21315943740773946 fooreturning foogreen!0.3222442464902997 foomy foogreen!0.8483575657010078 foohome foogreen!0.10339960863348097 fooafter foogreen!0.2440519310766831 foofinishing foogreen!0.39699181797914207 foomy foogreen!1.2218113988637924 fooclass foogreen!0.1232976937899366 foo. foogreen!0.10928708070423454 fooi foogreen!0.2562549489084631 foowas foogreen!0.8099888218566775 fooin foogreen!2.9650430660694838 fooqueue foogreen!0.507337914314121 footo foogreen!0.727736041881144 fooget foogreen!0.7367140497080982 fooon foogreen!0.711284636054188 foothe foogreen!194.2763775587082 foomicro foogreen!786.8869304656982 foobus foogreen!0.4422159108798951 fooand foogreen!0.43104542419314384 foothere foogreen!0.4694198723882437 foowas foogreen!0.5085613229312003 fooa foogreen!0.4430979897733778 foogirl foogreen!0.36199347232468426 fooopposite foogreen!0.31067250529304147 footo foogreen!0.2927705936599523 foome foogreen!0.24646619567647576 foojust foogreen!0.23911069729365408 foothen foogreen!0.11775700113503262 fooa foogreen!0.002219072712250636 fooyoung foogreen!0.0019248132048232947 fooman foogreen!0.32698659924790263 footried foogreen!0.3118939639534801 footo foogreen!0.5727249081246555 footouch foogreen!0.5670131067745388 fooher foogreen!0.7104063988663256 fooon foogreen!0.6698771030642092 foothe foogreen!0.4756081907544285 foobreast foogreen!0.26600153069011867 foo.\u201d", + "In S1, the regular BiLSTM with attention model for classification on \u201cage of harasser\u201d put some attention on phrases other than the harasser, and hence aggregated noise. This could explain why the regular BiLSTM model got lower performance than the CNN model. However, when training with key element extractions, it put almost all attention on the harasser \u201cyoung man\u201d (S2), which helped the model make correct prediction of \u201cyoung harasser\u201d. When predicting the \u201ctype of location\u201d (S3), the joint learning model directed its attention to \u201cmicro bus\u201d.", + "CNN Based Models: Since CNN is efficient for capturing the most useful information BIBREF22, it is quite suitable for the classification tasks in this study. It achieved better performance than the BiLSTM model. The joint learning method boosted the performance even higher. This is because the classifications are related to the extracted key elements, and the word representation learned by the first layer of CNNs (Figure FIGREF6) is more informative than word embedding. By plotting of t-SNEs BIBREF23 of the two kinds of word vectors, we can see the word representations in the joint learning model made the words more separable (Figure 1 in supplementary file). In addition, no improvement was found with the J-CNN* model, which demonstrated the joint learning with extraction is essential for the improvement.", + "With supervised attentive pooling, the model can get additional knowledge from key element labels. It helped the model in cases when certain location phrases were mentioned but the incidents did not happen at those locations. For instance, \u201cI was followed on my way home .\u201d, max pooling will very likely to predict it as \u201cprivate places\u201d. But, it is actually unknown. In other cases, with supervised attentive pooling, the model can distinguish \u201cmetro\u201d and \u201cmetro station\u201d, which are \u201ctransportation\u201d and \u201cstop/station\u201d respectively. Therefore, the model further improved on classifications on \u201ctype of location\u201d with supervised attention in terms of macro F1. For some tasks, like \u201ctime of day\u201d, there are fewer cases with such disambiguation and hence max pooling worked well. Supervised attention improved macro F1 in location and harasser classifications, because it made more correct predictions in cases that mentioned location and harasser. But the majority did not mention them. Therefore, the accuracy of J-SACNN did not increase, compared with the other models.", + "Classification on Harassment Forms: In Table TABREF18, we also compared the performance of binary classifications on harassment forms with the results reported by Karlekar and Bansal karlekar2018safecity. Joint learning models achieved higher accuracy. In some harassment stories, the whole text or a span of the text consists of trigger words of multiple forms, such as \u201cstare, whistles, start to sing, commenting\u201d. The supervised attention mechanism will force the model to look at all such words rather than just the one related to the harassment form for classification and hence it can introduce noise. This can explain why J-SACNN got lower accuracy in two of the harassment form classifications, compared to J-ACNN. In addition, J-CNN model did best in \u201cogling\u201d classification." + ], + [ + "We plotted the distribution of harassment incidents in each categorization dimension (Figure FIGREF19). It displays statistics that provide important evidence as to the scale of harassment and that can serve as the basis for more effective interventions to be developed by authorities ranging from advocacy organizations to policy makers. It provides evidence to support some commonly assumed factors about harassment: First, we demonstrate that harassment occurred more frequently during the night time than the day time. Second, it shows that besides unspecified strangers (not shown in the figure), conductors and drivers are top the list of identified types of harassers, followed by friends and relatives.", + "Furthermore, we uncovered that there exist strong correlations between the age of perpetrators and the location of harassment, between the single/multiple harasser(s) and location, and between age and single/multiple harasser(s) (Figure FIGREF20). The significance of the correlation is tested by chi-square independence with p value less than 0.05. Identifying these patterns will enable interventions to be differentiated for and targeted at specific populations. For instance, the young harassers often engage in harassment activities as groups. This points to the influence of peer pressure and masculine behavioral norms for men and boys on these activities. We also found that the majority of young perpetrators engaged in harassment behaviors on the streets. These findings suggest that interventions with young men and boys, who are readily influenced by peers, might be most effective when education is done peer-to-peer. It also points to the locations where such efforts could be made, including both in schools and on the streets. In contrast, we found that adult perpetrators of sexual harassment are more likely to act alone. Most of the adult harassers engaged in harassment on public transportation. These differences in adult harassment activities and locations, mean that interventions should be responsive to these factors. For example, increasing the security measures on transit at key times and locations.", + "In addition, we also found that the correlations between the forms of harassment with the age, single/multiple harasser, type of harasser, and location (Figure FIGREF21). For example, young harassers are more likely to engage in behaviors of verbal harassment, rather than physical harassment as compared to adults. It was a single perpetrator that engaged in touching or groping more often, rather than groups of perpetrators. In contrast, commenting happened more frequently when harassers were in groups. Last but not least, public transportation is where people got indecently touched most frequently both by fellow passengers and by conductors and drivers. The nature and location of the harassment are particularly significant in developing strategies for those who are harassed or who witness the harassment to respond and manage the everyday threat of harassment. For example, some strategies will work best on public transport, a particular closed, shared space setting, while other strategies might be more effective on the open space of the street.", + "These results can provide valuable information for all members of the public. Sharing stories of harassment has been found by researchers to shift people\u2019s cognitive and emotional orientation towards their traumatic experiences BIBREF24. Greater awareness of patterns and scale of harassment experiences promises to ensure those who have been subjected to this violence that they are not alone, empowering others to report incidents, and ensuring them that efforts are being made to prevent others from experiencing the same harassment. These results also provide various authorities tools to identify potential harassment patterns and to make more effective interventions to prevent further harassment incidents. For instance, the authorities can increase targeted educational efforts at youth and adults, and be guided in utilizing limited resources the most effectively to offer more safety measures, including policing and community-based responses. For example, focusing efforts on highly populated public transportation during the nighttime, when harassment is found to be most likely to occur." + ], + [ + "We provided a large number of annotated personal stories of sexual harassment. Analyzing and identifying the social patterns of harassment behavior is essential to changing these patterns and social tolerance for them. We demonstrated the joint learning NLP models with strong performances to automatically extract key elements and categorize the stories. Potentiality, the approaches and models proposed in this study can be applied to sexual harassment stories from other sources, which can process and summarize the harassment stories and help those who have experienced harassment and authorities to work faster, such as by automatically filing reports BIBREF6. Furthermore, we discovered meaningful patterns in the situations where harassment commonly occurred. The volume of social media data is huge, and the more we can extract from these data, the more powerful we can be as part of the efforts to build a safer and more inclusive communities. Our work can increase the understanding of sexual harassment in society, ease the processing of such incidents by advocates and officials, and most importantly, raise awareness of this urgent problem." + ], + [ + "We thank the Safecity for granting the permission of using the data." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0756/instruction.md b/qasper-0756/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..e520576990b4a82dc54164e1a6c09960a2c3ae46 --- /dev/null +++ b/qasper-0756/instruction.md @@ -0,0 +1,85 @@ +Name of Paper: What we write about when we write about causality: Features of causal statements across large-scale social discourse + +Question: How do they extract causality from text? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Dataset, filtering, and corpus selection", + "Tagging and corpus comparison", + "Cause-trees", + "Sentiment analysis", + "Topic modeling", + "Results", + "Discussion", + "Acknowledgments" + ], + "paragraphs": [ + [ + "Social media and online social networks now provide vast amounts of data on human online discourse and other activities BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 . With so much communication taking place online and with social media being capable of hosting powerful misinformation campaigns BIBREF7 such as those claiming vaccines cause autism BIBREF8 , BIBREF9 , it is more important than ever to better understand the discourse of causality and the interplay between online communication and the statement of cause and effect.", + "Causal inference is a crucial way that humans comprehend the world, and it has been a major focus of philosophy, statistics, mathematics, psychology, and the cognitive sciences. Philosophers such as Hume and Kant have long argued whether causality is a human-centric illusion or the discovery of a priori truth BIBREF10 , BIBREF11 . Causal inference in science is incredibly important, and researchers have developed statistical measures such as Granger causality BIBREF12 , mathematical and probabilistic frameworks BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 , and text mining procedures BIBREF17 , BIBREF18 , BIBREF19 to better infer causal influence from data. In the cognitive sciences, the famous perception experiments of Michotte et al. led to a long line of research exploring the cognitive biases that humans possess when attempting to link cause and effect BIBREF20 , BIBREF21 , BIBREF22 .", + "How humans understand and communicate cause and effect relationships is complicated, and is influenced by language structure BIBREF23 , BIBREF24 , BIBREF25 , BIBREF26 and sentiment or valence BIBREF27 . A key finding is that the perceived emphasis or causal weight changes between the agent (the grammatical construct responsible for a cause) and the patient (the construct effected by the cause) depending on the types of verbs used to describe the cause and effect. Researchers have hypothesized BIBREF28 that this is because of the innate weighting property of the verbs in the English language that humans use to attribute causes and effects. Another finding is the role of a valence bias: the volume and intensity of causal reasoning may increase due to negative feedback or negative events BIBREF27 .", + "Despite these long lines of research, causal attributions made via social media or online social networks have not been well studied. The goal of this paper is to explore the language and topics of causal statements in a large corpus of social media taken from Twitter. We hypothesize that language and sentiment biases play a significant role in these statements, and that tools from natural language processing and computational linguistics can be used to study them. We do not attempt to study the factual correctness of these statements or offer any degree of verification, nor do we exhaustively identify and extract all causal statements from these data. Instead, here we focus on statements that are with high certainty causal statements, with the goal to better understand key characteristics about causal statements that differ from everyday online communication.", + "The rest of this paper is organized as follows: In Sec. \"Materials and Methods\" we discuss our materials and methods, including the dataset we studied, how we preprocessed that data and extracted a `causal' corpus and a corresponding `control' corpus, and the details of the statistical and language analysis tools we studied these corpora with. In Sec. \"Results\" we present results using these tools to compare the causal statements to control statements. We conclude with a discussion in Sec. \"Discussion\" ." + ], + [ + "Data was collected from a 10% uniform sample of Twitter posts made during 2013, specifically the Gardenhose API. Twitter activity consists of short posts called tweets which are limited to 140 characters. Retweets, where users repost a tweet to spread its content, were not considered. (The spread of causal statements will be considered in future work.) We considered only English-language tweets for this study. To avoid cross-language effects, we kept only tweets with a user-reported language of `English' and, as a second constraint, individual tweets needed to match more English stopwords than any other language's set of stopwords. Stopwords considered for each language were determined using NLTK's database BIBREF29 . A tweet will be referred to as a `document' for the rest of this work.", + "All document text was processed the same way. Punctuation, XML characters, and hyperlinks were removed, as were Twitter-specific \u201cat-mentions\u201d and \u201chashtags\u201d (see also the Appendix). There is useful information here, but it is either not natural language text, or it is Twitter-specific, or both. Documents were broken into individual words (unigrams) on whitespace. Casing information was retained, as we will use it for our Named Entity analysis, but otherwise all words were considered lowercase only (see also the Appendix). Stemming BIBREF30 and lemmatization BIBREF31 were not performed.", + "Causal documents were chosen to contain one occurrence only of the exact unigrams: `caused', `causing', or `causes'. The word `cause' was not included due to its use as a popular contraction for `because'. One `cause-word' per document restricted the analysis to single relationships between two relata. Documents that contain bidirectional words (`associate', `relate', `connect', `correlate', and any of their stems) were also not selected for analysis. This is because our focus is on causality, an inherently one-sided relationship between two objects. We also did not consider additional synonyms of these cause words, although that could be pursued for future work. Control documents were also selected. These documents did not contain any of `caused', `causing', or `causes', nor any bidirectional words, and are further matched temporally to obtain the same number of control documents as causal documents in each fifteen-minute period during 2013. Control documents were otherwise selected randomly; causal synonyms may be present. The end result of this procedure identified 965,560 causal and 965,560 control documents. Each of the three \u201ccause-words\u201d, `caused', `causes', and `causing' appeared in 38.2%, 35.0%, and 26.8% of causal documents, respectively." + ], + [ + "Documents were further studied by annotating their unigrams with Parts-of-Speech (POS) and Named Entities (NE) tags. POS tagging was done using NLTK v3.1 BIBREF29 which implements an averaged perceptron classifier BIBREF32 trained on the Brown Corpus BIBREF33 . (POS tagging is affected by punctuation; we show in the Appendix that our results are relatively robust to the removal of punctuation.) POS tags denote the nouns, verbs, and other grammatical constructs present in a document. Named Entity Recognition (NER) was performed using the 4-class, distributional similarity tagger provided as part of the Stanford CoreNLP v3.6.0 toolkit BIBREF34 . NER aims to identify and classify proper words in a text. The NE classifications considered were: Organization, Location, Person, and Misc. The Stanford NER tagger uses a conditional random field model BIBREF35 trained on diverse sets of manually-tagged English-language data (CoNLL-2003) BIBREF34 . Conditional random fields allow dependencies between words so that `New York' and `New York Times', for example, are classified separately as a location and organization, respectively. These taggers are commonly used and often provide reasonably accurate results, but there is always potential ambiguity in written text and improving upon these methods remains an active area of research.", + "Unigrams, POS, and NEs were compared between the cause and control corpora using odds ratios (ORs): ", + "$$\\operatorname{OR}(x) = \\frac{p_C(x)/ (1-p_C(x))}{p_N(x) / (1-p_N(x))},$$ (Eq. 1) ", + " where $p_C(x)$ and $p_N(x)$ are the probabilities that a unigram, POS, or NE $x$ occurs in the causal and control corpus, respectively. These probabilities were computed for each corpus separately as $p(x) = f(x) / \\sum _{x^{\\prime } \\in V} f(x^{\\prime })$ , where $f(x)$ is the total number of occurrences of $x$ in the corpus and $V$ is the relevant set of unigrams, POS, or NEs. Confidence intervals for the ORs were computed using Wald's methodology BIBREF36 .", + "As there are many unique unigrams in the text, when computing unigram ORs we focused on the most meaningful unigrams within each corpus by using the following filtering criteria: we considered only the $\\operatorname{OR}$ s of the 1500 most frequent unigrams in that corpus that also have a term-frequency-inverse-document-frequency (tf-idf) score above the 90th percentile for that corpus BIBREF37 . The tf-idf was computed as ", + "$$\\mbox{tf-idf}(w) = \\log f(w) \\times \\log \\left(D\u0311{\\mathit {df}(w)} \\right) ,$$ (Eq. 2) ", + "where $D$ is the total number of documents in the corpus, and $\\mathit {df}(w)$ is the number of documents in the corpus containing unigram $w$ . Intuitively, unigrams with higher tf-idf scores appear frequently, but are not so frequent that they are ubiquitous through all documents. Filtering via tf-idf is standard practice in the information retrieval and data mining fields." + ], + [ + "For a better understanding of the higher-order language structure present in text phrases, cause-trees were constructed. A cause-tree starts with a root cause word (either `caused', `causing' or `causes'), then the two most probable words following (preceding) the root are identified. Next, the root word plus one of the top probable words is combined into a bigram and the top two most probable words following (preceding) this bigram are found. Repeatedly applying this process builds a binary tree representing the $n$ -grams that begin with (terminate at) the root word. This process can continue until a certain $n$ -gram length is reached or until there are no more documents long enough to search." + ], + [ + "Sentimental analysis was applied to estimate the emotional content of documents. Two levels of analysis were used: a method where individual unigrams were given crowdsourced numeric sentiment scores, and a second method involving a trained classifier that can incorporate document-level phrase information.", + "For the first sentiment analysis, each unigram $w$ was assigned a crowdsourced \u201clabMT\u201d sentiment score $s(w)$ BIBREF5 . (Unlike BIBREF5 , scores were recentered by subtracting the mean, $s(w) \\leftarrow s(w)-\\left$ .) Unigrams determined by volunteer raters to have a negative emotional sentiment (`hate',`death', etc.) have $s(w) < 0$ , while unigrams determined to have a positive emotional sentiment (`love', `happy', etc.) tend to have $s(w) > 0$ . Unigrams that have labMT scores and are above the 90th percentile of tf-idf for the corpus form the set $\\tilde{V}$ . (Unigrams in $\\tilde{V}$ need not be among the 1500 most frequent unigrams.) The set $\\tilde{V}$ captures 87.9% (91.5%) of total unigrams in the causal (control) corpus. Crucially, the tf-idf filtering ensures that the words `caused', `causes', and `causing', which have a slight negative sentiment, are not included and do not introduce a systematic bias when comparing the two corpora.", + "This sentiment measure works on a per-unigram basis, and is therefore best suited for large bodies of text, not short documents BIBREF5 . Instead of considering individual documents, the distributions of labMT scores over all unigrams for each corpus was used to compare the corpora. In addition, a single sentiment score for each corpus was computed as the average sentiment score over all unigrams in that corpus, weighed by unigram frequency: $\\sum _{w \\in \\tilde{V}} {f(w) s(w)} \\Big / \\sum _{w^{\\prime } \\in \\tilde{V}} f(w^{\\prime })$ .", + "To supplement this sentiment analysis method, we applied a second method capable of estimating with reasonable accuracy the sentiment of individual documents. We used the sentiment classifier BIBREF38 included in the Stanford CoreNLP v3.6.0 toolkit to documents in each corpus. Documents were individually classified into one of five categories: very negative, negative, neutral, positive, very positive. The data used to train this classifier is taken from positive and negative reviews of movies (Stanford Sentiment Treebank v1.0) BIBREF38 ." + ], + [ + "Lastly, we applied topic modeling to the causal corpus to determine what are the topical foci most discussed in causal statements. Topics were built from the causal corpus using Latent Dirichlet Allocation (LDA) BIBREF39 . Under LDA each document is modeled as a bag-of-words or unordered collection of unigrams. Topics are considered as mixtures of unigrams by estimating conditional distributions over unigrams: $P(w|T)$ , the probability of unigram $w$ given topic $T$ and documents are considered as mixtures of topics via $P(T|d)$ , the probability of topic $T$ given document $d$ . These distributions are then found via statistical inference given the observed distributions of unigrams across documents. The total number of topics is a parameter chosen by the practitioner. For this study we used the MALLET v2.0.8RC3 topic modeling toolkit BIBREF40 for model inference. By inspecting the most probable unigrams per topic (according to $P(w|T)$ ), we found 10 topics provided meaningful and distinct topics." + ], + [ + "We have collected approximately 1M causal statements made on Twitter over the course of 2013, and for a control we gathered the same number of statements selected at random but controlling for time of year (see Methods). We applied Parts-of-Speech (POS) and Named Entity (NE) taggers to all these texts. Some post-processed and tagged example documents, both causal and control, are shown in Fig. 1 A. We also applied sentiment analysis methods to these documents (Methods) and we have highlighted very positive and very negative words throughout Fig. 1 .", + "In Fig. 1 B we present odds ratios for how frequently unigrams (words), POS, or NE appear in causal documents relative to control documents. The three unigrams most strongly skewed towards causal documents were `stress', `problems', and `trouble', while the three most skewed towards control documents were `photo', `ready', and `cute'. While these are only a small number of the unigrams present, this does imply a negative sentiment bias among causal statements (we return to this point shortly).", + "Figure 1 B also presents odds ratios for POS tags, to help us measure the differences in grammatical structure between causal and control documents (see also the Appendix for the effects of punctuation and casing on these odds ratios). The causal corpus showed greater odds for plural nouns (Penn Treebank tag: NNS), plural proper nouns (NNPS), Wh-determiners/pronouns (WDT, WP$) such as `whichever',`whatever', `whose', or `whosever', and predeterminers (PDT) such as `all' or `both'. Predeterminers quantify noun phrases such as `all' in `after all the events that caused you tears', showing that many causal statements, despite the potential brevity of social media, can encompass or delineate classes of agents and/or patients. On the other hand, the causal corpus has lower odds than the control corpus for list items (LS), proper singular nouns (NNP), and interjections (UH).", + "Lastly, Fig. 1 B contains odds ratios for NE tags, allowing us to quantify the types of proper nouns that are more or less likely to appear in causal statements. Of the four tags, only the \u201cPerson\u201d tag is less likely in the causal corpus than the control. (This matches the odds ratio for the proper singular noun discussed above.) Perhaps surprisingly, these results together imply that causal statements are less likely to involve individual persons than non-causal statements. There is considerable celebrity news and gossip on social media BIBREF4 ; discussions of celebrities may not be especially focused on attributing causes to these celebrities. All other NE tags, Organization, Location, and Miscellaneous, occur more frequently in the causal corpus than the control. All the odds ratios in Fig. 1 B were significant at the $\\alpha = 0.05$ level except the List item marker (LS) POS tag.", + "The unigram analysis in Fig. 1 does not incorporate higher-order phrase structure present in written language. To explore these structures specifically in the causal corpus, we constructed \u201ccause-trees\u201d, shown in Fig. 2 . Inspired by association mining BIBREF41 , a cause-tree is a binary tree rooted at either `caused', `causes', or `causing', that illustrates the most frequently occurring $n$ -grams that either begin or end with that root cause word (see Methods for details).", + "The \u201ccauses\u201d tree shows the focused writing (sentence segments) that many people use to express either the relationship between their own actions and a cause-and-effect (\u201ceven if it causes\u201d), or the uncontrollable effect a cause may have on themselves: \u201ccauses me to have\u201d shows a person's inability to control a causal event (\u201c[...] i have central heterochromia which causes me to have dual colors in both eyes\u201d). The `causing' tree reveals our ability to confine causal patterns to specific areas, and also our ability to be affected by others causal decisions. Phrases like \u201ccausing a scene in/at\u201d and \u201ccausing a ruckus in/at\u201d (from documents like \u201ccausing a ruckus in the hotel lobby typical [...]\u201d) show people commonly associate bounds on where causal actions take place. The causing tree also shows people's tendency to emphasize current negativity: Phrases like \u201cpain this is causing\u201d coming from documents like \u201ccant you see the pain you are causing her\u201d supports the sentiment bias that causal attribution is more likely for negative cause-effect associations. Finally, the `caused' tree focuses heavily on negative events and indicates people are more likely to remember negative causal events. Documents with phrases from the caused tree (\u201c[...] appalling tragedy [...] that caused the death\u201d, \u201c[...] live with this pain that you caused when i was so young [...]\u201d) exemplify the negative events that are focused on are large-scale tragedies or very personal negative events in one's life.", + "Taken together, the popularity of negative sentiment unigrams (Fig. 1 ) and $n$ -grams (Fig. 2 ) among causal documents shows that emotional sentiment or \u201cvalence\u201d may play a role in how people perform causal attribution BIBREF27 . The \u201cif it bleeds, it leads\u201d mentality among news media, where violent and negative news are more heavily reported, may appeal to this innate causal association mechanism. (On the other hand, many news media themselves use social media for reporting.) The prevalence of negative sentiment also contrasts with the \u201cbetter angels of our nature\u201d evidence of Pinker BIBREF42 , illustrating one bias that shows why many find the results of Ref. BIBREF42 surprising.", + "Given this apparent sentiment skew, we further studied sentiment (Fig. 3 ). We compared the sentiment between the corpora in four different ways to investigate the observation (Figs. 1 B and 2 ) that people focus more about negative concepts when they discuss causality. First, we computed the mean sentiment score of each corpus using crowdsourced \u201clabMT\u201d scores weighted by unigram frequency (see Methods). We also applied tf-idf filtering (Methods) to exclude very common words, including the three cause-words, from the mean sentiment score. The causal corpus text was slightly negative on average while the control corpus was slightly positive (Fig. 3 A). The difference in mean sentiment score was significant (t-test: $p < 0.01$ ).", + "Second, we moved from the mean score to the distribution of sentiment across all (scored) unigrams in the causal and control corpora (Fig. 3 B). The causal corpus contained a large group of negative sentiment unigrams, with labMT scores in the approximate range $-3 < s < -1/2$ ; the control corpus had significantly fewer unigrams in this score range.", + "Third, in Fig. 3 C we used POS tags to categorize scored unigrams into nouns, verbs, and adjectives. Studying the distributions for each, we found that nouns explain much of the overall difference observed in Fig. 3 B, with verbs showing a similar but smaller difference between the two corpora. Adjectives showed little difference. The distributions in Fig. 3 C account for 87.8% of scored text in the causal corpus and 77.2% of the control corpus. The difference in sentiment between corpora was significant for all distributions (t-test: $p < 0.01$ ).", + "Fourth, to further confirm that the causal documents tend toward negative sentiment, we applied a separate, independent sentiment analysis using the Stanford NLP sentiment toolkit BIBREF38 to classify the sentiment of individual documents not unigrams (see Methods). Instead of a numeric sentiment score, this classifier assigns documents to one of five categories ranging from very negative to very positive. The classifier showed that the causal corpus contains more negative and very negative documents than the control corpus, while the control corpus contains more neutral, positive, and very positive documents (Fig. 3 D).", + "We have found language (Figs. 1 and 2 ) and sentiment (Fig. 3 ) differences between causal statements made on social media compared with other social media statements. But what is being discussed? What are the topical foci of causal statements? To study this, for our last analysis we applied topic models to the causal statements. Topic modeling finds groups of related terms (unigrams) by considering similarities between how those terms co-occur across a set of documents.", + "We used the popular topic modeling method Latent Dirichlet Allocation (LDA) BIBREF39 . We ranked unigrams by how strongly associated they were with the topic. Inspecting these unigrams we found that a 10-topic model discovered meaningful topics. See Methods for full details. The top unigrams for each topic are shown in Tab. 1 .", + "Topics in the causal corpus tend to fall into three main categories: (i) news, covering current events, weather, etc.; (ii) medicine and health, covering cancer, obesity, stress, etc.; and (iii) relationships, covering problems, stress, crisis, drama, sorry, etc.", + "While the topics are quite different, they are all similar in their use of negative sentiment words. The negative/global features in the `news' topic are captured in the most representative words: damage, fire, power, etc. Similar to news, the `accident' topic balances the more frequent day-to-day minor frustrations with the less frequent but more severe impacts of car accidents. The words `traffic' and `delays' are the most probable words for this topic, and are common, low-impact occurrences. On the contrary, `crash', `car', `accident' and `death' are the next most probable words for the accident topic, and generally show a focus on less-common but higher-impact events.", + "The `medical' topic also focused on negative words; highly probable words for this topic included `cancer', `break', `disease', `blood', etc. Meanwhile, the `body' topic contained words like: `stress', `lose', and `weight', giving a focus on on our more personal struggles with body image. Besides body image, the `injuries' topic uses specific pronouns (`his', `him', `her') in references to a person's own injuries or the injuries of others such as athletes.", + "Aside from more factual information, social information is well represented in causal statements. The `problems' topic shows people attribute their problems to many others with terms like: `dont', `people', `they', `them'. The `stress' topic also uses general words such as `more', `than', or `people' to link stress to all people, and in the same vein, the `crisis' topic focuses on problems within organizations such as governments. The `drama' and `sorry' topics tend towards more specific causal statements. Drama used the words: `like', `she', and `her' while documents in the sorry topic tended to address other people.", + "The topics of causal documents discovered by LDA showed that both general and specific statements are made regarding news, medicine, and relationships when individuals make causal attributions online." + ], + [ + "The power of online communication is the speed and ease with which information can be propagated by potentially any connected users. Yet these strengths come at a cost: rumors and misinformation also spread easily. Causal misattribution is at the heart of many rumors, conspiracy theories, and misinformation campaigns.", + "Given the central role of causal statements, further studies of the interplay of information propagation and online causal attributions are crucial. Are causal statements more likely to spread online and, if so, in which ways? What types of social media users are more or less likely to make causal statements? Will a user be more likely to make a causal statement if they have recently been exposed to one or more causal statements from other users?", + "The topics of causal statements also bring forth important questions to be addressed: how timely are causal statements? Are certain topics always being discussed in causal statements? Are there causal topics that are very popular for only brief periods and then forgotten? Temporal dynamics of causal statements are also interesting: do time-of-day or time-of-year factors play a role in how causal statements are made? Our work here focused on a limited subset of causal statements, but more generally, these results may inform new methods for automatically detecting causal statements from unstructured, natural language text BIBREF17 . Better computational tools focused on causal statements are an important step towards further understanding misinformation campaigns and other online activities. Lastly, an important but deeply challenging open question is how, if it is even possible, to validate the accuracy of causal statements. Can causal statements be ranked by some confidence metric(s)? We hope to pursue these and other questions in future research.", + "Parts-of-speech tagging depends on punctuation and casing, which we filtered in our data, so a study of how robust the POS algorithm is to punctuation and casing removal is important. We computed POS tags for the corpora with and without casing as well as with and without punctuation (which includes hashtags, links and at-symbols). Two tags mentioned in Fig. 1 B, NNPS and LS (which was not significant), were affected by punctuation removal. Otherwise, there is a strong correlation (Fig. 4 ) between Odds Ratios (causal vs. control) with punctuation and without punctuation, including casing and without casing ( $\\rho = 0.71$ and $0.80$ , respectively), indicating the POS differences between the corpora were primarily not due to the removal of punctuation or casing." + ], + [ + "We thank R. Gallagher for useful comments and gratefully acknowledge the resources provided by the Vermont Advanced Computing Core. This material is based upon work supported by the National Science Foundation under Grant No. ISS-1447634." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0757/environment/Dockerfile b/qasper-0757/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0757/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/* diff --git a/qasper-0757/instruction.md b/qasper-0757/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..1a23edfedf3c0513e5ced72b8122a321d72089db --- /dev/null +++ b/qasper-0757/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: What we write about when we write about causality: Features of causal statements across large-scale social discourse + +Question: What is the source of the "control" corpus? \ No newline at end of file diff --git a/qasper-0758/instruction.md b/qasper-0758/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..93e7fdadb1f35c2eacf690578b03a4bf53a76a6e --- /dev/null +++ b/qasper-0758/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: What we write about when we write about causality: Features of causal statements across large-scale social discourse + +Question: What are the selection criteria for "causal statements"? \ No newline at end of file diff --git a/qasper-0759/environment/Dockerfile b/qasper-0759/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0759/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/* diff --git a/qasper-0759/instruction.md b/qasper-0759/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..64402ab5ead9c99774c22e5b0ca11ded0bf2ff9a --- /dev/null +++ b/qasper-0759/instruction.md @@ -0,0 +1,85 @@ +Name of Paper: What we write about when we write about causality: Features of causal statements across large-scale social discourse + +Question: Do they use expert annotations, crowdsourcing, or only automatic methods to analyze the corpora? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Dataset, filtering, and corpus selection", + "Tagging and corpus comparison", + "Cause-trees", + "Sentiment analysis", + "Topic modeling", + "Results", + "Discussion", + "Acknowledgments" + ], + "paragraphs": [ + [ + "Social media and online social networks now provide vast amounts of data on human online discourse and other activities BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 . With so much communication taking place online and with social media being capable of hosting powerful misinformation campaigns BIBREF7 such as those claiming vaccines cause autism BIBREF8 , BIBREF9 , it is more important than ever to better understand the discourse of causality and the interplay between online communication and the statement of cause and effect.", + "Causal inference is a crucial way that humans comprehend the world, and it has been a major focus of philosophy, statistics, mathematics, psychology, and the cognitive sciences. Philosophers such as Hume and Kant have long argued whether causality is a human-centric illusion or the discovery of a priori truth BIBREF10 , BIBREF11 . Causal inference in science is incredibly important, and researchers have developed statistical measures such as Granger causality BIBREF12 , mathematical and probabilistic frameworks BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 , and text mining procedures BIBREF17 , BIBREF18 , BIBREF19 to better infer causal influence from data. In the cognitive sciences, the famous perception experiments of Michotte et al. led to a long line of research exploring the cognitive biases that humans possess when attempting to link cause and effect BIBREF20 , BIBREF21 , BIBREF22 .", + "How humans understand and communicate cause and effect relationships is complicated, and is influenced by language structure BIBREF23 , BIBREF24 , BIBREF25 , BIBREF26 and sentiment or valence BIBREF27 . A key finding is that the perceived emphasis or causal weight changes between the agent (the grammatical construct responsible for a cause) and the patient (the construct effected by the cause) depending on the types of verbs used to describe the cause and effect. Researchers have hypothesized BIBREF28 that this is because of the innate weighting property of the verbs in the English language that humans use to attribute causes and effects. Another finding is the role of a valence bias: the volume and intensity of causal reasoning may increase due to negative feedback or negative events BIBREF27 .", + "Despite these long lines of research, causal attributions made via social media or online social networks have not been well studied. The goal of this paper is to explore the language and topics of causal statements in a large corpus of social media taken from Twitter. We hypothesize that language and sentiment biases play a significant role in these statements, and that tools from natural language processing and computational linguistics can be used to study them. We do not attempt to study the factual correctness of these statements or offer any degree of verification, nor do we exhaustively identify and extract all causal statements from these data. Instead, here we focus on statements that are with high certainty causal statements, with the goal to better understand key characteristics about causal statements that differ from everyday online communication.", + "The rest of this paper is organized as follows: In Sec. \"Materials and Methods\" we discuss our materials and methods, including the dataset we studied, how we preprocessed that data and extracted a `causal' corpus and a corresponding `control' corpus, and the details of the statistical and language analysis tools we studied these corpora with. In Sec. \"Results\" we present results using these tools to compare the causal statements to control statements. We conclude with a discussion in Sec. \"Discussion\" ." + ], + [ + "Data was collected from a 10% uniform sample of Twitter posts made during 2013, specifically the Gardenhose API. Twitter activity consists of short posts called tweets which are limited to 140 characters. Retweets, where users repost a tweet to spread its content, were not considered. (The spread of causal statements will be considered in future work.) We considered only English-language tweets for this study. To avoid cross-language effects, we kept only tweets with a user-reported language of `English' and, as a second constraint, individual tweets needed to match more English stopwords than any other language's set of stopwords. Stopwords considered for each language were determined using NLTK's database BIBREF29 . A tweet will be referred to as a `document' for the rest of this work.", + "All document text was processed the same way. Punctuation, XML characters, and hyperlinks were removed, as were Twitter-specific \u201cat-mentions\u201d and \u201chashtags\u201d (see also the Appendix). There is useful information here, but it is either not natural language text, or it is Twitter-specific, or both. Documents were broken into individual words (unigrams) on whitespace. Casing information was retained, as we will use it for our Named Entity analysis, but otherwise all words were considered lowercase only (see also the Appendix). Stemming BIBREF30 and lemmatization BIBREF31 were not performed.", + "Causal documents were chosen to contain one occurrence only of the exact unigrams: `caused', `causing', or `causes'. The word `cause' was not included due to its use as a popular contraction for `because'. One `cause-word' per document restricted the analysis to single relationships between two relata. Documents that contain bidirectional words (`associate', `relate', `connect', `correlate', and any of their stems) were also not selected for analysis. This is because our focus is on causality, an inherently one-sided relationship between two objects. We also did not consider additional synonyms of these cause words, although that could be pursued for future work. Control documents were also selected. These documents did not contain any of `caused', `causing', or `causes', nor any bidirectional words, and are further matched temporally to obtain the same number of control documents as causal documents in each fifteen-minute period during 2013. Control documents were otherwise selected randomly; causal synonyms may be present. The end result of this procedure identified 965,560 causal and 965,560 control documents. Each of the three \u201ccause-words\u201d, `caused', `causes', and `causing' appeared in 38.2%, 35.0%, and 26.8% of causal documents, respectively." + ], + [ + "Documents were further studied by annotating their unigrams with Parts-of-Speech (POS) and Named Entities (NE) tags. POS tagging was done using NLTK v3.1 BIBREF29 which implements an averaged perceptron classifier BIBREF32 trained on the Brown Corpus BIBREF33 . (POS tagging is affected by punctuation; we show in the Appendix that our results are relatively robust to the removal of punctuation.) POS tags denote the nouns, verbs, and other grammatical constructs present in a document. Named Entity Recognition (NER) was performed using the 4-class, distributional similarity tagger provided as part of the Stanford CoreNLP v3.6.0 toolkit BIBREF34 . NER aims to identify and classify proper words in a text. The NE classifications considered were: Organization, Location, Person, and Misc. The Stanford NER tagger uses a conditional random field model BIBREF35 trained on diverse sets of manually-tagged English-language data (CoNLL-2003) BIBREF34 . Conditional random fields allow dependencies between words so that `New York' and `New York Times', for example, are classified separately as a location and organization, respectively. These taggers are commonly used and often provide reasonably accurate results, but there is always potential ambiguity in written text and improving upon these methods remains an active area of research.", + "Unigrams, POS, and NEs were compared between the cause and control corpora using odds ratios (ORs): ", + "$$\\operatorname{OR}(x) = \\frac{p_C(x)/ (1-p_C(x))}{p_N(x) / (1-p_N(x))},$$ (Eq. 1) ", + " where $p_C(x)$ and $p_N(x)$ are the probabilities that a unigram, POS, or NE $x$ occurs in the causal and control corpus, respectively. These probabilities were computed for each corpus separately as $p(x) = f(x) / \\sum _{x^{\\prime } \\in V} f(x^{\\prime })$ , where $f(x)$ is the total number of occurrences of $x$ in the corpus and $V$ is the relevant set of unigrams, POS, or NEs. Confidence intervals for the ORs were computed using Wald's methodology BIBREF36 .", + "As there are many unique unigrams in the text, when computing unigram ORs we focused on the most meaningful unigrams within each corpus by using the following filtering criteria: we considered only the $\\operatorname{OR}$ s of the 1500 most frequent unigrams in that corpus that also have a term-frequency-inverse-document-frequency (tf-idf) score above the 90th percentile for that corpus BIBREF37 . The tf-idf was computed as ", + "$$\\mbox{tf-idf}(w) = \\log f(w) \\times \\log \\left(D\u0311{\\mathit {df}(w)} \\right) ,$$ (Eq. 2) ", + "where $D$ is the total number of documents in the corpus, and $\\mathit {df}(w)$ is the number of documents in the corpus containing unigram $w$ . Intuitively, unigrams with higher tf-idf scores appear frequently, but are not so frequent that they are ubiquitous through all documents. Filtering via tf-idf is standard practice in the information retrieval and data mining fields." + ], + [ + "For a better understanding of the higher-order language structure present in text phrases, cause-trees were constructed. A cause-tree starts with a root cause word (either `caused', `causing' or `causes'), then the two most probable words following (preceding) the root are identified. Next, the root word plus one of the top probable words is combined into a bigram and the top two most probable words following (preceding) this bigram are found. Repeatedly applying this process builds a binary tree representing the $n$ -grams that begin with (terminate at) the root word. This process can continue until a certain $n$ -gram length is reached or until there are no more documents long enough to search." + ], + [ + "Sentimental analysis was applied to estimate the emotional content of documents. Two levels of analysis were used: a method where individual unigrams were given crowdsourced numeric sentiment scores, and a second method involving a trained classifier that can incorporate document-level phrase information.", + "For the first sentiment analysis, each unigram $w$ was assigned a crowdsourced \u201clabMT\u201d sentiment score $s(w)$ BIBREF5 . (Unlike BIBREF5 , scores were recentered by subtracting the mean, $s(w) \\leftarrow s(w)-\\left$ .) Unigrams determined by volunteer raters to have a negative emotional sentiment (`hate',`death', etc.) have $s(w) < 0$ , while unigrams determined to have a positive emotional sentiment (`love', `happy', etc.) tend to have $s(w) > 0$ . Unigrams that have labMT scores and are above the 90th percentile of tf-idf for the corpus form the set $\\tilde{V}$ . (Unigrams in $\\tilde{V}$ need not be among the 1500 most frequent unigrams.) The set $\\tilde{V}$ captures 87.9% (91.5%) of total unigrams in the causal (control) corpus. Crucially, the tf-idf filtering ensures that the words `caused', `causes', and `causing', which have a slight negative sentiment, are not included and do not introduce a systematic bias when comparing the two corpora.", + "This sentiment measure works on a per-unigram basis, and is therefore best suited for large bodies of text, not short documents BIBREF5 . Instead of considering individual documents, the distributions of labMT scores over all unigrams for each corpus was used to compare the corpora. In addition, a single sentiment score for each corpus was computed as the average sentiment score over all unigrams in that corpus, weighed by unigram frequency: $\\sum _{w \\in \\tilde{V}} {f(w) s(w)} \\Big / \\sum _{w^{\\prime } \\in \\tilde{V}} f(w^{\\prime })$ .", + "To supplement this sentiment analysis method, we applied a second method capable of estimating with reasonable accuracy the sentiment of individual documents. We used the sentiment classifier BIBREF38 included in the Stanford CoreNLP v3.6.0 toolkit to documents in each corpus. Documents were individually classified into one of five categories: very negative, negative, neutral, positive, very positive. The data used to train this classifier is taken from positive and negative reviews of movies (Stanford Sentiment Treebank v1.0) BIBREF38 ." + ], + [ + "Lastly, we applied topic modeling to the causal corpus to determine what are the topical foci most discussed in causal statements. Topics were built from the causal corpus using Latent Dirichlet Allocation (LDA) BIBREF39 . Under LDA each document is modeled as a bag-of-words or unordered collection of unigrams. Topics are considered as mixtures of unigrams by estimating conditional distributions over unigrams: $P(w|T)$ , the probability of unigram $w$ given topic $T$ and documents are considered as mixtures of topics via $P(T|d)$ , the probability of topic $T$ given document $d$ . These distributions are then found via statistical inference given the observed distributions of unigrams across documents. The total number of topics is a parameter chosen by the practitioner. For this study we used the MALLET v2.0.8RC3 topic modeling toolkit BIBREF40 for model inference. By inspecting the most probable unigrams per topic (according to $P(w|T)$ ), we found 10 topics provided meaningful and distinct topics." + ], + [ + "We have collected approximately 1M causal statements made on Twitter over the course of 2013, and for a control we gathered the same number of statements selected at random but controlling for time of year (see Methods). We applied Parts-of-Speech (POS) and Named Entity (NE) taggers to all these texts. Some post-processed and tagged example documents, both causal and control, are shown in Fig. 1 A. We also applied sentiment analysis methods to these documents (Methods) and we have highlighted very positive and very negative words throughout Fig. 1 .", + "In Fig. 1 B we present odds ratios for how frequently unigrams (words), POS, or NE appear in causal documents relative to control documents. The three unigrams most strongly skewed towards causal documents were `stress', `problems', and `trouble', while the three most skewed towards control documents were `photo', `ready', and `cute'. While these are only a small number of the unigrams present, this does imply a negative sentiment bias among causal statements (we return to this point shortly).", + "Figure 1 B also presents odds ratios for POS tags, to help us measure the differences in grammatical structure between causal and control documents (see also the Appendix for the effects of punctuation and casing on these odds ratios). The causal corpus showed greater odds for plural nouns (Penn Treebank tag: NNS), plural proper nouns (NNPS), Wh-determiners/pronouns (WDT, WP$) such as `whichever',`whatever', `whose', or `whosever', and predeterminers (PDT) such as `all' or `both'. Predeterminers quantify noun phrases such as `all' in `after all the events that caused you tears', showing that many causal statements, despite the potential brevity of social media, can encompass or delineate classes of agents and/or patients. On the other hand, the causal corpus has lower odds than the control corpus for list items (LS), proper singular nouns (NNP), and interjections (UH).", + "Lastly, Fig. 1 B contains odds ratios for NE tags, allowing us to quantify the types of proper nouns that are more or less likely to appear in causal statements. Of the four tags, only the \u201cPerson\u201d tag is less likely in the causal corpus than the control. (This matches the odds ratio for the proper singular noun discussed above.) Perhaps surprisingly, these results together imply that causal statements are less likely to involve individual persons than non-causal statements. There is considerable celebrity news and gossip on social media BIBREF4 ; discussions of celebrities may not be especially focused on attributing causes to these celebrities. All other NE tags, Organization, Location, and Miscellaneous, occur more frequently in the causal corpus than the control. All the odds ratios in Fig. 1 B were significant at the $\\alpha = 0.05$ level except the List item marker (LS) POS tag.", + "The unigram analysis in Fig. 1 does not incorporate higher-order phrase structure present in written language. To explore these structures specifically in the causal corpus, we constructed \u201ccause-trees\u201d, shown in Fig. 2 . Inspired by association mining BIBREF41 , a cause-tree is a binary tree rooted at either `caused', `causes', or `causing', that illustrates the most frequently occurring $n$ -grams that either begin or end with that root cause word (see Methods for details).", + "The \u201ccauses\u201d tree shows the focused writing (sentence segments) that many people use to express either the relationship between their own actions and a cause-and-effect (\u201ceven if it causes\u201d), or the uncontrollable effect a cause may have on themselves: \u201ccauses me to have\u201d shows a person's inability to control a causal event (\u201c[...] i have central heterochromia which causes me to have dual colors in both eyes\u201d). The `causing' tree reveals our ability to confine causal patterns to specific areas, and also our ability to be affected by others causal decisions. Phrases like \u201ccausing a scene in/at\u201d and \u201ccausing a ruckus in/at\u201d (from documents like \u201ccausing a ruckus in the hotel lobby typical [...]\u201d) show people commonly associate bounds on where causal actions take place. The causing tree also shows people's tendency to emphasize current negativity: Phrases like \u201cpain this is causing\u201d coming from documents like \u201ccant you see the pain you are causing her\u201d supports the sentiment bias that causal attribution is more likely for negative cause-effect associations. Finally, the `caused' tree focuses heavily on negative events and indicates people are more likely to remember negative causal events. Documents with phrases from the caused tree (\u201c[...] appalling tragedy [...] that caused the death\u201d, \u201c[...] live with this pain that you caused when i was so young [...]\u201d) exemplify the negative events that are focused on are large-scale tragedies or very personal negative events in one's life.", + "Taken together, the popularity of negative sentiment unigrams (Fig. 1 ) and $n$ -grams (Fig. 2 ) among causal documents shows that emotional sentiment or \u201cvalence\u201d may play a role in how people perform causal attribution BIBREF27 . The \u201cif it bleeds, it leads\u201d mentality among news media, where violent and negative news are more heavily reported, may appeal to this innate causal association mechanism. (On the other hand, many news media themselves use social media for reporting.) The prevalence of negative sentiment also contrasts with the \u201cbetter angels of our nature\u201d evidence of Pinker BIBREF42 , illustrating one bias that shows why many find the results of Ref. BIBREF42 surprising.", + "Given this apparent sentiment skew, we further studied sentiment (Fig. 3 ). We compared the sentiment between the corpora in four different ways to investigate the observation (Figs. 1 B and 2 ) that people focus more about negative concepts when they discuss causality. First, we computed the mean sentiment score of each corpus using crowdsourced \u201clabMT\u201d scores weighted by unigram frequency (see Methods). We also applied tf-idf filtering (Methods) to exclude very common words, including the three cause-words, from the mean sentiment score. The causal corpus text was slightly negative on average while the control corpus was slightly positive (Fig. 3 A). The difference in mean sentiment score was significant (t-test: $p < 0.01$ ).", + "Second, we moved from the mean score to the distribution of sentiment across all (scored) unigrams in the causal and control corpora (Fig. 3 B). The causal corpus contained a large group of negative sentiment unigrams, with labMT scores in the approximate range $-3 < s < -1/2$ ; the control corpus had significantly fewer unigrams in this score range.", + "Third, in Fig. 3 C we used POS tags to categorize scored unigrams into nouns, verbs, and adjectives. Studying the distributions for each, we found that nouns explain much of the overall difference observed in Fig. 3 B, with verbs showing a similar but smaller difference between the two corpora. Adjectives showed little difference. The distributions in Fig. 3 C account for 87.8% of scored text in the causal corpus and 77.2% of the control corpus. The difference in sentiment between corpora was significant for all distributions (t-test: $p < 0.01$ ).", + "Fourth, to further confirm that the causal documents tend toward negative sentiment, we applied a separate, independent sentiment analysis using the Stanford NLP sentiment toolkit BIBREF38 to classify the sentiment of individual documents not unigrams (see Methods). Instead of a numeric sentiment score, this classifier assigns documents to one of five categories ranging from very negative to very positive. The classifier showed that the causal corpus contains more negative and very negative documents than the control corpus, while the control corpus contains more neutral, positive, and very positive documents (Fig. 3 D).", + "We have found language (Figs. 1 and 2 ) and sentiment (Fig. 3 ) differences between causal statements made on social media compared with other social media statements. But what is being discussed? What are the topical foci of causal statements? To study this, for our last analysis we applied topic models to the causal statements. Topic modeling finds groups of related terms (unigrams) by considering similarities between how those terms co-occur across a set of documents.", + "We used the popular topic modeling method Latent Dirichlet Allocation (LDA) BIBREF39 . We ranked unigrams by how strongly associated they were with the topic. Inspecting these unigrams we found that a 10-topic model discovered meaningful topics. See Methods for full details. The top unigrams for each topic are shown in Tab. 1 .", + "Topics in the causal corpus tend to fall into three main categories: (i) news, covering current events, weather, etc.; (ii) medicine and health, covering cancer, obesity, stress, etc.; and (iii) relationships, covering problems, stress, crisis, drama, sorry, etc.", + "While the topics are quite different, they are all similar in their use of negative sentiment words. The negative/global features in the `news' topic are captured in the most representative words: damage, fire, power, etc. Similar to news, the `accident' topic balances the more frequent day-to-day minor frustrations with the less frequent but more severe impacts of car accidents. The words `traffic' and `delays' are the most probable words for this topic, and are common, low-impact occurrences. On the contrary, `crash', `car', `accident' and `death' are the next most probable words for the accident topic, and generally show a focus on less-common but higher-impact events.", + "The `medical' topic also focused on negative words; highly probable words for this topic included `cancer', `break', `disease', `blood', etc. Meanwhile, the `body' topic contained words like: `stress', `lose', and `weight', giving a focus on on our more personal struggles with body image. Besides body image, the `injuries' topic uses specific pronouns (`his', `him', `her') in references to a person's own injuries or the injuries of others such as athletes.", + "Aside from more factual information, social information is well represented in causal statements. The `problems' topic shows people attribute their problems to many others with terms like: `dont', `people', `they', `them'. The `stress' topic also uses general words such as `more', `than', or `people' to link stress to all people, and in the same vein, the `crisis' topic focuses on problems within organizations such as governments. The `drama' and `sorry' topics tend towards more specific causal statements. Drama used the words: `like', `she', and `her' while documents in the sorry topic tended to address other people.", + "The topics of causal documents discovered by LDA showed that both general and specific statements are made regarding news, medicine, and relationships when individuals make causal attributions online." + ], + [ + "The power of online communication is the speed and ease with which information can be propagated by potentially any connected users. Yet these strengths come at a cost: rumors and misinformation also spread easily. Causal misattribution is at the heart of many rumors, conspiracy theories, and misinformation campaigns.", + "Given the central role of causal statements, further studies of the interplay of information propagation and online causal attributions are crucial. Are causal statements more likely to spread online and, if so, in which ways? What types of social media users are more or less likely to make causal statements? Will a user be more likely to make a causal statement if they have recently been exposed to one or more causal statements from other users?", + "The topics of causal statements also bring forth important questions to be addressed: how timely are causal statements? Are certain topics always being discussed in causal statements? Are there causal topics that are very popular for only brief periods and then forgotten? Temporal dynamics of causal statements are also interesting: do time-of-day or time-of-year factors play a role in how causal statements are made? Our work here focused on a limited subset of causal statements, but more generally, these results may inform new methods for automatically detecting causal statements from unstructured, natural language text BIBREF17 . Better computational tools focused on causal statements are an important step towards further understanding misinformation campaigns and other online activities. Lastly, an important but deeply challenging open question is how, if it is even possible, to validate the accuracy of causal statements. Can causal statements be ranked by some confidence metric(s)? We hope to pursue these and other questions in future research.", + "Parts-of-speech tagging depends on punctuation and casing, which we filtered in our data, so a study of how robust the POS algorithm is to punctuation and casing removal is important. We computed POS tags for the corpora with and without casing as well as with and without punctuation (which includes hashtags, links and at-symbols). Two tags mentioned in Fig. 1 B, NNPS and LS (which was not significant), were affected by punctuation removal. Otherwise, there is a strong correlation (Fig. 4 ) between Odds Ratios (causal vs. control) with punctuation and without punctuation, including casing and without casing ( $\\rho = 0.71$ and $0.80$ , respectively), indicating the POS differences between the corpora were primarily not due to the removal of punctuation or casing." + ], + [ + "We thank R. Gallagher for useful comments and gratefully acknowledge the resources provided by the Vermont Advanced Computing Core. This material is based upon work supported by the National Science Foundation under Grant No. ISS-1447634." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0760/instruction.md b/qasper-0760/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..928052382ead52074fe1eb91b809891e817fa35f --- /dev/null +++ b/qasper-0760/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: What we write about when we write about causality: Features of causal statements across large-scale social discourse + +Question: how do they collect the comparable corpus? \ No newline at end of file diff --git a/qasper-0761/environment/Dockerfile b/qasper-0761/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0761/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/* diff --git a/qasper-0761/instruction.md b/qasper-0761/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..8fd37a9b9d5f319a006509ab6478f987ab79e9c4 --- /dev/null +++ b/qasper-0761/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: What we write about when we write about causality: Features of causal statements across large-scale social discourse + +Question: How do they collect the control corpus? \ No newline at end of file diff --git a/qasper-0766/environment/Dockerfile b/qasper-0766/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0766/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/* diff --git a/qasper-0766/instruction.md b/qasper-0766/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..6d9648fb9f70c174b87eebccf63819e1e6dbde55 --- /dev/null +++ b/qasper-0766/instruction.md @@ -0,0 +1,90 @@ +Name of Paper: Unsupervised Ranking Model for Entity Coreference Resolution + +Question: Are resolution mode variables hand crafted? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Notations and Definitions", + "Generative Ranking Model", + "Resolution Mode Variables", + "Features", + "Model Learning", + "Mention Detection", + "Experimental Setup", + "Results and Comparison", + "Conclusion", + "Acknowledgements" + ], + "paragraphs": [ + [ + "Entity coreference resolution has become a critical component for many Natural Language Processing (NLP) tasks. Systems requiring deep language understanding, such as information extraction BIBREF2 , semantic event learning BIBREF3 , BIBREF4 , and named entity linking BIBREF5 , BIBREF6 all benefit from entity coreference information.", + "Entity coreference resolution is the task of identifying mentions (i.e., noun phrases) in a text or dialogue that refer to the same real-world entities. In recent years, several supervised entity coreference resolution systems have been proposed, which, according to ng:2010:ACL, can be categorized into three classes \u2014 mention-pair models BIBREF7 , entity-mention models BIBREF8 , BIBREF9 , BIBREF10 and ranking models BIBREF11 , BIBREF12 , BIBREF13 \u2014 among which ranking models recently obtained state-of-the-art performance. However, the manually annotated corpora that these systems rely on are highly expensive to create, in particular when we want to build data for resource-poor languages BIBREF14 . That makes unsupervised approaches, which only require unannotated text for training, a desirable solution to this problem.", + "Several unsupervised learning algorithms have been applied to coreference resolution. haghighi-klein:2007:ACLMain presented a mention-pair nonparametric fully-generative Bayesian model for unsupervised coreference resolution. Based on this model, ng:2008:EMNLP probabilistically induced coreference partitions via EM clustering. poon-domingos:2008:EMNLP proposed an entity-mention model that is able to perform joint inference across mentions by using Markov Logic. Unfortunately, these unsupervised systems' performance on accuracy significantly falls behind those of supervised systems, and are even worse than the deterministic rule-based systems. Furthermore, there is no previous work exploring the possibility of developing an unsupervised ranking model which achieved state-of-the-art performance under supervised settings for entity coreference resolution.", + "In this paper, we propose an unsupervised generative ranking model for entity coreference resolution. Our experimental results on the English data from the CoNLL-2012 shared task BIBREF0 show that our unsupervised system outperforms the Stanford deterministic system BIBREF1 by 3.01% absolute on the CoNLL official metric. The contributions of this work are (i) proposing the first unsupervised ranking model for entity coreference resolution. (ii) giving empirical evaluations of this model on benchmark data sets. (iii) considerably narrowing the gap to supervised coreference resolution accuracy." + ], + [ + "In the following, $D = \\lbrace m_0, m_1, \\ldots , m_n\\rbrace $ represents a generic input document which is a sequence of coreference mentions, including the artificial root mention (denoted by $m_0$ ). The method to detect and extract these mentions is discussed later in Section \"Mention Detection\" . Let $C = \\lbrace c_1, c_2, \\ldots , c_n\\rbrace $ denote the coreference assignment of a given document, where each mention $m_i$ has an associated random variable $c_i$ taking values in the set $\\lbrace 0, i, \\ldots , i-1\\rbrace $ ; this variable specifies $m_i$ 's selected antecedent ( $c_i \\in \\lbrace 1, 2, \\ldots , i-1\\rbrace $ ), or indicates that it begins a new coreference chain ( $c_i = 0$ )." + ], + [ + "The following is a straightforward way to build a generative model for coreference: ", + "$$\\begin{array}{rcl}\nP(D, C) & = & P(D|C)P(C) \\\\\n& = & \\prod \\limits _{j=1}^{n}P(m_j|m_{c_j})\\prod \\limits _{j=1}^{n}P(c_j|j)\n\\end{array}$$ (Eq. 3) ", + "where we factorize the probabilities $P(D|C)$ and $P(C)$ into each position $j$ by adopting appropriate independence assumptions that given the coreference assignment $c_j$ and corresponding coreferent mention $m_{c_j}$ , the mention $m_j$ is independent with other mentions in front of it. This independent assumption is similar to that in the IBM 1 model on machine translation BIBREF15 , where it assumes that given the corresponding English word, the aligned foreign word is independent with other English and foreign words. We do not make any independent assumptions among different features (see Section \"Features\" for details).", + "Inference in this model is efficient, because we can compute $c_j$ separately for each mention: $\nc^*_j = \\operatornamewithlimits{argmax}\\limits _{c_j} P(m_j|m_{c_j}) P(c_j|j)\n$ ", + "The model is a so-called ranking model because it is able to identify the most probable candidate antecedent given a mention to be resolved." + ], + [ + "According to previous work BIBREF17 , BIBREF18 , BIBREF1 , antecedents are resolved by different categories of information for different mentions. For example, the Stanford system BIBREF1 uses string-matching sieves to link two mentions with similar text and precise-construct sieve to link two mentions which satisfy special syntactic or semantic relations such as apposition or acronym. Motivated by this, we introduce resolution mode variables $\\Pi = \\lbrace \\pi _1, \\ldots , \\pi _n\\rbrace $ , where for each mention $j$ the variable $\\pi _j \\in \\lbrace str, prec, attr\\rbrace $ indicates in which mode the mention should be resolved. In our model, we define three resolution modes \u2014 string-matching (str), precise-construct (prec), and attribute-matching (attr) \u2014 and $\\Pi $ is deterministic when $D$ is given (i.e. $P(\\Pi |D)$ is a point distribution). We determine $\\pi _j$ for each mention $m_j$ in the following way:", + " $\\pi _j = str$ , if there exists a mention $m_i, i < j$ such that the two mentions satisfy the String Match sieve, the Relaxed String Match sieve, or the Strict Head Match A sieve in the Stanford multi-sieve system BIBREF1 .", + " $\\pi _j = prec$ , if there exists a mention $m_i, i < j$ such that the two mentions satisfy the Speaker Identification sieve, or the Precise Constructs sieve.", + " $\\pi _j = attr$ , if there is no mention $m_i, i < j$ satisfies the above two conditions.", + "Now, we can extend the generative model in Eq. 3 to: $\n\\begin{array}{rcl}\n& & P(D, C) = P(D, C, \\Pi ) \\\\\n& = & \\prod \\limits _{j=1}^{n}P(m_j|m_{c_j}, \\pi _j) P(c_j|\\pi _j, j) P(\\pi _j|j)\n\\end{array}\n$ ", + "where we define $P(\\pi _j|j)$ to be uniform distribution. We model $P(m_j|m_{c_j}, \\pi _j)$ and $P(c_j|\\pi _j, j)$ in the following way: $\n\\begin{array}{l}\nP(m_j|m_{c_j}, \\pi _j) = t(m_j|m_{c_j}, \\pi _j) \\\\\nP(c_j|\\pi _j, j) = \\left\\lbrace \\begin{array}{ll}\nq(c_j|\\pi _j, j) & \\pi _j = attr \\\\\n\\frac{1}{j} & \\textrm {otherwise}\n\\end{array}\\right.\n\\end{array}\n$ ", + "where $\\theta = \\lbrace t, q\\rbrace $ are parameters of our model. Note that in the attribute-matching mode ( $\\pi _j = attr$ ) we model $P(c_j|\\pi _j, j)$ with parameter $q$ , while in the other two modes, we use the uniform distribution. It makes sense because the position information is important for coreference resolved by matching attributes of two mentions such as resolving pronoun coreference, but not that important for those resolved by matching text or special relations like two mentions referring the same person and matching by the name. [t] Learning Model with EM Initialization: Initialize $\\theta _0 = \\lbrace t_0, q_0\\rbrace $ ", + " $t=0$ to $T$ set all counts $c(\\ldots ) = 0$ ", + "each document $D$ $j=1$ to $n$ $k=0$ to $j - 1$ $L_{jk} = \\frac{t(m_j|m_k,\\pi _j)q(k|\\pi _j, j)}{\\sum \\limits _{i = 0}^{j-1} t(m_j|m_i,\\pi _j)q(i|\\pi _j, j)}$ ", + " $c(m_j, m_k, \\pi _j) \\mathrel {+}= L_{jk}$ ", + " $c(m_k, \\pi _j) \\mathrel {+}= L_{jk}$ ", + " $c(k, j, \\pi _j) \\mathrel {+}= L_{jk}$ ", + " $c(j, \\pi _j) \\mathrel {+}= L_{jk}$ Recalculate the parameters $t(m|m^{\\prime }, \\pi ) = \\frac{c(m, m^{\\prime }, \\pi )}{c(m^{\\prime }, \\pi )}$ ", + " $q(k, j, \\pi ) = \\frac{c(k, j, \\pi )}{c(j, \\pi )}$ " + ], + [ + "In this section, we describe the features we use to represent mentions. Specifically, as shown in Table 1 , we use different features under different resolution modes. It should be noted that only the Distance feature is designed for parameter $q$ , all other features are designed for parameter $t$ ." + ], + [ + "For model learning, we run EM algorithm BIBREF19 on our Model, treating $D$ as observed data and $C$ as latent variables. We run EM with 10 iterations and select the parameters achieving the best performance on the development data. Each iteration takes around 12 hours with 10 CPUs parallelly. The best parameters appear at around the 5th iteration, according to our experiments.The detailed derivation of the learning algorithm is shown in Appendix A. The pseudo-code is shown is Algorithm \"Resolution Mode Variables\" . We use uniform initialization for all the parameters in our model.", + "Several previous work has attempted to use EM for entity coreference resolution. cherry-bergsma:2005 and charniak-elsner:2009 applied EM for pronoun anaphora resolution. ng:2008:EMNLP probabilistically induced coreference partitions via EM clustering. Recently, moosavi2014 proposed an unsupervised model utilizing the most informative relations and achieved competitive performance with the Stanford system." + ], + [ + "The basic rules we used to detect mentions are similar to those of Lee:2013:CL, except that their system uses a set of filtering rules designed to discard instances of pleonastic it, partitives, certain quantified noun phrases and other spurious mentions. Our system keeps partitives, quantified noun phrases and bare NP mentions, but discards pleonastic it and other spurious mentions." + ], + [ + "Datasets. Due to the availability of readily parsed data, we select the APW and NYT sections of Gigaword Corpus (years 1994-2010) BIBREF20 to train the model. Following previous work BIBREF3 , we remove duplicated documents and the documents which include fewer than 3 sentences. The development and test data are the English data from the CoNLL-2012 shared task BIBREF0 , which is derived from the OntoNotes corpus BIBREF21 . The corpora statistics are shown in Table 2 . Our system is evaluated with automatically extracted mentions on the version of the data with automatic preprocessing information (e.g., predicted parse trees).", + "Evaluation Metrics. We evaluate our model on three measures widely used in the literature: MUC BIBREF22 , B $^{3}$ BIBREF23 , and Entity-based CEAF (CEAF $_e$ ) BIBREF24 . In addition, we also report results on another two popular metrics: Mention-based CEAF (CEAF $_m$ ) and BLANC BIBREF25 . All the results are given by the latest version of CoNLL-2012 scorer " + ], + [ + "Table 3 illustrates the results of our model together as baseline with two deterministic systems, namely Stanford: the Stanford system BIBREF10 and Multigraph: the unsupervised multigraph system BIBREF26 , and one unsupervised system, namely MIR: the unsupervised system using most informative relations BIBREF27 . Our model outperforms the three baseline systems on all the evaluation metrics. Specifically, our model achieves improvements of 2.93% and 3.01% on CoNLL F1 score over the Stanford system, the winner of the CoNLL 2011 shared task, on the CoNLL 2012 development and test sets, respectively. The improvements on CoNLL F1 score over the Multigraph model are 1.41% and 1.77% on the development and test sets, respectively. Comparing with the MIR model, we obtain significant improvements of 2.62% and 3.02% on CoNLL F1 score.", + "To make a thorough empirical comparison with previous studies, Table 3 (below the dashed line) also shows the results of some state-of-the-art supervised coreference resolution systems \u2014 IMS: the second best system in the CoNLL 2012 shared task BIBREF28 ; Latent-Tree: the latent tree model BIBREF29 obtaining the best results in the shared task; Berkeley: the Berkeley system with the final feature set BIBREF12 ; LaSO: the structured perceptron system with non-local features BIBREF30 ; Latent-Strc: the latent structure system BIBREF31 ; Model-Stack: the entity-centric system with model stacking BIBREF32 ; and Non-Linear: the non-linear mention-ranking model with feature representations BIBREF33 . Our unsupervised ranking model outperforms the supervised IMS system by 1.02% on the CoNLL F1 score, and achieves competitive performance with the latent tree model. Moreover, our approach considerably narrows the gap to other supervised systems listed in Table 3 ." + ], + [ + "We proposed a new generative, unsupervised ranking model for entity coreference resolution into which we introduced resolution mode variables to distinguish mentions resolved by different categories of information. Experimental results on the data from CoNLL-2012 shared task show that our system significantly improves the accuracy on different evaluation metrics over the baseline systems.", + "One possible direction for future work is to differentiate more resolution modes. Another one is to add more precise or even event-based features to improve the model's performance." + ], + [ + "This research was supported in part by DARPA grant FA8750-12-2-0342 funded under the DEFT program. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of DARPA.", + "Appendix A. Derivation of Model Learning", + "Formally, we iteratively estimate the model parameters $\\theta $ , employing the following EM algorithm:", + "For simplicity, we denote: $\n{\\small \\begin{array}{rcl}\nP(C|D; \\theta ) & = & \\tilde{P}(C|D) \\\\\nP(C|D; \\theta ^{\\prime }) & = & P(C|D)\n\\end{array}}\n$ ", + "In addition, we use $\\tau (\\pi _j|j)$ to denote the probability $P(\\pi _j|j)$ which is uniform distribution in our model. Moreover, we use the following notation for convenience: $\n{\\small \\theta (m_j, m_k, j, k, \\pi _j) = t(m_j|m_k, \\pi _j) q(k|\\pi _j, j) \\tau (\\pi _j|j)\n}\n$ ", + "Then, we have $\n{\\scriptsize {\n\\begin{array}{rl}\n& E_{\\tilde{P}(c|D)} [\\log P(D, C)] \\\\\n= & \\sum \\limits _{C} \\tilde{P}(C|D) \\log P(D, C) \\\\\n= & \\sum \\limits _{C} \\tilde{P}(C|D) \\big (\\sum \\limits _{j=1}^{n} \\log t(m_j|m_{c_j}, \\pi _j) + \\log q(c_j|\\pi _j, j) + \\log \\tau (\\pi _j|j) \\big ) \\\\\n= & \\sum \\limits _{j=1}^{n} \\sum \\limits _{k=0}^{j-1} L_{jk} \\big (\\log t(m_j|m_k, \\pi _j) + \\log q(k|\\pi _j, j) + \\log \\tau (\\pi _j|j) \\big )\n\\end{array}}}\n$ ", + "Then the parameters $t$ and $q$ that maximize $E_{\\tilde{P}(c|D)} [\\log P(D, C)]$ satisfy that $\n{\\small \\begin{array}{rcl}\nt(m_j|m_k, \\pi _j) & = & \\frac{L_{jk}}{\\sum \\limits _{i = 1}^{n} L_{ik}} \\\\\nq(k|\\pi _j, j) & = & \\frac{L_{jk}}{\\sum \\limits _{i = 0}^{j-1} L_{ji}}\n\\end{array}}\n$ ", + "where $L_{jk}$ can be calculated by $\n{\\small \\begin{array}{rcl}\nL_{jk} & = & \\sum \\limits _{C, c_j=k} \\tilde{P}(C|D) = \\frac{\\sum \\limits _{C, c_j=k} \\tilde{P}(C, D)}{\\sum \\limits _{C} \\tilde{P}(C, D)} \\\\\n& = & \\frac{\\sum \\limits _{C, c_j=k}\\prod \\limits _{i = 1}^{n}\\tilde{\\theta }(m_i, m_{c_i}, c_i, i, \\pi _i)}{\\sum \\limits _{C}\\prod \\limits _{i = 1}^{n}\\tilde{\\theta }(m_i, m_{c_i}, c_i, i, \\pi _i)} \\\\\n& = & \\frac{\\tilde{\\theta }(m_j, m_k, k, j, \\pi _j)\\sum \\limits _{C(-j)}\\tilde{P}(C(-j)|D)}{\\sum \\limits _{i=0}^{j-1}\\tilde{\\theta }(m_j, m_i, i, j, \\pi _j)\\sum \\limits _{C(-j)}\\tilde{P}(C(-j)|D)} \\\\\n& = & \\frac{\\tilde{\\theta }(m_j, m_k, k, j, \\pi _j)}{\\sum \\limits _{i=0}^{j-1}\\tilde{\\theta }(m_j, m_i, i, j, \\pi _j)} \\\\\n& = & \\frac{\\tilde{t}(m_j|m_k, \\pi _j) \\tilde{q}(k|\\pi _j, j) \\tilde{\\tau }(\\pi _j|j)}{\\sum \\limits _{i=0}^{j-1}\\tilde{t}(m_j|m_i, \\pi _j) \\tilde{q}(i|\\pi _j, j) \\tilde{\\tau }(\\pi _j|j)} \\\\\n& = & \\frac{\\tilde{t}(m_j|m_k, \\pi _j) \\tilde{q}(k|\\pi _j, j)}{\\sum \\limits _{i=0}^{j-1}\\tilde{t}(m_j|m_i, \\pi _j) \\tilde{q}(i|\\pi _j, j)}\n\\end{array}}\n$ ", + "where $C(-j) = \\lbrace c_1, \\ldots , c_{j-1}, c_{j+1}, \\ldots , c_{n}\\rbrace $ . The above derivations correspond to the learning algorithm in Algorithm \"Resolution Mode Variables\" . " + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0767/instruction.md b/qasper-0767/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..12072999b5de777d7f0e6c8365dfa752ddc2cbc3 --- /dev/null +++ b/qasper-0767/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Unsupervised Ranking Model for Entity Coreference Resolution + +Question: What are resolution model variables? \ No newline at end of file diff --git a/qasper-0768/environment/Dockerfile b/qasper-0768/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0768/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/* diff --git a/qasper-0768/instruction.md b/qasper-0768/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..6071f21216b906520002b015f81e69682e527df8 --- /dev/null +++ b/qasper-0768/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Unsupervised Ranking Model for Entity Coreference Resolution + +Question: Is the model presented in the paper state of the art? \ No newline at end of file diff --git a/qasper-0769/instruction.md b/qasper-0769/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..4de7b49e42435d40697f2d6f513f19ea89a0372d --- /dev/null +++ b/qasper-0769/instruction.md @@ -0,0 +1,65 @@ +Name of Paper: The First Evaluation of Chinese Human-Computer Dialogue Technology + +Question: What problems are found with the evaluation scheme? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "The First Evaluation of Chinese Human-Computer Dialogue Technology", + "Task 1: User Intent Classification", + "Task 2: Online Testing of Task-oriented Dialogue", + "Evaluation Data", + "Evaluation Results", + "Conclusion", + "Acknowledgements" + ], + "paragraphs": [ + [ + "Recently, human-computer dialogue has been emerged as a hot topic, which has attracted the attention of both academia and industry. In research, the natural language understanding (NLU), dialogue management (DM) and natural language generation (NLG) have been promoted by the technologies of big data and deep learning BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 . Following the development of machine reading comprehension BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , the NLU technology has made great progress. The development of DM technology is from rule-based approach and supervised learning based approach to reinforcement learning based approach BIBREF15 . The NLG technology is through pattern-based approach, sentence planning approach and end-to-end deep learning approach BIBREF16 , BIBREF17 , BIBREF18 . In application, there are massive products that are based on the technology of human-computer dialogue, such as Apple Siri, Amazon Echo, Microsoft Cortana, Facebook Messenger and Google Allo etc.", + "Although the blooming of human-computer dialogue technology in both academia and industry, how to evaluate a dialogue system, especially an open domain chit-chat system, is still an open question. Figure FIGREF6 presents a brief comparison of the open domain chit-chat system and the task-oriented dialogue system.", + "From Figure FIGREF6 , we can see that it is quite different between the open domain chit-chat system and the task-oriented dialogue system. For the open domain chit-chat system, as it has no exact goal in a conversation, given an input message, the responses can be various. For example, for the input message \u201cHow is it going today?\u201d, the responses can be \u201cI'm fine!\u201d, \u201cNot bad.\u201d, \u201cI feel so depressed!\u201d, \u201cWhat a bad day!\u201d, etc. There may be infinite number of responses for an open domain messages. Hence, it is difficult to construct a gold standard (usually a reference set) to evaluate a response which is generated by an open domain chit-chat system. For the task-oriented system, although there are some objective evaluation metrics, such as the number of turns in a dialogue, the ratio of task completion, etc., there is no gold standard for automatically evaluating two (or more) dialogue systems when considering the satisfaction of the human and the fluency of the generated dialogue.", + "To promote the development of the evaluation technology for dialogue systems, especially considering the language characteristics of Chinese, we organize the first evaluation of Chinese human-computer dialogue technology. In this paper, we will present the evaluation scheme and the released corpus in detail.", + "The rest of this paper is as follows. In Section 2, we will briefly introduce the first evaluation of Chinese human-computer dialogue technology, which includes the descriptions and the evaluation metrics of the two tasks. We then present the evaluation data and final results in Section 3 and 4 respectively, following the conclusion and acknowledgements in the last two sections." + ], + [ + "The First Evaluation of Chinese Human-Computer Dialogue Technology includes two tasks, namely user intent classification and online testing of task-oriented dialogue." + ], + [ + "In using of human-computer dialogue based applications, human may have various intent, for example, chit-chatting, asking questions, booking air tickets, inquiring weather, etc. Therefore, after receiving an input message (text or ASR result) from a user, the first step is to classify the user intent into a specific domain for further processing. Table TABREF7 shows an example of user intent with category information.", + "In task 1, there are two top categories, namely, chit-chat and task-oriented dialogue. The task-oriented dialogue also includes 30 sub categories. In this evaluation, we only consider to classify the user intent in single utterance.", + "It is worth noting that besides the released data for training and developing, we also allow to collect external data for training and developing. To considering that, the task 1 is indeed includes two sub tasks. One is a closed evaluation, in which only the released data can be used for training and developing. The other is an open evaluation that allow to collect external data for training and developing. For task 1, we use F1-score as evaluation metric." + ], + [ + "For the task-oriented dialogue systems, the best way for evaluation is to use the online human-computer dialogue. After finishing an online human-computer dialogue with a dialogue system, the human then manually evaluate the system by using the metrics of user satisfaction degree, dialogue fluency, etc. Therefore, in the task 2, we use an online testing of task-oriented dialogue for dialogue systems. For a human tester, we will give a complete intent with an initial sentence, which is used to start the online human-computer dialogue. Table TABREF12 shows an example of the task-oriented human-computer dialogue. Here \u201cU\u201d and \u201cR\u201d denote user and robot respectively. The complete intent is as following:", + "\u201c\u00e6\u009f\u00a5\u00e8\u00af\u00a2\u00e6\u0098\u008e\u00e5\u00a4\u00a9\u00e4\u00bb\u008e\u00e5\u0093\u0088\u00e5\u00b0\u0094\u00e6\u00bb\u00a8\u00e5\u0088\u00b0\u00e5\u008c\u0097\u00e4\u00ba\u00ac\u00e7\u009a\u0084\u00e6\u0099\u009a\u00e9\u0097\u00b4\u00e8\u00bd\u00af\u00e5\u008d\u00a7\u00e7\u0081\u00ab\u00e8\u00bd\u00a6\u00e7\u00a5\u00a8\u00ef\u00bc\u008c\u00e4\u00b8\u008a\u00e4\u00b8\u008b\u00e9\u0093\u00ba\u00e5\u009d\u0087\u00e5\u008f\u00af\u00e3\u0080\u0082", + "Inquire the soft berth ticket at tomorrow evening, from Harbin to Beijing, either upper or lower berth is okay.\u201d", + "In task 2, there are three categories. They are \u201cair tickets\u201d, \u201ctrain tickets\u201d and \u201chotel\u201d. Correspondingly, there are three type of tasks. All the tasks are in the scope of the three categories. However, a complete user intent may include more than one task. For example, a user may first inquiring the air tickets. However, due to the high price, the user decide to buy a train tickets. Furthermore, the user may also need to book a hotel room at the destination.", + "We use manual evaluation for task 2. For each system and each complete user intent, the initial sentence, which is used to start the dialogue, is the same. The tester then begin to converse to each system. A dialogue is finished if the system successfully returns the information which the user inquires or the number of dialogue turns is larger than 30 for a single task. For building the dialogue systems of participants, we release an example set of complete user intent and three data files of flight, train and hotel in JSON format. There are five evaluation metrics for task 2 as following.", + "Task completion ratio: The number of completed tasks divided by the number of total tasks.", + "User satisfaction degree: There are five scores -2, -1, 0, 1, 2, which denote very dissatisfied, dissatisfied, neutral, satisfied and very satisfied, respectively.", + "Response fluency: There are three scores -1, 0, 1, which indicate nonfluency, neutral, fluency.", + "Number of dialogue turns: The number of utterances in a task-completed dialogue.", + "Guidance ability for out of scope input: There are two scores 0, 1, which represent able to guide or unable to guide.", + "For the number of dialogue turns, we have a penalty rule that for a dialogue task, if the system cannot return the result (or accomplish the task) in 30 turns, the dialogue task is end by force. Meanwhile, if a system cannot accomplish a task in less than 30 dialogue turns, the number of dialogue turns is set to 30." + ], + [ + "In the evaluation, all the data for training, developing and test is provided by the iFLYTEK Corporation.", + "For task 1, as the descriptions in Section SECREF10 , the two top categories are chit-chat (chat in Table TABREF13 ) and task-oriented dialogue. Meanwhile, the task-oriented dialogue also includes 30 sub categories. Actually, the task 1 is a 31 categories classification task. In task 1, besides the data we released for training and developing, we also allow the participants to extend the training and developing corpus. Hence, there are two sub tasks for the task 1. One is closed test, which means the participants can only use the released data for training and developing. The other is open test, which allows the participants to explore external corpus for training and developing. Note that there is a same test set for both the closed test and the open test.", + "For task 2, we release 11 examples of the complete user intent and 3 data file, which includes about one month of flight, hotel and train information, for participants to build their dialogue systems. The current date for online test is set to April 18, 2017. If the tester says \u201ctoday\u201d, the systems developed by the participants should understand that he/she indicates the date of April 18, 2017." + ], + [ + "There are 74 participants who are signing up the evaluation. The final number of participants is 28 and the number of submitted systems is 43. Table TABREF14 and TABREF15 show the evaluation results of the closed test and open test of the task 1 respectively. Due to the space limitation, we only present the top 5 results of task 1. We will add the complete lists of the evaluation results in the version of full paper.", + "Note that for task 2, there are 7 submitted systems. However, only 4 systems can provide correct results or be connected in a right way at the test phase. Therefore, Table TABREF16 shows the complete results of the task 2." + ], + [ + "In this paper, we introduce the first evaluation of Chinese human-computer dialogue technology. In detail, we first present the two tasks of the evaluation as well as the evaluation metrics. We then describe the released data for evaluation. Finally, we also show the evaluation results of the two tasks. As the evaluation data is provided by the iFLYTEK Corporation from their real online applications, we believe that the released data will further promote the research of human-computer dialogue and fill the blank of the data on the two tasks." + ], + [ + "We would like to thank the Social Media Processing (SMP) committee of Chinese Information Processing Society of China. We thank all the participants of the first evaluation of Chinese human-computer dialogue technology. We also thank the testers from the voice resource department of the iFLYTEK Corporation for their effort to the online real-time human-computer dialogue test and offline dialogue evaluation. We thank Lingzhi Li, Yangzi Zhang, Jiaqi Zhu and Xiaoming Shi from the research center for social computing and information retrieval for their support on the data annotation, establishing the system testing environment and the communication to the participants and help connect their systems to the testing environment." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0792/environment/Dockerfile b/qasper-0792/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0792/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/* diff --git a/qasper-0792/instruction.md b/qasper-0792/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..abd72581806a77a69f63229c5441957cfba3edbb --- /dev/null +++ b/qasper-0792/instruction.md @@ -0,0 +1,180 @@ +Name of Paper: A New Corpus for Low-Resourced Sindhi Language with Word Embeddings + +Question: Are trained word embeddings used for any other NLP task? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related work", + "Methodology", + "Methodology ::: Task description", + "Methodology ::: Corpus acquisition", + "Methodology ::: Preprocessing", + "Methodology ::: Word embedding models", + "Methodology ::: GloVe", + "Methodology ::: Continuous bag-of-words", + "Methodology ::: Skip gram", + "Methodology ::: Hyperparameters ::: Sub-sampling", + "Methodology ::: Hyperparameters ::: Dynamic context window", + "Methodology ::: Hyperparameters ::: Sub-word model", + "Methodology ::: Hyperparameters ::: Position-dependent weights", + "Methodology ::: Hyperparameters ::: Shifted point-wise mutual information", + "Methodology ::: Hyperparameters ::: Deleting rare words", + "Methodology ::: Evaluation methods", + "Methodology ::: Evaluation methods ::: Cosine similarity", + "Methodology ::: Evaluation methods ::: WordSim353", + "Statistical analysis of corpus", + "Statistical analysis of corpus ::: Letter occurrences", + "Statistical analysis of corpus ::: Letter n-grams frequency", + "Statistical analysis of corpus ::: Word Frequencies", + "Statistical analysis of corpus ::: Stop words", + "Experiments and results", + "Experiments and results ::: Hyperparameter optimization", + "Word similarity comparison of Word Embeddings ::: Nearest neighboring words", + "Word similarity comparison of Word Embeddings ::: Word pair relationship", + "Word similarity comparison of Word Embeddings ::: Comparison with WordSim353", + "Word similarity comparison of Word Embeddings ::: Visualization", + "Discussion and future work", + "Conclusion" + ], + "paragraphs": [ + [ + "Sindhi is a rich morphological, mutltiscript, and multidilectal language. It belongs to the Indo-Aryan language family BIBREF0, with significant cultural and historical background. Presently, it is recognized as is an official language BIBREF1 in Sindh province of Pakistan, also being taught as a compulsory subject in Schools and colleges. Sindhi is also recognized as one of the national languages in India. Ulhasnagar, Rajasthan, Gujarat, and Maharashtra are the largest Indian regions of Sindhi native speakers. It is also spoken in other countries except for Pakistan and India, where native Sindhi speakers have migrated, such as America, Canada, Hong Kong, British, Singapore, Tanzania, Philippines, Kenya, Uganda, and South, and East Africa. Sindhi has rich morphological structure BIBREF2 due to a large number of homogeneous words. Historically, it was written in multiple writing systems, which differ from each other in terms of orthography and morphology. The Persian-Arabic is the standard script of Sindhi, which was officially accepted in 1852 by the British government. However, the Sindhi-Devanagari is also a popular writing system in India being written in left to right direction like the Hindi language. Formerly, Khudabadi, Gujrati, Landa, Khojki, and Gurumukhi were also adopted as its writing systems. Even though, Sindhi has great historical and literal background, presently spoken by nearly 75 million people BIBREF1. The research on SNLP was coined in 2002, however, IT grabbed research attention after the development of its Unicode system BIBREF3. But still, Sindhi stands among the low-resourced languages due to the scarcity of core language processing resources of the raw and annotated corpus, which can be utilized for training robust word embeddings or the use of machine learning algorithms. Since the development of annotated datasets requires time and human resources.", + "The Language Resources (LRs) are fundamental elements for the development of high quality NLP systems based on automatic or NN based approaches. The LRs include written or spoken corpora, lexicons, and annotated corpora for specific computational purposes. The development of such resources has received great research interest for the digitization of human languages BIBREF4. Many world languages are rich in such language processing resources integrated in their software tools including English BIBREF5 BIBREF6, Chinese BIBREF7 and other languages BIBREF8 BIBREF9. The Sindhi language lacks the basic computational resources BIBREF10 of a large text corpus, which can be utilized for training robust word embeddings and developing language independent NLP applications including semantic analysis, sentiment analysis, parts of the speech tagging, named entity recognition, machine translation BIBREF11, multitasking BIBREF12, BIBREF13. Presently Sindhi Persian-Arabic is frequently used for online communication, newspapers, public institutions in Pakistan, and India BIBREF1. But little work has been carried out for the development of LRs such as raw corpus BIBREF14, BIBREF15, annotated corpus BIBREF16, BIBREF17, BIBREF1, BIBREF18. In the best of our knowledge, Sindhi lacks the large unlabelled corpus which can be utilized for generating and evaluating word embeddings for Statistical Sindhi Language Processing (SSLP).", + "One way to to break out this loop is to learn word embeddings from unlabelled corpora, which can be utilized to bootstrap other downstream NLP tasks. The word embedding is a new term of semantic vector space BIBREF19, distributed representations BIBREF20, and distributed semantic models. It is a language modeling approach BIBREF21 used for the mapping of words and phrases into $n$-dimensional dense vectors of real numbers that effectively capture the semantic and syntactic relationship with neighboring words in a geometric way BIBREF22 BIBREF23. Such as \u201cEinstein\u201d and \u201cScientist\u201d would have greater similarity compared with \u201cEinstein\u201d and \u201cdoctor.\u201d In this way, word embeddings accomplish the important linguistic concept of \u201ca word is characterized by the company it keeps\". More recently NN based models yield state-of-the-art performance in multiple NLP tasks BIBREF24 BIBREF25 with the word embeddings. One of the advantages of such techniques is they use unsupervised approaches for learning representations and do not require annotated corpus which is rare for low-resourced Sindhi language. Such representions can be trained on large unannotated corpora, and then generated representations can be used in the NLP tasks which uses a small amount of labelled data.", + "In this paper, we address the problems of corpus construction by collecting a large corpus of more than 61 million words from multiple web resources using the web-scrappy framework. After the collection of the corpus, we carefully preprocessed for the filtration of noisy text, e.g., the HTML tags and vocabulary of the English language. The statistical analysis is also presented for the letter, word frequencies and identification of stop-words. Finally, the corpus is utilized to generate Sindhi word embeddings using state-of-the-art GloVe BIBREF26 SG and CBoW BIBREF27 BIBREF20 BIBREF24 algorithms. The popular intrinsic evaluation method BIBREF20 BIBREF28 BIBREF29 of calculating cosine similarity between word vectors and WordSim353 BIBREF30 are employed to measure the performance of the learned Sindhi word embeddings. We translated English WordSim353 word pairs into Sindhi using bilingual English to Sindhi dictionary. The intrinsic approach typically involves a pre-selected set of query terms BIBREF23 and semantically related target words, which we refer to as query words. Furthermore, we also compare the proposed word embeddings with recently revealed Sindhi fastText (SdfastText) BIBREF25 word representations. To the best of our knowledge, this is the first comprehensive work on the development of large corpus and generating word embeddings along with systematic evaluation for low-resourced Sindhi Persian-Arabic. The synopsis of our novel contributions is listed as follows:", + "We present a large corpus of more than 61 million words obtained from multiple web resources and reveal a list of Sindhi stop words.", + "We develop a text cleaning pipeline for the preprocessing of the raw corpus.", + "Generate word embeddings using GloVe, CBoW, and SG Word2Vec algorithms also evaluate and compare them using the intrinsic evaluation approaches of cosine similarity matrix and WordSim353.", + "We are the first to evaluate SdfastText word representations and compare them with our proposed Sindhi word embeddings.", + "The remaining sections of the paper are organized as; Section SECREF2 presents the literature survey regarding computational resources, Sindhi corpus construction, and word embedding models. Afterwards, Section SECREF3 presents the employed methodology, Section SECREF4 consist of statistical analysis of the developed corpus. Section SECREF5 present the experimental setup. The intrinsic evaluation results along with comparison are given in Section SECREF6. The discussion and future work are given in Section SECREF7, and lastly, Section SECREF8 presents the conclusion." + ], + [ + "The natural language resources refer to a set of language data and descriptions BIBREF31 in machine readable form, used for building, improving, and evaluating NLP algorithms or softwares. Such resources include written or spoken corpora, lexicons, and annotated corpora for specific computational purposes. Many world languages are rich in such language processing resources integrated in the software tools including NLTK for English BIBREF5, Stanford CoreNLP BIBREF6, LTP for Chinese BIBREF7, TectoMT for German, Russian, Arabic BIBREF8 and multilingual toolkit BIBREF9. But Sindhi language is at an early stage for the development of such resources and software tools.", + "The corpus construction for NLP mainly involves important steps of acquisition, preprocessing, and tokenization. Initially, BIBREF14 discussed the morphological structure and challenges concerned with the corpus development along with orthographical and morphological features in the Persian-Arabic script. The raw and annotated corpus BIBREF1 for Sindhi Persian-Arabic is a good supplement towards the development of resources, including raw and annotated datasets for parts of speech tagging, morphological analysis, transliteration between Sindhi Persian-Arabic and Sindhi-Devanagari, and machine translation system. But the corpus is acquired only form Wikipedia-dumps. A survey-based study BIBREF4 provides all the progress made in the Sindhi Natural Language Processing (SNLP) with the complete gist of adopted techniques, developed tools and available resources which show that work on resource development on Sindhi needs more sophisticated efforts. The raw corpus is utilized for word segmentation BIBREF32 of Sindhi Persian-Arabic. More recently, an initiative towards the development of resources is taken BIBREF16 by open sourcing annotated dataset of Sindhi Persian-Arabic obtained from news and social blogs. The existing and proposed work is presented in Table TABREF9 on the corpus development, word segmentation, and word embeddings, respectively.", + "The power of word embeddings in NLP was empirically estimated by proposing a neural language model BIBREF21 and multitask learning BIBREF12, but recently usage of word embeddings in deep neural algorithms has become integral element BIBREF33 for performance acceleration in deep NLP applications. The CBoW and SG BIBREF27 BIBREF20 popular word2vec neural architectures yielded high quality vector representations in lower computational cost with integration of character-level learning on large corpora in terms of semantic and syntactic word similarity later extended BIBREF33 BIBREF24. Both approaches produce state-of-the-art accuracy with fast training performance, better representations of less frequent words and efficient representation of phrases as well. BIBREF34 proposed NN based approach for generating morphemic-level word embeddings, which surpassed all the existing embedding models in intrinsic evaluation. A count-based GloVe model BIBREF26 also yielded state-of-the-art results in an intrinsic evaluation and downstream NLP tasks.", + "The performance of Word embeddings is evaluated using intrinsic BIBREF23 BIBREF29 and extrinsic evaluation BIBREF28 methods. The performance of word embeddings can be measured with intrinsic and extrinsic evaluation approaches. The intrinsic approach is used to measure the internal quality of word embeddings such as querying nearest neighboring words and calculating the semantic or syntactic similarity between similar word pairs. A method of direct comparison for intrinsic evaluation of word embeddings measures the neighborhood of a query word in vector space. The key advantage of that method is to reduce bias and create insight to find data-driven relevance judgment. An extrinsic evaluation approach is used to evaluate the performance in downstream NLP tasks, such as parts-of-speech tagging or named-entity recognition BIBREF23, but the Sindhi language lacks annotated corpus for such type of evaluation. Moreover, extrinsic evaluation is time consuming and difficult to interpret. Therefore, we opt intrinsic evaluation method BIBREF28 to get a quick insight into the quality of proposed Sindhi word embeddings by measuring the cosine distance between similar words and using WordSim353 dataset. A study reveals that the choice of optimized hyper-parameters BIBREF35 has a great impact on the quality of pretrained word embeddings as compare to desing a novel algorithm. Therefore, we optimized the hyperparameters for generating robust Sindhi word embeddings using CBoW, SG and GloVe models. The embedding visualization is also useful to visualize the similarity of word clusters. Therefore, we use t-SNE BIBREF36 dimensionality reduction algorithm for compressing high dimensional embedding into 2-dimensional $x$,$y$ coordinate pairs with PCA BIBREF37. The PCA is useful to combine input features by dropping the least important features while retaining the most valuable features." + ], + [ + "This section presents the employed methodology in detail for corpus acquisition, preprocessing, statistical analysis, and generating Sindhi word embeddings." + ], + [ + "We initiate this work from scratch by collecting large corpus from multiple web resources. After preprocessing and statistical analysis of the corpus, we generate Sindhi word embeddings with state-of-the-art CBoW, SG, and GloVe algorithms. The generated word embeddings are evaluated using the intrinsic evaluation approaches of cosine similarity between nearest neighbors, word pairs, and WordSim-353 for distributional semantic similarity. Moreover, we use t-SNE with PCA for the comparison of the distance between similar words via visualization." + ], + [ + "The corpus is a collection of human language text BIBREF31 built with a specific purpose. However, the statistical analysis of the corpus provides quantitative, reusable data, and an opportunity to examine intuitions and ideas about language. Therefore, the corpus has great importance for the study of written language to examine the text. In fact, realizing the necessity of large text corpus for Sindhi, we started this research by collecting raw corpus from multiple web resource using web-scrappy framwork for extraction of news columns of daily Kawish and Awami Awaz Sindhi newspapers, Wikipedia dumps, short stories and sports news from Wichaar social blog, news from Focus Word press blog, historical writings, novels, stories, books from Sindh Salamat literary websites, novels, history and religious books from Sindhi Adabi Board and tweets regarding news and sports are collected from twitter." + ], + [ + "The preprocessing of text corpus obtained from multiple web resources is a challenging task specially it becomes more complicated when working on low-resourced language like Sindhi due to the lack of open-source preprocessing tools such as NLTK BIBREF5 for English. Therefore, we design a preprocessing pipeline depicted in Figure FIGREF22 for the filtration of unwanted data and vocabulary of other languages such as English to prepare input for word embeddings. Whereas, the involved preprocessing steps are described in detail below the Figure FIGREF22. Moreover, we reveal the list of Sindhi stop words BIBREF38 which is labor intensive and requires human judgment as well. Hence, the most frequent and least important words are classified as stop words with the help of a Sindhi linguistic expert. The partial list of Sindhi stop words is given in TABREF61. We use python programming language for designing the preprocessing pipeline using regex and string functions.", + "Input: The collected text documents were concatenated for the input in UTF-8 format.", + "Replacement symbols: The punctuation marks of a full stop, hyphen, apostrophe, comma, quotation, and exclamation marks replaced with white space for authentic tokenization because without replacing these symbols with white space the words were found joined with their next or previous corresponding words.", + "Filtration of noisy data: The text acquisition from web resources contain a huge amount of noisy data. Therefore, we filtered out unimportant data such as the rest of the punctuation marks, special characters, HTML tags, all types of numeric entities, email, and web addresses.", + "Normalization: In this step, We tokenize the corpus then normalize to lower-case for the filtration of multiple white spaces, English vocabulary, and duplicate words. The stop words were only filtered out for preparing input for GloVe. However, the sub-sampling approach in CBoW and SG can discard most frequent or stop words automatically." + ], + [ + "The NN based approaches have produced state-of-the-art performance in NLP with the usage of robust word embedings generated from the large unlabelled corpus. Therefore, word embeddings have become the main component for setting up new benchmarks in NLP using deep learning approaches. Most recently, the use cases of word embeddings are not only limited to boost statistical NLP applications but can also be used to develop language resources such as automatic construction of WordNet BIBREF39 using the unsupervised approach.", + "The word embedding can be precisely defined as the encoding of vocabulary $V$ into $N$ and the word $w$ from $V$ to vector $\\overrightarrow{w} $ into $N$-dimensional embedding space. They can be broadly categorized into predictive and count based methods, being generated by employing co-occurrence statistics, NN algorithms, and probabilistic models. The GloVe BIBREF26 algorithm treats each word as a single entity in the corpus and generates a vector of each word. However, CBoW and SG BIBREF27 BIBREF20, later extended BIBREF33 BIBREF24, well-known as word2vec rely on simple two layered NN architecture which uses linear activation function in hidden layer and softmax in the output layer. The work2vec model treats each word as a bag-of-character n-gram." + ], + [ + "The GloVe is a log-bilinear regression model BIBREF26 which combines two methods of local context window and global matrix factorization for training word embeddings of a given vocabulary in an unsupervised way. It weights the contexts using the harmonic function, for example, a context word four tokens away from an occurrence will be counted as $\\frac{1}{4}$. The Glove\u2019s implementation represents word $w \\in V_{w}$ and context $c \\in V_{c}$ in $D$-dimensional vectors $\\overrightarrow{w}$ and $\\overrightarrow{c}$ in a following way,", + "Where, $b^{\\overrightarrow{w}}$ is row vector $\\left|V_{w}\\right|$ and $b^{\\overrightarrow{c}}$ is $\\left|V_{c}\\right|$ is column vector." + ], + [ + "The standard CBoW is the inverse of SG BIBREF27 model, which predicts input word on behalf of the context. The length of input in the CBoW model depends on the setting of context window size which determines the distance to the left and right of the target word. Hence the context is a window that contain neighboring words such as by giving $w=\\left\\lbrace w_{1}, w_{2}, \\dots \\dots w_{t}\\right\\rbrace $ a sequence of words $T$, the objective of the CBoW is to maximize the probability of given neighboring words such as,", + "Where, $c_{t}$ is context of $t^{\\text{th}}$ word for example with window $w_{t-c}, \\ldots w_{t-1}, w_{t+1}, \\ldots w_{t+c}$ of size $2 c$." + ], + [ + "The SG model predicts surrounding words by giving input word BIBREF20 with training objective of learning good word embeddings that efficiently predict the neighboring words. The goal of skip-gram is to maximize average log-probability of words $w=\\left\\lbrace w_{1}, w_{2}, \\dots \\dots w_{t}\\right\\rbrace $ across the entire training corpus,", + "Where, $c_{t}$ denotes the context of words indices set of nearby $w_{t}$ words in the training corpus." + ], + [ + "Th sub-sampling BIBREF20 approach is useful to dilute most frequent or stop words, also accelerates learning rate, and increases accuracy for learning rare word vectors. Numerous words in English, e.g., \u2018the\u2019, \u2018you\u2019, \u2019that\u2019 do not have more importance, but these words appear very frequently in the text. However, considering all the words equally would also lead to over-fitting problem of model parameters BIBREF24 on the frequent word embeddings and under-fitting on the rest. Therefore, it is useful to count the imbalance between rare and repeated words. The sub-sampling technique randomly removes most frequent words with some threshold $t$ and probability $p$ of words and frequency $f$ of words in the corpus.", + "Where each word$w_{i}$ is discarded with computed probability in training phase, $f(w_i )$ is frequency of word $w_{i}$ and $t>0$ are parameters." + ], + [ + "The traditional word embedding models usually use a fixed size of a context window. For instance, if the window size ws=6, then the target word apart from 6 tokens will be treated similarity as the next word. The scheme is used to assign more weight to closer words, as closer words are generally considered to be more important to the meaning of the target word. The CBoW, SG and GloVe models employ this weighting scheme. The GloVe model weights the contexts using a harmonic function, for example, a context word four tokens away from an occurrence will be counted as $\\frac{1}{4}$. However, CBoW and SG implementation equally consider the contexts by dividing the ws with the distance from target word, e.g. ws=6 will weigh its context by $\\frac{6}{6} \\frac{5}{6} \\frac{4}{6} \\frac{3}{6} \\frac{2}{6} \\frac{1}{6}$." + ], + [ + "The sub-word model BIBREF24 can learn the internal structure of words by sharing the character representations across words. In that way, the vector for each word is made of the sum of those character $n-gram$. Such as, a vector of a word \u201ctable\u201d is a sum of $n-gram$ vectors by setting the letter $n-gram$ size $min=3$ to $max=6$ as, $, abl, able, able>, ble, ble>, le>$, we can get all sub-words of \"table\" with minimum length of $minn=3$ and maximum length of $maxn=6$. The $<$ and $>$ symbols are used to separate prefix and suffix words from other character sequences. In this way, the sub-word model utilizes the principles of morphology, which improves the quality of infrequent word representations. In addition to character $n-grams$, the input word $w$ is also included in the set of character $n-gram$, to learn the representation of each word. We obtain scoring function using a input dictionary of $n-grams$ with size $K$ by giving word $w$ , where $K_{w} \\subset \\lbrace 1, \\ldots , K\\rbrace $. A word representation $Z_{k}$ is associated to each $n-gram$ $Z$. Hence, each word is represented by the sum of character $n-gram$ representations, where, $s$ is the scoring function in the following equation," + ], + [ + "The position-dependent weighting approach BIBREF40 is used to avoid direct encoding of representations for words and their positions which can lead to over-fitting problem. The approach learns positional representations in contextual word representations and used to reweight word embedding. Thus, it captures good contextual representations at lower computational cost,", + "Where, $p$ is individual position in context window associated with $d_{p}$ vector. Afterwards the context vector reweighted by their positional vectors is average of context words. The relative positional set is $P$ in context window and $v_{C}$ is context vector of $w_{t}$ respectively." + ], + [ + "The use sparse Shifted Positive Point-wise Mutual Information (SPPMI) BIBREF41 word-context matrix in learning word representations improves results on two word similarity tasks. The CBoW and SG have $k$ (number of negatives) BIBREF27 BIBREF20 hyperparameter, which affects the value that both models try to optimize for each $(w, c): P M I(w, c)-\\log k$. Parameter $k$ has two functions of better estimation of negative examples, and it performs as before observing the probability of positive examples (actual occurrence of $w,c$)." + ], + [ + "Before creating a context window, the automatic deletion of rare words also leads to performance gain in CBoW, SG and GloVe models, which further increases the actual size of context windows." + ], + [ + "The intrinsic evaluation is based on semantic similarity BIBREF23 in word embeddings. The word similarity measure approach states BIBREF35 that the words are similar if they appear in the similar context. We measure word similarity of proposed Sindhi word embeddings using dot product method and WordSim353." + ], + [ + "The cosine similarity between two non-zero vectors is a popular measure that calculates the cosine of the angle between them which can be derived by using the Euclidean dot product method. The dot product is a multiplication of each component from both vectors added together. The result of a dot product between two vectors isn\u2019t another vector but a single value or a scalar. The dot product for two vectors can be defined as: $\\overrightarrow{a}=\\left(a_{1}, a_{2}, a_{3}, \\dots , a_{n}\\right)$ and $\\overrightarrow{b}=\\left({b}_{1}, {b}_{2}, {b}_{3}, \\ldots , {b}_{n}\\right)$ where $a_{n}$ and $b_{n}$ are the components of the vector and $n$ is dimension of vectors such as,", + "However, the cosine of two non-zero vectors can be derived by using the Euclidean dot product formula,", + "Given $a_{i}$ two vectors of attributes $a$ and $b$, the cosine similarity, $\\cos ({\\theta })$, is represented using a dot product and magnitude as,", + "where $a_{i}$ and $b_{i}$ are components of vector $\\overrightarrow{a}$ and $\\overrightarrow{b}$, respectively." + ], + [ + "The WordSim353 BIBREF42 is popular for the evaluation of lexical similarity and relatedness. The similarity score is assigned with 13 to 16 human subjects with semantic relations BIBREF30 for 353 English noun pairs. Due to the lack of annotated datasets in the Sindhi language, we translated WordSim353 using English to Sindhi bilingual dictionary for the evaluation of our proposed Sindhi word embeddings and SdfastText. We use the Spearman correlation coefficient for the semantic and syntactic similarity comparison which is used to used to discover the strength of linear or nonlinear relationships if there are no repeated data values. A perfect Spearman\u2019s correlation of $+1$ or $-1$ discovers the strength of a link between two sets of data (word-pairs) when observations are monotonically increasing or decreasing functions of each other in a following way,", + "where $r_s$ is the rank correlation coefficient, $n$ denote the number of observations, and $d^i$ is the rank difference between $i^{th}$ observations." + ], + [ + "The large corpus acquired from multiple resources is rich in vocabulary. We present the complete statistics of collected corpus (see Table TABREF52) with number of sentences, words and unique tokens." + ], + [ + "The frequency of letter occurrences in human language is not arbitrarily organized but follow some specific rules which enable us to describe some linguistic regularities. The Zipf\u2019s law BIBREF43 suggests that if the frequency of letter or word occurrence ranked in descending order such as,", + "Where, $F_{r}$ is the letter frequency of rth rank, $a$ and $b$ are parameters of input text. The comparative letter frequency in the corpus is the total number of occurrences of a letter divided by the total number of letters present in the corpus. The letter frequencies in our developed corpus are depicted in Figure FIGREF55; however, the corpus contains 187,620,276 total number of the character set. Sindhi Persian-Arabic alphabet consists of 52 letters but in the vocabulary 59 letters are detected, additional seven letters are modified uni-grams and standalone honorific symbols." + ], + [ + "We denote the combination of letter occurrences in a word as n-grams, where each letter is a gram in a word. The letter n-gram frequency is carefully analyzed in order to find the length of words which is essential to develop NLP systems, including learning of word embeddings such as choosing the minimum or maximum length of sub-word for character-level representation learning BIBREF24. We calculate the letter n-grams in words along with their percentage in the developed corpus (see Table TABREF57). The bi-gram words are most frequent, mostly consists of stop words and secondly, 4-gram words have a higher frequency." + ], + [ + "The word frequency count is an observation of word occurrences in the text. The commonly used words are considered to be with higher frequency, such as the word \u201cthe\" in English. Similarly, the frequency of rarely used words to be lower. Such frequencies can be calculated at character or word-level. We calculate word frequencies by counting a word $w$ occurrence in the corpus $c$, such as,", + "Where the frequency of $w$ is the sum of every occurrence $k$ of $w$ in $c$." + ], + [ + "The most frequent and least important words in NLP are often classified as stop words. The removal of such words can boost the performance of the NLP model BIBREF38, such as sentiment analysis and text classification. But the construction of such words list is time consuming and requires user decisions. Firstly, we determined Sindhi stop words by counting their term frequencies using Eq. DISPLAY_FORM59, and secondly, by analysing their grammatical status with the help of Sindhi linguistic expert because all the frequent words are not stop words (see Figure FIGREF62). After determining the importance of such words with the help of human judgment, we placed them in the list of stop words. The total number of detected stop words is 340 in our developed corpus. The partial list of most frequent Sindhi stop words is depicted in Table TABREF61 along with their frequency. The filtration of stop words is an essential preprocessing step for learning GloVe BIBREF26 word embeddings; therefore, we filtered out stop words for preparing input for the GloVe model. However, the sub-sampling approach BIBREF33 BIBREF24 is used to discard such most frequent words in CBoW and SG models." + ], + [ + "Hyperparameter optimization BIBREF23is more important than designing a novel algorithm. We carefully choose to optimize the dictionary and algorithm-based parameters of CBoW, SG and GloVe algorithms. Hence, we conducted a large number of experiments for training and evaluation until the optimization of most suitable hyperparameters depicted in Table TABREF64 and discussed in Section SECREF63. The choice of optimized hyperparameters is based on The high cosine similarity score in retrieving nearest neighboring words, the semantic, syntactic similarity between word pairs, WordSim353, and visualization of the distance between twenty nearest neighbours using t-SNE respectively. All the experiments are conducted on GTX 1080-TITAN GPU." + ], + [ + "The state-of-the-art SG, CBoW BIBREF27 BIBREF33 BIBREF20 BIBREF24 and Glove BIBREF26 word embedding algorithms are evaluated by parameter tuning for development of Sindhi word embeddings. These parameters can be categories into dictionary and algorithm based, respectively. The integration of character n-gram in learning word representations is an ideal method especially for rich morphological languages because this approach has the ability to compute rare and misspelled words. Sindhi is also a rich morphological language. Therefore more robust embeddings became possible to train with the hyperparameter optimization of SG, CBoW and GloVe algorithms. We tuned and evaluated the hyperparameters of three algorithms individually which are discussed as follows:", + "Number of Epochs: Generally, more epochs on the corpus often produce better results but more epochs take long training time. Therefore, we evaluate 10, 20, 30 and 40 epochs for each word embedding model, and 40 epochs constantly produce good results.", + "Learning rate (lr): We tried lr of $0.05$, $0.1$, and $0.25$, the optimal lr $(0.25)$ gives the better results for training all the embedding models.", + "Dimensions ($D$): We evaluate and compare the quality of $100-D$, $200-D$, and $300-D$ using WordSim353 on different $ws$, and the optimal $300-D$ are evaluated with cosine similarity matrix for querying nearest neighboring words and calculating the similarity between word pairs. The embedding dimensions have little affect on the quality of the intrinsic evaluation process. However, the selection of embedding dimensions might have more impact on the accuracy in certain downstream NLP applications. The lower embedding dimensions are faster to train and evaluate.", + "Character n-grams: The selection of minimum (minn) and the maximum (maxn) length of character $n-grams$ is an important parameter for learning character-level representations of words in CBoW and SG models. Therefore, the n-grams from $3-9$ were tested to analyse the impact on the accuracy of embedding. We optimized the length of character n-grams from $minn=2$ and $maxn=7$ by keeping in view the word frequencies depicted in Table TABREF57.", + "Window size (ws): The large ws means considering more context words and similarly less ws means to limit the size of context words. By changing the size of the dynamic context window, we tried the ws of 3, 5, 7 the optimal ws=7 yield consistently better performance.", + "Negative Sampling (NS): : The more negative examples yield better results, but more negatives take long training time. We tried 10, 20, and 30 negative examples for CBoW and SG. The best negative examples of 20 for CBoW and SG significantly yield better performance in average training time.", + "Minimum word count (minw): We evaluated the range of minimum word counts from 1 to 8 and analyzed that the size of input vocabulary is decreasing at a large scale by ignoring more words similarly the vocabulary size was increasing by considering rare words. Therefore, by ignoring words with a frequency of less than 4 in CBoW, SG, and GloVe consistently yields better results with the vocabulary of 200,000 words.", + "Loss function (ls): we use hierarchical softmax (hs) for CBoW, negative sampling (ns) for SG and default loss function for GloVe BIBREF26.", + "The recommended verbosity level, number of buckets, sampling threshold, number of threads are used for training CBoW, SG BIBREF24, and GloVe BIBREF26." + ], + [ + "The cosine similarity matrix BIBREF35 is a popular approach to compute the relationship between all embedding dimensions of their distinct relevance to query word. The words with similar context get high cosine similarity and geometrical relatedness to Euclidean distance, which is a common and primary method to measure the distance between a set of words and nearest neighbors. Each word contains the most similar top eight nearest neighboring words determined by the highest cosine similarity score using Eq. DISPLAY_FORM48. We present the English translation of both query and retrieved words also discuss with their English meaning for ease of relevance judgment between the query and retrieved words.To take a closer look at the semantic and syntactic relationship captured in the proposed word embeddings, Table TABREF74 shows the top eight nearest neighboring words of five different query words Friday, Spring, Cricket, Red, Scientist taken from the vocabulary. As the first query word Friday returns the names of days Saturday, Sunday, Monday, Tuesday, Wednesday, Thursday in an unordered sequence. The SdfastText returns five names of days Sunday, Thursday, Monday, Tuesday and Wednesday respectively. The GloVe model also returns five names of days. However, CBoW and SG gave six names of days except Wednesday along with different writing forms of query word Friday being written in the Sindhi language which shows that CBoW and SG return more relevant words as compare to SdfastText and GloVe. The CBoW returned Add and GloVe returns Honorary words which are little similar to the querry word but SdfastText resulted two irrelevant words Kameeso (N) which is a name (N) of person in Sindhi and Phrase is a combination of three Sindhi words which are not tokenized properly. Similarly, nearest neighbors of second query word Spring are retrieved accurately as names and seasons and semantically related to query word Spring by CBoW, SG and Glove but SdfastText returned four irrelevant words of Dilbahar (N), Pharase, Ashbahar (N) and Farzana (N) out of eight. The third query word is Cricket, the name of a popular game. The first retrieved word in CBoW is Kabadi (N) that is a popular national game in Pakistan. Including Kabadi (N) all the returned words by CBoW, SG and GloVe are related to Cricket game or names of other games. But the first word in SdfastText contains a punctuation mark in retrieved word Gone.Cricket that are two words joined with a punctuation mark (.), which shows the tokenization error in preprocessing step, sixth retrieved word Misspelled is a combination of three words not related to query word, and Played, Being played are also irrelevant and stop words. Moreover, fourth query word Red gave results that contain names of closely related to query word and different forms of query word written in the Sindhi language. The last returned word Unknown by SdfastText is irrelevant and not found in the Sindhi dictionary for translation. The last query word Scientist also contains semantically related words by CBoW, SG, and GloVe, but the first Urdu word given by SdfasText belongs to the Urdu language which means that the vocabulary may also contain words of other languages. Another unknown word returned by SdfastText does not have any meaning in the Sindhi dictionary. More interesting observations in the presented results are the diacritized words retrieved from our proposed word embeddings and The authentic tokenization in the preprocessing step presented in Figure FIGREF22. However, SdfastText has returned tri-gram words of Phrase in query words Friday, Spring, a Misspelled word in Cricket and Scientist query words. Hence, the overall performance of our proposed SG, CBoW, and GloVe demonstrate high semantic relatedness in retrieving the top eight nearest neighbor words." + ], + [ + "Generally, closer words are considered more important to a word\u2019s meaning. The word embeddings models have the ability to capture the lexical relations between words. Identifying such relationship that connects words is important in NLP applications. We measure that semantic relationship by calculating the dot product of two vectors using Eq. DISPLAY_FORM48. The high cosine similarity score denotes the closer words in the embedding matrix, while less cosine similarity score means the higher distance between word pairs. We present the cosine similarity score of different semantically or syntactically related word pairs taken from the vocabulary in Table TABREF77 along with English translation, which shows the average similarity of 0.632, 0.650, 0.591 yields by CBoW, SG and GloVe respectively. The SG model achieved a high average similarity score of 0.650 followed by CBoW with a 0.632 average similarity score. The GloVe also achieved a considerable average score of 0.591 respectively. However, the average similarity score of SdfastText is 0.388 and the word pair Microsoft-Bill Gates is not available in the vocabulary of SdfastText. This shows that along with performance, the vocabulary in SdfastText is also limited as compared to our proposed word embeddings.", + "Moreover, the average semantic relatedness similarity score between countries and their capitals is shown in Table TABREF78 with English translation, where SG also yields the best average score of 0.663 followed by CBoW with 0.611 similarity score. The GloVe also yields better semantic relatedness of 0.576 and the SdfastText yield an average score of 0.391. The first query word China-Beijing is not available the vocabulary of SdfastText. However, the similarity score between Afghanistan-Kabul is lower in our proposed CBoW, SG, GloVe models because the word Kabul is the name of the capital of Afghanistan as well as it frequently appears as an adjective in Sindhi text which means able." + ], + [ + "We evaluate the performance of our proposed word embeddings using the WordSim353 dataset by translation English word pairs to Sindhi. Due to vocabulary differences between English and Sindhi, we were unable to find the authentic meaning of six terms, so we left these terms untranslated. So our final Sindhi WordSim353 consists of 347 word pairs. Table TABREF80 shows the Spearman correlation results using Eq. DISPLAY_FORM51 on different dimensional embeddings on the translated WordSim353. The Table TABREF80 presents complete results with the different ws for CBoW, SG and GloVe in which the ws=7 subsequently yield better performance than ws of 3 and 5, respectively. The SG model outperforms CBoW and GloVe in semantic and syntactic similarity by achieving the performance of 0.629 with ws=7. In comparison with English BIBREF27 achieved the average semantic and syntactic similarity of 0.637, 0.656 with CBoW and SG, respectively. Therefore, despite the challenges in translation from English to Sindhi, our proposed Sindhi word embeddings have efficiently captured the semantic and syntactic relationship." + ], + [ + "We use t-Distributed Stochastic Neighboring (t-SNE) dimensionality BIBREF36 reduction algorithm with PCA BIBREF37 for exploratory embeddings analysis in 2-dimensional map. The t-SNE is a non-linear dimensionality reduction algorithm for visualization of high dimensional datasets. It starts the probability calculation of similar word clusters in high-dimensional space and calculates the probability of similar points in the corresponding low-dimensional space. The purpose of t-SNE for visualization of word embeddings is to keep similar words close together in 2-dimensional $x,y$ coordinate pairs while maximizing the distance between dissimilar words. The t-SNE has a perplexity (PPL) tunable parameter used to balance the data points at both the local and global levels. We visualize the embeddings using PPL=20 on 5000-iterations of 300-D models. We use the same query words (see Table TABREF74) by retrieving the top 20 nearest neighboring word clusters for a better understanding of the distance between similar words. Every query word has a distinct color for the clear visualization of a similar group of words. The closer word clusters show the high similarity between the query and retrieved word clusters. The word clusters in SG (see Fig. FIGREF83) are closer to their group of semantically related words. Secondly, the CBoW model depicted in Fig. FIGREF82 and GloVe Fig. FIGREF84 also show the better cluster formation of words than SdfastText Fig. FIGREF85, respectively." + ], + [ + "In this era of the information age, the existence of LRs plays a vital role in the digital survival of natural languages because the NLP tools are used to process a flow of un-structured data from disparate sources. It is imperative to mention that presently, Sindhi Persian-Arabic is frequently used in online communication, newspapers, public institutions in Pakistan and India. Due to the growing use of Sindhi on web platforms, the need for its LRs is also increasing for the development of language technology tools. But little work has been carried out for the development of resources which is not sufficient to design a language independent or machine learning algorithms. The present work is a first comprehensive initiative on resource development along with their evaluation for statistical Sindhi language processing. More recently, the NN based approaches have produced a state-of-the-art performance in NLP by exploiting unsupervised word embeddings learned from the large unlabelled corpus. Such word embeddings have also motivated the work on low-resourced languages. Our work mainly consists of novel contributions of resource development along with comprehensive evaluation for the utilization of NN based approaches in SNLP applications. The large corpus obtained from multiple web resources is utilized for the training of word embeddings using SG, CBoW and Glove models. The intrinsic evaluation along with comparative results demonstrates that the proposed Sindhi word embeddings have accurately captured the semantic information as compare to recently revealed SdfastText word vectors. The SG yield best results in nearest neighbors, word pair relationship and semantic similarity. The performance of CBoW is also close to SG in all the evaluation matrices. The GloVe also yields better word representations; however SG and CBoW models surpass the GloVe model in all evaluation matrices. Hyperparameter optimization is as important as designing a new algorithm. The choice of optimal parameters is a key aspect of performance gain in learning robust word embeddings. Moreover, We analysed that the size of the corpus and careful preprocessing steps have a large impact on the quality of word embeddings. However, in algorithmic perspective, the character-level learning approach in SG and CBoW improves the quality of representation learning, and overall window size, learning rate, number of epochs are the core parameters that largely influence the performance of word embeddings models. Ultimately, the new corpus of low-resourced Sindhi language, list of stop words and pretrained word embeddings along with empirical evaluation, will be a good supplement for future research in SSLP applications. In the future, we aim to use the corpus for annotation projects such as parts-of-speech tagging, named entity recognition. The proposed word embeddings will be refined further by creating custom benchmarks and the extrinsic evaluation approach will be employed for the performance analysis of proposed word embeddings. Moreover, we will also utilize the corpus using Bi-directional Encoder Representation Transformer BIBREF13 for learning deep contextualized Sindhi word representations. Furthermore, the generated word embeddings will be utilized for the automatic construction of Sindhi WordNet." + ], + [ + "In this paper, we mainly present three novel contributions of large corpus development contains large vocabulary of more than 61 million tokens, 908,456 unique words. Secondly, the list of Sindhi stop words is constructed by finding their high frequency and least importance with the help of Sindhi linguistic expert. Thirdly, the unsupervised Sindhi word embeddings are generated using state-of-the-art CBoW, SG and GloVe algorithms and evaluated using popular intrinsic evaluation approaches of cosine similarity matrix and WordSim353 for the first time in Sindhi language processing. We translate English WordSim353 using the English-Sindhi bilingual dictionary, which will also be a good resource for the evaluation of Sindhi word embeddings. Moreover, the proposed word embeddings are also compared with recently revealed SdfastText word representations.", + "Our empirical results demonstrate that our proposed Sindhi word embeddings have captured high semantic relatedness in nearest neighboring words, word pair relationship, country, and capital and WordSim353. The SG yields the best performance than CBoW and GloVe models subsequently. However, the performance of GloVe is low on the same vocabulary because of character-level learning of word representations and sub-sampling approaches in SG and CBoW. Our proposed Sindhi word embeddings have surpassed SdfastText in the intrinsic evaluation matrix. Also, the vocabulary of SdfastText is limited because they are trained on a small Wikipedia corpus of Sindhi Persian-Arabic. We will further investigate the extrinsic performance of proposed word embeddings on the Sindhi text classification task in the future. The proposed resources along with systematic evaluation will be a sophisticated addition to the computational resources for statistical Sindhi language processing." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0793/instruction.md b/qasper-0793/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..db02d51411c3a4deb2b02f1a9170c73269bae8b1 --- /dev/null +++ b/qasper-0793/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: A New Corpus for Low-Resourced Sindhi Language with Word Embeddings + +Question: How many uniue words are in the dataset? \ No newline at end of file diff --git a/qasper-0794/instruction.md b/qasper-0794/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..d6c4056197cff0b762e3f25e81ed7e13174ac863 --- /dev/null +++ b/qasper-0794/instruction.md @@ -0,0 +1,180 @@ +Name of Paper: A New Corpus for Low-Resourced Sindhi Language with Word Embeddings + +Question: How is the data collected, which web resources were used? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related work", + "Methodology", + "Methodology ::: Task description", + "Methodology ::: Corpus acquisition", + "Methodology ::: Preprocessing", + "Methodology ::: Word embedding models", + "Methodology ::: GloVe", + "Methodology ::: Continuous bag-of-words", + "Methodology ::: Skip gram", + "Methodology ::: Hyperparameters ::: Sub-sampling", + "Methodology ::: Hyperparameters ::: Dynamic context window", + "Methodology ::: Hyperparameters ::: Sub-word model", + "Methodology ::: Hyperparameters ::: Position-dependent weights", + "Methodology ::: Hyperparameters ::: Shifted point-wise mutual information", + "Methodology ::: Hyperparameters ::: Deleting rare words", + "Methodology ::: Evaluation methods", + "Methodology ::: Evaluation methods ::: Cosine similarity", + "Methodology ::: Evaluation methods ::: WordSim353", + "Statistical analysis of corpus", + "Statistical analysis of corpus ::: Letter occurrences", + "Statistical analysis of corpus ::: Letter n-grams frequency", + "Statistical analysis of corpus ::: Word Frequencies", + "Statistical analysis of corpus ::: Stop words", + "Experiments and results", + "Experiments and results ::: Hyperparameter optimization", + "Word similarity comparison of Word Embeddings ::: Nearest neighboring words", + "Word similarity comparison of Word Embeddings ::: Word pair relationship", + "Word similarity comparison of Word Embeddings ::: Comparison with WordSim353", + "Word similarity comparison of Word Embeddings ::: Visualization", + "Discussion and future work", + "Conclusion" + ], + "paragraphs": [ + [ + "Sindhi is a rich morphological, mutltiscript, and multidilectal language. It belongs to the Indo-Aryan language family BIBREF0, with significant cultural and historical background. Presently, it is recognized as is an official language BIBREF1 in Sindh province of Pakistan, also being taught as a compulsory subject in Schools and colleges. Sindhi is also recognized as one of the national languages in India. Ulhasnagar, Rajasthan, Gujarat, and Maharashtra are the largest Indian regions of Sindhi native speakers. It is also spoken in other countries except for Pakistan and India, where native Sindhi speakers have migrated, such as America, Canada, Hong Kong, British, Singapore, Tanzania, Philippines, Kenya, Uganda, and South, and East Africa. Sindhi has rich morphological structure BIBREF2 due to a large number of homogeneous words. Historically, it was written in multiple writing systems, which differ from each other in terms of orthography and morphology. The Persian-Arabic is the standard script of Sindhi, which was officially accepted in 1852 by the British government. However, the Sindhi-Devanagari is also a popular writing system in India being written in left to right direction like the Hindi language. Formerly, Khudabadi, Gujrati, Landa, Khojki, and Gurumukhi were also adopted as its writing systems. Even though, Sindhi has great historical and literal background, presently spoken by nearly 75 million people BIBREF1. The research on SNLP was coined in 2002, however, IT grabbed research attention after the development of its Unicode system BIBREF3. But still, Sindhi stands among the low-resourced languages due to the scarcity of core language processing resources of the raw and annotated corpus, which can be utilized for training robust word embeddings or the use of machine learning algorithms. Since the development of annotated datasets requires time and human resources.", + "The Language Resources (LRs) are fundamental elements for the development of high quality NLP systems based on automatic or NN based approaches. The LRs include written or spoken corpora, lexicons, and annotated corpora for specific computational purposes. The development of such resources has received great research interest for the digitization of human languages BIBREF4. Many world languages are rich in such language processing resources integrated in their software tools including English BIBREF5 BIBREF6, Chinese BIBREF7 and other languages BIBREF8 BIBREF9. The Sindhi language lacks the basic computational resources BIBREF10 of a large text corpus, which can be utilized for training robust word embeddings and developing language independent NLP applications including semantic analysis, sentiment analysis, parts of the speech tagging, named entity recognition, machine translation BIBREF11, multitasking BIBREF12, BIBREF13. Presently Sindhi Persian-Arabic is frequently used for online communication, newspapers, public institutions in Pakistan, and India BIBREF1. But little work has been carried out for the development of LRs such as raw corpus BIBREF14, BIBREF15, annotated corpus BIBREF16, BIBREF17, BIBREF1, BIBREF18. In the best of our knowledge, Sindhi lacks the large unlabelled corpus which can be utilized for generating and evaluating word embeddings for Statistical Sindhi Language Processing (SSLP).", + "One way to to break out this loop is to learn word embeddings from unlabelled corpora, which can be utilized to bootstrap other downstream NLP tasks. The word embedding is a new term of semantic vector space BIBREF19, distributed representations BIBREF20, and distributed semantic models. It is a language modeling approach BIBREF21 used for the mapping of words and phrases into $n$-dimensional dense vectors of real numbers that effectively capture the semantic and syntactic relationship with neighboring words in a geometric way BIBREF22 BIBREF23. Such as \u201cEinstein\u201d and \u201cScientist\u201d would have greater similarity compared with \u201cEinstein\u201d and \u201cdoctor.\u201d In this way, word embeddings accomplish the important linguistic concept of \u201ca word is characterized by the company it keeps\". More recently NN based models yield state-of-the-art performance in multiple NLP tasks BIBREF24 BIBREF25 with the word embeddings. One of the advantages of such techniques is they use unsupervised approaches for learning representations and do not require annotated corpus which is rare for low-resourced Sindhi language. Such representions can be trained on large unannotated corpora, and then generated representations can be used in the NLP tasks which uses a small amount of labelled data.", + "In this paper, we address the problems of corpus construction by collecting a large corpus of more than 61 million words from multiple web resources using the web-scrappy framework. After the collection of the corpus, we carefully preprocessed for the filtration of noisy text, e.g., the HTML tags and vocabulary of the English language. The statistical analysis is also presented for the letter, word frequencies and identification of stop-words. Finally, the corpus is utilized to generate Sindhi word embeddings using state-of-the-art GloVe BIBREF26 SG and CBoW BIBREF27 BIBREF20 BIBREF24 algorithms. The popular intrinsic evaluation method BIBREF20 BIBREF28 BIBREF29 of calculating cosine similarity between word vectors and WordSim353 BIBREF30 are employed to measure the performance of the learned Sindhi word embeddings. We translated English WordSim353 word pairs into Sindhi using bilingual English to Sindhi dictionary. The intrinsic approach typically involves a pre-selected set of query terms BIBREF23 and semantically related target words, which we refer to as query words. Furthermore, we also compare the proposed word embeddings with recently revealed Sindhi fastText (SdfastText) BIBREF25 word representations. To the best of our knowledge, this is the first comprehensive work on the development of large corpus and generating word embeddings along with systematic evaluation for low-resourced Sindhi Persian-Arabic. The synopsis of our novel contributions is listed as follows:", + "We present a large corpus of more than 61 million words obtained from multiple web resources and reveal a list of Sindhi stop words.", + "We develop a text cleaning pipeline for the preprocessing of the raw corpus.", + "Generate word embeddings using GloVe, CBoW, and SG Word2Vec algorithms also evaluate and compare them using the intrinsic evaluation approaches of cosine similarity matrix and WordSim353.", + "We are the first to evaluate SdfastText word representations and compare them with our proposed Sindhi word embeddings.", + "The remaining sections of the paper are organized as; Section SECREF2 presents the literature survey regarding computational resources, Sindhi corpus construction, and word embedding models. Afterwards, Section SECREF3 presents the employed methodology, Section SECREF4 consist of statistical analysis of the developed corpus. Section SECREF5 present the experimental setup. The intrinsic evaluation results along with comparison are given in Section SECREF6. The discussion and future work are given in Section SECREF7, and lastly, Section SECREF8 presents the conclusion." + ], + [ + "The natural language resources refer to a set of language data and descriptions BIBREF31 in machine readable form, used for building, improving, and evaluating NLP algorithms or softwares. Such resources include written or spoken corpora, lexicons, and annotated corpora for specific computational purposes. Many world languages are rich in such language processing resources integrated in the software tools including NLTK for English BIBREF5, Stanford CoreNLP BIBREF6, LTP for Chinese BIBREF7, TectoMT for German, Russian, Arabic BIBREF8 and multilingual toolkit BIBREF9. But Sindhi language is at an early stage for the development of such resources and software tools.", + "The corpus construction for NLP mainly involves important steps of acquisition, preprocessing, and tokenization. Initially, BIBREF14 discussed the morphological structure and challenges concerned with the corpus development along with orthographical and morphological features in the Persian-Arabic script. The raw and annotated corpus BIBREF1 for Sindhi Persian-Arabic is a good supplement towards the development of resources, including raw and annotated datasets for parts of speech tagging, morphological analysis, transliteration between Sindhi Persian-Arabic and Sindhi-Devanagari, and machine translation system. But the corpus is acquired only form Wikipedia-dumps. A survey-based study BIBREF4 provides all the progress made in the Sindhi Natural Language Processing (SNLP) with the complete gist of adopted techniques, developed tools and available resources which show that work on resource development on Sindhi needs more sophisticated efforts. The raw corpus is utilized for word segmentation BIBREF32 of Sindhi Persian-Arabic. More recently, an initiative towards the development of resources is taken BIBREF16 by open sourcing annotated dataset of Sindhi Persian-Arabic obtained from news and social blogs. The existing and proposed work is presented in Table TABREF9 on the corpus development, word segmentation, and word embeddings, respectively.", + "The power of word embeddings in NLP was empirically estimated by proposing a neural language model BIBREF21 and multitask learning BIBREF12, but recently usage of word embeddings in deep neural algorithms has become integral element BIBREF33 for performance acceleration in deep NLP applications. The CBoW and SG BIBREF27 BIBREF20 popular word2vec neural architectures yielded high quality vector representations in lower computational cost with integration of character-level learning on large corpora in terms of semantic and syntactic word similarity later extended BIBREF33 BIBREF24. Both approaches produce state-of-the-art accuracy with fast training performance, better representations of less frequent words and efficient representation of phrases as well. BIBREF34 proposed NN based approach for generating morphemic-level word embeddings, which surpassed all the existing embedding models in intrinsic evaluation. A count-based GloVe model BIBREF26 also yielded state-of-the-art results in an intrinsic evaluation and downstream NLP tasks.", + "The performance of Word embeddings is evaluated using intrinsic BIBREF23 BIBREF29 and extrinsic evaluation BIBREF28 methods. The performance of word embeddings can be measured with intrinsic and extrinsic evaluation approaches. The intrinsic approach is used to measure the internal quality of word embeddings such as querying nearest neighboring words and calculating the semantic or syntactic similarity between similar word pairs. A method of direct comparison for intrinsic evaluation of word embeddings measures the neighborhood of a query word in vector space. The key advantage of that method is to reduce bias and create insight to find data-driven relevance judgment. An extrinsic evaluation approach is used to evaluate the performance in downstream NLP tasks, such as parts-of-speech tagging or named-entity recognition BIBREF23, but the Sindhi language lacks annotated corpus for such type of evaluation. Moreover, extrinsic evaluation is time consuming and difficult to interpret. Therefore, we opt intrinsic evaluation method BIBREF28 to get a quick insight into the quality of proposed Sindhi word embeddings by measuring the cosine distance between similar words and using WordSim353 dataset. A study reveals that the choice of optimized hyper-parameters BIBREF35 has a great impact on the quality of pretrained word embeddings as compare to desing a novel algorithm. Therefore, we optimized the hyperparameters for generating robust Sindhi word embeddings using CBoW, SG and GloVe models. The embedding visualization is also useful to visualize the similarity of word clusters. Therefore, we use t-SNE BIBREF36 dimensionality reduction algorithm for compressing high dimensional embedding into 2-dimensional $x$,$y$ coordinate pairs with PCA BIBREF37. The PCA is useful to combine input features by dropping the least important features while retaining the most valuable features." + ], + [ + "This section presents the employed methodology in detail for corpus acquisition, preprocessing, statistical analysis, and generating Sindhi word embeddings." + ], + [ + "We initiate this work from scratch by collecting large corpus from multiple web resources. After preprocessing and statistical analysis of the corpus, we generate Sindhi word embeddings with state-of-the-art CBoW, SG, and GloVe algorithms. The generated word embeddings are evaluated using the intrinsic evaluation approaches of cosine similarity between nearest neighbors, word pairs, and WordSim-353 for distributional semantic similarity. Moreover, we use t-SNE with PCA for the comparison of the distance between similar words via visualization." + ], + [ + "The corpus is a collection of human language text BIBREF31 built with a specific purpose. However, the statistical analysis of the corpus provides quantitative, reusable data, and an opportunity to examine intuitions and ideas about language. Therefore, the corpus has great importance for the study of written language to examine the text. In fact, realizing the necessity of large text corpus for Sindhi, we started this research by collecting raw corpus from multiple web resource using web-scrappy framwork for extraction of news columns of daily Kawish and Awami Awaz Sindhi newspapers, Wikipedia dumps, short stories and sports news from Wichaar social blog, news from Focus Word press blog, historical writings, novels, stories, books from Sindh Salamat literary websites, novels, history and religious books from Sindhi Adabi Board and tweets regarding news and sports are collected from twitter." + ], + [ + "The preprocessing of text corpus obtained from multiple web resources is a challenging task specially it becomes more complicated when working on low-resourced language like Sindhi due to the lack of open-source preprocessing tools such as NLTK BIBREF5 for English. Therefore, we design a preprocessing pipeline depicted in Figure FIGREF22 for the filtration of unwanted data and vocabulary of other languages such as English to prepare input for word embeddings. Whereas, the involved preprocessing steps are described in detail below the Figure FIGREF22. Moreover, we reveal the list of Sindhi stop words BIBREF38 which is labor intensive and requires human judgment as well. Hence, the most frequent and least important words are classified as stop words with the help of a Sindhi linguistic expert. The partial list of Sindhi stop words is given in TABREF61. We use python programming language for designing the preprocessing pipeline using regex and string functions.", + "Input: The collected text documents were concatenated for the input in UTF-8 format.", + "Replacement symbols: The punctuation marks of a full stop, hyphen, apostrophe, comma, quotation, and exclamation marks replaced with white space for authentic tokenization because without replacing these symbols with white space the words were found joined with their next or previous corresponding words.", + "Filtration of noisy data: The text acquisition from web resources contain a huge amount of noisy data. Therefore, we filtered out unimportant data such as the rest of the punctuation marks, special characters, HTML tags, all types of numeric entities, email, and web addresses.", + "Normalization: In this step, We tokenize the corpus then normalize to lower-case for the filtration of multiple white spaces, English vocabulary, and duplicate words. The stop words were only filtered out for preparing input for GloVe. However, the sub-sampling approach in CBoW and SG can discard most frequent or stop words automatically." + ], + [ + "The NN based approaches have produced state-of-the-art performance in NLP with the usage of robust word embedings generated from the large unlabelled corpus. Therefore, word embeddings have become the main component for setting up new benchmarks in NLP using deep learning approaches. Most recently, the use cases of word embeddings are not only limited to boost statistical NLP applications but can also be used to develop language resources such as automatic construction of WordNet BIBREF39 using the unsupervised approach.", + "The word embedding can be precisely defined as the encoding of vocabulary $V$ into $N$ and the word $w$ from $V$ to vector $\\overrightarrow{w} $ into $N$-dimensional embedding space. They can be broadly categorized into predictive and count based methods, being generated by employing co-occurrence statistics, NN algorithms, and probabilistic models. The GloVe BIBREF26 algorithm treats each word as a single entity in the corpus and generates a vector of each word. However, CBoW and SG BIBREF27 BIBREF20, later extended BIBREF33 BIBREF24, well-known as word2vec rely on simple two layered NN architecture which uses linear activation function in hidden layer and softmax in the output layer. The work2vec model treats each word as a bag-of-character n-gram." + ], + [ + "The GloVe is a log-bilinear regression model BIBREF26 which combines two methods of local context window and global matrix factorization for training word embeddings of a given vocabulary in an unsupervised way. It weights the contexts using the harmonic function, for example, a context word four tokens away from an occurrence will be counted as $\\frac{1}{4}$. The Glove\u2019s implementation represents word $w \\in V_{w}$ and context $c \\in V_{c}$ in $D$-dimensional vectors $\\overrightarrow{w}$ and $\\overrightarrow{c}$ in a following way,", + "Where, $b^{\\overrightarrow{w}}$ is row vector $\\left|V_{w}\\right|$ and $b^{\\overrightarrow{c}}$ is $\\left|V_{c}\\right|$ is column vector." + ], + [ + "The standard CBoW is the inverse of SG BIBREF27 model, which predicts input word on behalf of the context. The length of input in the CBoW model depends on the setting of context window size which determines the distance to the left and right of the target word. Hence the context is a window that contain neighboring words such as by giving $w=\\left\\lbrace w_{1}, w_{2}, \\dots \\dots w_{t}\\right\\rbrace $ a sequence of words $T$, the objective of the CBoW is to maximize the probability of given neighboring words such as,", + "Where, $c_{t}$ is context of $t^{\\text{th}}$ word for example with window $w_{t-c}, \\ldots w_{t-1}, w_{t+1}, \\ldots w_{t+c}$ of size $2 c$." + ], + [ + "The SG model predicts surrounding words by giving input word BIBREF20 with training objective of learning good word embeddings that efficiently predict the neighboring words. The goal of skip-gram is to maximize average log-probability of words $w=\\left\\lbrace w_{1}, w_{2}, \\dots \\dots w_{t}\\right\\rbrace $ across the entire training corpus,", + "Where, $c_{t}$ denotes the context of words indices set of nearby $w_{t}$ words in the training corpus." + ], + [ + "Th sub-sampling BIBREF20 approach is useful to dilute most frequent or stop words, also accelerates learning rate, and increases accuracy for learning rare word vectors. Numerous words in English, e.g., \u2018the\u2019, \u2018you\u2019, \u2019that\u2019 do not have more importance, but these words appear very frequently in the text. However, considering all the words equally would also lead to over-fitting problem of model parameters BIBREF24 on the frequent word embeddings and under-fitting on the rest. Therefore, it is useful to count the imbalance between rare and repeated words. The sub-sampling technique randomly removes most frequent words with some threshold $t$ and probability $p$ of words and frequency $f$ of words in the corpus.", + "Where each word$w_{i}$ is discarded with computed probability in training phase, $f(w_i )$ is frequency of word $w_{i}$ and $t>0$ are parameters." + ], + [ + "The traditional word embedding models usually use a fixed size of a context window. For instance, if the window size ws=6, then the target word apart from 6 tokens will be treated similarity as the next word. The scheme is used to assign more weight to closer words, as closer words are generally considered to be more important to the meaning of the target word. The CBoW, SG and GloVe models employ this weighting scheme. The GloVe model weights the contexts using a harmonic function, for example, a context word four tokens away from an occurrence will be counted as $\\frac{1}{4}$. However, CBoW and SG implementation equally consider the contexts by dividing the ws with the distance from target word, e.g. ws=6 will weigh its context by $\\frac{6}{6} \\frac{5}{6} \\frac{4}{6} \\frac{3}{6} \\frac{2}{6} \\frac{1}{6}$." + ], + [ + "The sub-word model BIBREF24 can learn the internal structure of words by sharing the character representations across words. In that way, the vector for each word is made of the sum of those character $n-gram$. Such as, a vector of a word \u201ctable\u201d is a sum of $n-gram$ vectors by setting the letter $n-gram$ size $min=3$ to $max=6$ as, $, abl, able, able>, ble, ble>, le>$, we can get all sub-words of \"table\" with minimum length of $minn=3$ and maximum length of $maxn=6$. The $<$ and $>$ symbols are used to separate prefix and suffix words from other character sequences. In this way, the sub-word model utilizes the principles of morphology, which improves the quality of infrequent word representations. In addition to character $n-grams$, the input word $w$ is also included in the set of character $n-gram$, to learn the representation of each word. We obtain scoring function using a input dictionary of $n-grams$ with size $K$ by giving word $w$ , where $K_{w} \\subset \\lbrace 1, \\ldots , K\\rbrace $. A word representation $Z_{k}$ is associated to each $n-gram$ $Z$. Hence, each word is represented by the sum of character $n-gram$ representations, where, $s$ is the scoring function in the following equation," + ], + [ + "The position-dependent weighting approach BIBREF40 is used to avoid direct encoding of representations for words and their positions which can lead to over-fitting problem. The approach learns positional representations in contextual word representations and used to reweight word embedding. Thus, it captures good contextual representations at lower computational cost,", + "Where, $p$ is individual position in context window associated with $d_{p}$ vector. Afterwards the context vector reweighted by their positional vectors is average of context words. The relative positional set is $P$ in context window and $v_{C}$ is context vector of $w_{t}$ respectively." + ], + [ + "The use sparse Shifted Positive Point-wise Mutual Information (SPPMI) BIBREF41 word-context matrix in learning word representations improves results on two word similarity tasks. The CBoW and SG have $k$ (number of negatives) BIBREF27 BIBREF20 hyperparameter, which affects the value that both models try to optimize for each $(w, c): P M I(w, c)-\\log k$. Parameter $k$ has two functions of better estimation of negative examples, and it performs as before observing the probability of positive examples (actual occurrence of $w,c$)." + ], + [ + "Before creating a context window, the automatic deletion of rare words also leads to performance gain in CBoW, SG and GloVe models, which further increases the actual size of context windows." + ], + [ + "The intrinsic evaluation is based on semantic similarity BIBREF23 in word embeddings. The word similarity measure approach states BIBREF35 that the words are similar if they appear in the similar context. We measure word similarity of proposed Sindhi word embeddings using dot product method and WordSim353." + ], + [ + "The cosine similarity between two non-zero vectors is a popular measure that calculates the cosine of the angle between them which can be derived by using the Euclidean dot product method. The dot product is a multiplication of each component from both vectors added together. The result of a dot product between two vectors isn\u2019t another vector but a single value or a scalar. The dot product for two vectors can be defined as: $\\overrightarrow{a}=\\left(a_{1}, a_{2}, a_{3}, \\dots , a_{n}\\right)$ and $\\overrightarrow{b}=\\left({b}_{1}, {b}_{2}, {b}_{3}, \\ldots , {b}_{n}\\right)$ where $a_{n}$ and $b_{n}$ are the components of the vector and $n$ is dimension of vectors such as,", + "However, the cosine of two non-zero vectors can be derived by using the Euclidean dot product formula,", + "Given $a_{i}$ two vectors of attributes $a$ and $b$, the cosine similarity, $\\cos ({\\theta })$, is represented using a dot product and magnitude as,", + "where $a_{i}$ and $b_{i}$ are components of vector $\\overrightarrow{a}$ and $\\overrightarrow{b}$, respectively." + ], + [ + "The WordSim353 BIBREF42 is popular for the evaluation of lexical similarity and relatedness. The similarity score is assigned with 13 to 16 human subjects with semantic relations BIBREF30 for 353 English noun pairs. Due to the lack of annotated datasets in the Sindhi language, we translated WordSim353 using English to Sindhi bilingual dictionary for the evaluation of our proposed Sindhi word embeddings and SdfastText. We use the Spearman correlation coefficient for the semantic and syntactic similarity comparison which is used to used to discover the strength of linear or nonlinear relationships if there are no repeated data values. A perfect Spearman\u2019s correlation of $+1$ or $-1$ discovers the strength of a link between two sets of data (word-pairs) when observations are monotonically increasing or decreasing functions of each other in a following way,", + "where $r_s$ is the rank correlation coefficient, $n$ denote the number of observations, and $d^i$ is the rank difference between $i^{th}$ observations." + ], + [ + "The large corpus acquired from multiple resources is rich in vocabulary. We present the complete statistics of collected corpus (see Table TABREF52) with number of sentences, words and unique tokens." + ], + [ + "The frequency of letter occurrences in human language is not arbitrarily organized but follow some specific rules which enable us to describe some linguistic regularities. The Zipf\u2019s law BIBREF43 suggests that if the frequency of letter or word occurrence ranked in descending order such as,", + "Where, $F_{r}$ is the letter frequency of rth rank, $a$ and $b$ are parameters of input text. The comparative letter frequency in the corpus is the total number of occurrences of a letter divided by the total number of letters present in the corpus. The letter frequencies in our developed corpus are depicted in Figure FIGREF55; however, the corpus contains 187,620,276 total number of the character set. Sindhi Persian-Arabic alphabet consists of 52 letters but in the vocabulary 59 letters are detected, additional seven letters are modified uni-grams and standalone honorific symbols." + ], + [ + "We denote the combination of letter occurrences in a word as n-grams, where each letter is a gram in a word. The letter n-gram frequency is carefully analyzed in order to find the length of words which is essential to develop NLP systems, including learning of word embeddings such as choosing the minimum or maximum length of sub-word for character-level representation learning BIBREF24. We calculate the letter n-grams in words along with their percentage in the developed corpus (see Table TABREF57). The bi-gram words are most frequent, mostly consists of stop words and secondly, 4-gram words have a higher frequency." + ], + [ + "The word frequency count is an observation of word occurrences in the text. The commonly used words are considered to be with higher frequency, such as the word \u201cthe\" in English. Similarly, the frequency of rarely used words to be lower. Such frequencies can be calculated at character or word-level. We calculate word frequencies by counting a word $w$ occurrence in the corpus $c$, such as,", + "Where the frequency of $w$ is the sum of every occurrence $k$ of $w$ in $c$." + ], + [ + "The most frequent and least important words in NLP are often classified as stop words. The removal of such words can boost the performance of the NLP model BIBREF38, such as sentiment analysis and text classification. But the construction of such words list is time consuming and requires user decisions. Firstly, we determined Sindhi stop words by counting their term frequencies using Eq. DISPLAY_FORM59, and secondly, by analysing their grammatical status with the help of Sindhi linguistic expert because all the frequent words are not stop words (see Figure FIGREF62). After determining the importance of such words with the help of human judgment, we placed them in the list of stop words. The total number of detected stop words is 340 in our developed corpus. The partial list of most frequent Sindhi stop words is depicted in Table TABREF61 along with their frequency. The filtration of stop words is an essential preprocessing step for learning GloVe BIBREF26 word embeddings; therefore, we filtered out stop words for preparing input for the GloVe model. However, the sub-sampling approach BIBREF33 BIBREF24 is used to discard such most frequent words in CBoW and SG models." + ], + [ + "Hyperparameter optimization BIBREF23is more important than designing a novel algorithm. We carefully choose to optimize the dictionary and algorithm-based parameters of CBoW, SG and GloVe algorithms. Hence, we conducted a large number of experiments for training and evaluation until the optimization of most suitable hyperparameters depicted in Table TABREF64 and discussed in Section SECREF63. The choice of optimized hyperparameters is based on The high cosine similarity score in retrieving nearest neighboring words, the semantic, syntactic similarity between word pairs, WordSim353, and visualization of the distance between twenty nearest neighbours using t-SNE respectively. All the experiments are conducted on GTX 1080-TITAN GPU." + ], + [ + "The state-of-the-art SG, CBoW BIBREF27 BIBREF33 BIBREF20 BIBREF24 and Glove BIBREF26 word embedding algorithms are evaluated by parameter tuning for development of Sindhi word embeddings. These parameters can be categories into dictionary and algorithm based, respectively. The integration of character n-gram in learning word representations is an ideal method especially for rich morphological languages because this approach has the ability to compute rare and misspelled words. Sindhi is also a rich morphological language. Therefore more robust embeddings became possible to train with the hyperparameter optimization of SG, CBoW and GloVe algorithms. We tuned and evaluated the hyperparameters of three algorithms individually which are discussed as follows:", + "Number of Epochs: Generally, more epochs on the corpus often produce better results but more epochs take long training time. Therefore, we evaluate 10, 20, 30 and 40 epochs for each word embedding model, and 40 epochs constantly produce good results.", + "Learning rate (lr): We tried lr of $0.05$, $0.1$, and $0.25$, the optimal lr $(0.25)$ gives the better results for training all the embedding models.", + "Dimensions ($D$): We evaluate and compare the quality of $100-D$, $200-D$, and $300-D$ using WordSim353 on different $ws$, and the optimal $300-D$ are evaluated with cosine similarity matrix for querying nearest neighboring words and calculating the similarity between word pairs. The embedding dimensions have little affect on the quality of the intrinsic evaluation process. However, the selection of embedding dimensions might have more impact on the accuracy in certain downstream NLP applications. The lower embedding dimensions are faster to train and evaluate.", + "Character n-grams: The selection of minimum (minn) and the maximum (maxn) length of character $n-grams$ is an important parameter for learning character-level representations of words in CBoW and SG models. Therefore, the n-grams from $3-9$ were tested to analyse the impact on the accuracy of embedding. We optimized the length of character n-grams from $minn=2$ and $maxn=7$ by keeping in view the word frequencies depicted in Table TABREF57.", + "Window size (ws): The large ws means considering more context words and similarly less ws means to limit the size of context words. By changing the size of the dynamic context window, we tried the ws of 3, 5, 7 the optimal ws=7 yield consistently better performance.", + "Negative Sampling (NS): : The more negative examples yield better results, but more negatives take long training time. We tried 10, 20, and 30 negative examples for CBoW and SG. The best negative examples of 20 for CBoW and SG significantly yield better performance in average training time.", + "Minimum word count (minw): We evaluated the range of minimum word counts from 1 to 8 and analyzed that the size of input vocabulary is decreasing at a large scale by ignoring more words similarly the vocabulary size was increasing by considering rare words. Therefore, by ignoring words with a frequency of less than 4 in CBoW, SG, and GloVe consistently yields better results with the vocabulary of 200,000 words.", + "Loss function (ls): we use hierarchical softmax (hs) for CBoW, negative sampling (ns) for SG and default loss function for GloVe BIBREF26.", + "The recommended verbosity level, number of buckets, sampling threshold, number of threads are used for training CBoW, SG BIBREF24, and GloVe BIBREF26." + ], + [ + "The cosine similarity matrix BIBREF35 is a popular approach to compute the relationship between all embedding dimensions of their distinct relevance to query word. The words with similar context get high cosine similarity and geometrical relatedness to Euclidean distance, which is a common and primary method to measure the distance between a set of words and nearest neighbors. Each word contains the most similar top eight nearest neighboring words determined by the highest cosine similarity score using Eq. DISPLAY_FORM48. We present the English translation of both query and retrieved words also discuss with their English meaning for ease of relevance judgment between the query and retrieved words.To take a closer look at the semantic and syntactic relationship captured in the proposed word embeddings, Table TABREF74 shows the top eight nearest neighboring words of five different query words Friday, Spring, Cricket, Red, Scientist taken from the vocabulary. As the first query word Friday returns the names of days Saturday, Sunday, Monday, Tuesday, Wednesday, Thursday in an unordered sequence. The SdfastText returns five names of days Sunday, Thursday, Monday, Tuesday and Wednesday respectively. The GloVe model also returns five names of days. However, CBoW and SG gave six names of days except Wednesday along with different writing forms of query word Friday being written in the Sindhi language which shows that CBoW and SG return more relevant words as compare to SdfastText and GloVe. The CBoW returned Add and GloVe returns Honorary words which are little similar to the querry word but SdfastText resulted two irrelevant words Kameeso (N) which is a name (N) of person in Sindhi and Phrase is a combination of three Sindhi words which are not tokenized properly. Similarly, nearest neighbors of second query word Spring are retrieved accurately as names and seasons and semantically related to query word Spring by CBoW, SG and Glove but SdfastText returned four irrelevant words of Dilbahar (N), Pharase, Ashbahar (N) and Farzana (N) out of eight. The third query word is Cricket, the name of a popular game. The first retrieved word in CBoW is Kabadi (N) that is a popular national game in Pakistan. Including Kabadi (N) all the returned words by CBoW, SG and GloVe are related to Cricket game or names of other games. But the first word in SdfastText contains a punctuation mark in retrieved word Gone.Cricket that are two words joined with a punctuation mark (.), which shows the tokenization error in preprocessing step, sixth retrieved word Misspelled is a combination of three words not related to query word, and Played, Being played are also irrelevant and stop words. Moreover, fourth query word Red gave results that contain names of closely related to query word and different forms of query word written in the Sindhi language. The last returned word Unknown by SdfastText is irrelevant and not found in the Sindhi dictionary for translation. The last query word Scientist also contains semantically related words by CBoW, SG, and GloVe, but the first Urdu word given by SdfasText belongs to the Urdu language which means that the vocabulary may also contain words of other languages. Another unknown word returned by SdfastText does not have any meaning in the Sindhi dictionary. More interesting observations in the presented results are the diacritized words retrieved from our proposed word embeddings and The authentic tokenization in the preprocessing step presented in Figure FIGREF22. However, SdfastText has returned tri-gram words of Phrase in query words Friday, Spring, a Misspelled word in Cricket and Scientist query words. Hence, the overall performance of our proposed SG, CBoW, and GloVe demonstrate high semantic relatedness in retrieving the top eight nearest neighbor words." + ], + [ + "Generally, closer words are considered more important to a word\u2019s meaning. The word embeddings models have the ability to capture the lexical relations between words. Identifying such relationship that connects words is important in NLP applications. We measure that semantic relationship by calculating the dot product of two vectors using Eq. DISPLAY_FORM48. The high cosine similarity score denotes the closer words in the embedding matrix, while less cosine similarity score means the higher distance between word pairs. We present the cosine similarity score of different semantically or syntactically related word pairs taken from the vocabulary in Table TABREF77 along with English translation, which shows the average similarity of 0.632, 0.650, 0.591 yields by CBoW, SG and GloVe respectively. The SG model achieved a high average similarity score of 0.650 followed by CBoW with a 0.632 average similarity score. The GloVe also achieved a considerable average score of 0.591 respectively. However, the average similarity score of SdfastText is 0.388 and the word pair Microsoft-Bill Gates is not available in the vocabulary of SdfastText. This shows that along with performance, the vocabulary in SdfastText is also limited as compared to our proposed word embeddings.", + "Moreover, the average semantic relatedness similarity score between countries and their capitals is shown in Table TABREF78 with English translation, where SG also yields the best average score of 0.663 followed by CBoW with 0.611 similarity score. The GloVe also yields better semantic relatedness of 0.576 and the SdfastText yield an average score of 0.391. The first query word China-Beijing is not available the vocabulary of SdfastText. However, the similarity score between Afghanistan-Kabul is lower in our proposed CBoW, SG, GloVe models because the word Kabul is the name of the capital of Afghanistan as well as it frequently appears as an adjective in Sindhi text which means able." + ], + [ + "We evaluate the performance of our proposed word embeddings using the WordSim353 dataset by translation English word pairs to Sindhi. Due to vocabulary differences between English and Sindhi, we were unable to find the authentic meaning of six terms, so we left these terms untranslated. So our final Sindhi WordSim353 consists of 347 word pairs. Table TABREF80 shows the Spearman correlation results using Eq. DISPLAY_FORM51 on different dimensional embeddings on the translated WordSim353. The Table TABREF80 presents complete results with the different ws for CBoW, SG and GloVe in which the ws=7 subsequently yield better performance than ws of 3 and 5, respectively. The SG model outperforms CBoW and GloVe in semantic and syntactic similarity by achieving the performance of 0.629 with ws=7. In comparison with English BIBREF27 achieved the average semantic and syntactic similarity of 0.637, 0.656 with CBoW and SG, respectively. Therefore, despite the challenges in translation from English to Sindhi, our proposed Sindhi word embeddings have efficiently captured the semantic and syntactic relationship." + ], + [ + "We use t-Distributed Stochastic Neighboring (t-SNE) dimensionality BIBREF36 reduction algorithm with PCA BIBREF37 for exploratory embeddings analysis in 2-dimensional map. The t-SNE is a non-linear dimensionality reduction algorithm for visualization of high dimensional datasets. It starts the probability calculation of similar word clusters in high-dimensional space and calculates the probability of similar points in the corresponding low-dimensional space. The purpose of t-SNE for visualization of word embeddings is to keep similar words close together in 2-dimensional $x,y$ coordinate pairs while maximizing the distance between dissimilar words. The t-SNE has a perplexity (PPL) tunable parameter used to balance the data points at both the local and global levels. We visualize the embeddings using PPL=20 on 5000-iterations of 300-D models. We use the same query words (see Table TABREF74) by retrieving the top 20 nearest neighboring word clusters for a better understanding of the distance between similar words. Every query word has a distinct color for the clear visualization of a similar group of words. The closer word clusters show the high similarity between the query and retrieved word clusters. The word clusters in SG (see Fig. FIGREF83) are closer to their group of semantically related words. Secondly, the CBoW model depicted in Fig. FIGREF82 and GloVe Fig. FIGREF84 also show the better cluster formation of words than SdfastText Fig. FIGREF85, respectively." + ], + [ + "In this era of the information age, the existence of LRs plays a vital role in the digital survival of natural languages because the NLP tools are used to process a flow of un-structured data from disparate sources. It is imperative to mention that presently, Sindhi Persian-Arabic is frequently used in online communication, newspapers, public institutions in Pakistan and India. Due to the growing use of Sindhi on web platforms, the need for its LRs is also increasing for the development of language technology tools. But little work has been carried out for the development of resources which is not sufficient to design a language independent or machine learning algorithms. The present work is a first comprehensive initiative on resource development along with their evaluation for statistical Sindhi language processing. More recently, the NN based approaches have produced a state-of-the-art performance in NLP by exploiting unsupervised word embeddings learned from the large unlabelled corpus. Such word embeddings have also motivated the work on low-resourced languages. Our work mainly consists of novel contributions of resource development along with comprehensive evaluation for the utilization of NN based approaches in SNLP applications. The large corpus obtained from multiple web resources is utilized for the training of word embeddings using SG, CBoW and Glove models. The intrinsic evaluation along with comparative results demonstrates that the proposed Sindhi word embeddings have accurately captured the semantic information as compare to recently revealed SdfastText word vectors. The SG yield best results in nearest neighbors, word pair relationship and semantic similarity. The performance of CBoW is also close to SG in all the evaluation matrices. The GloVe also yields better word representations; however SG and CBoW models surpass the GloVe model in all evaluation matrices. Hyperparameter optimization is as important as designing a new algorithm. The choice of optimal parameters is a key aspect of performance gain in learning robust word embeddings. Moreover, We analysed that the size of the corpus and careful preprocessing steps have a large impact on the quality of word embeddings. However, in algorithmic perspective, the character-level learning approach in SG and CBoW improves the quality of representation learning, and overall window size, learning rate, number of epochs are the core parameters that largely influence the performance of word embeddings models. Ultimately, the new corpus of low-resourced Sindhi language, list of stop words and pretrained word embeddings along with empirical evaluation, will be a good supplement for future research in SSLP applications. In the future, we aim to use the corpus for annotation projects such as parts-of-speech tagging, named entity recognition. The proposed word embeddings will be refined further by creating custom benchmarks and the extrinsic evaluation approach will be employed for the performance analysis of proposed word embeddings. Moreover, we will also utilize the corpus using Bi-directional Encoder Representation Transformer BIBREF13 for learning deep contextualized Sindhi word representations. Furthermore, the generated word embeddings will be utilized for the automatic construction of Sindhi WordNet." + ], + [ + "In this paper, we mainly present three novel contributions of large corpus development contains large vocabulary of more than 61 million tokens, 908,456 unique words. Secondly, the list of Sindhi stop words is constructed by finding their high frequency and least importance with the help of Sindhi linguistic expert. Thirdly, the unsupervised Sindhi word embeddings are generated using state-of-the-art CBoW, SG and GloVe algorithms and evaluated using popular intrinsic evaluation approaches of cosine similarity matrix and WordSim353 for the first time in Sindhi language processing. We translate English WordSim353 using the English-Sindhi bilingual dictionary, which will also be a good resource for the evaluation of Sindhi word embeddings. Moreover, the proposed word embeddings are also compared with recently revealed SdfastText word representations.", + "Our empirical results demonstrate that our proposed Sindhi word embeddings have captured high semantic relatedness in nearest neighboring words, word pair relationship, country, and capital and WordSim353. The SG yields the best performance than CBoW and GloVe models subsequently. However, the performance of GloVe is low on the same vocabulary because of character-level learning of word representations and sub-sampling approaches in SG and CBoW. Our proposed Sindhi word embeddings have surpassed SdfastText in the intrinsic evaluation matrix. Also, the vocabulary of SdfastText is limited because they are trained on a small Wikipedia corpus of Sindhi Persian-Arabic. We will further investigate the extrinsic performance of proposed word embeddings on the Sindhi text classification task in the future. The proposed resources along with systematic evaluation will be a sophisticated addition to the computational resources for statistical Sindhi language processing." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0795/environment/Dockerfile b/qasper-0795/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..965507458d0d703efd0af039b23f47131d70c351 --- /dev/null +++ b/qasper-0795/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/* diff --git a/qasper-0795/instruction.md b/qasper-0795/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..bfad00ee3a069c59535fe36e02fa7bd0184d3bea --- /dev/null +++ b/qasper-0795/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: The Wiki Music dataset: A tool for computational analysis of popular music + +Question: What trends are found in musical preferences? \ No newline at end of file diff --git a/qasper-0800/instruction.md b/qasper-0800/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..27dd53147c183e093fe421fdba7d020cd586d6c9 --- /dev/null +++ b/qasper-0800/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: An Annotated Corpus of Emerging Anglicisms in Spanish Newspaper Headlines + +Question: Does the paper motivate the use of CRF as the baseline model? \ No newline at end of file diff --git a/qasper-0801/instruction.md b/qasper-0801/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..8e9cae0ce8b0932435b6cd5ea6709bdfa4406778 --- /dev/null +++ b/qasper-0801/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: An Annotated Corpus of Emerging Anglicisms in Spanish Newspaper Headlines + +Question: What are the handcrafted features used? \ No newline at end of file diff --git a/qasper-0806/instruction.md b/qasper-0806/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..4eba257d6be089ec2ea65fa9da91d08ecdda7bff --- /dev/null +++ b/qasper-0806/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Efficient Attention using a Fixed-Size Memory Representation + +Question: Which baseline methods are used? \ No newline at end of file diff --git a/qasper-0807/instruction.md b/qasper-0807/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..93c126ac17e1ec9f34301c8c30df44db9ee1066a --- /dev/null +++ b/qasper-0807/instruction.md @@ -0,0 +1,88 @@ +Name of Paper: Efficient Attention using a Fixed-Size Memory Representation + +Question: How much is the BLEU score? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Sequence-to-Sequence Model with Attention", + "Memory-Based Attention Model", + "Model Interpretations", + "Position Encodings (PE)", + "Toy Copying Experiment", + "Machine Translation", + "Visualizing Attention", + "Related Work", + "Conclusion" + ], + "paragraphs": [ + [ + "Sequence-to-sequence models BIBREF0 , BIBREF1 have achieved state of the art results across a wide variety of tasks, including Neural Machine Translation (NMT) BIBREF2 , BIBREF3 , text summarization BIBREF4 , BIBREF5 , speech recognition BIBREF6 , BIBREF7 , image captioning BIBREF8 , and conversational modeling BIBREF9 , BIBREF10 .", + "The most popular approaches are based on an encoder-decoder architecture consisting of two recurrent neural networks (RNNs) and an attention mechanism that aligns target to source tokens BIBREF2 , BIBREF11 . The typical attention mechanism used in these architectures computes a new attention context at each decoding step based on the current state of the decoder. Intuitively, this corresponds to looking at the source sequence after the output of every single target token.", + "Inspired by how humans process sentences, we believe it may be unnecessary to look back at the entire original source sequence at each step. We thus propose an alternative attention mechanism (section \"Memory-Based Attention Model\" ) that leads to smaller computational time complexity. Our method predicts $K$ attention context vectors while reading the source, and learns to use a weighted average of these vectors at each step of decoding. Thus, we avoid looking back at the source sequence once it has been encoded. We show (section \"Experiments\" ) that this speeds up inference while performing on-par with the standard mechanism on both toy and real-world WMT translation datasets. We also show that our mechanism leads to larger speedups as sequences get longer. Finally, by visualizing the attention scores (section \"Visualizing Attention\" ), we verify that the proposed technique learns meaningful alignments, and that different attention context vectors specialize on different parts of the source." + ], + [ + "Our models are based on an encoder-decoder architecture with attention mechanism BIBREF2 , BIBREF11 . An encoder function takes as input a sequence of source tokens $\\mathbf {x} = (x_1, ..., x_m)$ and produces a sequence of states $\\mathbf {s} = (s_1, ..., s_m)$ .The decoder is an RNN that predicts the probability of a target sequence $\\mathbf {y} = (y_1, ..., y_T \\mid \\mathbf {s})$ . The probability of each target token $y_i \\in \\lbrace 1, ... ,|V|\\rbrace $ is predicted based on the recurrent state in the decoder RNN, $h_i$ , the previous words, $y_{