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qasper-0110/instruction.md

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+Name of Paper: Enriching BERT with Knowledge Graph Embeddings for Document Classification
+
+Question: How do they combine text representations with the knowledge graph embeddings?

qasper-0142/instruction.md

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+Name of Paper: Active Learning for Chinese Word Segmentation in Medical Text
+
+Question: How does the active learning model work?

qasper-0143/instruction.md

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+Name of Paper: Active Learning for Chinese Word Segmentation in Medical Text
+
+Question: Which neural network architectures are employed?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Chinese Word Segmentation",
+    "Active Learning",
+    "Active Learning for Chinese Word Segmentation",
+    "CRF-based Word Segmenter",
+    "Information Entropy Based Scoring Model",
+    "Datasets",
+    "Parameter Settings",
+    "Experimental Results",
+    "Conclusion and Future Work",
+    "Acknowledgment"
+  ],
+  "paragraphs": [
+    [
+      "Electronic health records (EHRs) systematically collect patients' clinical information, such as health profiles, histories of present illness, past medical histories, examination results and treatment plans BIBREF0 . By analyzing EHRs, many useful information, closely related to patients, can be discovered BIBREF1 . Since Chinese EHRs are recorded without explicit word delimiters (e.g., \u201cUTF8gkai\u7cd6\u5c3f\u75c5\u916e\u75c7\u9178\u4e2d\u6bd2\u201d (diabetic ketoacidosis)), Chinese word segmentation (CWS) is a prerequisite for processing EHRs. Currently, state-of-the-art CWS methods usually require large amounts of manually-labeled data to reach their full potential. However, there are many challenges inherent in labeling EHRs. First, EHRs have many medical terminologies, such as \u201cUTF8gkai\u9ad8\u8840\u538b\u6027\u5fc3\u810f\u75c5\u201d (hypertensive heart disease) and \u201cUTF8gkai\u7f57\u6c0f\u82ac\u201d (Rocephin), so only annotators with medical backgrounds can be qualified to label EHRs. Second, EHRs may involve personal privacies of patients. Therefore, they cannot be openly published on a large scale for labeling. The above two problems lead to the high annotation cost and insufficient training corpus in the research of CWS in medical text.",
+      "CWS was usually formulated as a sequence labeling task BIBREF2 , which can be solved by supervised learning approaches, such as hidden markov model (HMM) BIBREF3 and conditional random field (CRF) BIBREF4 . However, these methods rely heavily on handcrafted features. To relieve the efforts of feature engineering, neural network-based methods are beginning to thrive BIBREF5 , BIBREF6 , BIBREF7 . However, due to insufficient annotated training data, conventional models for CWS trained on open corpus often suffer from significant performance degradation when transferred to a domain-specific text. Moreover, the task in medical domain is rarely dabbled, and only one related work on transfer learning is found in recent literatures BIBREF8 . However, researches related to transfer learning mostly remain in general domains, causing a major problem that a considerable amount of manually annotated data is required, when introducing the models into specific domains.",
+      "One of the solutions for this obstacle is to use active learning, where only a small scale of samples are selected and labeled in an active manner. Active learning methods are favored by the researchers in many natural language processing (NLP) tasks, such as text classification BIBREF9 and named entity recognition (NER) BIBREF10 . However, only a handful of works are conducted on CWS BIBREF2 , and few focuses on medical domain tasks.",
+      "Given the aforementioned challenges and current researches, we propose a word segmentation method based on active learning. To model the segmentation history, we incorporate a sampling strategy consisting of word score, link score and sequence score, which effectively evaluates the segmentation decisions. Specifically, we combine information branch and gated neural network to determine if the segment is a legal word, i.e., word score. Meanwhile, we use the hidden layer output of the long short-term memory (LSTM) BIBREF11 to find out how the word is linked to its surroundings, i.e., link score. The final decision on the selection of labeling samples is made by calculating the average of word and link scores on the whole segmented sentence, i.e., sequence score. Besides, to capture coherence over characters, we additionally add K-means clustering features to the input of CRF-based word segmenter.",
+      "To sum up, the main contributions of our work are summarized as follows:",
+      "The rest of this paper is organized as follows. Section SECREF2 briefly reviews the related work on CWS and active learning. Section SECREF3 presents an active learning method for CWS. We experimentally evaluate our proposed method in Section SECREF4 . Finally, Section SECREF5 concludes the paper and envisions on future work."
+    ],
+    [
+      "In past decades, researches on CWS have a long history and various methods have been proposed BIBREF13 , BIBREF14 , BIBREF15 , which is an important task for Chinese NLP BIBREF7 . These methods are mainly focus on two categories: supervised learning and deep learning BIBREF2 .",
+      "Supervised Learning Methods. Initially, supervised learning methods were widely-used in CWS. Xue BIBREF13 employed a maximum entropy tagger to automatically assign Chinese characters. Zhao et al. BIBREF16 used a conditional random field for tag decoding and considered both feature template selection and tag set selection. However, these methods greatly rely on manual feature engineering BIBREF17 , while handcrafted features are difficult to design, and the size of these features is usually very large BIBREF6 .",
+      "Deep Learning Methods. Recently, neural networks have been applied in CWS tasks. To name a few, Zheng et al. BIBREF14 used deep layers of neural networks to learn feature representations of characters. Chen et al. BIBREF6 adopted LSTM to capture the previous important information. Chen et al. BIBREF18 proposed a gated recursive neural network (GRNN), which contains reset and update gates to incorporate the complicated combinations of characters. Jiang and Tang BIBREF19 proposed a sequence-to-sequence transformer model to avoid overfitting and capture character information at the distant site of a sentence. Yang et al. BIBREF20 investigated subword information for CWS and integrated subword embeddings into a Lattice LSTM (LaLSTM) network. However, general word segmentation models do not work well in specific field due to lack of annotated training data.",
+      "Currently, a handful of domain-specific CWS approaches have been studied, but they focused on decentralized domains. In the metallurgical field, Shao et al. BIBREF15 proposed a domain-specific CWS method based on Bi-LSTM model. In the medical field, Xing et al. BIBREF8 proposed an adaptive multi-task transfer learning framework to fully leverage domain-invariant knowledge from high resource domain to medical domain. Meanwhile, transfer learning still greatly focuses on the corpus in general domain. When it comes to the specific domain, large amounts of manually-annotated data is necessary. Active learning can solve this problem to a certain extent. However, due to the challenges faced by performing active learning on CWS, only a few studies have been conducted. On judgements, Yan et al. BIBREF21 adopted the local annotation strategy, which selects substrings around the informative characters in active learning. However, their method still stays at the statistical level. Unlike the above method, we propose an active learning approach for CWS in medical text, which combines information entropy with neural network to effectively reduce annotation cost."
+    ],
+    [
+      "Active learning BIBREF22 mainly aims to ease the data collection process by automatically deciding which instances should be labeled by annotators to train a model as quickly and effectively as possible BIBREF23 . The sampling strategy plays a key role in active learning. In the past decade, the rapid development of active learning has resulted in various sampling strategies, such as uncertainty sampling BIBREF24 , query-by-committee BIBREF25 and information gain BIBREF26 . Currently, the most mainstream sampling strategy is uncertainty sampling. It focuses its selection on samples closest to the decision boundary of the classifier and then chooses these samples for annotators to relabel BIBREF27 .",
+      "The formal definition of uncertainty sampling is to select a sample INLINEFORM0 that maximizes the entropy INLINEFORM1 over the probability of predicted classes: DISPLAYFORM0 ",
+      "where INLINEFORM0 is a multi-dimensional feature vector, INLINEFORM1 is its binary label, and INLINEFORM2 is the predicted probability, through which a classifier trained on training sets can map features to labels. However, in some complicated tasks, such as CWS and NER, only considering the uncertainty of classifier is obviously not enough."
+    ],
+    [
+      "Active learning methods can generally be described into two parts: a learning engine and a selection engine BIBREF28 . The learning engine is essentially a classifier, which is mainly used for training of classification problems. The selection engine is based on the sampling strategy, which chooses samples that need to be relabeled by annotators from unlabeled data. Then, relabeled samples are added to training set for classifier to re-train, thus continuously improving the accuracy of the classifier. In this paper, a CRF-based segmenter and a scoring model are employed as learning engine and selection engine, respectively.",
+      "Fig. FIGREF7 and Algorithm SECREF3 demonstrate the procedure of CWS based on active learning. First, we train a CRF-based segmenter by train set. Then, the segmenter is employed to annotate the unlabeled set roughly. Subsequently, information entropy based scoring model picks INLINEFORM0 -lowest ranking samples for annotators to relabel. Meanwhile, the train sets and unlabeled sets are updated. Finally, we re-train the segmenter. The above steps iterate until the desired accuracy is achieved or the number of iterations has reached a predefined threshold. [!ht] Active Learning for Chinese Word Segmentation labeled data INLINEFORM1 , unlabeled data INLINEFORM2 , the number of iterations INLINEFORM3 , the number of samples selected per iteration INLINEFORM4 , partitioning function INLINEFORM5 , size INLINEFORM6 a word segmentation model INLINEFORM7 with the smallest test set loss INLINEFORM8 Initialize: INLINEFORM9 ",
+      " train a word segmenter INLINEFORM0 ",
+      " estimate the test set loss INLINEFORM0 ",
+      " label INLINEFORM0 by INLINEFORM1 ",
+      " INLINEFORM0 to INLINEFORM1 INLINEFORM2 compute INLINEFORM3 by branch information entropy based scoring model",
+      " select INLINEFORM0 -lowest ranking samples INLINEFORM1 ",
+      "relabel INLINEFORM0 by annotators",
+      "form a new labeled dataset INLINEFORM0 ",
+      "form a new unlabeled dataset INLINEFORM0 ",
+      "train a word segmenter INLINEFORM0 ",
+      "estimate the new test loss INLINEFORM0 ",
+      "compute the loss reduction INLINEFORM0 ",
+      " INLINEFORM0 INLINEFORM1 ",
+      " INLINEFORM0 ",
+      " INLINEFORM0 INLINEFORM1 with the smallest test set loss INLINEFORM2 INLINEFORM3 "
+    ],
+    [
+      "CWS can be formalized as a sequence labeling problem with character position tags, which are (`B', `M', `E', `S'). So, we convert the labeled data into the `BMES' format, in which each character in the sequence is assigned into a label as follows one by one: B=beginning of a word, M=middle of a word, E=end of a word and S=single word.",
+      "In this paper, we use CRF as a training model for CWS task. Given the observed sequence, CRF has a single exponential model for the joint probability of the entire sequence of labels, while maximum entropy markov model (MEMM) BIBREF29 uses per-state exponential models for the conditional probabilities of next states BIBREF4 . Therefore, it can solve the label bias problem effectively. Compared with neural networks, it has less dependency on the corpus size.",
+      "First, we pre-process EHRs at the character-level, separating each character of raw EHRs. For instance, given a sentence INLINEFORM0 , where INLINEFORM1 represents the INLINEFORM2 -th character, the separated form is INLINEFORM3 . Then, we employ Word2Vec BIBREF30 to train pre-processed EHRs to get character embeddings. To capture interactions between adjacent characters, K-means clustering algorithm BIBREF31 is utilized to feature the coherence over characters. In general, K-means divides INLINEFORM4 EHR characters into INLINEFORM5 groups of clusters and the similarity of EHR characters in the same cluster is higher. With each iteration, K-means can classify EHR characters into the nearest cluster based on distance to the mean vector. Then, recalculating and adjusting the mean vectors of these clusters until the mean vector converges. K-means features explicitly show the difference between two adjacent characters and even multiple characters. Finally, we additionally add K-means clustering features to the input of CRF-based segmenter. The segmenter makes positional tagging decisions over individual characters. For example, a Chinese segmented sentence UTF8gkai\u201c\u75c5\u4eba/\u957f\u671f/\u4e8e/\u6211\u9662/\u80be\u75c5\u79d1/\u4f4f\u9662/\u6cbb\u7597/\u3002/\" (The patient was hospitalized for a long time in the nephrology department of our hospital.) is labeled as `BEBESBEBMEBEBES'."
+    ],
+    [
+      "To select the most appropriate sentences in a large number of unlabeled corpora, we propose a scoring model based on information entropy and neural network as the sampling strategy of active learning, which is inspired by Cai and Zhao BIBREF32 . The score of a segmented sentence is computed as follows. First, mapping the segmented sentence to a sequence of candidate word embeddings. Then, the scoring model takes the word embedding sequence as input, scoring over each individual candidate word from two perspectives: (1) the possibility that the candidate word itself can be regarded as a legal word; (2) the rationality of the link that the candidate word directly follows previous segmentation history. Fig. FIGREF10 illustrates the entire scoring model. A gated neural network is employed over character embeddings to generate distributed representations of candidate words, which are sent to a LSTM model.",
+      "We use gated neural network and information entropy to capture the likelihood of the segment being a legal word. The architecture of word score model is depicted in Fig. FIGREF12 .",
+      "Gated Combination Neural Network (GCNN)",
+      "To effectively learn word representations through character embeddings, we use GCNN BIBREF32 . The architecture of GCNN is demonstrated in Fig. FIGREF13 , which includes update gate and reset gate. The gated mechanism not only captures the characteristics of the characters themselves, but also utilizes the interaction between the characters. There are two types of gates in this network structure: reset gates and update gates. These two gated vectors determine the final output of the gated recurrent neural network, where the update gate helps the model determine what to be passed, and the reset gate primarily helps the model decide what to be cleared. In particular, the word embedding of a word with INLINEFORM0 characters can be computed as: DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 are update gates for new combination vector INLINEFORM2 and the i-th character INLINEFORM3 respectively, the combination vector INLINEFORM4 is formalized as: DISPLAYFORM0 ",
+      "where INLINEFORM0 and INLINEFORM1 are reset gates for characters.",
+      "Left and Right Branch Information Entropy In general, each string in a sentence may be a word. However, compared with a string which is not a word, the string of a word is significantly more independent. The branch information entropy is usually used to judge whether each character in a string is tightly linked through the statistical characteristics of the string, which reflects the likelihood of a string being a word. The left and right branch information entropy can be formalized as follows: DISPLAYFORM0 DISPLAYFORM1 ",
+      "where INLINEFORM0 denotes the INLINEFORM1 -th candidate word, INLINEFORM2 denotes the character set, INLINEFORM3 denotes the probability that character INLINEFORM4 is on the left of word INLINEFORM5 and INLINEFORM6 denotes the probability that character INLINEFORM7 is on the right of word INLINEFORM8 . INLINEFORM9 and INLINEFORM10 respectively represent the left and right branch information entropy of the candidate word INLINEFORM11 . If the left and right branch information entropy of a candidate word is relatively high, the probability that the candidate word can be combined with the surrounded characters to form a word is low, thus the candidate word is likely to be a legal word.",
+      "To judge whether the candidate words in a segmented sentence are legal words, we compute the left and right entropy of each candidate word, then take average as the measurement standard: DISPLAYFORM0 ",
+      "We represent a segmented sentence with INLINEFORM0 candidate words as [ INLINEFORM1 , INLINEFORM2 ,..., INLINEFORM3 ], so the INLINEFORM4 ( INLINEFORM5 ) of the INLINEFORM6 -th candidate word is computed by its average entropy: DISPLAYFORM0 ",
+      "In this paper, we use LSTM to capture the coherence between words in a segmented sentence. This neural network is mainly an optimization for traditional RNN. RNN is widely used to deal with time-series prediction problems. The result of its current hidden layer is determined by the input of the current layer and the output of the previous hidden layer BIBREF33 . Therefore, RNN can remember historical results. However, traditional RNN has problems of vanishing gradient and exploding gradient when training long sequences BIBREF34 . By adding a gated mechanism to RNN, LSTM effectively solves these problems, which motivates us to get the link score with LSTM. Formally, the LSTM unit performs the following operations at time step INLINEFORM0 : DISPLAYFORM0 DISPLAYFORM1 ",
+      "where INLINEFORM0 , INLINEFORM1 , INLINEFORM2 are the inputs of LSTM, all INLINEFORM3 and INLINEFORM4 are a set of parameter matrices to be trained, and INLINEFORM5 is a set of bias parameter matrices to be trained. INLINEFORM6 and INLINEFORM7 operation respectively represent matrix element-wise multiplication and sigmoid function. In the LSTM unit, there are two hidden layers ( INLINEFORM8 , INLINEFORM9 ), where INLINEFORM10 is the internal memory cell for dealing with vanishing gradient, while INLINEFORM11 is the main output of the LSTM unit for complex operations in subsequent layers.",
+      "We denotes INLINEFORM0 as the word embedding of time step INLINEFORM1 , a prediction INLINEFORM2 of next word embedding INLINEFORM3 can be computed by hidden layer INLINEFORM4 : DISPLAYFORM0 ",
+      "Therefore, link score of next word embedding INLINEFORM0 can be computed as: DISPLAYFORM0 ",
+      "Due to the structure of LSTM, vector INLINEFORM0 contains important information of entire segmentation decisions. In this way, the link score gets the result of the sequence-level word segmentation, not just word-level.",
+      "Intuitively, we can compute the score of a segmented sequence by summing up word scores and link scores. However, we find that a sequence with more candidate words tends to have higher sequence scores. Therefore, to alleviate the impact of the number of candidate words on sequence scores, we calculate final scores as follows: DISPLAYFORM0 ",
+      "where INLINEFORM0 denotes the INLINEFORM1 -th segmented sequence with INLINEFORM2 candidate words, and INLINEFORM3 represents the INLINEFORM4 -th candidate words in the segmented sequence.",
+      "When training the model, we seek to minimize the sequence score of the corrected segmented sentence and the predicted segmented sentence. DISPLAYFORM0 ",
+      "where INLINEFORM0 is the loss function."
+    ],
+    [
+      "We collect 204 EHRs with cardiovascular diseases from the Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine and each contains 27 types of records. We choose 4 different types with a total of 3868 records from them, which are first course reports, medical records, chief ward round records and discharge records. The detailed information of EHRs are listed in Table TABREF32 .",
+      "We split our datasets as follows. First, we randomly select 3200 records from 3868 records as unlabeled set. Then, we manually annotate remaining 668 records as labeled set, which contains 1170 sentences. Finally, we divide labeled set into train set and test set with the ratio of 7:3 randomly. Statistics of datasets are listed in Table TABREF33 ."
+    ],
+    [
+      "To determine suitable parameters, we divide training set into two sets, the first 80% sentences as training set and the rest 20% sentences as validation set.",
+      "Character embedding dimensions and K-means clusters are two main parameters in the CRF-based word segmenter.",
+      "In this paper, we choose character-based CRF without any features as baseline. First, we use Word2Vec to train character embeddings with dimensions of [`50', `100', `150', `200', `300', `400'] respectively, thus we obtain 6 different dimensional character embeddings. Second, these six types of character embeddings are used as the input to K-means algorithm with the number of clusters [`50', `100', `200', `300', `400', `500', `600'] respectively to capture the corresponding features of character embeddings. Then, we add K-means clustering features to baseline for training. As can be seen from Fig. FIGREF36 , when the character embedding dimension INLINEFORM0 = 150 and the number of clusters INLINEFORM1 = 400, CRF-based word segmenter performs best, so these two parameters are used in subsequent experiments.",
+      "Hyper-parameters of neural network have a great impact on the performance. The hyper-parameters we choose are listed in Table TABREF38 .",
+      "The dimension of character embeddings is set as same as the parameter used in CRF-based word segmenter and the number of hidden units is also set to be the same as it. Maximum word length is ralated to the number of parameters in GCNN unit. Since there are many long medical terminologies in EHRs, we set the maximum word length as 6. In addition, dropout is an effective way to prevent neural networks from overfitting BIBREF35 . To avoid overfitting, we drop the input layer of the scoring model with the rate of 20%."
+    ],
+    [
+      "Our work experimentally compares two mainstream CWS tools (LTP and Jieba) on training and testing sets. These two tools are widely used and recognized due to their high INLINEFORM0 -score of word segmentation in general fields. However, in specific fields, there are many terminologies and uncommon words, which lead to the unsatisfactory performance of segmentation results. To solve the problem of word segmentation in specific fields, these two tools provide a custom dictionary for users. In the experiments, we also conduct a comparative experiment on whether external domain dictionary has an effect on the experimental results. We manually construct the dictionary when labeling EHRs.",
+      "From the results in Table TABREF41 , we find that Jieba benefits a lot from the external dictionary. However, the Recall of LTP decreases when joining the domain dictionary. Generally speaking, since these two tools are trained by general domain corpus, the results are not ideal enough to cater to the needs of subsequent NLP of EHRs when applied to specific fields.",
+      "To investigate the effectiveness of K-means features in CRF-based segmenter, we also compare K-means with 3 different clustering features, including MeanShift BIBREF36 , SpectralClustering BIBREF37 and DBSCAN BIBREF38 on training and testing sets. From the results in Table TABREF43 , by adding additional clustering features in CRF-based segmenter, there is a significant improvement of INLINEFORM0 -score, which indicates that clustering features can effectively capture the semantic coherence between characters. Among these clustering features, K-means performs best, so we utlize K-means results as additional features for CRF-based segmenter.",
+      "In this experiment, since uncertainty sampling is the most popular strategy in real applications for its simpleness and effectiveness BIBREF27 , we compare our proposed strategy with uncertainty sampling in active learning. We conduct our experiments as follows. First, we employ CRF-based segmenter to annotate the unlabeled set. Then, sampling strategy in active learning selects a part of samples for annotators to relabel. Finally, the relabeled samples are added to train set for segmenter to re-train. Our proposed scoring strategy selects samples according to the sequence scores of the segmented sentences, while uncertainty sampling suggests relabeling samples that are closest to the segmenter\u2019s decision boundary.",
+      "Generally, two main parameters in active learning are the numbers of iterations and samples selected per iteration. To fairly investigate the influence of two parameters, we compare our proposed strategy with uncertainty sampling on the same parameter. We find that though the number of iterations is large enough, it has a limited impact on the performance of segmenter. Therefore, we choose 30 as the number of iterations, which is a good trade-off between speed and performance. As for the number of samples selected per iteration, there are 6078 sentences in unlabeled set, considering the high cost of relabeling, we set four sizes of samples selected per iteration, which are 2%, 5%, 8% and 11%.",
+      "The experimental results of two sampling strategies with 30 iterations on four different proportions of relabeled data are shown in Fig. FIGREF45 , where x-axis represents the number of iterations and y-axis denotes the INLINEFORM0 -score of the segmenter. Scoring strategy shows consistent improvements over uncertainty sampling in the early iterations, indicating that scoring strategy is more capable of selecting representative samples.",
+      "Furthermore, we also investigate the relations between the best INLINEFORM0 -score and corresponding number of iteration on two sampling strategies, which is depicted in Fig. FIGREF46 .",
+      "It is observed that in our proposed scoring model, with the proportion of relabeled data increasing, the iteration number of reaching the optimal word segmentation result is decreasing, but the INLINEFORM0 -score of CRF-based word segmenter is also gradually decreasing. When the proportion is 2%, the segmenter reaches the highest INLINEFORM1 -score: 90.62%. Obviously, our proposed strategy outperforms uncertainty sampling by a large margin. Our proposed method needs only 2% relabeled samples to obtain INLINEFORM2 -score of 90.62%, while uncertainty sampling requires 8% samples to reach its best INLINEFORM3 -score of 88.98%, which indicates that with our proposed method, we only need to manually relabel a small number of samples to achieve a desired segmentation result."
+    ],
+    [
+      "To relieve the efforts of EHRs annotation, we propose an effective word segmentation method based on active learning, in which the sampling strategy is a scoring model combining information entropy with neural network. Compared with the mainstream uncertainty sampling, our strategy selects samples from statistical perspective and deep learning level. In addition, to capture coherence between characters, we add K-means clustering features to CRF-based word segmenter. Based on EHRs collected from the Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, we evaluate our method on CWS task. Compared with uncertainty sampling, our method requires 6% less relabeled samples to achieve better performance, which proves that our method can save the cost of manual annotation to a certain extent.",
+      "In future, we plan to employ other widely-used deep neural networks, such as convolutional neural network and attention mechanism, in the research of EHRs segmentation. Then, we believe that our method can be applied to other tasks as well, so we will fully investigate the application of our method in other tasks, such as NER and relation extraction."
+    ],
+    [
+      "The authors would like to appreciate any suggestions or comments from the anonymous reviewers. This work was supported by the National Natural Science Foundation of China (No. 61772201) and the National Key R&D Program of China for \u201cPrecision medical research\" (No. 2018YFC0910550)."
+    ]
+  ]
+}
+```

qasper-0144/instruction.md

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+Name of Paper: InScript: Narrative texts annotated with script information
+
+Question: What are the key points in the role of script knowledge that can be studied?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Motivation",
+    "Collection via Amazon M-Turk",
+    "Data Statistics",
+    "Annotation",
+    "Annotation Schema",
+    "Development of the Schema",
+    "First Annotation Phase",
+    "Modification of the Schema",
+    "Special Cases",
+    "Inter-Annotator Agreement",
+    "Annotated Corpus Statistics",
+    "Comparison to the DeScript Corpus",
+    "Conclusion",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "A script is \u201ca standardized sequence of events that describes some stereotypical human activity such as going to a restaurant or visiting a doctor\u201d BIBREF0 . Script events describe an action/activity along with the involved participants. For example, in the script describing a visit to a restaurant, typical events are entering the restaurant, ordering food or eating. Participants in this scenario can include animate objects like the waiter and the customer, as well as inanimate objects such as cutlery or food.",
+      "Script knowledge has been shown to play an important role in text understanding (cullingford1978script, miikkulainen1995script, mueller2004understanding, Chambers2008, Chambers2009, modi2014inducing, rudinger2015learning). It guides the expectation of the reader, supports coreference resolution as well as common-sense knowledge inference and enables the appropriate embedding of the current sentence into the larger context. Figure 1 shows the first few sentences of a story describing the scenario taking a bath. Once the taking a bath scenario is evoked by the noun phrase (NP) \u201ca bath\u201d, the reader can effortlessly interpret the definite NP \u201cthe faucet\u201d as an implicitly present standard participant of the taking a bath script. Although in this story, \u201centering the bath room\u201d, \u201cturning on the water\u201d and \u201cfilling the tub\u201d are explicitly mentioned, a reader could nevertheless have inferred the \u201cturning on the water\u201d event, even if it was not explicitly mentioned in the text. Table 1 gives an example of typical events and participants for the script describing the scenario taking a bath.",
+      "A systematic study of the influence of script knowledge in texts is far from trivial. Typically, text documents (e.g. narrative texts) describing various scenarios evoke many different scripts, making it difficult to study the effect of a single script. Efforts have been made to collect scenario-specific script knowledge via crowdsourcing, for example the OMICS and SMILE corpora (singh2002open, Regneri:2010, Regneri2013), but these corpora describe script events in a pointwise telegram style rather than in full texts.",
+      "This paper presents the InScript corpus (Narrative Texts Instantiating Script structure). It is a corpus of simple narrative texts in the form of stories, wherein each story is centered around a specific scenario. The stories have been collected via Amazon Mechanical Turk (M-Turk). In this experiment, turkers were asked to write down a concrete experience about a bus ride, a grocery shopping event etc. We concentrated on 10 scenarios and collected 100 stories per scenario, giving a total of 1,000 stories with about 200,000 words. Relevant verbs and noun phrases in all stories are annotated with event types and participant types respectively. Additionally, the texts have been annotated with coreference information in order to facilitate the study of the interdependence between script structure and coreference.",
+      "The InScript corpus is a unique resource that provides a basis for studying various aspects of the role of script knowledge in language processing by humans. The acquisition of this corpus is part of a larger research effort that aims at using script knowledge to model the surprisal and information density in written text. Besides InScript, this project also released a corpus of generic descriptions of script activities called DeScript (for Describing Script Structure, Wanzare2016). DeScript contains a range of short and textually simple phrases that describe script events in the style of OMICS or SMILE (singh2002open, Regneri:2010). These generic telegram-style descriptions are called Event Descriptions (EDs); a sequence of such descriptions that cover a complete script is called an Event Sequence Description (ESD). Figure 2 shows an excerpt of a script in the baking a cake scenario. The figure shows event descriptions for 3 different events in the DeScript corpus (left) and fragments of a story in the InScript corpus (right) that instantiate the same event type."
+    ],
+    [
+      "We selected 10 scenarios from different available scenario lists (e.g. Regneri:2010 , VanDerMeer2009, and the OMICS corpus BIBREF1 ), including scripts of different complexity (Taking a bath vs. Flying in an airplane) and specificity (Riding a public bus vs. Repairing a flat bicycle tire). For the full scenario list see Table 2 .",
+      "Texts were collected via the Amazon Mechanical Turk platform, which provides an opportunity to present an online task to humans (a.k.a. turkers). In order to gauge the effect of different M-Turk instructions on our task, we first conducted pilot experiments with different variants of instructions explaining the task. We finalized the instructions for the full data collection, asking the turkers to describe a scenario in form of a story as if explaining it to a child and to use a minimum of 150 words. The selected instruction variant resulted in comparably simple and explicit scenario-related stories. In the future we plan to collect more complex stories using different instructions. In total 190 turkers participated. All turkers were living in the USA and native speakers of English. We paid USD $0.50 per story to each turker. On average, the turkers took 9.37 minutes per story with a maximum duration of 17.38 minutes."
+    ],
+    [
+      "Statistics for the corpus are given in Table 2 . On average, each story has a length of 12 sentences and 217 words with 98 word types on average. Stories are coherent and concentrate mainly on the corresponding scenario. Neglecting auxiliaries, modals and copulas, on average each story has 32 verbs, out of which 58% denote events related to the respective scenario. As can be seen in Table 2 , there is some variation in stories across scenarios: The flying in an airplane scenario, for example, is most complex in terms of the number of sentences, tokens and word types that are used. This is probably due to the inherent complexity of the scenario: Taking a flight, for example, is more complicated and takes more steps than taking a bath. The average count of sentences, tokens and types is also very high for the baking a cake scenario. Stories from the scenario often resemble cake recipes, which usually contain very detailed steps, so people tend to give more detailed descriptions in the stories.",
+      "For both flying in an airplane and baking a cake, the standard deviation is higher in comparison to other scenarios. This indicates that different turkers described the scenario with a varying degree of detail and can also be seen as an indicator for the complexity of both scenarios. In general, different people tend to describe situations subjectively, with a varying degree of detail. In contrast, texts from the taking a bath and planting a tree scenarios contain a relatively smaller number of sentences and fewer word types and tokens. Both planting a tree and taking a bath are simpler activities, which results in generally less complex texts.",
+      "The average pairwise word type overlap can be seen as a measure of lexical variety among stories: If it is high, the stories resemble each other more. We can see that stories in the flying in an airplane and baking a cake scenarios have the highest values here, indicating that most turkers used a similar vocabulary in their stories.",
+      "In general, the response quality was good. We had to discard 9% of the stories as these lacked the quality we were expecting. In total, we selected 910 stories for annotation."
+    ],
+    [
+      "This section deals with the annotation of the data. We first describe the final annotation schema. Then, we describe the iterative process of corpus annotation and the refinement of the schema. This refinement was necessary due to the complexity of the annotation."
+    ],
+    [
+      "For each of the scenarios, we designed a specific annotation template. A script template consists of scenario-specific event and participant labels. An example of a template is shown in Table 1 . All NP heads in the corpus were annotated with a participant label; all verbs were annotated with an event label. For both participants and events, we also offered the label unclear if the annotator could not assign another label. We additionally annotated coreference chains between NPs. Thus, the process resulted in three layers of annotation: event types, participant types and coreference annotation. These are described in detail below.",
+      "As a first layer, we annotated event types. There are two kinds of event type labels, scenario-specific event type labels and general labels. The general labels are used across every scenario and mark general features, for example whether an event belongs to the scenario at all. For the scenario-specific labels, we designed an unique template for every scenario, with a list of script-relevant event types that were used as labels. Such labels include for example ScrEv_close_drain in taking a bath as in Example UID10 (see Figure 1 for a complete list for the taking a bath scenario)",
+      "I start by closing $_{\\textsc {\\scriptsize ScrEv\\_close\\_drain}}$ the drain at the bottom of the tub.",
+      "The general labels that were used in addition to the script-specific labels in every scenario are listed below:",
+      "ScrEv_other. An event that belongs to the scenario, but its event type occurs too infrequently (for details, see below, Section \"Modification of the Schema\" ). We used the label \u201cother\" because event classification would become too finegrained otherwise.",
+      "Example: After I am dried I put my new clothes on and clean up $_{\\textsc {\\scriptsize ScrEv\\_other}}$ the bathroom.",
+      "RelNScrEv. Related non-script event. An event that can plausibly happen during the execution of the script and is related to it, but that is not part of the script.",
+      "Example: After finding on what I wanted to wear, I went into the bathroom and shut $_{\\textsc {\\scriptsize RelNScrEv}}$ the door.",
+      "UnrelEv. An event that is unrelated to the script.",
+      "Example: I sank into the bubbles and took $_{\\textsc {\\scriptsize UnrelEv}}$ a deep breath.",
+      "Additionally, the annotators were asked to annotate verbs and phrases that evoke the script without explicitly referring to a script event with the label Evoking, as shown in Example UID10 . Today I took a bath $_{\\textsc {\\scriptsize Evoking}}$ in my new apartment.",
+      "As in the case of the event type labels, there are two kinds of participant labels: general labels and scenario-specific labels. The latter are part of the scenario-specific templates, e.g. ScrPart_drain in the taking a bath scenario, as can be seen in Example UID15 .",
+      "I start by closing the drain $_{\\textsc {\\scriptsize ScrPart\\_drain}}$ at the bottom of the tub.",
+      "The general labels that are used across all scenarios mark noun phrases with scenario-independent features. There are the following general labels:",
+      "ScrPart_other. A participant that belongs to the scenario, but its participant type occurs only infrequently.",
+      "Example: I find my bath mat $_{\\textsc {\\scriptsize ScrPart\\_other}}$ and lay it on the floor to keep the floor dry.",
+      "NPart. Non-participant. A referential NP that does not belong to the scenario.",
+      "Example: I washed myself carefully because I did not want to spill water onto the floor $_{\\textsc {\\scriptsize NPart}}$ .labeled",
+      "SuppVComp. A support verb complement. For further discussion of this label, see Section \"Special Cases\" ",
+      "Example: I sank into the bubbles and took a deep breath $_{\\textsc {\\scriptsize SuppVComp}}$ .",
+      "Head_of_Partitive. The head of a partitive or a partitive-like construction. For a further discussion of this label cf. Section \"Special Cases\" ",
+      "Example: I grabbed a bar $_{\\textsc {\\scriptsize Head\\_of\\_Partitive}}$ of soap and lathered my body.",
+      "No_label. A non-referential noun phrase that cannot be labeled with another label. Example: I sat for a moment $_{\\textsc {\\scriptsize No\\_label}}$ , relaxing, allowing the warm water to sooth my skin.",
+      "All NPs labeled with one of the labels SuppVComp, Head_of_Partitive or No_label are considered to be non-referential. No_label is used mainly in four cases in our data: non-referential time expressions (in a while, a million times better), idioms (no matter what), the non-referential \u201cit\u201d (it felt amazing, it is better) and other abstracta (a lot better, a little bit).",
+      "In the first annotation phase, annotators were asked to mark verbs and noun phrases that have an event or participant type, that is not listed in the template, as MissScrEv/ MissScrPart (missing script event or participant, resp.). These annotations were used as a basis for extending the templates (see Section \"Modification of the Schema\" ) and replaced later by newly introduced labels or ScrEv_other and ScrPart_other respectively.",
+      "All noun phrases were annotated with coreference information indicating which entities denote the same discourse referent. The annotation was done by linking heads of NPs (see Example UID21 , where the links are indicated by coindexing). As a rule, we assume that each element of a coreference chain is marked with the same participant type label.",
+      "I $ _{\\textsc {\\scriptsize Coref1}}$ washed my $ _{\\textsc {\\scriptsize Coref1}}$ entire body $ _{\\textsc {\\scriptsize Coref2}}$ , starting with my $ _{\\textsc {\\scriptsize Coref1}}$ face $ _{\\textsc {\\scriptsize Coref3}} $ and ending with the toes $ _{\\textsc {\\scriptsize Coref4}} $ . I $ _{\\textsc {\\scriptsize Coref1}}$ always wash my $ _{\\textsc {\\scriptsize Coref1}}$ toes $_{\\textsc {\\scriptsize Coref4}}$ very thoroughly ...",
+      "The assignment of an entity to a referent is not always trivial, as is shown in Example UID21 . There are some cases in which two discourse referents are grouped in a plural NP. In the example, those things refers to the group made up of shampoo, soap and sponge. In this case, we asked annotators to introduce a new coreference label, the name of which indicates which referents are grouped together (Coref_group_washing_tools). All NPs are then connected to the group phrase, resulting in an additional coreference chain.",
+      "I $ _{\\textsc {\\scriptsize Coref1}}$ made sure that I $ _{\\textsc {\\scriptsize Coref1}}$ have my $ _{\\textsc {\\scriptsize Coref1}}$ shampoo $ _{\\textsc {\\scriptsize Coref2 + Coref\\_group\\_washing\\_tools}}$ , soap $_{\\textsc {\\scriptsize Coref3 + Coref\\_group\\_washing\\_tools}}$ and sponge $ _{\\textsc {\\scriptsize Coref4 + Coref\\_group\\_washing\\_tools}}$ ready to get in. Once I $ _{\\textsc {\\scriptsize Coref1}}$ have those things $ _{\\textsc {\\scriptsize Coref\\_group\\_washing\\_tools}}$ I $ _{\\textsc {\\scriptsize Coref1}}$ sink into the bath. ... I $ _{\\textsc {\\scriptsize Coref1}}$ applied some soap $ _{\\textsc {\\scriptsize Coref1}}$0 on my $ _{\\textsc {\\scriptsize Coref1}}$1 body and used the sponge $ _{\\textsc {\\scriptsize Coref1}}$2 to scrub a bit. ... I $ _{\\textsc {\\scriptsize Coref1}}$3 rinsed the shampoo $ _{\\textsc {\\scriptsize Coref1}}$4 . Example UID21 thus contains the following coreference chains: Coref1: I $ _{\\textsc {\\scriptsize Coref1}}$5 I $ _{\\textsc {\\scriptsize Coref1}}$6 my $ _{\\textsc {\\scriptsize Coref1}}$7 I $ _{\\textsc {\\scriptsize Coref1}}$8 I $ _{\\textsc {\\scriptsize Coref1}}$9 I $ _{\\textsc {\\scriptsize Coref1}}$0 my $ _{\\textsc {\\scriptsize Coref1}}$1 I",
+      "Coref2: shampoo $\\rightarrow $ shampoo",
+      "Coref3: soap $\\rightarrow $ soap",
+      "Coref4: sponge $\\rightarrow $ sponge",
+      "Coref_group_washing_ tools: shampoo $\\rightarrow $ soap $\\rightarrow $ sponge $\\rightarrow $ things"
+    ],
+    [
+      "The templates were carefully designed in an iterated process. For each scenario, one of the authors of this paper provided a preliminary version of the template based on the inspection of some of the stories. For a subset of the scenarios, preliminary templates developed at our department for a psycholinguistic experiment on script knowledge were used as a starting point. Subsequently, the authors manually annotated 5 randomly selected texts for each of the scenarios based on the preliminary template. Necessary extensions and changes in the templates were discussed and agreed upon. Most of the cases of disagreement were related to the granularity of the event and participant types. We agreed on the script-specific functional equivalence as a guiding principle. For example, reading a book, listening to music and having a conversation are subsumed under the same event label in the flight scenario, because they have the common function of in-flight entertainment in the scenario. In contrast, we assumed different labels for the cake tin and other utensils (bowls etc.), since they have different functions in the baking a cake scenario and accordingly occur with different script events.",
+      "Note that scripts and templates as such are not meant to describe an activity as exhaustively as possible and to mention all steps that are logically necessary. Instead, scripts describe cognitively prominent events in an activity. An example can be found in the flight scenario. While more than a third of the turkers mentioned the event of fastening the seat belts in the plane (buckle_seat_belt), no person wrote about undoing their seat belts again, although in reality both events appear equally often. Consequently, we added an event type label for buckling up, but no label for undoing the seat belts."
+    ],
+    [
+      "We used the WebAnno annotation tool BIBREF2 for our project. The stories from each scenario were distributed among four different annotators. In a calibration phase, annotators were presented with some sample texts for test annotations; the results were discussed with the authors. Throughout the whole annotation phase, annotators could discuss any emerging issues with the authors. All annotations were done by undergraduate students of computational linguistics. The annotation was rather time-consuming due to the complexity of the task, and thus we decided for single annotation mode. To assess annotation quality, a small sample of texts was annotated by all four annotators and their inter-annotator agreement was measured (see Section \"Inter-Annotator Agreement\" ). It was found to be sufficiently high.",
+      "Annotation of the corpus together with some pre- and post-processing of the data required about 500 hours of work. All stories were annotated with event and participant types (a total of 12,188 and 43,946 instances, respectively). On average there were 7 coreference chains per story with an average length of 6 tokens."
+    ],
+    [
+      "After the first annotation round, we extended and changed the templates based on the results. As mentioned before, we used MissScrEv and MissScrPart labels to mark verbs and noun phrases instantiating events and participants for which no appropriate labels were available in the templates. Based on the instances with these labels (a total of 941 and 1717 instances, respectively), we extended the guidelines to cover the sufficiently frequent cases. In order to include new labels for event and participant types, we tried to estimate the number of instances that would fall under a certain label. We added new labels according to the following conditions:",
+      "For the participant annotations, we added new labels for types that we expected to appear at least 10 times in total in at least 5 different stories (i.e. in approximately 5% of the stories).",
+      "For the event annotations, we chose those new labels for event types that would appear in at least 5 different stories.",
+      "In order to avoid too fine a granularity of the templates, all other instances of MissScrEv and MissScrPart were re-labeled with ScrEv_other and ScrPart_other. We also relabeled participants and events from the first annotation phase with ScrEv_other and ScrPart_other, if they did not meet the frequency requirements. The event label air_bathroom (the event of letting fresh air into the room after the bath), for example, was only used once in the stories, so we relabeled that instance to ScrEv_other.",
+      "Additionally, we looked at the DeScript corpus BIBREF3 , which contains manually clustered event paraphrase sets for the 10 scenarios that are also covered by InScript (see Section \"Comparison to the DeScript Corpus\" ). Every such set contains event descriptions that describe a certain event type. We extended our templates with additional labels for these events, if they were not yet part of the template."
+    ],
+    [
+      "Noun-noun compounds were annotated twice with the same label (whole span plus the head noun), as indicated by Example UID31 . This redundant double annotation is motivated by potential processing requirements.",
+      "I get my (wash (cloth $ _{\\textsc {\\scriptsize ScrPart\\_washing\\_tools}} ))$ , $_{\\textsc {\\scriptsize ScrPart\\_washing\\_tools}} $ and put it under the water.",
+      "A special treatment was given to support verb constructions such as take time, get home or take a seat in Example UID32 . The semantics of the verb itself is highly underspecified in such constructions; the event type is largely dependent on the object NP. As shown in Example UID32 , we annotate the head verb with the event type described by the whole construction and label its object with SuppVComp (support verb complement), indicating that it does not have a proper reference.",
+      "I step into the tub and take $ _{\\textsc {\\scriptsize ScrEv\\_sink\\_water}} $ a seat $ _{\\textsc {\\scriptsize SuppVComp}} $ .",
+      "We used the Head_of_Partitive label for the heads in partitive constructions, assuming that the only referential part of the construction is the complement. This is not completely correct, since different partitive heads vary in their degree of concreteness (cf. Examples UID33 and UID33 ), but we did not see a way to make the distinction sufficiently transparent to the annotators. Our seats were at the back $ _{\\textsc {\\scriptsize Head\\_of\\_Partitive}} $ of the train $ _{\\textsc {\\scriptsize ScrPart\\_train}} $ . In the library you can always find a couple $ _{\\textsc {\\scriptsize Head\\_of\\_Partitive}} $ of interesting books $ _{\\textsc {\\scriptsize ScrPart\\_book}} $ .",
+      "Group denoting NPs sometimes refer to groups whose members are instances of different participant types. In Example UID34 , the first-person plural pronoun refers to the group consisting of the passenger (I) and a non-participant (my friend). To avoid a proliferation of event type labels, we labeled these cases with Unclear.",
+      "I $ _{\\textsc {\\scriptsize {ScrPart\\_passenger}}}$ wanted to visit my $_{\\textsc {\\scriptsize {ScrPart\\_passenger}}}$ friend $ _{\\textsc {\\scriptsize {NPart}}}$ in New York. ... We $_{\\textsc {\\scriptsize Unclear}}$ met at the train station.",
+      "We made an exception for the Getting a Haircut scenario, where the mixed participant group consisting of the hairdresser and the customer occurs very often, as in Example UID34 . Here, we introduced the additional ad-hoc participant label Scr_Part_hairdresser_customer.",
+      "While Susan $_{\\textsc {\\scriptsize {ScrPart\\_hairdresser}}}$ is cutting my $_{\\textsc {\\scriptsize {ScrPart\\_customer}}}$ hair we $_{\\textsc {\\scriptsize Scr\\_Part\\_hairdresser\\_customer}}$ usually talk a bit."
+    ],
+    [
+      "In order to calculate inter-annotator agreement, a total of 30 stories from 6 scenarios were randomly chosen for parallel annotation by all 4 annotators after the first annotation phase. We checked the agreement on these data using Fleiss' Kappa BIBREF4 . The results are shown in Figure 4 and indicate moderate to substantial agreement BIBREF5 . Interestingly, if we calculated the Kappa only on the subset of cases that were annotated with script-specific event and participant labels by all annotators, results were better than those of the evaluation on all labeled instances (including also unrelated and related non-script events). This indicates one of the challenges of the annotation task: In many cases it is difficult to decide whether a particular event should be considered a central script event, or an event loosely related or unrelated to the script.",
+      "For coreference chain annotation, we calculated the percentage of pairs which were annotated by at least 3 annotators (qualified majority vote) compared to the set of those pairs annotated by at least one person (see Figure 4 ). We take the result of 90.5% between annotators to be a good agreement."
+    ],
+    [
+      "Figure 5 gives an overview of the number of event and participant types provided in the templates. Taking a flight and getting a haircut stand out with a large number of both event and participant types, which is due to the inherent complexity of the scenarios. In contrast, planting a tree and going on a train contain the fewest labels. There are 19 event and participant types on average.",
+      "Figure 6 presents overview statistics about the usage of event labels, participant labels and coreference chain annotations. As can be seen, there are usually many more mentions of participants than events. For coreference chains, there are some chains that are really long (which also results in a large scenario-wise standard deviation). Usually, these chains describe the protagonist.",
+      "We also found again that the flying in an airplane scenario stands out in terms of participant mentions, event mentions and average number of coreference chains.",
+      "Figure 7 shows for every participant label in the baking a cake scenario the number of stories which they occurred in. This indicates how relevant a participant is for the script. As can be seen, a small number of participants are highly prominent: cook, ingredients and cake are mentioned in every story. The fact that the protagonist appears most often consistently holds for all other scenarios, where the acting person appears in every story, and is mentioned most frequently.",
+      "Figure 8 shows the distribution of participant/event type labels over all appearances over all scenarios on average. The groups stand for the most frequently appearing label, the top 2 to 5 labels in terms of frequency and the top 6 to 10. ScrEv_other and ScrPart_other are shown separately. As can be seen, the most frequently used participant label (the protagonist) makes up about 40% of overall participant instances. The four labels that follow the protagonist in terms of frequency together appear in 37% of the cases. More than 2 out of 3 participants in total belong to one of only 5 labels.",
+      "In contrast, the distribution for events is more balanced. 14% of all event instances have the most prominent event type. ScrEv_other and ScrPart_other both appear as labels in at most 5% of all event and participant instantiations: The specific event and participant type labels in our templates cover by far most of the instances.",
+      "In Figure 9 , we grouped participants similarly into the first, the top 2-5 and top 6-10 most frequently appearing participant types. The figure shows for each of these groups the average frequency per story, and in the rightmost column the overall average. The results correspond to the findings from the last paragraph."
+    ],
+    [
+      "As mentioned previously, the InScript corpus is part of a larger research project, in which also a corpus of a different kind, the DeScript corpus, was created. DeScript covers 40 scenarios, and also contains the 10 scenarios from InScript. This corpus contains texts that describe scripts on an abstract and generic level, while InScript contains instantiations of scripts in narrative texts. Script events in DeScript are described in a very simple, telegram-style language (see Figure 2 ). Since one of the long-term goals of the project is to align the InScript texts with the script structure given from DeScript, it is interesting to compare both resources.",
+      "The InScript corpus exhibits much more lexical variation than DeScript. Many approaches use the type-token ratio to measure this variance. However, this measure is known to be sensitive to text length (see e.g. Tweedie1998), which would result in very small values for InScript and relatively large ones for DeScript, given the large average difference of text lengths between the corpora. Instead, we decided to use the Measure of Textual Lexical Diversity (MTLD) (McCarthy2010, McCarthy2005), which is familiar in corpus linguistics. This metric measures the average number of tokens in a text that are needed to retain a type-token ratio above a certain threshold. If the MTLD for a text is high, many tokens are needed to lower the type-token ratio under the threshold, so the text is lexically diverse. In contrast, a low MTLD indicates that only a few words are needed to make the type-token ratio drop, so the lexical diversity is smaller. We use the threshold of 0.71, which is proposed by the authors as a well-proven value.",
+      "Figure 10 compares the lexical diversity of both resources. As can be seen, the InScript corpus with its narrative texts is generally much more diverse than the DeScript corpus with its short event descriptions, across all scenarios. For both resources, the flying in an airplane scenario is most diverse (as was also indicated above by the mean word type overlap). However, the difference in the variation of lexical variance of scenarios is larger for DeScript than for InScript. Thus, the properties of a scenario apparently influence the lexical variance of the event descriptions more than the variance of the narrative texts. We used entropy BIBREF6 over lemmas to measure the variance of lexical realizations for events. We excluded events for which there were less than 10 occurrences in DeScript or InScript. Since there is only an event annotation for 50 ESDs per scenario in DeScript, we randomly sampled 50 texts from InScript for computing the entropy to make the numbers more comparable.",
+      "Figure 11 shows as an example the entropy values for the event types in the going on a train scenario. As can be seen in the graph, the entropy for InScript is in general higher than for DeScript. In the stories, a wider variety of verbs is used to describe events. There are also large differences between events: While wait has a really low entropy, spend_time_train has an extremely high entropy value. This event type covers many different activities such as reading, sleeping etc."
+    ],
+    [
+      "In this paper we described the InScript corpus of 1,000 narrative texts annotated with script structure and coreference information. We described the annotation process, various difficulties encountered during annotation and different remedies that were taken to overcome these. One of the future research goals of our project is also concerned with finding automatic methods for text-to-script mapping, i.e. for the alignment of text segments with script states. We consider InScript and DeScript together as a resource for studying this alignment. The corpus shows rich lexical variation and will serve as a unique resource for the study of the role of script knowledge in natural language processing."
+    ],
+    [
+      "This research was funded by the German Research Foundation (DFG) as part of SFB 1102 'Information Density and Linguistic Encoding'."
+    ]
+  ]
+}
+```

qasper-0175/instruction.md

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

qasper-0180/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Facilitating on-line opinion dynamics by mining expressions of causation. The case of climate change debates on The Guardian
+
+Question: Does the paper report the results of previous models applied to the same tasks?

qasper-0181/instruction.md

@@ -0,0 +1,120 @@
+Name of Paper: Facilitating on-line opinion dynamics by mining expressions of causation. The case of climate change debates on The Guardian
+
+Question: How is the quality of the discussion evaluated?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction ::: Background",
+    "Introduction ::: Objective",
+    "Introduction ::: Data: the communicative setting of TheGuardian.com",
+    "Mining opinions and beliefs from texts",
+    "Mining opinions and beliefs from texts ::: Causal mapping methods and the climate change debate",
+    "Mining opinions and beliefs from texts ::: Automated causation tracking with the Penelope semantic frame extractor",
+    "Analyses and applications",
+    "Analyses and applications ::: Aggregation",
+    "Analyses and applications ::: Spatial renditions of TheGuardian.com's opinion landscape",
+    "Analyses and applications ::: Spatial renditions of TheGuardian.com's opinion landscape ::: A macro-level overview: causes addressed in the climate change debate",
+    "Analyses and applications ::: Spatial renditions of TheGuardian.com's opinion landscape ::: Micro-level investigations: opinions on nuclear power and global warming",
+    "From opinion observation to debate facilitation",
+    "From opinion observation to debate facilitation ::: Debate facilitation through models of alignment and polarization",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Over the past two decades, the rise of social media and the digitization of news and discussion platforms have radically transformed how individuals and groups create, process and share news and information. As Alan Rusbridger, former-editor-in-chief of the newspaper The Guardian has it, these technologically-driven shifts in the ways people communicate, organize themselves and express their beliefs and opinions, have",
+      "empower[ed] those that were never heard, creating a a new form of politics and turning traditional news corporations inside out. It is impossible to think of Donald Trump; of Brexit; of Bernie Sanders; of Podemos; of the growth of the far right in Europe; of the spasms of hope and violent despair in the Middle East and North Africa without thinking also of the total inversion of how news is created, shared and distributed. Much of it is liberating and and inspiring. Some of it is ugly and dark. And something - the centuries-old craft of journalism - is in danger of being lost BIBREF0.",
+      "Rusbridger's observation that the present media-ecology puts traditional notions of politics, journalism, trust and truth at stake is a widely shared one BIBREF1, BIBREF2, BIBREF3. As such, it has sparked interdisciplinary investigations, diagnoses and ideas for remedies across the economical, socio-political, and technological spectrum, challenging our existing assumptions and epistemologies BIBREF4, BIBREF5. Among these lines of inquiry, particular strands of research from the computational social sciences are addressing pressing questions of how emerging technologies and digital methods might be operationalized to regain a grip on the dynamics that govern the flow of on-line news and its associated multitudes of voices, opinions and conflicts. Could the information circulating on on-line (social) news platforms for instance be mined to better understand and analyze the problems facing our contemporary society? Might such data mining and analysis help us to monitor the growing number of social conflicts and crises due to cultural differences and diverging world-views? And finally, would such an approach potentially facilitate early detection of conflicts and even ways to resolve them before they turn violent?",
+      "Answering these questions requires further advances in the study of cultural conflict based on digital media data. This includes the development of fine-grained representations of cultural conflict based on theoretically-informed text analysis, the integration of game-theoretical approaches to models of polarization and alignment, as well as the construction of accessible tools and media-monitoring observatories: platforms that foster insight into the complexities of social behaviour and opinion dynamics through automated computational analyses of (social) media data. Through an interdisciplinary approach, the present article aims to make both a practical and theoretical contribution to these aspects of the study of opinion dynamics and conflict in new media environments."
+    ],
+    [
+      "The objective of the present article is to critically examine possibilities and limitations of machine-guided exploration and potential facilitation of on-line opinion dynamics on the basis of an experimental data analytics pipeline or observatory for mining and analyzing climate change-related user comments from the news website of The Guardian (TheGuardian.com). Combining insights from the social and political sciences with computational methods for the linguistic analysis of texts, this observatory provides a series of spatial (network) representations of the opinion landscapes on climate change on the basis of causation frames expressed in news website comments. This allows for the exploration of opinion spaces at different levels of detail and aggregation.",
+      "Technical and theoretical questions related to the proposed method and infrastructure for the exploration and facilitation of debates will be discussed in three sections. The first section concerns notions of how to define what constitutes a belief or opinion and how these can be mined from texts. To this end, an approach based on the automated extraction of semantic frames expressing causation is proposed. The observatory thus builds on the theoretical premise that expressions of causation such as `global warming causes rises in sea levels' can be revelatory for a person or group's underlying belief systems. Through a further technical description of the observatory's data-analytical components, section two of the paper deals with matters of spatially modelling the output of the semantic frame extractor and how this might be achieved without sacrificing nuances of meaning. The final section of the paper, then, discusses how insights gained from technologically observing opinion dynamics can inform conceptual modelling efforts and approaches to on-line opinion facilitation. As such, the paper brings into view and critically evaluates the fundamental conceptual leap from machine-guided observation to debate facilitation and intervention.",
+      "Through the case examples from The Guardian's website and the theoretical discussions explored in these sections, the paper intends to make a twofold contribution to the fields of media studies, opinion dynamics and computational social science. Firstly, the paper introduces and chains together a number of data analytics components for social media monitoring (and facilitation) that were developed in the context of the <project name anonymized for review> infrastructure project. The <project name anonymized for review> infrastructure makes the components discussed in this paper available as open web services in order to foster reproducibility and further experimentation and development <infrastructure reference URL anonymized for review>. Secondly, and supplementing these technological and methodological gains, the paper addresses a number of theoretical, epistemological and ethical questions that are raised by experimental approaches to opinion exploration and facilitation. This notably includes methodological questions on the preservation of meaning through text and data mining, as well as the role of human interpretation, responsibility and incentivisation in observing and potentially facilitating opinion dynamics."
+    ],
+    [
+      "In order to study on-line opinion dynamics and build the corresponding climate change opinion observatory discussed in this paper, a corpus of climate-change related news articles and news website comments was analyzed. Concretely, articles from the \u2018climate change\u2019 subsection from the news website of The Guardian dated from 2009 up to April 2019 were processed, along with up to 200 comments and associated metadata for articles where commenting was enabled at the time of publication. The choice for studying opinion dynamics using data from The Guardian is motivated by this news website's prominent position in the media landscape as well as its communicative setting, which is geared towards user engagement. Through this interaction with readers, the news platform embodies many of the recent shifts that characterize our present-day media ecology.",
+      "TheGuardian.com is generally acknowledged to be one of the UK's leading online newspapers, with 8,2 million unique visitors per month as of May 2013 BIBREF6. The website consists of a core news site, as well as a range of subsections that allow for further classification and navigation of articles. Articles related to climate change can for instance be accessed by navigating through the `News' section, over the subsection `environment', to the subsubsection `climate change' BIBREF7. All articles on the website can be read free of charge, as The Guardian relies on a business model that combines revenues from advertising, voluntary donations and paid subscriptions.",
+      "Apart from offering high-quality, independent journalism on a range of topics, a distinguishing characteristic of The Guardian is its penchant for reader involvement and engagement. Adopting to the changing media landscape and appropriating business models that fit the transition from print to on-line news media, the Guardian has transformed itself into a platform that enables forms of citizen journalism, blogging, and welcomes readers comments on news articles BIBREF0. In order for a reader to comment on articles, it is required that a user account is made, which provides a user with a unique user name and a user profile page with a stable URL. According to the website's help pages, providing users with an identity that is consistently recognized by the community fosters proper on-line community behaviour BIBREF8. Registered users can post comments on content that is open to commenting, and these comments are moderated by a dedicated moderation team according to The Guardian's community standards and participation guidelines BIBREF9. In support of digital methods and innovative approaches to journalism and data mining, The Guardian has launched an open API (application programming interface) through which developers can access different types of content BIBREF10. It should be noted that at the moment of writing this article, readers' comments are not accessible through this API. For the scientific and educational purposes of this paper, comments were thus consulted using a dedicated scraper.",
+      "Taking into account this community and technologically-driven orientation, the communicative setting of The Guardian from which opinions are to be mined and the underlying belief system revealed, is defined by articles, participating commenters and comment spheres (that is, the actual comments aggregated by user, individual article or collection of articles) (see Figure FIGREF4).",
+      "In this setting, articles (and previous comments on those articles) can be commented on by participating commenters, each of which bring to the debate his or her own opinions or belief system. What this belief system might consists of can be inferred on a number of levels, with varying degrees of precision. On the most general level, a generic description of the profile of the average reader of The Guardian can be informative. Such profiles have been compiled by market researchers with the purpose of informing advertisers about the demographic that might be reached through this news website (and other products carrying The Guardian's brand). As of the writing of this article, the audience The Guardian is presented to advertisers as a `progressive' audience:",
+      "Living in a world of unprecedented societal change, with the public narratives around politics, gender, body image, sexuality and diet all being challenged. The Guardian is committed to reflecting the progressive agenda, and reaching the crowd that uphold those values. It\u2019s helpful that we reach over half of progressives in the UK BIBREF11.",
+      "A second, equally high-level indicator of the beliefs that might be present on the platform, are the links through which articles on climate change can be accessed. An article on climate change might for instance be consulted through the environment section of the news website, but also through the business section. Assuming that business interests might potentially be at odds with environmental concerns, it could be hypothesized that the particular comment sphere for that article consists of at least two potentially clashing frames of mind or belief systems.",
+      "However, as will be expanded upon further in this article, truly capturing opinion dynamics requires a more systemic and fine-grained approach. The present article therefore proposes a method for harvesting opinions from the actual comment texts. The presupposition is thereby that comment spheres are marked by a diversity of potentially related opinions and beliefs. Opinions might for instance be connected through the reply structure that marks the comment section of an article, but this connection might also manifest itself on a semantic level (that is, the level of meaning or the actual contents of the comments). To capture this multidimensional, interconnected nature of the comment spheres, it is proposed to represent comment spheres as networks, where the nodes represent opinions and beliefs, and edges the relationships between these beliefs (see the spatial representation of beliefs infra). The use of precision language tools to extract such beliefs and their mutual relationships, as will be explored in the following sections, can open up new pathways of model validation and creation."
+    ],
+    [
+      "In traditional experimental settings, survey techniques and associated statistical models provide researchers with established methods to gauge and analyze the opinions of a population. When studying opinion landscapes through on-line social media, however, harvesting beliefs from big textual data such as news website comments and developing or appropriating models for their analysis is a non-trivial task BIBREF12, BIBREF13, BIBREF14.",
+      "In the present context, two challenges related to data-gathering and text mining need to be addressed: (1) defining what constitutes an expression of an opinion or belief, and (2) associating this definition with a pattern that might be extracted from texts. Recent scholarship in the fields of natural language processing (NLP) and argumentation mining has yielded a range of instruments and methods for the (automatic) identification of argumentative claims in texts BIBREF15, BIBREF16. Adding to these instruments and methods, the present article proposes an approach in which belief systems or opinions on climate change are accessed through expressions of causation."
+    ],
+    [
+      "The climate change debate is often characterized by expressions of causation, that is, expressions linking a certain cause with a certain effect. Cultural or societal clashes on climate change might for instance concern diverging assessments of whether global warming is man-made or not BIBREF17. Based on such examples, it can be stated that expressions of causation are closely associated with opinions or beliefs, and that as such, these expressions can be considered a valuable indicator for the range and diversity of the opinions and beliefs that constitute the climate change debate. The observatory under discussion therefore focuses on the extraction and analysis of linguistic patterns called causation frames. As will be further demonstrated in this section, the benefit of this causation-based approach is that it offers a systemic approach to opinion dynamics that comprises different layers of meaning, notably the cognitive or social meaningfulness of patterns on account of their being expressions of causation, as well as further lexical and semantic information that might be used for analysis and comparison.",
+      "The study of expressions of causation as a method for accessing and assessing belief systems and opinions has been formalized and streamlined since the 1970s. Pioneered by political scientist Robert Axelrod and others, this causal mapping method (also referred to as `cognitive mapping') was introduced as a means of reconstructing and evaluating administrative and political decision-making processes, based on the principle that",
+      "the notion of causation is vital to the process of evaluating alternatives. Regardless of philosophical difficulties involved in the meaning of causation, people do evaluate complex policy alternatives in terms of the consequences a particular choice would cause, and ultimately of what the sum of these effects would be. Indeed, such causal analysis is built into our language, and it would be very difficult for us to think completely in other terms, even if we tried BIBREF18.",
+      "Axelrod's causal mapping method comprises a set of conventions to graphically represent networks of causes and effects (the nodes in a network) as well as the qualitative aspects of this relation (the network\u2019s directed edges, notably assertions of whether the causal linkage is positive or negative). These causes and effects are to be extracted from relevant sources by means of a series of heuristics and an encoding scheme (it should be noted that for this task Axelrod had human readers in mind). The graphs resulting from these efforts provide a structural overview of the relations among causal assertions (and thus beliefs):",
+      "The basic elements of the proposed system are quite simple. The concepts a person uses are represented as points, and the causal links between these concepts are represented as arrows between these points. This gives a pictorial representation of the causal assertions of a person as a graph of points and arrows. This kind of representation of assertions as a graph will be called a cognitive map. The policy alternatives, all of the various causes and effects, the goals, and the ultimate utility of the decision maker can all be thought of as concept variables, and represented as points in the cognitive map. The real power of this approach appears when a cognitive map is pictured in graph form; it is then relatively easy to see how each of the concepts and causal relationships relate to each other, and to see the overall structure of the whole set of portrayed assertions BIBREF18.",
+      "In order to construct these cognitive maps based on textual information, Margaret Tucker Wrightson provides a set of reading and coding rules for extracting cause concepts, linkages (relations) and effect concepts from expressions in the English language. The assertion `Our present topic is the militarism of Germany, which is maintaining a state of tension in the Baltic Area' might for instance be encoded as follows: `the militarism of Germany' (cause concept), /+/ (a positive relationship), `maintaining a state of tension in the Baltic area' (effect concept) BIBREF19. Emphasizing the role of human interpretation, it is acknowledged that no strict set of rules can capture the entire spectrum of causal assertions:",
+      "The fact that the English language is as varied as those who use it makes the coder's task complex and difficult. No set of rules will completely solve the problems he or she might encounter. These rules, however, provide the coder with guidelines which, if conscientiously followed, will result in outcomes meeting social scientific standards of comparative validity and reliability BIBREF19.",
+      "To facilitate the task of encoders, the causal mapping method has gone through various iterations since its original inception, all the while preserving its original premises. Recent software packages have for instance been devised to support the data encoding and drawing process BIBREF20. As such, causal or cognitive mapping has become an established opinion and decision mining method within political science, business and management, and other domains. It has notably proven to be a valuable method for the study of recent societal and cultural conflicts. Thomas Homer-Dixon et al. for instance rely on cognitive-affective maps created from survey data to analyze interpretations of the housing crisis in Germany, Israeli attitudes toward the Western Wall, and moderate versus skeptical positions on climate change BIBREF21. Similarly, Duncan Shaw et al. venture to answer the question of `Why did Brexit happen?' by building causal maps of nine televised debates that were broadcast during the four weeks leading up to the Brexit referendum BIBREF22.",
+      "In order to appropriate the method of causal mapping to the study of on-line opinion dynamics, it needs to expanded from applications at the scale of human readers and relatively small corpora of archival documents and survey answers, to the realm of `big' textual data and larger quantities of information. This attuning of cognitive mapping methods to the large-scale processing of texts required for media monitoring necessarily involves a degree of automation, as will be explored in the next section."
+    ],
+    [
+      "As outlined in the previous section, causal mapping is based on the extraction of so-called cause concepts, (causal) relations, and effect concepts from texts. The complexity of each of these these concepts can range from the relatively simple (as illustrated by the easily-identifiable cause and effect relation in the example of `German militarism' cited earlier), to more complex assertions such as `The development of international cooperation in all fields across the ideological frontiers will gradually remove the hostility and fear that poison international relations', which contains two effect concepts (viz. `the hostility that poisons international relations' and `the fear that poisons international relations'). As such, this statement would have to be encoded as a double relationship BIBREF19.",
+      "The coding guidelines in BIBREF19 further reflect that extracting cause and effect concepts from texts is an operation that works on both the syntactical and semantic levels of assertions. This can be illustrated by means of the guidelines for analyzing the aforementioned causal assertion on German militarism:",
+      "1. The first step is the realization of the relationship. Does a subject affect an object? 2. Having recognized that it does, the isolation of the cause and effects concepts is the second step. As the sentence structure indicates, \"the militarism of Germany\" is the causal concept, because it is the initiator of the action, while the direct object clause, \"a state of tension in the Baltic area,\" constitutes that which is somehow influenced, the effect concept BIBREF19.",
+      "In the field of computational linguistics, from which the present paper borrows part of its methods, this procedure for extracting information related to causal assertions from texts can be considered an instance of an operation called semantic frame extraction BIBREF23. A semantic frame captures a coherent part of the meaning of a sentence in a structured way. As documented in the FrameNet project BIBREF24, the Causation frame is defined as follows:",
+      "A Cause causes an Effect. Alternatively, an Actor, a participant of a (implicit) Cause, may stand in for the Cause. The entity Affected by the Causation may stand in for the overall Effect situation or event BIBREF25.",
+      "In a linguistic utterance such as a statement in a news website comment, the Causation frame can be evoked by a series of lexical units, such as `cause', `bring on', etc. In the example `If such a small earthquake CAUSES problems, just imagine a big one!', the Causation frame is triggered by the verb `causes', which therefore is called the frame evoking element. The Cause slot is filled by `a small earthquake', the Effect slot by `problems' BIBREF25.",
+      "In order to automatically mine cause and effects concepts from the corpus of comments on The Guardian, the present paper uses the Penelope semantic frame extractor: a tool that exploits the fact that semantic frames can be expressed as form-meaning mappings called constructions. Notably, frames were extracted from Guardian comments by focusing on the following lexical units (verbs, prepositions and conjunctions), listed in FrameNet as frame evoking elements of the Causation frame: Cause.v, Due to.prep, Because.c, Because of.prep, Give rise to.v, Lead to.v or Result in.v.",
+      "As illustrated by the following examples, the strings output by the semantic frame extractor adhere closely to the original utterance, preserving all of the the comments' causation frames real-world noisiness:",
+      "The output of the semantic frame extractor as such is used as the input for the ensuing pipeline components in the climate change opinion observatory. The aim of a further analysis of these frames is to find patterns in the beliefs and opinions they express. As will be discussed in the following section, which focuses on applications and cases, maintaining semantic nuances in this further analytic process foregrounds the role of models and aggregation levels."
+    ],
+    [
+      "Based on the presupposition that relations between causation frames reveal beliefs, the output of the semantic frame extractor creates various opportunities for exploring opinion landscapes and empirically validating conceptual models for opinion dynamics.",
+      "In general, any alignment of conceptual models and real-world data is an exercise in compromising, as the idealized, abstract nature of models is likely to be at odds with the messiness of the actual data. Finding such a compromise might for instance involve a reduction of the simplicity or elegance of the model, or, on the other hand, an increased aggregation (and thus reduced granularity) of the data.",
+      "Addressing this challenge, the current section reflects on questions of data modelling, aggregation and meaning by exploring, through case examples, different spatial representations of opinion landscapes mined from the TheGuardian.com's comment sphere. These spatial renditions will be understood as network visualizations in which nodes represent argumentative statements (beliefs) and edges the degree of similarity between these statements. On the most general level, then, such a representation can consists of an overview of all the causes expressed in the corpus of climate change-related Guardian comments. This type of visualization provides a birds-eye view of the entire opinion landscape as mined from the comment texts. In turn, such a general overview might elicit more fine-grained, micro-level investigations, in which a particular cause is singled out and its more specific associated effects are mapped. These macro and micro level overviews come with their own proper potential for theory building and evaluation, as well as distinct requirements for the depth or detail of meaning that needs to be represented. To get the most general sense of an opinion landscape one might for instance be more tolerant of abstract renditions of beliefs (e.g. by reducing statements to their most frequently used terms), but for more fine-grained analysis one requires more context and nuance (e.g. adhering as closely as possible to the original comment)."
+    ],
+    [
+      "As follows from the above, one of the most fundamental questions when building automated tools to observe opinion dynamics that potentially aim at advising means of debate facilitation concerns the level of meaning aggregation. A clear argumentative or causal association between, for instance, climate change and catastrophic events such as floods or hurricanes may become detectable by automatic causal frame tracking at the scale of large collections of articles where this association might appear statistically more often, but detection comes with great challenges when the aim is to classify certain sets of only a few statements in more free expression environments such as comment spheres.",
+      "In other words, the problem of meaning aggregation is closely related to issues of scale and aggregation over utterances. The more fine-grained the semantic resolution is, that is, the more specific the cause or effect is that one is interested in, the less probable it is to observe the same statement twice. Moreover, with every independent variable (such as time, different commenters or user groups, etc.), less data on which fine-grained opinion statements are to be detected is available. In the present case of parsed comments from TheGuardian.com, providing insights into the belief system of individual commenters, even if all their statements are aggregated over time, relies on a relatively small set of argumentative statements. This relative sparseness is in part due to the fact that the scope of the semantic frame extractor is confined to the frame evoking elements listed earlier, thus omitting more implicit assertions of causation (i.e. expressions of causation that can only be derived from context and from reading between the lines).",
+      "Similarly, as will be explored in the ensuing paragraphs, matters of scale and aggregation determine the types of further linguistic analyses that can be performed on the output of the frame extractor. Within the field of computational linguistics, various techniques have been developed to represent the meaning of words as vectors that capture the contexts in which these words are typically used. Such analyses might reveal patterns of statistical significance, but it is also likely that in creating novel, numerical representations of the original utterances, the semantic structure of argumentatively linked beliefs is lost.",
+      "In sum, developing opinion observatories and (potential) debate facilitators entails finding a trade-off, or, in fact, a middle way between macro- and micro-level analyses. On the one hand, one needs to leverage automated analysis methods at the scale of larger collections to maximum advantage. But one also needs to integrate opportunities to interactively zoom into specific aspects of interest and provide more fine-grained information at these levels down to the actual statements. This interplay between macro- and micro-level analyses is explored in the case studies below."
+    ],
+    [
+      "The main purpose of the observatory under discussion is to provide insight into the belief structures that characterize the opinion landscape on climate change. For reasons outlined above, this raises questions of how to represent opinions and, correspondingly, determining which representation is most suited as the atomic unit of comparison between opinions. In general terms, the desired outcome of further processing of the output of the semantic frame extractor is a network representation in which similar cause or effect strings are displayed in close proximity to one another. A high-level description of the pipeline under discussion thus goes as follows. In a first step, it can be decided whether one wants to map cause statements or effect statements. Next, the selected statements are grouped per commenter (i.e. a list is made of all cause statements or effect statements per commenter). These statements are filtered in order to retain only nouns, adjectives and verbs (thereby also omitting frequently occurring verbs such as \u2018to be\u2019). The remaining words are then lemmatized, that is, reduced to their dictionary forms. This output is finally translated into a network representation, whereby nodes represent (aggregated) statements, and edges express the semantic relatedness between statements (based on a set overlap whereby the number of shared lemmata are counted).",
+      "As illustrated by two spatial renditions that were created using this approach and visualized using the network analysis tool Gephi BIBREF26, the labels assigned to these nodes (lemmata, full statements, or other) can be appropriated to the scope of the analysis."
+    ],
+    [
+      "Suppose one wants to get a first idea about the scope and diversity of an opinion landscape, without any preconceived notions of this landscape's structure or composition. One way of doing this would be to map all of the causes that are mentioned in comments related to articles on climate change, that is, creating an overview of all the causes that have been retrieved by the frame extractor in a single representation. Such a representation would not immediately provide the granularity to state what the beliefs or opinions in the debates actually are, but rather, it might inspire a sense of what those opinions might be about, thus pointing towards potentially interesting phenomena that might warrant closer examination.",
+      "Figure FIGREF10, a high-level overview of the opinion landscape, reveals a number of areas to which opinions and beliefs might pertain. The top-left clusters in the diagram for instance reveal opinions about the role of people and countries, whereas on the right-hand side, we find a complementary cluster that might point to beliefs concerning the influence of high or increased CO2-emissions. In between, there is a cluster on power and energy sources, reflecting the energy debate's association to both issues of human responsibility and CO2 emissions. As such, the overview can already inspire, potentially at best, some very general hypotheses about the types of opinions that figure in the climate change debate."
+    ],
+    [
+      "Based on the range of topics on which beliefs are expressed, a micro-level analysis can be conducted to reveal what those beliefs are and, for instance, whether they align or contradict each other. This can be achieved by singling out a cause of interest, and mapping out its associated effects.",
+      "As revealed by the global overview of the climate change opinion landscape, a portion of the debate concerns power and energy sources. One topic with a particularly interesting role in this debate is nuclear power. Figure FIGREF12 illustrates how a more detailed representation of opinions on this matter can be created by spatially representing all of the effects associated with causes containing the expression `nuclear power'. Again, similar beliefs (in terms of words used in the effects) are positioned closer to each other, thus facilitating the detection of clusters. Commenters on The Guardian for instance express concerns about the deaths or extinction that might be caused by this energy resource. They also voice opinions on its cleanliness, whether or not it might decrease pollution or be its own source of pollution, and how it reduces CO2-emissions in different countries.",
+      "Whereas the detailed opinion landscape on `nuclear power' is relatively limited in terms of the number of mined opinions, other topics might reveal more elaborate belief systems. This is for instance the case for the phenomenon of `global warming'. As shown in Figure FIGREF13, opinions on global warming are clustered around the idea of `increases', notably in terms of evaporation, drought, heat waves, intensity of cyclones and storms, etc. An adjacent cluster is related to `extremes', such as extreme summers and weather events, but also extreme colds."
+    ],
+    [
+      "The observatory introduced in the preceding paragraphs provides preliminary insights into the range and scope of the beliefs that figure in climate change debates on TheGuardian.com. The observatory as such takes a distinctly descriptive stance, and aims to satisfy, at least in part, the information needs of researchers, activists, journalists and other stakeholders whose main concern is to document, investigate and understand on-line opinion dynamics. However, in the current information sphere, which is marked by polarization, misinformation and a close entanglement with real-world conflicts, taking a mere descriptive or neutral stance might not serve every stakeholder's needs. Indeed, given the often skewed relations between power and information, questions arise as to how media observations might in turn be translated into (political, social or economic) action. Knowledge about opinion dynamics might for instance inform interventions that remedy polarization or disarm conflict. In other words, the construction of (social) media observatories unavoidably lifts questions about the possibilities, limitations and, especially, implications of the machine-guided and human-incentivized facilitation of on-line discussions and debates.",
+      "Addressing these questions, the present paragraph introduces and explores the concept of a debate facilitator, that is, a device that extends the capabilities of the previously discussed observatory to also promote more interesting and constructive discussions. Concretely, we will conceptualize a device that reveals how the personal opinion landscapes of commenters relate to each other (in terms of overlap or lack thereof), and we will discuss what steps might potentially be taken on the basis of such representation to balance the debate. Geared towards possible interventions in the debate, such a device may thus go well beyond the observatory's objectives of making opinion processes and conflicts more transparent, which concomitantly raises a number of serious concerns that need to be acknowledged.",
+      "On rather fundamental ground, tools that steer debates in one way or another may easily become manipulative and dangerous instruments in the hands of certain interest groups. Various aspects of our daily lives are for instance already implicitly guided by recommender systems, the purpose and impact of which can be rather opaque. For this reason, research efforts across disciplines are directed at scrutinizing and rendering such systems more transparent BIBREF28. Such scrutiny is particularly pressing in the context of interventions on on-line communication platforms, which have already been argued to enforce affective communication styles that feed rather than resolve conflict. The objectives behind any facilitation device should therefore be made maximally transparent and potential biases should be fully acknowledged at every level, from data ingest to the dissemination of results BIBREF29. More concretely, the endeavour of constructing opinion observatories and facilitators foregrounds matters of `openness' of data and tools, security, ensuring data quality and representative sampling, accounting for evolving data legislation and policy, building communities and trust, and envisioning beneficial implications. By documenting the development process for a potential facilitation device, the present paper aims to contribute to these on-going investigations and debates. Furthermore, every effort has been made to protect the identities of the commenters involved. In the words of media and technology visionary Jaron Lanier, developers and computational social scientists entering this space should remain fundamentally aware of the fact that `digital information is really just people in disguise' BIBREF30.",
+      "With these reservations in mind, the proposed approach can be situated among ongoing efforts that lead from debate observation to facilitation. One such pathway, for instance, involves the construction of filters to detect hate speech, misinformation and other forms of expression that might render debates toxic BIBREF31, BIBREF32. Combined with community outreach, language-based filtering and detection tools have proven to raise awareness among social media users about the nature and potential implications of their on-line contributions BIBREF33. Similarly, advances can be expected from approaches that aim to extend the scope of analysis beyond descriptions of a present debate situation in order to model how a debate might evolve over time and how intentions of the participants could be included in such an analysis.",
+      "Progress in any of these areas hinges on a further integration of real-world data in the modelling process, as well as a further socio-technical and media-theoretical investigation of how activity on social media platforms and technologies correlate to real-world conflicts. The remainder of this section therefore ventures to explore how conceptual argument communication models for polarization and alignment BIBREF34 might be reconciled with real-world data, and how such models might inform debate facilitation efforts."
+    ],
+    [
+      "As discussed in previous sections, news websites like TheGuardian.com establish a communicative settings in which agents (users, commenters) exchange arguments about different issues or topics. For those seeking to establish a healthy debate, it could thus be of interest to know how different users relate to each other in terms of their beliefs about a certain issue or topic (in this case climate change). Which beliefs are for instance shared by users and which ones are not? In other words, can we map patterns of alignment or polarization among users?",
+      "Figure FIGREF15 ventures to demonstrate how representations of opinion landscapes (generated using the methods outlined above) can be enriched with user information to answer such questions. Specifically, the graph represents the beliefs of two among the most active commenters in the corpus. The opinions of each user are marked using a colour coding scheme: red nodes represent the beliefs of the first user, blue nodes represent the beliefs of the second user. Nodes with a green colour represent beliefs that are shared by both users.",
+      "Taking into account again the factors of aggregation that were discussed in the previous section, Figure FIGREF15 supports some preliminary observations about the relationship between the two users in terms of their beliefs. Generally, given the fact that the graph concerns the two most active commenters on the website, it can be seen that the rendered opinion landscape is quite extensive. It is also clear that the belief systems of both users are not unrelated, as nodes of all colours can be found distributed throughout the graph. This is especially the case for the right-hand top cluster and right-hand bottom cluster of the graph, where green, red, and blue nodes are mixed. Since both users are discussing on articles on climate change, a degree of affinity between opinions or beliefs is to be expected.",
+      "Upon closer examination, a number of disparities between the belief systems of the two commenters can be detected. Considering the left-hand top cluster and center of the graph, it becomes clear that exclusively the red commenter is using a selection of terms related to the economical and socio-political realm (e.g. `people', `american', `nation', `government') and industry (e.g. `fuel', `industry', `car', etc.). The blue commenter, on the other hand, exclusively engages in using a range of terms that could be deemed more technical and scientific in nature (e.g. `feedback', `property', `output', `trend', `variability', etc.). From the graph, it also follows that the blue commenter does not enter into the red commenter's `social' segments of the graph as frequently as the red commenter enters the more scientifically-oriented clusters of the graph (although in the latter cases the red commenter does not use the specific technical terminology of the blue commenter). The cluster where both beliefs mingle the most (and where overlap can be observed), is the top right cluster. This overlap is constituted by very general terms (e.g. `climate', `change', and `science'). In sum, the graph reveals that the commenters' beliefs are positioned most closely to each other on the most general aspects of the debate, whereas there is less relatedness on the social and more technical aspects of the debate. In this regard, the depicted situation seemingly evokes currently on-going debates about the role or responsibilities of the people or individuals versus that of experts when it comes to climate change BIBREF35, BIBREF36, BIBREF37.",
+      "What forms of debate facilitation, then, could be based on these observations? And what kind of collective effects can be expected? As follows from the above, beliefs expressed by the two commenters shown here (which are selected based on their active participation rather than actual engagement or dialogue with one another) are to some extent complementary, as the blue commenter, who displays a scientifically-oriented system of beliefs, does not readily engage with the social topics discussed by the red commenter. As such, the overall opinion landscape of the climate change could potentially be enriched with novel perspectives if the blue commenter was invited to engage in a debate about such topics as industry and government. Similarly, one could explore the possibility of providing explanatory tools or additional references on occasions where the debate takes a more technical turn.",
+      "However, argument-based models of collective attitude formation BIBREF38, BIBREF34 also tell us to be cautious about such potential interventions. Following the theory underlying these models, different opinion groups prevailing during different periods of a debate will activate different argumentative associations. Facilitating exchange between users with complementary arguments supporting similar opinions may enforce biased argument pools BIBREF39 and lead to increasing polarization at the collective level. In the example considered here the two commenters agree on the general topic, but the analysis suggests that they might have different opinions about the adequate direction of specific climate change action. A more fine\u2013grained automatic detection of cognitive and evaluative associations between arguments and opinions is needed for a reliable use of models to predict what would come out of facilitating exchange between two specific users. In this regard, computational approaches to the linguistic analysis of texts such as semantic frame extraction offer productive opportunities for empirically modelling opinion dynamics. Extraction of causation frames allows one to disentangle cause-effect relations between semantic units, which provides a productive step towards mapping and measuring structures of cognitive associations. These opportunities are to be explored by future work."
+    ],
+    [
+      "Ongoing transitions from a print-based media ecology to on-line news and discussion platforms have put traditional forms of news production and consumption at stake. Many challenges related to how information is currently produced and consumed come to a head in news website comment sections, which harbour the potential of providing new insights into how cultural conflicts emerge and evolve. On the basis of an observatory for analyzing climate change-related comments from TheGuardian.com, this article has critically examined possibilities and limitations of the machine-assisted exploration and possible facilitation of on-line opinion dynamics and debates.",
+      "Beyond technical and modelling pathways, this examination brings into view broader methodological and epistemological aspects of the use of digital methods to capture and study the flow of on-line information and opinions. Notably, the proposed approaches lift questions of computational analysis and interpretation that can be tied to an overarching tension between `distant' and `close reading' BIBREF40. In other words, monitoring on-line opinion dynamics means embracing the challenges and associated trade-offs that come with investigating large quantities of information through computational, text-analytical means, but doing this in such a way that nuance and meaning are not lost in the process.",
+      "Establishing productive cross-overs between the level of opinions mined at scale (for instance through the lens of causation frames) and the detailed, closer looks at specific conversations, interactions and contexts depends on a series of preliminaries. One of these is the continued availability of high-quality, accessible data. As the current on-line media ecology is recovering from recent privacy-related scandals (e.g. Cambridge Analytica), such data for obvious reasons is not always easy to come by. In the same legal and ethical vein, reproducibility and transparency of models is crucial to the further development of analytical tools and methods. As the experiments discussed in this paper have revealed, a key factor in this undertaking are human faculties of interpretation. Just like the encoding schemes introduced by Axelrod and others before the wide-spread use of computational methods, present-day pipelines and tools foreground the role of human agents as the primary source of meaning attribution.",
+      "<This project has received funding from the European Union\u2019s Horizon 2020 research and innovation programme under grant agreement No 732942 (Opinion Dynamics and Cultural Conflict in European Spaces \u2013 www.Odycceus.eu).>"
+    ]
+  ]
+}
+```

qasper-0188/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: "Hinglish"Language -- Modeling a Messy Code-Mixed Language
+
+Question: Was each text augmentation technique experimented individually?

qasper-0322/instruction.md

@@ -0,0 +1,88 @@
+Name of Paper: Exploring End-to-End Techniques for Low-Resource Speech Recognition
+
+Question: What kind of spontaneous speech is used?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related work",
+    "Basic setup",
+    "Experiments with architecture",
+    "Loss modification: segmenting during training",
+    "Using different features",
+    "Varying model size and number of layers",
+    "Training the best model",
+    "Conclusions and future work",
+    "Acknowledgements"
+  ],
+  "paragraphs": [
+    [
+      "Although development of the first speech recognition systems began half a century ago, there has been a significant increase of the accuracy of ASR systems and number of their applications for the recent ten years, even for low-resource languages BIBREF0 , BIBREF1 .",
+      "This is mainly due to widespread applying of deep learning and very effective performance of neural networks in hybrid recognition systems (DNN-HMM). However, for last few years there has been a trend to change traditional ASR training paradigm. End-to-end training systems gradually displace complex multistage learning process (including training of GMMs BIBREF2 , clustering of allophones\u2019 states, aligning of speech to clustered senones, training neural networks with cross-entropy loss, followed by retraining with sequence-discriminative criterion). The new approach implies training the system in one global step, working only with acoustic data and reference texts, and significantly simplifies or even completely excludes in some cases the decoding process. It also avoids the problem of out-of-vocabulary words (OOV), because end-to-end system, trained with parts of the words as targets, can construct new words itself using graphemes or subword units, while traditional DNN-HMM systems are limited with language model vocabulary.",
+      "The whole variety of end-to-end systems can be divided into 3 main categories: Connectionist Temporal Classification (CTC) BIBREF3 ; Sequence-to-sequence models with attention mechanism BIBREF4 ; RNN-Transducers BIBREF5 .",
+      "Connectionist Temporal Classification (CTC) approach uses loss functions that utilize all possible alignments between reference text and audio data. Targets for CTC-based system can be phonemes, graphemes, syllables and other subword units and even whole words. However, a lot more data is usually required to train such systems well, compared to traditional hybrid systems.",
+      "Sequence-to-sequence models are used to map entire input sequences to output sequences without any assumptions about their alignment. The most popular architecture for sequence-to-sequence models is encoder-decoder model with attention. Encoder and decoder are usually constructed using recurrent neural networks, basic attention mechanism calculates energy weights that emphasize importance of encoder vectors for decoding on this step, and then sums all these vectors with energy weights. Encoder-decoder models with attention mechanism show results close to traditional DNN-HMM systems and in some cases surpass them, but for a number of reasons their usage is still rather limited. First of all, this is related to the fact, that such systems show best results when the duration of real utterances is close to the duration of utterances from training data. However, when the duration difference increases, the performance degrades significantly BIBREF4 .",
+      "Moreover, the entire utterance must be preprocessed by encoder before start of decoder's work. This is the reason, why it is hard to apply the approach to recognize long recordings or streaming audio. Segmenting long recordings into shorter utterances solves the duration issue, but leads to a context break, and eventually negatively affects recognition accuracy. Secondly, the computational complexity of encoder-decoder models is high because of recurrent networks usage, so these models are rather slow and hard to parallelize.",
+      "The idea of RNN-Transducer is an extension of CTC and provides the ability to model inner dependencies separately and jointly between elements of both input (audio frames) and output (phonemes and other subword units) sequences. Despite of mathematical elegance, such systems are very complicated and hard to implement, so they are still rarely used, although several impressive results were obtained using this technique.",
+      "CTC-based approach is easier to implement, better scaled and has many \u201cdegrees of freedom\u201d, which allows to significantly improve baseline systems and achieve results close to state-of-the-art. Moreover, CTC-based systems are well compatible with traditional WFST-decoders and can be easily integrated with conventional ASR systems.",
+      "Besides, as already mentioned, CTC-systems are rather sensitive to the amount of training data, so it is very relevant to study how to build effective CTC-based recognition system using a small amount of training samples. It is especially actual for low-resource languages, where we have only a few dozen hours of speech. Building ASR system for low-resource languages is one of the aims of international Babel program, funded by the Intelligence Advanced Research Projects Activity (IARPA). Within the program extensive research was carried out, resulting in creation of a number of modern ASR systems for low-resource languages. Recently, end-to-end approaches were applied to this task, showing expectedly worse results than traditional systems, although the difference is rather small.",
+      "In this paper we explore a number of ways to improve end-to-end CTC-based systems in low-resource scenarios using the Turkish language dataset from the IARPA Babel collection. In the next section we describe in more details different versions of CTC-systems and their application for low-resource speech recognition. Section 3 describes the experiments and their results. Section 4 summarizes the results and discusses possible ways for further work."
+    ],
+    [
+      "Development of CTC-based systems originates from the paper BIBREF3 where CTC loss was introduced. This loss is a total probability of labels sequence given observation sequence, which takes into account all possible alignments induced by a given words sequence.",
+      "Although a number of possible alignments increases exponentially with sequences\u2019 lengths, there is an efficient algorithm to compute CTC loss based on dynamic programming principle (known as Forward-Backward algorithm). This algorithm operates with posterior probabilities of any output sequence element observation given the time frame and CTC loss is differentiable with respect to these probabilities.",
+      "Therefore, if an acoustic model is based on the neural network which estimates these posteriors, its training may be performed with a conventional error back-propagation gradient descent BIBREF6 . Training of ASR system based on such a model does not require an explicit alignment of input utterance to the elements of output sequence and thus may be performed in end-to-end fashion. It is also important that CTC loss accumulates the information about the whole output sequence, and hence its optimization is in some sense an alternative to the traditional fine-tuning of neural network acoustic models by means of sequence-discriminative criteria such as sMBR BIBREF7 etc. The implementation of CTC is conventionally based on RNN/LSTM networks, including bidirectional ones as acoustic models, since they are known to model long context effectively.",
+      "The important component of CTC is a special \u201cblank\u201d symbol which fills in gaps between meaningful elements of output sequence to equalize its length to the number of frames in the input sequence. It corresponds to a separate output neuron, and blank symbols are deleted from the recognized sequence to obtain the final result. In BIBREF8 a modification of CTC loss was proposed, referred as Auto SeGmentation criterion (ASG loss), which does not use blank symbols. Instead of using \u201cblank\u201d, a simple transition probability model for an output symbols is introduced. This leads to a significant simplification and speedup of computations. Moreover, the improved recognition results compared to basic CTC loss were obtained.",
+      "DeepSpeech BIBREF9 developed by Baidu Inc. was one of the first systems that demonstrated an effectiveness of CTC-based speech recognition in LVCSR tasks. Being trained on 2300 hours of English Conversational Telephone Speech data, it demonstrated state-of-the-art results on Hub5'00 evaluation set. Research in this direction continued and resulted in DeepSpeech2 architecture BIBREF10 , composed of both convolutional and recurrent layers. This system demonstrates improved accuracy of recognition of both English and Mandarin speech. Another successful example of applying CTC to LVCSR tasks is EESEN system BIBREF11 . It integrates an RNN-based model trained with CTC criterion to the conventional WFST-based decoder from the Kaldi toolkit BIBREF12 . The paper BIBREF13 shows that end-to-end systems may be successfully built from convolutional layers only instead of recurrent ones. It was demonstrated that using Gated Convolutional Units (GLU-CNNs) and training with ASG-loss leads to the state-of-the-art results on the LibriSpeech database (960 hours of training data).",
+      "Recently, a new modification of DeepSpeech2 architecture was proposed in BIBREF14 . Several lower convolutional layers were replaced with a deep residual network with depth-wise separable convolutions. This modification along with using strong regularization and data augmentation techniques leads to the results close to DeepSpeech2 in spite of significantly lower amount of data used for training. Indeed, one of the models was trained with only 80 hours of speech data (which were augmented with noisy and speed-perturbed versions of original data).",
+      "These results suggest that CTC can be successfully applied for the training of ASR systems for low-resource languages, in particular, for those included in Babel research program (the amount of training data for them is normally 40 to 80 hours of speech).",
+      "Currently, Babel corpus contains data for more than 20 languages, and for most of them quite good traditional ASR system were built BIBREF15 , BIBREF16 , BIBREF17 . In order to improve speech recognition accuracy for a given language, data from other languages is widely used as well. It can be used to train multilingual system via multitask learning or to obtain high-level multilingual representations, usually bottleneck features, extracted from a pre-trained multilingual network.",
+      "One of the first attempts to build ASR system for low-resource BABEL languages using CTC-based end-to-end training was made recently BIBREF18 . Despite the obtained results are somewhat worse compared to the state-of-the-art traditional systems, they still demonstrate that CTC-based approach is viable for building low-resource ASR systems. The aim of our work is to investigate some ways to improve the obtained results."
+    ],
+    [
+      "For all experiments we used conversational speech from IARPA Babel Turkish Language Pack (LDC2016S10). This corpus contains about 80 hours of transcribed speech for training and 10 hours for development. The dataset is rather small compared to widely used benchmarks for conversational speech: English Switchboard corpus (300 hours, LDC97S62) and Fisher dataset (2000 hours, LDC2004S13 and LDC2005S13).",
+      "As targets we use 32 symbols: 29 lowercase characters of Turkish alphabet BIBREF19 , apostrophe, space and special \u3008blank\u3009 character that means \u201cno output\u201d. Thus we do not use any prior linguistic knowledge and also avoid OOV problem as the system can construct new words directly.",
+      "All models are trained with CTC-loss. Input features are 40 mel-scaled log filterbank enegries (FBanks) computed every 10 ms with 25 ms window, concatenated with deltas and delta-deltas (120 features in vector). We also tried to use spectrogram and experimented with different normalization techniques.",
+      "For decoding we used character-based beam search BIBREF20 with 3-gram language model build with SRILM package BIBREF21 finding sequence of characters INLINEFORM0 that maximizes the following objective BIBREF9 : INLINEFORM1 ",
+      "where INLINEFORM0 is language model weight and INLINEFORM1 is word insertion penalty.",
+      "For all experiments we used INLINEFORM0 , INLINEFORM1 , and performed decoding with beam width equal to 100 and 2000, which is not very large compared to 7000 and more active hypotheses used in traditional WFST decoders (e.g. many Kaldi recipes do decoding with INLINEFORM2 ).",
+      "To compare with other published results BIBREF18 , BIBREF22 we used Sclite BIBREF23 scoring package to measure results of decoding with beam width 2000, that takes into account incomplete words and spoken noise in reference texts and doesn't penalize model if it incorrectly recognize these pieces.",
+      "Also we report WER (word error rate) for simple argmax decoder (taking labels with maximum output on each time step and than applying CTC decoding rule \u2013 collapse repeated labels and remove \u201cblanks\u201d)."
+    ],
+    [
+      "We tried to explore the behavior of different neural network architectures in case when rather small data is available. We used multi-layer bidirectional LSTM networks, tried fully-convolutional architecture similar to Wav2Letter BIBREF8 and explored DeepSpeech-like architecture developed by Salesforce (DS-SF) BIBREF14 .",
+      "The convolutional model consists of 11 convolutional layers with batch normalization after each layer. The DeepSpeech-like architecture consists of 5-layers residual network with depth-wise separable convolutions followed by 4-layer bidirectional Gated Recurrent Unit (GRU) as described in BIBREF14 .",
+      "Our baseline bidirectional LSTM is 6-layers network with 320 hidden units per direction as in BIBREF18 . Also we tried to use bLSTM to label every second frame (20 ms) concatenating every first output from first layer with second and taking this as input for second model layer.",
+      "The performance of our baseline models is shown in Table TABREF6 ."
+    ],
+    [
+      "It is known that CTC-loss is very unstable for long utterances BIBREF3 , and smaller utterances are more useful for this task. Some techniques were developed to help model converge faster, e.g. sortagrad BIBREF10 (using shorter segments at the beginning of training).",
+      "To compute CTC-loss we use all possible alignments between audio features and reference text, but only some of the alignments make sense. Traditional DNN-HMM systems also use iterative training with finding best alignment and then training neural network to approximate this alignment. Therefore, we propose the following algorithm to use segmentation during training:",
+      "compute CTC-alignment (find the sequence of targets with minimal loss that can be mapped to real targets by collapsing repeated characters and removing blanks)",
+      "perform greedy decoding (argmax on each step)",
+      "find \u201cwell-recognized\u201d words with INLINEFORM0 ( INLINEFORM1 is a hyperparameter): segment should start and end with space; word is \u201cwell-recognized\u201d when argmax decoding is equal to computed alignment",
+      "if the word is \u201cwell-recognized\u201d, divide the utterance into 5 segments: left segment before space, left space, the word, right space and right segment",
+      "compute CTC-loss for all this segments separately and do back-propagation as usual",
+      "The results of training with this criterion are shown in Table TABREF13 . The proposed criterion doesn't lead to consistent improvement while decoding with large beam width (2000), but shows significant improvement when decoding with smaller beam (100). We plan to further explore utilizing alignment information during training."
+    ],
+    [
+      "We explored different normalization techniques. FBanks with cepstral mean normalization (CMN) perform better than raw FBanks. We found using variance with mean normalization (CMVN) unnecessary for the task. Using deltas and delta-deltas improves model, so we used them in other experiments. Models trained with spectrogram features converge slower and to worse minimum, but the difference when using CMN is not very big compared to FBanks."
+    ],
+    [
+      "Experiments with varying number of hidden units of 6-layer bLSTM models are presented in Table TABREF17 . Models with 512 and 768 hidden units are worse than with 320, but model with 1024 hidden units is significantly better than others. We also observed that model with 6 layers performs better than others."
+    ],
+    [
+      "To train our best model we chose the best network from our experiments (6-layer bLSTM with 1024 hidden units), trained it with Adam optimizer and fine-tuned with SGD with momentum using exponential learning rate decay. The best model trained with speed and volume perturbation BIBREF24 achieved 45.8% WER, which is the best published end-to-end result on Babel Turkish dataset using in-domain data. For comparison, WER of model trained using in-domain data in BIBREF18 is 53.1%, using 4 additional languages (including English Switchboard dataset) \u2013 48.7%. It is also not far from Kaldi DNN-HMM system BIBREF22 with 43.8% WER."
+    ],
+    [
+      "In this paper we explored different end-to-end architectures in low-resource ASR task using Babel Turkish dataset. We considered different ways to improve performance and proposed promising CTC-loss modification that uses segmentation during training. Our final system achieved 45.8% WER using in-domain data only, which is the best published result for Turkish end-to-end systems. Our work also shows than well-tuned end-to-end system can achieve results very close to traditional DNN-HMM systems even for low-resource languages. In future work we plan to further investigate different loss modifications (Gram-CTC, ASG) and try to use RNN-Transducers and multi-task learning."
+    ],
+    [
+      "This work was financially supported by the Ministry of Education and Science of the Russian Federation, Contract 14.575.21.0132 (IDRFMEFI57517X0132)."
+    ]
+  ]
+}
+```

qasper-0346/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Exploring Hate Speech Detection in Multimodal Publications
+
+Question: How many tweats does MMHS150k contains, 150000?

qasper-0347/instruction.md

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

qasper-0349/instruction.md

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

qasper-0370/instruction.md

@@ -0,0 +1,150 @@
+Name of Paper: CamemBERT: a Tasty French Language Model
+
+Question: Was CamemBERT compared against multilingual BERT on these tasks?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work ::: From non-contextual to contextual word embeddings",
+    "Related Work ::: Non-contextual word embeddings for languages other than English",
+    "Related Work ::: Contextualised models for languages other than English",
+    "CamemBERT",
+    "CamemBERT ::: Architecture",
+    "CamemBERT ::: Pretraining objective",
+    "CamemBERT ::: Optimisation",
+    "CamemBERT ::: Segmentation into subword units",
+    "CamemBERT ::: Pretraining data",
+    "Evaluation ::: Part-of-speech tagging and dependency parsing",
+    "Evaluation ::: Part-of-speech tagging and dependency parsing ::: Baselines",
+    "Evaluation ::: Named Entity Recognition",
+    "Evaluation ::: Named Entity Recognition ::: Baselines",
+    "Evaluation ::: Natural Language Inference",
+    "Evaluation ::: Natural Language Inference ::: Baselines",
+    "Experiments",
+    "Experiments ::: Experimental Setup ::: Pretraining",
+    "Experiments ::: Experimental Setup ::: Fine-tuning",
+    "Experiments ::: Results ::: Part-of-Speech tagging and dependency parsing",
+    "Experiments ::: Results ::: Natural Language Inference: XNLI",
+    "Experiments ::: Results ::: Named-Entity Recognition",
+    "Experiments ::: Discussion",
+    "Conclusion",
+    "Acknowledgments",
+    "Appendix ::: Impact of Whole-Word Masking"
+  ],
+  "paragraphs": [
+    [
+      "Pretrained word representations have a long history in Natural Language Processing (NLP), from non-neural methods BIBREF0, BIBREF1, BIBREF2 to neural word embeddings BIBREF3, BIBREF4 and to contextualised representations BIBREF5, BIBREF6. Approaches shifted more recently from using these representations as an input to task-specific architectures to replacing these architectures with large pretrained language models. These models are then fine-tuned to the task at hand with large improvements in performance over a wide range of tasks BIBREF7, BIBREF8, BIBREF9, BIBREF10.",
+      "These transfer learning methods exhibit clear advantages over more traditional task-specific approaches, probably the most important being that they can be trained in an unsupervised manner. They nevertheless come with implementation challenges, namely the amount of data and computational resources needed for pretraining that can reach hundreds of gigabytes of uncompressed text and require hundreds of GPUs BIBREF11, BIBREF9. The latest transformer architecture has gone uses as much as 750GB of plain text and 1024 TPU v3 for pretraining BIBREF10. This has limited the availability of these state-of-the-art models to the English language, at least in the monolingual setting. Even though multilingual models give remarkable results, they are often larger and their results still lag behind their monolingual counterparts BIBREF12. This is particularly inconvenient as it hinders their practical use in NLP systems as well as the investigation of their language modeling capacity, something that remains to be investigated in the case of, for instance, morphologically rich languages.",
+      "We take advantage of the newly available multilingual corpus OSCAR BIBREF13 and train a monolingual language model for French using the RoBERTa architecture. We pretrain the model - which we dub CamemBERT- and evaluate it in four different downstream tasks for French: part-of-speech (POS) tagging, dependency parsing, named entity recognition (NER) and natural language inference (NLI). CamemBERT improves the state of the art for most tasks over previous monolingual and multilingual approaches, which confirms the effectiveness of large pretrained language models for French.",
+      "We summarise our contributions as follows:",
+      "We train a monolingual BERT model on the French language using recent large-scale corpora.",
+      "We evaluate our model on four downstream tasks (POS tagging, dependency parsing, NER and natural language inference (NLI)), achieving state-of-the-art results in most tasks, confirming the effectiveness of large BERT-based models for French.",
+      "We release our model in a user-friendly format for popular open-source libraries so that it can serve as a strong baseline for future research and be useful for French NLP practitioners."
+    ],
+    [
+      "The first neural word vector representations were non-contextualised word embeddings, most notably word2vec BIBREF3, GloVe BIBREF4 and fastText BIBREF14, which were designed to be used as input to task-specific neural architectures. Contextualised word representations such as ELMo BIBREF5 and flair BIBREF6, improved the expressivity of word embeddings by taking context into account. They improved the performance of downstream tasks when they replaced traditional word representations. This paved the way towards larger contextualised models that replaced downstream architectures in most tasks. These approaches, trained with language modeling objectives, range from LSTM-based architectures such as ULMFiT BIBREF15 to the successful transformer-based architectures such as GPT2 BIBREF8, BERT BIBREF7, RoBERTa BIBREF9 and more recently ALBERT BIBREF16 and T5 BIBREF10."
+    ],
+    [
+      "Since the introduction of word2vec BIBREF3, many attempts have been made to create monolingual models for a wide range of languages. For non-contextual word embeddings, the first two attempts were by BIBREF17 and BIBREF18 who created word embeddings for a large number of languages using Wikipedia. Later BIBREF19 trained fastText word embeddings for 157 languages using Common Crawl and showed that using crawled data significantly increased the performance of the embeddings relatively to those trained only on Wikipedia."
+    ],
+    [
+      "Following the success of large pretrained language models, they were extended to the multilingual setting with multilingual BERT , a single multilingual model for 104 different languages trained on Wikipedia data, and later XLM BIBREF12, which greatly improved unsupervised machine translation. A few monolingual models have been released: ELMo models for Japanese, Portuguese, German and Basque and BERT for Simplified and Traditional Chinese and German.",
+      "However, to the best of our knowledge, no particular effort has been made toward training models for languages other than English, at a scale similar to the latest English models (e.g. RoBERTa trained on more than 100GB of data)."
+    ],
+    [
+      "Our approach is based on RoBERTa BIBREF9, which replicates and improves the initial BERT by identifying key hyper-parameters for more robust performance.",
+      "In this section, we describe the architecture, training objective, optimisation setup and pretraining data that was used for CamemBERT.",
+      "CamemBERT differs from RoBERTa mainly with the addition of whole-word masking and the usage of SentencePiece tokenisation BIBREF20."
+    ],
+    [
+      "Similar to RoBERTa and BERT, CamemBERT is a multi-layer bidirectional Transformer BIBREF21. Given the widespread usage of Transformers, we do not describe them in detail here and refer the reader to BIBREF21. CamemBERT uses the original BERT $_{\\small \\textsc {BASE}}$ configuration: 12 layers, 768 hidden dimensions, 12 attention heads, which amounts to 110M parameters."
+    ],
+    [
+      "We train our model on the Masked Language Modeling (MLM) task. Given an input text sequence composed of $N$ tokens $x_1, ..., x_N$, we select $15\\%$ of tokens for possible replacement. Among those selected tokens, 80% are replaced with the special $<$mask$>$ token, 10% are left unchanged and 10% are replaced by a random token. The model is then trained to predict the initial masked tokens using cross-entropy loss.",
+      "Following RoBERTa we dynamically mask tokens instead of fixing them statically for the whole dataset during preprocessing. This improves variability and makes the model more robust when training for multiple epochs.",
+      "Since we segment the input sentence into subwords using SentencePiece, the input tokens to the models can be subwords. An upgraded version of BERT and BIBREF22 have shown that masking whole words instead of individual subwords leads to improved performance. Whole-word masking (WWM) makes the training task more difficult because the model has to predict a whole word instead of predicting only part of the word given the rest. As a result, we used WWM for CamemBERT by first randomly sampling 15% of the words in the sequence and then considering all subword tokens in each of these 15% words for candidate replacement. This amounts to a proportion of selected tokens that is close to the original 15%. These tokens are then either replaced by $<$mask$>$ tokens (80%), left unchanged (10%) or replaced by a random token.",
+      "Subsequent work has shown that the next sentence prediction task (NSP) originally used in BERT does not improve downstream task performance BIBREF12, BIBREF9, we do not use NSP as a consequence."
+    ],
+    [
+      "Following BIBREF9, we optimise the model using Adam BIBREF23 ($\\beta _1 = 0.9$, $\\beta _2 = 0.98$) for 100k steps. We use large batch sizes of 8192 sequences. Each sequence contains at most 512 tokens. We enforce each sequence to only contain complete sentences. Additionally, we used the DOC-SENTENCES scenario from BIBREF9, consisting of not mixing multiple documents in the same sequence, which showed slightly better results."
+    ],
+    [
+      "We segment the input text into subword units using SentencePiece BIBREF20. SentencePiece is an extension of Byte-Pair encoding (BPE) BIBREF24 and WordPiece BIBREF25 that does not require pre-tokenisation (at the word or token level), thus removing the need for language-specific tokenisers. We use a vocabulary size of 32k subword tokens. These are learned on $10^7$ sentences sampled from the pretraining dataset. We do not use subword regularisation (i.e. sampling from multiple possible segmentations) in our implementation for simplicity."
+    ],
+    [
+      "Pretrained language models can be significantly improved by using more data BIBREF9, BIBREF10. Therefore we used French text extracted from Common Crawl, in particular, we use OSCAR BIBREF13 a pre-classified and pre-filtered version of the November 2018 Common Craw snapshot.",
+      "OSCAR is a set of monolingual corpora extracted from Common Crawl, specifically from the plain text WET format distributed by Common Crawl, which removes all HTML tags and converts all text encodings to UTF-8. OSCAR follows the same approach as BIBREF19 by using a language classification model based on the fastText linear classifier BIBREF26, BIBREF27 pretrained on Wikipedia, Tatoeba and SETimes, which supports 176 different languages.",
+      "OSCAR performs a deduplication step after language classification and without introducing a specialised filtering scheme, other than only keeping paragraphs containing 100 or more UTF-8 encoded characters, making OSCAR quite close to the original Crawled data.",
+      "We use the unshuffled version of the French OSCAR corpus, which amounts to 138GB of uncompressed text and 32.7B SentencePiece tokens."
+    ],
+    [
+      "We fist evaluate CamemBERT on the two downstream tasks of part-of-speech (POS) tagging and dependency parsing. POS tagging is a low-level syntactic task, which consists in assigning to each word its corresponding grammatical category. Dependency parsing consists in predicting the labeled syntactic tree capturing the syntactic relations between words.",
+      "We run our experiments using the Universal Dependencies (UD) paradigm and its corresponding UD POS tag set BIBREF28 and UD treebank collection version 2.2 BIBREF29, which was used for the CoNLL 2018 shared task. We perform our work on the four freely available French UD treebanks in UD v2.2: GSD, Sequoia, Spoken, and ParTUT.",
+      "GSD BIBREF30 is the second-largest treebank available for French after the FTB (described in subsection SECREF25), it contains data from blogs, news articles, reviews, and Wikipedia. The Sequoia treebank BIBREF31, BIBREF32 comprises more than 3000 sentences, from the French Europarl, the regional newspaper L\u2019Est R\u00e9publicain, the French Wikipedia and documents from the European Medicines Agency. Spoken is a corpus converted automatically from the Rhapsodie treebank BIBREF33, BIBREF34 with manual corrections. It consists of 57 sound samples of spoken French with orthographic transcription and phonetic transcription aligned with sound (word boundaries, syllables, and phonemes), syntactic and prosodic annotations. Finally, ParTUT is a conversion of a multilingual parallel treebank developed at the University of Turin, and consisting of a variety of text genres, including talks, legal texts, and Wikipedia articles, among others; ParTUT data is derived from the already-existing parallel treebank Par(allel)TUT BIBREF35 . Table TABREF23 contains a summary comparing the sizes of the treebanks.",
+      "We evaluate the performance of our models using the standard UPOS accuracy for POS tagging, and Unlabeled Attachment Score (UAS) and Labeled Attachment Score (LAS) for dependency parsing. We assume gold tokenisation and gold word segmentation as provided in the UD treebanks."
+    ],
+    [
+      "To demonstrate the value of building a dedicated version of BERT for French, we first compare CamemBERT to the multilingual cased version of BERT (designated as mBERT). We then compare our models to UDify BIBREF36. UDify is a multitask and multilingual model based on mBERT that is near state-of-the-art on all UD languages including French for both POS tagging and dependency parsing.",
+      "It is relevant to compare CamemBERT to UDify on those tasks because UDify is the work that pushed the furthest the performance in fine-tuning end-to-end a BERT-based model on downstream POS tagging and dependency parsing. Finally, we compare our model to UDPipe Future BIBREF37, a model ranked 3rd in dependency parsing and 6th in POS tagging during the CoNLL 2018 shared task BIBREF38. UDPipe Future provides us a strong baseline that does not make use of any pretrained contextual embedding.",
+      "We will compare to the more recent cross-lingual language model XLM BIBREF12, as well as the state-of-the-art CoNLL 2018 shared task results with predicted tokenisation and segmentation in an updated version of the paper."
+    ],
+    [
+      "Named Entity Recognition (NER) is a sequence labeling task that consists in predicting which words refer to real-world objects, such as people, locations, artifacts and organisations. We use the French Treebank (FTB) BIBREF39 in its 2008 version introduced by cc-clustering:09short and with NER annotations by sagot2012annotation. The NER-annotated FTB contains more than 12k sentences and more than 350k tokens extracted from articles of the newspaper Le Monde published between 1989 and 1995. In total, it contains 11,636 entity mentions distributed among 7 different types of entities, namely: 2025 mentions of \u201cPerson\u201d, 3761 of \u201cLocation\u201d, 2382 of \u201cOrganisation\u201d, 3357 of \u201cCompany\u201d, 67 of \u201cProduct\u201d, 15 of \u201cPOI\u201d (Point of Interest) and 29 of \u201cFictional Character\u201d.",
+      "A large proportion of the entity mentions in the treebank are multi-word entities. For NER we therefore report the 3 metrics that are commonly used to evaluate models: precision, recall, and F1 score. Here precision measures the percentage of entities found by the system that are correctly tagged, recall measures the percentage of named entities present in the corpus that are found and the F1 score combines both precision and recall measures giving a general idea of a model's performance."
+    ],
+    [
+      "Most of the advances in NER haven been achieved on English, particularly focusing on the CoNLL 2003 BIBREF40 and the Ontonotes v5 BIBREF41, BIBREF42 English corpora. NER is a task that was traditionally tackled using Conditional Random Fields (CRF) BIBREF43 which are quite suited for NER; CRFs were later used as decoding layers for Bi-LSTM architectures BIBREF44, BIBREF45 showing considerable improvements over CRFs alone. These Bi-LSTM-CRF architectures were later enhanced with contextualised word embeddings which yet again brought major improvements to the task BIBREF5, BIBREF6. Finally, large pretrained architectures settled the current state of the art showing a small yet important improvement over previous NER-specific architectures BIBREF7, BIBREF46.",
+      "In non-English NER the CoNLL 2002 shared task included NER corpora for Spanish and Dutch corpora BIBREF47 while the CoNLL 2003 included a German corpus BIBREF40. Here the recent efforts of BIBREF48 settled the state of the art for Spanish and Dutch, while BIBREF6 did it for German.",
+      "In French, no extensive work has been done due to the limited availability of NER corpora. We compare our model with the strong baselines settled by BIBREF49, who trained both CRF and BiLSTM-CRF architectures on the FTB and enhanced them using heuristics and pretrained word embeddings."
+    ],
+    [
+      "We also evaluate our model on the Natural Language Inference (NLI) task, using the French part of the XNLI dataset BIBREF50. NLI consists in predicting whether a hypothesis sentence is entailed, neutral or contradicts a premise sentence.",
+      "The XNLI dataset is the extension of the Multi-Genre NLI (MultiNLI) corpus BIBREF51 to 15 languages by translating the validation and test sets manually into each of those languages. The English training set is also machine translated for all languages. The dataset is composed of 122k train, 2490 valid and 5010 test examples. As usual, NLI performance is evaluated using accuracy.",
+      "To evaluate a model on a language other than English (such as French), we consider the two following settings:",
+      "TRANSLATE-TEST: The French test set is machine translated into English, and then used with an English classification model. This setting provides a reasonable, although imperfect, way to circumvent the fact that no such data set exists for French, and results in very strong baseline scores.",
+      "TRANSLATE-TRAIN: The French model is fine-tuned on the machine-translated English training set and then evaluated on the French test set. This is the setting that we used for CamemBERT."
+    ],
+    [
+      "For the TRANSLATE-TEST setting, we report results of the English RoBERTa to act as a reference.",
+      "In the TRANSLATE-TRAIN setting, we report the best scores from previous literature along with ours. BiLSTM-max is the best model in the original XNLI paper, mBERT which has been reported in French in BIBREF52 and XLM (MLM+TLM) is the best-presented model from BIBREF50."
+    ],
+    [
+      "In this section, we measure the performance of CamemBERT by evaluating it on the four aforementioned tasks: POS tagging, dependency parsing, NER and NLI."
+    ],
+    [
+      "We use the RoBERTa implementation in the fairseq library BIBREF53. Our learning rate is warmed up for 10k steps up to a peak value of $0.0007$ instead of the original $0.0001$ given our large batch size (8192). The learning rate fades to zero with polynomial decay. We pretrain our model on 256 Nvidia V100 GPUs (32GB each) for 100k steps during 17h."
+    ],
+    [
+      "For each task, we append the relevant predictive layer on top of CamemBERT's Transformer architecture. Following the work done on BERT BIBREF7, for sequence tagging and sequence labeling we append a linear layer respectively to the $<$s$>$ special token and to the first subword token of each word. For dependency parsing, we plug a bi-affine graph predictor head as inspired by BIBREF54 following the work done on multilingual parsing with BERT by BIBREF36. We refer the reader to these two articles for more details on this module.",
+      "We fine-tune independently CamemBERT for each task and each dataset. We optimise the model using the Adam optimiser BIBREF23 with a fixed learning rate. We run a grid search on a combination of learning rates and batch sizes. We select the best model on the validation set out of the 30 first epochs.",
+      "Although this might push the performances even further, for all tasks except NLI, we don't apply any regularisation techniques such as weight decay, learning rate warm-up or discriminative fine-tuning. We show that fine-tuning CamemBERT in a straight-forward manner leads to state-of-the-art results on most tasks and outperforms the existing BERT-based models in most cases.",
+      "The POS tagging, dependency parsing, and NER experiments are run using hugging face's Transformer library extended to support CamemBERT and dependency parsing BIBREF55. The NLI experiments use the fairseq library following the RoBERTa implementation."
+    ],
+    [
+      "For POS tagging and dependency parsing, we compare CamemBERT to three other near state-of-the-art models in Table TABREF32. CamemBERT outperforms UDPipe Future by a large margin for all treebanks and all metrics. Despite a much simpler optimisation process, CamemBERT beats UDify performances on all the available French treebanks.",
+      "CamemBERT also demonstrates higher performances than mBERT on those tasks. We observe a larger error reduction for parsing than for tagging. For POS tagging, we observe error reductions of respectively 0.71% for GSD, 0.81% for Sequoia, 0.7% for Spoken and 0.28% for ParTUT. For parsing, we observe error reductions in LAS of 2.96% for GSD, 3.33% for Sequoia, 1.70% for Spoken and 1.65% for ParTUT."
+    ],
+    [
+      "On the XNLI benchmark, CamemBERT obtains improved performance over multilingual language models on the TRANSLATE-TRAIN setting (81.2 vs. 80.2 for XLM) while using less than half the parameters (110M vs. 250M). However, its performance still lags behind models trained on the original English training set in the TRANSLATE-TEST setting, 81.2 vs. 82.91 for RoBERTa. It should be noted that CamemBERT uses far fewer parameters than RoBERTa (110M vs. 355M parameters)."
+    ],
+    [
+      "For named entity recognition, our experiments show that CamemBERT achieves a slightly better precision than the traditional CRF-based SEM architectures described above in Section SECREF25 (CRF and Bi-LSTM+CRF), but shows a dramatic improvement in finding entity mentions, raising the recall score by 3.5 points. Both improvements result in a 2.36 point increase in the F1 score with respect to the best SEM architecture (BiLSTM-CRF), giving CamemBERT the state of the art for NER on the FTB. One other important finding is the results obtained by mBERT. Previous work with this model showed increased performance in NER for German, Dutch and Spanish when mBERT is used as contextualised word embedding for an NER-specific model BIBREF48, but our results suggest that the multilingual setting in which mBERT was trained is simply not enough to use it alone and fine-tune it for French NER, as it shows worse performance than even simple CRF models, suggesting that monolingual models could be better at NER."
+    ],
+    [
+      "CamemBERT displays improved performance compared to prior work for the 4 downstream tasks considered. This confirms the hypothesis that pretrained language models can be effectively fine-tuned for various downstream tasks, as observed for English in previous work. Moreover, our results also show that dedicated monolingual models still outperform multilingual ones. We explain this point in two ways. First, the scale of data is possibly essential to the performance of CamemBERT. Indeed, we use 138GB of uncompressed text vs. 57GB for mBERT. Second, with more data comes more diversity in the pretraining distribution. Reaching state-of-the-art performances on 4 different tasks and 6 different datasets requires robust pretrained models. Our results suggest that the variability in the downstream tasks and datasets considered is handled more efficiently by a general language model than by Wikipedia-pretrained models such as mBERT."
+    ],
+    [
+      "CamemBERT improves the state of the art for multiple downstream tasks in French. It is also lighter than other BERT-based approaches such as mBERT or XLM. By releasing our model, we hope that it can serve as a strong baseline for future research in French NLP, and expect our experiments to be reproduced in many other languages. We will publish an updated version in the near future where we will explore and release models trained for longer, with additional downstream tasks, baselines (e.g. XLM) and analysis, we will also train additional models with potentially cleaner corpora such as CCNet BIBREF56 for more accurate performance evaluation and more complete ablation."
+    ],
+    [
+      "This work was partly funded by three French National grants from the Agence Nationale de la Recherche, namely projects PARSITI (ANR-16-CE33-0021), SoSweet (ANR-15-CE38-0011) and BASNUM (ANR-18-CE38-0003), as well as by the last author's chair in the PRAIRIE institute."
+    ],
+    [
+      "We analyze the addition of whole-word masking on the downstream performance of CamemBERT. As reported for English on other downstream tasks, whole word masking improves downstream performances for all tasks but NER as seen in Table TABREF46. NER is highly sensitive to capitalisation, prefixes, suffixes and other subword features that could be used by a model to correctly identify entity mentions. Thus the added information by learning the masking at a subword level rather than at whole-word level seems to have a detrimental effect on downstream NER results."
+    ]
+  ]
+}
+```

qasper-0371/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: CamemBERT: a Tasty French Language Model
+
+Question: How long was CamemBERT trained?

qasper-0376/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Vocabulary-based Method for Quantifying Controversy in Social Media
+
+Question: What social media platform is observed?

qasper-0379/instruction.md

@@ -0,0 +1,128 @@
+Name of Paper: Semantic Sentiment Analysis of Twitter Data
+
+Question: What difficulties does sentiment analysis on Twitter have, compared to sentiment analysis in other domains?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Synonyms",
+    "Glossary",
+    "Definition",
+    "Introduction",
+    "Key Points",
+    "Historical Background",
+    "Variants of the Task at SemEval",
+    "Features and Learning",
+    "Sentiment Polarity Lexicons",
+    "Key Applications",
+    "Future Directions",
+    "Cross-References",
+    "Recommended Reading"
+  ],
+  "paragraphs": [
+    [
+      "Microblog sentiment analysis; Twitter opinion mining"
+    ],
+    [
+      "Sentiment Analysis: This is text analysis aiming to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a piece of text."
+    ],
+    [
+      "Sentiment analysis on Twitter is the use of natural language processing techniques to identify and categorize opinions expressed in a tweet, in order to determine the author's attitude toward a particular topic or in general. Typically, discrete labels such as positive, negative, neutral, and objective are used for this purpose, but it is also possible to use labels on an ordinal scale, or even continuous numerical values."
+    ],
+    [
+      "Internet and the proliferation of smart mobile devices have changed the way information is created, shared, and spreads, e.g., microblogs such as Twitter, weblogs such as LiveJournal, social networks such as Facebook, and instant messengers such as Skype and WhatsApp are now commonly used to share thoughts and opinions about anything in the surrounding world. This has resulted in the proliferation of social media content, thus creating new opportunities to study public opinion at a scale that was never possible before.",
+      "Naturally, this abundance of data has quickly attracted business and research interest from various fields including marketing, political science, and social studies, among many others, which are interested in questions like these: Do people like the new Apple Watch? What do they hate about iPhone6? Do Americans support ObamaCare? What do Europeans think of Pope's visit to Palestine? How do we recognize the emergence of health problems such as depression? Do Germans like how Angela Merkel is handling the refugee crisis in Europe? What do republican voters in USA like/hate about Donald Trump? How do Scottish feel about the Brexit?",
+      "Answering these questions requires studying the sentiment of opinions people express in social media, which has given rise to the fast growth of the field of sentiment analysis in social media, with Twitter being especially popular for research due to its scale, representativeness, variety of topics discussed, as well as ease of public access to its messages BIBREF0 , BIBREF1 .",
+      "Despite all these opportunities, the rise of social media has also presented new challenges for natural language processing (NLP) applications, which had largely relied on NLP tools tuned for formal text genres such as newswire, and thus were not readily applicable to the informal language and style of social media. That language proved to be quite challenging with its use of creative spelling and punctuation, misspellings, slang, new words, URLs, and genre-specific terminology and abbreviations, e.g., RT for re-tweet and #hashtags. In addition to the genre difference, there is also a difference in length: social media messages are generally short, often length-limited by design as in Twitter, i.e., a sentence or a headline rather than a full document. How to handle such challenges has only recently been the subject of thorough research BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 ."
+    ],
+    [
+      "Sentiment analysis has a wide number of applications in areas such as market research, political and social sciences, and for studying public opinion in general, and Twitter is one of the most commonly-used platforms for this. This is due to its streaming nature, which allows for real-time analysis, to its social aspect, which encourages people to share opinions, and to the short size of the tweets, which simplifies linguistic analysis.",
+      "There are several formulations of the task of Sentiment Analysis on Twitter that look at different sizes of the target (e.g., at the level of words vs. phrases vs. tweets vs. sets of tweets), at different types of semantic targets (e.g., aspect vs. topic vs. overall tweet), at the explicitness of the target (e.g., sentiment vs. stance detection), at the scale of the expected label (2-point vs. 3-point vs. ordinal), etc. All these are explored at SemEval, the International Workshop on Semantic Evaluation, which has created a number of benchmark datasets and has enabled direct comparison between different systems and approaches, both as part of the competition and beyond.",
+      "Traditionally, the task has been addressed using supervised and semi-supervised methods, as well as using distant supervision, with the most important resource being sentiment polarity lexicons, and with feature-rich approaches as the dominant research direction for years. With the recent rise of deep learning, which in many cases eliminates the need for any explicit feature modeling, the importance of both lexicons and features diminishes, while at the same time attention is shifting towards learning from large unlabeled data, which is needed to train the high number of parameters of such complex models. Finally, as methods for sentiment analysis mature, more attention is also being paid to linguistic structure and to multi-linguality and cross-linguality."
+    ],
+    [
+      "Sentiment analysis emerged as a popular research direction in the early 2000s. Initially, it was regarded as standard document classification into topics such as business, sport, and politics BIBREF10 . However, researchers soon realized that it was quite different from standard document classification BIBREF11 , and that it crucially needed external knowledge in the form of sentiment polarity lexicons.",
+      "Around the same time, other researchers realized the importance of external sentiment lexicons, e.g., Turney BIBREF12 proposed an unsupervised approach to learn the sentiment orientation of words/phrases: positive vs. negative. Later work studied the linguistic aspects of expressing opinions, evaluations, and speculations BIBREF13 , the role of context in determining the sentiment orientation BIBREF14 , of deeper linguistic processing such as negation handling BIBREF15 , of finer-grained sentiment distinctions BIBREF16 , of positional information BIBREF17 , etc. Moreover, it was recognized that in many cases, it is crucial to know not just the polarity of the sentiment but also the topic toward which this sentiment is expressed BIBREF18 .",
+      "Until the rise of social media, research on opinion mining and sentiment analysis had focused primarily on learning about the language of sentiment in general, meaning that it was either genre-agnostic BIBREF19 or focused on newswire texts BIBREF20 and customer reviews (e.g., from web forums), most notably about movies BIBREF10 and restaurants BIBREF21 but also about hotels, digital cameras, cell phones, MP3 and DVD players BIBREF22 , laptops BIBREF21 , etc. This has given rise to several resources, mostly word and phrase polarity lexicons, which have proven to be very valuable for their respective domains and types of texts, but less useful for short social media messages.",
+      "Later, with the emergence of social media, sentiment analysis in Twitter became a hot research topic. Unfortunately, research in that direction was hindered by the unavailability of suitable datasets and lexicons for system training, development, and testing. While some Twitter-specific resources were developed, initially they were either small and proprietary, such as the i-sieve corpus BIBREF6 , were created only for Spanish like the TASS corpus BIBREF23 , or relied on noisy labels obtained automatically, e.g., based on emoticons and hashtags BIBREF24 , BIBREF25 , BIBREF10 .",
+      "This situation changed with the shared task on Sentiment Analysis on Twitter, which was organized at SemEval, the International Workshop on Semantic Evaluation, a semantic evaluation forum previously known as SensEval. The task ran in 2013, 2014, 2015, and 2016, attracting over 40 participating teams in all four editions. While the focus was on general tweets, the task also featured out-of-domain testing on SMS messages, LiveJournal messages, as well as on sarcastic tweets.",
+      "SemEval-2013 Task 2 BIBREF26 and SemEval-2014 Task 9 BIBREF27 focused on expression-level and message-level polarity. SemEval-2015 Task 10 BIBREF28 , BIBREF29 featured topic-based message polarity classification on detecting trends toward a topic and on determining the out-of-context (a priori) strength of association of Twitter terms with positive sentiment. SemEval-2016 Task 4 BIBREF30 introduced a 5-point scale, which is used for human review ratings on popular websites such as Amazon, TripAdvisor, Yelp, etc.; from a research perspective, this meant moving from classification to ordinal regression. Moreover, it focused on quantification, i.e., determining what proportion of a set of tweets on a given topic are positive/negative about it. It also featured a 5-point scale ordinal quantification subtask BIBREF31 .",
+      "Other related tasks have explored aspect-based sentiment analysis BIBREF32 , BIBREF33 , BIBREF21 , sentiment analysis of figurative language on Twitter BIBREF34 , implicit event polarity BIBREF35 , stance in tweets BIBREF36 , out-of-context sentiment intensity of phrases BIBREF37 , and emotion detection BIBREF38 . Some of these tasks featured languages other than English."
+    ],
+    [
+      "Tweet-level sentiment. The simplest and also the most popular task of sentiment analysis on Twitter is to determine the overall sentiment expressed by the author of a tweet BIBREF30 , BIBREF28 , BIBREF26 , BIBREF29 , BIBREF27 . Typically, this means choosing one of the following three classes to describe the sentiment: Positive, Negative, and Neutral. Here are some examples:",
+      "Positive: @nokia lumia620 cute and small and pocket-size, and available in the brigh test colours of day! #lumiacaption",
+      "Negative: I hate tweeting on my iPhone 5 it's so small :(",
+      "Neutral: If you work as a security in a samsung store...Does that make you guardian of the galaxy??",
+      "Sentiment polarity lexicons. Naturally, the overall sentiment in a tweet can be determined based on the sentiment-bearing words and phrases it contains as well as based on emoticons such as ;) and:(. For this purpose, researchers have been using lexicons of sentiment-bearing words. For example, cute is a positive word, while hate is a negative one, and the occurrence of these words in (1) and (2) can help determine the overall polarity of the respective tweet. We will discuss these lexicons in more detail below.",
+      "Prior sentiment polarity of multi-word phrases. Unfortunately, many sentiment-bearing words are not universally good or universally bad. For example, the polarity of an adjective could depend on the noun it modifies, e.g., hot coffee and unpredictable story express positive sentiment, while hot beer and unpredictable steering are negative. Thus, determining the out-of-context (a priori) strength of association of Twitter terms, especially multi-word terms, with positive/negative sentiment is an active research direction BIBREF28 , BIBREF29 .",
+      "Phrase-level polarity in context. Even when the target noun is the same, the polarity of the modifying adjective could be different in different tweets, e.g., small is positive in (1) but negative in (2), even though they both refer to a phone. Thus, there has been research in determining the sentiment polarity of a term in the context of a tweet BIBREF26 , BIBREF29 , BIBREF27 .",
+      "Sarcasm. Going back to tweet-level sentiment analysis, we should mention sarcastic tweets, which are particularly challenging as the sentiment they express is often the opposite of what the words they contain suggest BIBREF4 , BIBREF29 , BIBREF27 . For example, (4) and (5) express a negative sentiment even though they contain positive words and phrases such as thanks, love, and boosts my morale.",
+      "Negative: Thanks manager for putting me on the schedule for Sunday",
+      "Negative: I just love missing my train every single day. Really boosts my morale.",
+      "Sentiment toward a topic. Even though tweets are short, as they are limited to 140 characters by design (even though this was relaxed a bit as of September 19, 2016, and now media attachments such as images, videos, polls, etc., and quoted tweets no longer reduce the character count), they are still long enough to allow the tweet's author to mention several topics and to express potentially different sentiment toward each of them. A topic can be anything that people express opinions about, e.g., a product (e.g., iPhone6), a political candidate (e.g., Donald Trump), a policy (e.g., Obamacare), an event (e.g., Brexit), etc. For example, in (6) the author is positive about Donald Trump but negative about Hillary Clinton. A political analyzer would not be interested so much in the overall sentiment expressed in the tweet (even though one could argue that here it is positive overall), but rather in the sentiment with respect to a topic of his/her interest of study.",
+      "As a democrat I couldnt ethically support Hillary no matter who was running against her. Just so glad that its Trump, just love the guy!",
+      "(topic: Hillary INLINEFORM0 Negative)",
+      "(topic: Trump INLINEFORM0 Positive)",
+      "Aspect-based sentiment analysis. Looking again at (1) and (2), we can say that the sentiment is not about the phone (lumia620 and iPhone 5, respectively), but rather about some specific aspect thereof, namely, size. Similarly, in (7) instead of sentiment toward the topic lasagna, we can see sentiment toward two aspects thereof: quality (Positive sentiment) and quantity (Negative sentiment). Aspect-based sentiment analysis is an active research area BIBREF32 , BIBREF33 , BIBREF21 .",
+      "The lasagna is delicious but do not come here on an empty stomach.",
+      "Stance detection. A task related to, but arguably different in some respect from sentiment analysis, is that of stance detection. The goal here is to determine whether the author of a piece of text is in favor of, against, or neutral toward a proposition or a target BIBREF36 . For example, in (8) the author has a negative stance toward the proposition w\u200bomen have the right to abortion, even though the target is not mentioned at all. Similarly, in (9\u00a7) the author expresses a negative sentiment toward Mitt Romney, from which one can imply that s/he has a positive stance toward the target \u200bBarack Obama.",
+      "A\u200b foetus has rights too! Make your voice heard.",
+      "(Target: w\u200bomen have the right to abortion INLINEFORM0 Against)",
+      "A\u200bll Mitt Romney cares about is making money for the rich.",
+      "(Target: \u200bBarack Obama INLINEFORM0 InFavor)",
+      "Ordinal regression. The above tasks were offered in different granularities, e.g., 2-way (Positive, Negative), 3-way (Positive, Neutral, Negative), 4-way (Positive, Neutral, Negative, Objective), 5-way (HighlyPositive, Positive, Neutral, Negative, HighlyNegative), and sometimes even 11-way BIBREF34 . It is important to note that the 5-way and the 11-way scales are ordinal, i.e., the classes can be associated with numbers, e.g., INLINEFORM0 2, INLINEFORM1 1, 0, 1, and 2 for the 5-point scale. This changes the machine learning task as not all mistakes are equal anymore BIBREF16 . For example, misclassifying a HighlyNegative example as HighlyPositive is a bigger mistake than misclassifying it as Negative or as Neutral. From a machine learning perspective, this means moving from classification to ordinal regression. This also requires different evaluation measures BIBREF30 .",
+      "Quantification. Practical applications are hardly ever interested in the sentiment expressed in a specific tweet. Rather, they look at estimating the prevalence of positive and negative tweets about a given topic in a set of tweets from some time interval. Most (if not all) tweet sentiment classification studies conducted within political science BIBREF39 , BIBREF40 , BIBREF41 , economics BIBREF42 , BIBREF7 , social science BIBREF43 , and market research BIBREF44 , BIBREF45 use Twitter with an interest in aggregate data and not in individual classifications. Thus, some tasks, such as SemEval-2016 Task 4 BIBREF30 , replace classification with class prevalence estimation, which is also known as quantification in data mining and related fields. Note that quantification is not a mere byproduct of classification, since a good classifier is not necessarily a good quantifier, and vice versa BIBREF46 . Finally, in case of multiple labels on an ordinal scale, we have yet another machine learning problem: ordinal quantification. Both versions of quantification require specific evaluation measures and machine learning algorithms."
+    ],
+    [
+      "Pre-processing. Tweets are subject to standard preprocessing steps for text such as tokenization, stemming, lemmatization, stop-word removal, and part-of-speech tagging. Moreover, due to their noisy nature, they are also processed using some Twitter-specific techniques such as substitution/removal of URLs, of user mentions, of hashtags, and of emoticons, spelling correction, elongation normalization, abbreviation lookup, punctuation removal, detection of amplifiers and diminishers, negation scope detection, etc. For this, one typically uses Twitter-specific NLP tools such as part-of-speech and named entity taggers, syntactic parsers, etc. BIBREF47 , BIBREF48 , BIBREF49 .",
+      "Negation handling. Special handling is also done for negation. The most popular approach to negation handling is to transform any word that appeared in a negation context by adding a suffix _NEG to it, e.g., good would become good_NEG BIBREF50 , BIBREF10 . A negated context is typically defined as a text span between a negation word, e.g., no, not, shouldn't, and a punctuation mark or the end of the message. Alternatively, one could flip the polarity of sentiment words, e.g., the positive word good would become negative when negated. It has also been argued BIBREF51 that negation affects different words differently, and thus it was also proposed to build and use special sentiment polarity lexicons for words in negation contexts BIBREF52 .",
+      "Features. Traditionally, systems for Sentiment Analysis on Twitter have relied on handcrafted features derived from word-level (e.g., great, freshly roasted coffee, becoming president) and character-level INLINEFORM0 -grams (e.g., bec, beco, comin, oming), stems (e.g., becom), lemmata (e.g., become, roast), punctuation (e.g., exclamation and question marks), part-of-speech tags (e.g., adjectives, adverbs, verbs, nouns), word clusters (e.g., probably, probly, and maybe could be collapsed to the same word cluster), and Twitter-specific encodings such as emoticons (e.g., ;), :D), hashtags (#Brexit), user tags (e.g., @allenai_org), abbreviations (e.g., RT, BTW, F2F, OMG), elongated words (e.g., soooo, yaayyy), use of capitalization (e.g., proportion of ALL CAPS words), URLs, etc. Finally, the most important features are those based on the presence of words and phrases in sentiment polarity lexicons with positive/negative scores; examples of such features include number of positive terms, number of negative terms, ratio of the number of positive terms to the number of positive+negative terms, ratio of the number of negative terms to the number of positive+negative terms, sum of all positive scores, sum of all negative scores, sum of all scores, etc.",
+      "Supervised learning. Traditionally, the above features were fed into classifiers such as Maximum Entropy (MaxEnt) and Support Vector Machines (SVM) with various kernels. However, observation over the SemEval Twitter sentiment task in recent years shows growing interest in, and by now clear dominance of methods based on deep learning. In particular, the best-performing systems at SemEval-2015 and SemEval-2016 used deep convolutional networks BIBREF53 , BIBREF54 . Conversely, kernel machines seem to be less frequently used than in the past, and the use of learning methods other than the ones mentioned above is at this point scarce. All these models are examples of supervised learning as they need labeled training data.",
+      "Semi-supervised learning. We should note two things about the use of deep neural networks. First they can often do quite well without the need for explicit feature modeling, as they can learn the relevant features in their hidden layers starting from the raw text. Second, they have too many parameters, and thus they require a lot of training data, orders of magnitude more than it is realistic to have manually annotated. A popular way to solve this latter problem is to use self training, a form of semi-supervised learning, where first a system is trained on the available training data only, then this system is applied to make predictions on a large unannotated set of tweets, and finally it is trained for a few more iterations on its own predictions. This works because parts of the network, e.g., with convolution or with LSTMs BIBREF55 , BIBREF54 , BIBREF56 , need to learn something like a language model, i.e., which word is likely to follow which one. Training these parts needs no labels. While these parts can be also pre-trained, it is easier, and often better, to use self training.",
+      "Distantly-supervised learning. Another way to make use of large unannotated datasets is to rely on distant supervision BIBREF41 . For example, one can annotate tweets for sentiment polarity based on whether they contain a positive or a negative emoticon. This results in noisy labels, which can be used to train a system BIBREF54 , to induce sentiment-specific word embeddings BIBREF57 , sentiment-polarity lexicons BIBREF25 , etc.",
+      "Unsupervised learning. Fully unsupervised learning is not a popular method for addressing sentiment analysis tasks. Yet, some features used in sentiment analysis have been learned in an unsupervised way, e.g., Brown clusters to generalize over words BIBREF58 . Similarly, word embeddings are typically trained from raw tweets that have no annotation for sentiment (even though there is also work on sentiment-specific word embeddings BIBREF57 , which uses distant supervision)."
+    ],
+    [
+      "Despite the wide variety of knowledge sources explored so far in the literature, sentiment polarity lexicons remain the most commonly used resource for the task of sentiment analysis.",
+      "Until recently, such sentiment polarity lexicons were manually crafted and were thus of small to moderate size, e.g., LIWC BIBREF59 has 2,300 words, the General Inquirer BIBREF60 contains 4,206 words, Bing Liu's lexicon BIBREF22 includes 6,786 words, and MPQA BIBREF14 has about 8,000 words.",
+      "Early efforts toward building sentiment polarity lexicons automatically yielded lexicons of moderate sizes such as the SentiWordNet BIBREF19 , BIBREF61 . However, recent results have shown that automatically extracted large-scale lexicons (e.g., up to a million words and phrases) offer important performance advantages, as confirmed at shared tasks on Sentiment Analysis on Twitter at SemEval 2013-2016 BIBREF30 , BIBREF26 , BIBREF29 , BIBREF27 . Using such large-scale lexicons was crucial for the performance of the top-ranked systems. Similar observations were made in the related Aspect-Based Sentiment Analysis task at SemEval 2014 BIBREF21 . In both tasks, the winning systems benefitted from building and using massive sentiment polarity lexicons BIBREF25 , BIBREF62 .",
+      "The two most popular large-scale lexicons were the Hashtag Sentiment Lexicon and the Sentiment140 lexicon, which were developed by the team of NRC Canada for their participation in the SemEval-2013 shared task on sentiment analysis on Twitter. Similar automatically induced lexicons proved useful for other SemEval tasks, e.g., for SemEval-2016 Task 3 on Community Question Answering BIBREF63 , BIBREF30 .",
+      "The importance of building sentiment polarity lexicons has resulted in a special subtask BIBREF29 at SemEval-2015 (part of Task 4) and an entire task BIBREF37 at SemEval-2016 (namely, Task 7), on predicting the out-of-context sentiment intensity of words and phrases. Yet, we should note though that the utility of using sentiment polarity lexicons for sentiment analysis probably needs to be revisited, as the best system at SemEval-2016 Task 4 could win without using any lexicons BIBREF53 ; it relied on semi-supervised learning using a deep neural network.",
+      "Various approaches have been proposed in the literature for bootstrapping sentiment polarity lexicons starting from a small set of seeds: positive and negative terms (words and phrases). The dominant approach is that of Turney BIBREF12 , who uses pointwise mutual information and bootstrapping to build a large lexicon and to estimate the semantic orientation of each word in that lexicon. He starts with a small set of seed positive (e.g., excellent) and negative words (e.g., bad), and then uses these words to induce sentiment polarity orientation for new words in a large unannotated set of texts (in his case, product reviews). The idea is that words that co-occur in the same text with positive seed words are likely to be positive, while those that tend to co-occur with negative words are likely to be negative. To quantify this intuition, Turney defines the notion of sentiment orientation (SO) for a term INLINEFORM0 as follows:",
+      " INLINEFORM0 ",
+      "where PMI is the pointwise mutual information, INLINEFORM0 and INLINEFORM1 are placeholders standing for any of the seed positive and negative terms, respectively, and INLINEFORM2 is a target word/phrase from the large unannotated set of texts (here tweets).",
+      "A positive/negative value for INLINEFORM0 indicates positive/negative polarity for the word INLINEFORM1 , and its magnitude shows the corresponding sentiment strength. In turn, INLINEFORM2 , where INLINEFORM3 is the probability to see INLINEFORM4 with any of the seed positive words in the same tweet, INLINEFORM5 is the probability to see INLINEFORM6 in any tweet, and INLINEFORM7 is the probability to see any of the seed positive words in a tweet; INLINEFORM8 is defined similarly.",
+      "The pointwise mutual information is a notion from information theory: given two random variables INLINEFORM0 and INLINEFORM1 , the mutual information of INLINEFORM2 and INLINEFORM3 is the \u201camount of information\u201d (in units such as bits) obtained about the random variable INLINEFORM4 , through the random variable INLINEFORM5 BIBREF64 .",
+      "Let INLINEFORM0 and INLINEFORM1 be two values from the sample space of INLINEFORM2 and INLINEFORM3 , respectively. The pointwise mutual information between INLINEFORM4 and INLINEFORM5 is defined as follows: DISPLAYFORM0 ",
+      " INLINEFORM0 takes values between INLINEFORM1 , which happens when INLINEFORM2 = 0, and INLINEFORM3 if INLINEFORM4 .",
+      "In his experiments, Turney BIBREF12 used five positive and five negative words as seeds. His PMI-based approach further served as the basis for the creation of the two above-mentioned large-scale automatic lexicons for sentiment analysis in Twitter for English, initially developed by NRC for their participation in SemEval-2013 BIBREF25 . The Hashtag Sentiment Lexicon uses as seeds hashtags containing 32 positive and 36 negative words, e.g., #happy and #sad. Similarly, the Sentiment140 lexicon uses smileys as seed indicators for positive and negative sentiment, e.g., :), :-), and :)) as positive seeds, and :( and :-( as negative ones.",
+      "An alternative approach to lexicon induction has been proposed BIBREF65 , which, instead of using PMI, assigns positive/negative labels to the unlabeled tweets (based on the seeds), and then trains an SVM classifier on them, using word INLINEFORM0 -grams as features. These INLINEFORM1 -grams are then used as lexicon entries (words and phrases) with the learned classifier weights as polarity scores. Finally, it has been shown that sizable further performance gains can be obtained by starting with mid-sized seeds, i.e., hundreds of words and phrases BIBREF66 ."
+    ],
+    [
+      "Sentiment analysis on Twitter has applications in a number of areas, including political science BIBREF39 , BIBREF40 , BIBREF41 , economics BIBREF42 , BIBREF7 , social science BIBREF43 , and market research BIBREF44 , BIBREF45 . It is used to study company reputation online BIBREF45 , to measure customer satisfaction, to identify detractors and promoters, to forecast market growth BIBREF42 , to predict the future income from newly-released movies, to forecast the outcome of upcoming elections BIBREF41 , BIBREF7 , to study political polarization BIBREF39 , BIBREF9 , etc."
+    ],
+    [
+      "We expect the quest for more interesting formulations of the general sentiment analysis task to continue. We see competitions such as those at SemEval as the engine of this innovation, as they not only perform head-to-head comparisons, but also create databases and tools that enable follow-up research for many years afterward.",
+      "In terms of methods, we believe that deep learning BIBREF55 , BIBREF54 , BIBREF56 , together with semi-supervised and distantly-supervised methods BIBREF67 , BIBREF57 , will be the main focus of future research. We also expect more attention to be paid to linguistic structure and sentiment compositionality BIBREF68 , BIBREF69 . Moreover, we forecast more interest for languages other than English, and for cross-lingual methods BIBREF40 , BIBREF70 , BIBREF71 , which will allow leveraging on the rich resources that are already available for English. Last, but not least, the increase in opinion spam on Twitter will make it important to study astroturfing BIBREF72 and troll detection BIBREF73 , BIBREF74 , BIBREF75 ."
+    ],
+    [
+      "Microblog Sentiment Analysis 100590",
+      "Multi-classifier System for Sentiment Analysis and Opinion Mining 351",
+      "Sentiment Analysis in Social Media 120",
+      "Sentiment Analysis of Microblogging Data 110168",
+      "Sentiment Analysis of Reviews 110169",
+      "Sentiment Analysis, Basics of 110159",
+      "Sentiment Quantification of User-Generated Content 110170",
+      "Social Media Analysis for Monitoring Political Sentiment 110172",
+      "Twitter Microblog Sentiment Analysis 265",
+      "User Sentiment and Opinion Analysis 192"
+    ],
+    [
+      "For general research on sentiment analysis, we recommend the following surveys: BIBREF76 and BIBREF15 . For sentiment analysis on Twitter, we recommend the overview article on Sentiment Analysis on Twitter about the SemEval task BIBREF28 as well as the task description papers for different editions of the task BIBREF30 , BIBREF26 , BIBREF29 , BIBREF27 ."
+    ]
+  ]
+}
+```

qasper-0382/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: COSTRA 1.0: A Dataset of Complex Sentence Transformations
+
+Question: What annotations are available in the dataset?

qasper-0385/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: COSTRA 1.0: A Dataset of Complex Sentence Transformations
+
+Question: Is this dataset publicly available?

qasper-0511/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping
+
+Question: Do they train their own RE model?

qasper-0518/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation
+
+Question: Did the collection process use a WoZ method?

qasper-0520/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation
+
+Question: What baselines did they compare their model with?

qasper-0526/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Analysis of Risk Factor Domains in Psychosis Patient Health Records
+
+Question: What additional features are proposed for future work?

qasper-0527/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Analysis of Risk Factor Domains in Psychosis Patient Health Records
+
+Question: What are their initial results on this task?

qasper-0580/instruction.md

@@ -0,0 +1,82 @@
+Name of Paper: Detecting Potential Topics In News Using BERT, CRF and Wikipedia
+
+Question: How many layers does their system have?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction & Related Work",
+    "Data Preparation",
+    "Experiments ::: Model Architecture",
+    "Experiments ::: Training",
+    "Experiments ::: Results",
+    "Experiments ::: Discussions",
+    "Conclusion and Future Work"
+  ],
+  "paragraphs": [
+    [
+      "Named-Entity-Recognition(NER) approaches can be categorised broadly in three types. Detecting NER with predefined dictionaries and rulesBIBREF2, with some statistical approachesBIBREF3 and with deep learning approachesBIBREF4.",
+      "Stanford CoreNLP NER is a widely used baseline for many applications BIBREF5. Authors have used approaches of Gibbs sampling and conditional random field (CRF) for non-local information gathering and then Viterbi algorithm to infer the most likely state in the CRF sequence outputBIBREF6.",
+      "Deep learning approaches in NLP use document, word or token representations instead of one-hot encoded vectors. With the rise of transfer learning, pretrained Word2VecBIBREF7, GloVeBIBREF8, fasttextBIBREF9 which provides word embeddings were being used with recurrent neural networks (RNN) to detect NERs. Using LSTM layers followed by CRF layes with pretrained word-embeddings as input has been explored hereBIBREF10. Also, CNNs with character embeddings as inputs followed by bi-directional LSTM and CRF layers, were explored hereBIBREF11.",
+      "With the introduction of attentions and transformersBIBREF12 many deep architectures emerged in last few years. Approach of using these pretrained models like ElmoBIBREF13, FlairBIBREF14 and BERTBIBREF0 for word representations followed by variety of LSMT and CRF combinations were tested by authors in BIBREF15 and these approaches show state-of-the-art performance.",
+      "There are very few approaches where caseless NER task is explored. In this recent paperBIBREF16 authors have explored effects of \"Cased\" entities and how variety of networks perform and they show that the most effective strategy is a concatenation of cased and lowercased training data, producing a single model with high performance on both cased and uncased text.",
+      "In another paperBIBREF17, authors have proposed True-Case pre-training before using BiLSTM+CRF approach to detect NERs effectively. Though it shows good results over previous approaches, it is not useful in Indian Languages context as there is no concept of cases.",
+      "In our approach, we are focusing more on data preparation for our definition of topics using some of the state-of-art architectures based on BERT, LSTM/GRU and CRF layers as they have been explored in previous approaches mentioned above. Detecting caseless topics with higher recall and reasonable precision has been given a priority over f1 score. And comparisons have been made with available and ready-to-use open-source libraries from the productionization perspective."
+    ],
+    [
+      "We need good amount of data to try deep learning state-of-the-art algorithms. There are lot of open datasets available for names, locations, organisations, but not for topics as defined in Abstract above. Also defining and inferring topics is an individual preference and there are no fix set of rules for its definition. But according to our definition, we can use wikipedia titles as our target topics. English wikipedia dataset has more than 18 million titles if we consider all versions of them till now. We had to clean up the titles to remove junk titles as wikipedia title almost contains all the words we use daily. To remove such titles, we deployed simple rules as follows -",
+      "Remove titles with common words : \"are\", \"the\", \"which\"",
+      "Remove titles with numeric values : 29, 101",
+      "Remove titles with technical components, driver names, transistor names : X00, lga-775",
+      "Remove 1-gram titles except locations (almost 80% of these also appear in remaining n-gram titles)",
+      "After doing some more cleaning we were left with 10 million titles. We have a dump of 15 million English news articles published in past 4 years. Further, we reduced number of articles by removing duplicate and near similar articles. We used our pre-trained doc2vec models and cosine similarity to detect almost similar news articles. Then selected minimum articles required to cover all possible 2-grams to 5-grams. This step is done to save some training time without loosing accuracy. Do note that, in future we are planning to use whole dataset and hope to see gains in F1 and Recall further. But as per manual inspection, our dataset contains enough variations of sentences with rich vocabulary which contains names of celebrities, politicians, local authorities, national/local organisations and almost all locations, India and International, mentioned in the news text, in last 4 years.",
+      "We then created a parallel corpus format as shown in Table 1. Using pre-trained Bert-Tokenizer from hugging-face, converted words in sentences to tokenes. Caseless-BERT pre-trained tokenizer is used. Notice that some of the topic words are broken into tokens and NER tag has been repeated accordingly. For example, in Table 1 second row, word \"harassment\" is broken into \"har ##ass ##ment\". Similarly, one \"NER\" tag is repeated three times to keep the length of sequence-pair same. Finally, for around 3 million news articles, parallel corpus is created, which is of around 150 million sentences, with around 3 billion words (all lower cased) and with around 5 billion tokens approximately."
+    ],
+    [
+      "We tried multiple variations of LSTM and GRU layes, with/without CRF layer. There is a marginal gain in using GRU layers over LSTM. Also, we saw gain in using just one layers of GRU instead of more. Finally, we settled on the architecture, shown in Figure 1 for the final training, based on validation set scores with sample training set.",
+      "Text had to be tokenized using pytorch-pretrained-bert as explained above before passing to the network. Architecture is built using tensorflow/keras. Coding inspiration taken from BERT-keras and for CRF layer keras-contrib. If one is more comfortable in pytorch there are many examples available on github, but pytorch-bert-crf-ner is better for an easy start.",
+      "We used BERT-Multilingual model so that we can train and fine-tune the same model for other Indian languages. You can take BERT-base or BERT-large for better performance with only English dataset. Or you can use DistilBERT for English and DistilmBERT for 104 languages for faster pre-training and inferences. Also, we did not choose AutoML approach for hyper-parameter tuning which could have resulted in much more accurate results but at the same time could have taken very long time as well. So instead, chose and tweaked the parameters based on initial results.",
+      "We trained two models, one with sequence length 512 to capture document level important n-grams and second with sequence length 64 to capture sentence/paragraph level important n-grams. Through experiments it was evident that, sequence length plays a vital role in deciding context and locally/globally important n-grams. Final output is a concatenation of both the model outputs."
+    ],
+    [
+      "Trained the topic model on single 32gb NVidia-V100 and it took around 50 hours to train the model with sequence length 512. We had to take 256gb ram machine to accommodate all data in memory for faster read/write. Also, trained model with 64 sequence length in around 17 hours.",
+      "It is very important to note that sequence length decides how many bert-tokens you can pass for inference and also decides training time and accuracy. Ideally more is better because inference would be faster as well. For 64 sequence length, we are moving 64-token window over whole token-text and recognising topics in each window. So, one should choose sequence length according to their use case. Also, we have explained before our motivation of choosing 2 separate sequence lengths models.",
+      "We stopped the training for both the models when it crossed 70% precision, 90% recall on training and testing sets, as we were just looking to get maximum recall and not bothered about precision in our case. Both the models reach this point at around 16 epochs."
+    ],
+    [
+      "Comparison with existing open-source NER libraries is not exactly fair as they are NOT trained for detecting topics and important n-grams, also NOT trained for case-less text. But they are useful in testing and benchmarking if our model is detecting traditional NERs or not, which it should capture, as Wikipedia titles contains almost all Names, Places and Organisation names. You can check the sample output here",
+      "Comparisons have been made among Flair-NER, Stanford-caseless-NER (used english.conll.4class.caseless as it performed better than 3class and 7class), Spacy-NER and our models. Of which only Stanford-NER provides case-less models. In Table 2, scores are calculated by taking traditional NER list as reference. In Table 4, same is done with Wikipedia Titles reference set.",
+      "As you can see in Table 2 & 3, recall is great for our model but precision is not good as Model is also trying to detect new potential topics which are not there even in reference Wikipedia-Titles and NER sets. In capturing Wikipedia topics our model clearly surpasses other models in all scores.",
+      "Spacy results are good despite not being trained for case-less data. In terms of F1 and overall stability Spacy did better than Stanford NER, on our News Validation set. Similarly, Stanford did well in Precision but could not catch up with Spacy and our model in terms of Recall. Flair overall performed poorly, but as said before these open-source models are not trained for our particular use-case."
+    ],
+    [
+      "Lets check some examples for detailed analysis of the models and their results. Following is the economy related news.",
+      "Example 1 : around $1\u20131.5 trillion or around two percent of global gdp, are lost to corruption every year, president of the natural resource governance institute nrgi has said. speaking at a panel on integrity in public governance during the world bank group and international monetary fund annual meeting on sunday, daniel kaufmann, president of nrgi, presented the statistic, result of a study by the nrgi, an independent, non-profit organisation based in new york. however, according to kaufmann, the figure is only the direct costs of corruption as it does not factor in the opportunities lost on innovation and productivity, xinhua news agency reported. a country that addresses corruption and significantly improves rule of law can expect a huge increase in per capita income in the long run, the study showed. it will also see similar gains in reducing infant mortality and improving education, said kaufmann.",
+      "Detected NERs can be seen per model in Table 4. Our model do not capture numbers as we have removed all numbers from my wiki-titles as topics. Reason behind the same is that we can easily write regex to detect currency, prices, time, date and deep learning is not required for the same. Following are few important n-grams only our models was able to capture -",
+      "capita income",
+      "infant mortality",
+      "international monetary fund annual meeting",
+      "natural resource governance institute",
+      "public governance",
+      "At the same time, we can see that Spacy did much better than Stanford-caseless NER and Flair could not capture any of the NERs. Another example of a news in political domain and detected NERs can be seen per model in Table 5.",
+      "Example 2 : wearing the aam aadmi party's trademark cap and with copies of the party's five-year report card in hand, sunita kejriwal appears completely at ease. it's a cold winter afternoon in delhi, as the former indian revenue service (irs) officer hits the campaign trail to support her husband and batchmate, chief minister arvind kejriwal. emerging from the background for the first time, she is lending her shoulder to the aap bandwagon in the new delhi assembly constituency from where the cm, then a political novice, had emerged as the giant killer by defeating congress incumbent sheila dikshit in 2013.",
+      "Correct n-grams captured only by our model are -",
+      "aam aadmi party",
+      "aap bandwagon",
+      "delhi assembly constituency",
+      "giant killer",
+      "indian revenue service",
+      "political novice",
+      "In this example, Stanford model did better and captured names properly, for example \"sheila dikshit\" which Spacy could not detect but Spacy captureed almost all numeric values along with numbers expressed in words.",
+      "It is important to note that, our model captures NERs with some additional words around them. For example, \"president of nrgi\" is detected by the model but not \"ngri\". But model output does convey more information than the later. To capture the same for all models (and to make comparison fair), partial match has been enabled and if correct NER is part of predictied NER then later one is marked as matched. This could be the reason for good score for Spacy. Note that, partial match is disabled for Wikipedia Titles match task as shown in Table 3. Here, our model outperformed all the models."
+    ],
+    [
+      "Through this exercise, we were able to test out the best suitable model architecture and data preparation steps so that similar models could be trained for Indian languages. Building cased or caseless NERs for English was not the final goal and this has already been benchmarked and explored before in previous approaches explained in \"Related Work\" section. We didn't use traditional datasets for model performance comparisons & benchmarks. As mentioned before, all the comparisons are being done with open-source models and libraries from the productionization point of view. We used a english-news validation dataset which is important and relevant to our specific task and all validation datasets and raw output results can be found at our github link .",
+      "Wikipedia titles for Indian languages are very very less and resulting tagged data is even less to run deep architectures. We are trying out translations/transliterations of the English-Wiki-Titles to improve Indic-languages entity/topics data.",
+      "This approach is also useful in building news-summarizing models as it detects almost all important n-grams present in the news. Output of this model can be introduced in a summarization network to add more bias towards important words and bias for their inclusion."
+    ]
+  ]
+}
+```

qasper-0712/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning
+
+Question: What datasets are used?

qasper-0714/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning
+
+Question: How big is improvement in performance over Transformers?

qasper-0715/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Aspect Term Extraction with History Attention and Selective Transformation
+
+Question: How do they determine the opinion summary?

qasper-0723/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Using word embeddings to improve the discriminability of co-occurrence text networks
+
+Question: What is the reason that traditional co-occurrence networks fail in establishing links between similar words whenever they appear distant in the text?

qasper-0746/instruction.md

@@ -0,0 +1,89 @@
+Name of Paper: A multimodal deep learning approach for named entity recognition from social media
+
+Question: What datasets did they use?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Related Work ::: Unimodal Named Entity Recognition",
+    "Related Work ::: Multimodal Named Entity Recognition",
+    "The Proposed Approach",
+    "Experimental Evaluation",
+    "Experimental Evaluation ::: Dataset",
+    "Experimental Evaluation ::: Experimental Setup",
+    "Experimental Evaluation ::: Evaluation Results",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "A common social media delivery system such as Twitter supports various media types like video, image and text. This media allows users to share their short posts called Tweets. Users are able to share their tweets with other users that are usually following the source user. Hovewer there are rules to protect the privacy of users from unauthorized access to their timeline BIBREF0. The very nature of user interactions in Twitter micro-blogging social media is oriented towards their daily life, first witness news-reporting and engaging in various events (sports, political stands etc.). According to studies, news in twitter is propagated and reported faster than conventional news media BIBREF1. Thus, extracting first hand news and entities occurring in this fast and versatile online media gives valuable information. However, abridged and noisy content of Tweets makes it even more difficult and challenging for tasks such as named entity recognition and information retrieval BIBREF2.",
+      "The task of tracking and recovering information from social media posts is a concise definition of information retrieval in social media BIBREF3, BIBREF4. However many challenges are blocking useful solutions to this issue, namely, the noisy nature of user generated content and the perplexity of words used in short posts. Sometimes different entities are called exactly the same, for example \"Micheal Jordan\" refers to a basketball player and also a computer scientist in the field of artificial intelligence. The only thing that divides both of these is the context in which entity appeared. If the context refers to something related to AI, the reader can conclude \"Micheal Jordan\" is the scientist, and if the context is refers to sports and basketball then he is the basketball player. The task of distinguishing between different named entities that appear to have the same textual appearance is called named entity disambiguation. There is more useful data on the subject rather than on plain text. For example images and visual data are more descriptive than just text for tasks such as named entity recognition and disambiguation BIBREF5 while some methods only use the textual data BIBREF6.",
+      "The provided extra information is closely related to the textual data. As a clear example, figure FIGREF1 shows a tweet containing an image. The combination of these multimodal data in order to achieve better performance in NLP related tasks is a promising alternative explored recently.",
+      "An NLP task such as named entity recognition in social media is a most challenging task because users tend to invent, mistype and epitomize words. Sometimes these words correspond to named entities which makes the recognition task even more difficult BIBREF7. In some cases, the context that carries the entity (surrounding words and related image) is more descriptive than the entity word presentation BIBREF8.",
+      "To find a solution to the issues at hand, and keeping multimodal data in mind, recognition of named entities from social media has become a research interest which utilizes image compared to NER task in a conventional text. Researchers in this field have tried to propose multimodal architectures based on deep neural networks with multimodal input that are capable of combining text and image BIBREF9, BIBREF8, BIBREF10.",
+      "In this paper we draw a better solution in terms of performance by proposing a new novel method called CWI (Character-Word-Image model). We used multimodal deep neural network to overcome the NER task in micro-blogging social media.",
+      "The rest of the paper is organized as follows: section SECREF2 provides an insight view of previous methods; section SECREF3 describes the method we propose; section SECREF4 shows experimental evaluation and test results; finally, section SECREF5 concludes the whole article."
+    ],
+    [
+      "Many algorithms and methods have been proposed to detect, classify or extract information from single type of data such as audio, text, image etc. However, in the case of social media, data comes in a variety of types such as text, image, video or audio in a bounded style. Most of the time, it is very common to caption a video or image with textual information. This information about the video or image can refer to a person, location etc. From a multimodal learning perspective, jointly computing such data is considered to be more valuable in terms of representation and evaluation. Named entity recognition task, on the other hand, is the task of recognizing named entities from a sentence or group of sentences in a document format.",
+      "Named entity is formally defined as a word or phrase that clearly identifies an item from set of other similar items BIBREF11, BIBREF12. Equation DISPLAY_FORM2 expresses a sequence of tokens.",
+      "From this equation, the NER task is defined as recognition of tokens that correspond to interesting items. These items from natural language processing perspective are known as named entity categories; BIO2 proposes four major categories, namely, organization, person, location and miscellaneous BIBREF13. From the biomedical domain, gene, protein, drug and disease names are known as named entities BIBREF14, BIBREF15. Output of NER task is formulated in . $I_s\\in [1,N]$ and $I_e\\in [1,N]$ is the start and end indices of each named entity and $t$ is named entity type BIBREF16.",
+      "BIO2 tagging for named entity recognition is defined in equation . Table TABREF3 shows BIO2 tags and their respective meanings; B and I indicate beginning and inside of the entity respectively, while O shows the outside of it. Even though many tagging standards have been proposed for NER task, BIO is the foremost accepted by many real world applications BIBREF17.",
+      "A named entity recognizer gets $s$ as input and provides entity tags for each token. This sequential process requires information from the whole sentence rather than only tokens and for that reason, it is also considered to be a sequence tagging problem. Another analogous problem to this issue is part of speech tagging and some methods are capable of doing both. However, in cases where noise is present and input sequence has linguistic typos, many methods fail to overcome the problem. As an example, consider a sequence of tokens where a new token invented by social media users gets trended. This trending new word is misspelled and is used in a sequence along with other tokens in which the whole sequence does not follow known linguistic grammar. For this special case, classical methods and those which use engineered features do not perform well.",
+      "Using the sequence $s$ itself or adding more information to it divides two approaches to overcome this problem: unimodal and multimodal.",
+      "Although many approaches for NER have been proposed and reviewing them is not in the scope of this article, we focus on foremost analogues classical and deep learning approaches for named entity recognition in two subsections. In subsection SECREF4 unimodal approaches for named entity recognition are presented while in subsection SECREF7 emerging multimodal solutions are described."
+    ],
+    [
+      "The recognition of named entities from only textual data (unimodal learning approach) is a well studied and explored research criteria. For a prominent example of this category, the Stanford NER is a widely used baseline for many applications BIBREF18. The incorporation of non-local information in information extraction is proposed by the authors using of Gibbs sampling. The conditional random field (CRF) approach used in this article, creates a chain of cliques, where each clique represents the probabilistic relationship between two adjacent states. Also, Viterbi algorithm has been used to infer the most likely state in the CRF output sequence. Equation DISPLAY_FORM5 shows the proposed CRF method.",
+      "where $\\phi $ is the potential function.",
+      "CRF finds the most probable likelihood by modeling the input sequence of tokens $s$ as a normalized product of feature functions. In a simpler explanation, CRF outputs the most probable tags that follow each other. For example it is more likely to have an I-PER, O or any other that that starts with B- after B-PER rather than encountering tags that start with I-.",
+      "T-NER is another approach that is specifically aimed to answer NER task in twitter BIBREF19. A set of algorithms in their original work have been published to answer tasks such as POS (part of speech tagging), named entity segmentation and NER. Labeled LDA has been used by the authors in order to outperform baseline in BIBREF20 for NER task. Their approach strongly relies on dictionary, contextual and orthographic features.",
+      "Deep learning techniques use distributed word or character representation rather than raw one-hot vectors. Most of this research in NLP field use pretrained word embeddings such as Word2Vec BIBREF21, GloVe BIBREF22 or fastText BIBREF23. These low dimensional real valued dense vectors have proved to provide better representation for words compared to one-hot vector or other space vector models.",
+      "The combination of word embedding along with bidirectional long-short term memory (LSTM) neural networks are examined in BIBREF24. The authors also propose to add a CRF layer at the end of their neural network architecture in order to preserve output tag relativity. Utilization of recurrent neural networks (RNN) provides better sequential modeling over data. However, only using sequential information does not result in major improvements because these networks tend to rely on the most recent tokens. Instead of using RNN, authors used LSTM. The long and short term memory capability of these networks helps them to keep in memory what is important and forget what is not necessary to remember. Equation DISPLAY_FORM6 formulates forget-gate of an LSTM neural network, eq. shows input-gate, eq. notes output-gate and eq. presents memory-cell. Finally, eq. shows the hidden part of an LSTM unit BIBREF25, BIBREF26.",
+      "for all these equations, $\\sigma $ is activation function (sigmoid or tanh are commonly used for LSTM) and $\\circ $ is concatenation operation. $W$ and $U$ are weights and $b$ is the bias which should be learned over training process.",
+      "LSTM is useful for capturing the relation of tokens in a forward sequential form, however in natural language processing tasks, it is required to know the upcoming token. To overcome this problem, the authors have used a backward and forward LSTM combining output of both.",
+      "In a different approach, character embedding followed by a convolution layer is proposed in BIBREF27 for sequence labeling. The utilized architecture is followed by a bidirectional LSTM layer that ends in a CRF layer. Character embedding is a useful technique that the authors tried to use it in a combination with word embedding. Character embedding with the use of convolution as feature extractor from character level, captures relations between characters that form a word and reduces spelling noise. It also helps the model to have an embedding when pretrained word embedding is empty or initialized as random for new words. These words are encountered when they were not present in the training set, thus, in the test phase, model fails to provide a useful embedding."
+    ],
+    [
+      "Multimodal learning has become an emerging research interest and with the rise of deep learning techniques, it has become more visible in different research areas ranging from medical imaging to image segmentation and natural language processing BIBREF28, BIBREF29, BIBREF30, BIBREF31, BIBREF32, BIBREF33, BIBREF34, BIBREF35, BIBREF36, BIBREF9, BIBREF37, BIBREF38, BIBREF39, BIBREF40, BIBREF41, BIBREF42, BIBREF43, BIBREF44, BIBREF45. On the other hand, very little research has been focused on the extraction of named entities with joint image and textual data concerning short and noisy content BIBREF46, BIBREF47, BIBREF9, BIBREF8 while several studies have been explored in textual named entity recognition using neural models BIBREF48, BIBREF49, BIBREF24, BIBREF50, BIBREF27, BIBREF51, BIBREF10, BIBREF52.",
+      "State-of-the-art methods have shown acceptable evaluation on structured and well formatted short texts. Techniques based on deep learning such as utilization of convolutional neural networks BIBREF52, BIBREF49, recurrent neural networks BIBREF50 and long short term memory neural networks BIBREF27, BIBREF24 are aimed to solve NER problem.",
+      "The multimodal named entity recognizers can be categorized in two categories based on the tasks at hand, one tries to improve NER task with utilization of visual data BIBREF46, BIBREF8, BIBREF47, and the other tries to give further information about the task at hand such as disambiguation of named entities BIBREF9. We will refer to both of these tasks as MNER. To have a better understanding of MNER, equation DISPLAY_FORM9 formulates the available multimodal data while equations and are true for this task.",
+      "$i$ refers to image and the rest goes same as equation DISPLAY_FORM2 for word token sequence.",
+      "In BIBREF47 pioneering research was conducted using feature extraction from both image and textual data. The extracted features were fed to decision trees in order to output the named entity classes. Researchers have used multiple datasets ranging from buildings to human face images to train their image feature extractor (object detector and k-means clustering) and a text classifier has been trained on texts acquired from DBPedia.",
+      "Researchers in BIBREF46 proposed a MNER model with regards to triplet embedding of words, characters and image. Modality attention applied to this triplet indicates the importance of each embedding and their impact on the output while reducing the impact of irrelevant modals. Modality attention layer is applied to all embedding vectors for each modal, however the investigation of fine-grained attention mechanism is still unclear BIBREF53. The proposed method with Inception feature extraction BIBREF54 and pretrained GloVe word vectors shows good results on the dataset that the authors aggregated from Snapchat. This method shows around 0.5 for precision and F-measure for four entity types (person, location, organization and misc) while for segmentation tasks (distinguishing between a named entity and a non-named entity) it shows around 0.7 for the metrics mentioned.",
+      "An adaptive co-attention neural network with four generations are proposed in BIBREF8. The adaptive co-attention part is similar to the multimodal attention proposed in BIBREF46 that enabled the authors to have better results over the dataset they collected from Twitter. In their main proposal, convolutional layers are used for word representation, BiLSTM is utilized to combine word and character embeddings and an attention layer combines the best of the triplet (word, character and image features). VGG-Net16 BIBREF55 is used as a feature extractor for image while the impact of other deep image feature extractors on the proposed solution is unclear, however the results show its superiority over related unimodal methods."
+    ],
+    [
+      "In the present work, we propose a new multimodal deep approach (CWI) that is able to handle noise by co-learning semantics from three modalities, character, word and image. Our method is composed of three parts, convolutional character embedding, joint word embedding (fastText-GloVe) and InceptionV3 image feature extraction BIBREF54, BIBREF23, BIBREF22. Figure FIGREF11 shows CWI architecture in more detail.",
+      "Character Feature Extraction shown in the left part of figure FIGREF11 is a composition of six layers. Each sequence of words from a single tweet, $\\langle w_1, w_2, \\dots , w_n \\rangle $ is converted to a sequence of character representation $\\langle [c_{(0,0)}, c_{(0,1)}, \\dots , c_{(0,k)}], \\dots , [c_{(n,0)}, c_{(n,1)}, \\dots , c_{(n,k)}] \\rangle $ and in order to apply one dimensional convolution, it is required to be in a fixed length. $k$ shows the fixed length of the character sequence representing each word. Rather than using the one-hot representation of characters, a randomly initialized (uniform distribution) embedding layer is used. The first three convolution layers are followed by a one dimensional pooling layer. In each layer, kernel size is increased incrementally from 2 to 4 while the number of kernels are doubled starting from 16. Just like the first part, the second segment of this feature extractor uses three layers but with slight changes. Kernel size is reduced starting from 4 to 2 and the number of kernels is halved starting from 64. In this part, $\\otimes $ sign shows concatenation operation. TD + GN + SineRelu note targeted dropout, group normalization and sine-relu BIBREF56, BIBREF57, BIBREF58. These layers prevent the character feature extractor from overfitting. Equation DISPLAY_FORM12 defines SineRelu activation function which is slightly different from Relu.",
+      "Instead of using zero in the second part of this equation, $\\epsilon (\\sin {x}-\\cos {x})$ has been used for negative inputs, $\\epsilon $ is a hyperparameter that controls the amplitude of $\\sin {x}-\\cos {x}$ wave. This slight change prevents network from having dead-neurons and unlike Relu, it is differentiable everywhere. On the other hand, it has been proven that using GroupNormalization provides better results than BatchNormalization on various tasks BIBREF57.",
+      "However the dropout has major improvement on the neural network as an overfitting prevention technique BIBREF59, in our setup the TargtedDropout shows to provide better results. TargetedDropout randomly drops neurons whose output is over a threshold.",
+      "Word Feature Extraction is presented in the middle part of figure FIGREF11. Joint embeddings from pretrained word vectors of GloVe BIBREF22 and fastText BIBREF23 by concatenation operation results in 500 dimensional word embedding. In order to have forward and backward information for each hidden layer, we used a bidirectional long-short term memory BIBREF25, BIBREF26. For the words which were not in the pretrained tokens, we used a random initialization (uniform initialization) between -0.25 and 0.25 at each embedding. The result of this phase is extracted features for each word.",
+      "Image Feature Extraction is shown in the right part of figure FIGREF11. For this part, we have used InceptionV3 pretrained on ImageNet BIBREF60. Many models were available as first part of image feature extraction, however the main reason we used InceptionV3 as feature extractor backbone is better performance of it on ImageNet and the results obtained by this particular model were slightly better compared to others.",
+      "Instead of using headless version of InceptionV3 for image feature extraction, we have used the full model which outputs the 1000 classes of ImageNet. Each of these classes resembles an item, the set of these items can present a person, location or anything that is identified as a whole. To have better features extracted from the image, we have used an embedding layer. In other words, we looked at the top 5 extracted probabilities as words that is shown in eq. DISPLAY_FORM16; Based on our assumption, these five words present textual keywords related to the image and combination of these words should provide useful information about the objects in visual data. An LSTM unit has been used to output the final image features. These combined embeddings from the most probable items in image are the key to have extra information from a social media post.",
+      "where $IW$ is image-word vector, $x$ is output of InceptionV3 and $i$ is the image. $x$ is in domain of [0,1] and $\\sum \\limits _{\\forall k\\in x}k=1$ holds true, while $\\sum \\limits _{\\forall k\\in IW}k\\le 1$.",
+      "Multimodal Fusion in our work is presented as concatenation of three feature sets extracted from words, characters and images. Unlike previous methods, our original work does not include an attention layer to remove noisy features. Instead, we stacked LSTM units from word and image feature extractors to have better results. The last layer presented at the top right side of figure FIGREF11 shows this part. In our second proposed method, we have used attention layer applied to this triplet. Our proposed attention mechanism is able to detect on which modality to increase or decrease focus. Equations DISPLAY_FORM17, and show attention mechanism related to second proposed model.",
+      "Conditional Random Field is the last layer in our setup which forms the final output. The same implementation explained in eq. DISPLAY_FORM5 is used for our method."
+    ],
+    [
+      "The present section provides evaluation results of our model against baselines. Before diving into our results, a brief description of dataset and its statistics are provided."
+    ],
+    [
+      "In BIBREF8 a refined collection of tweets gathered from twitter is presented. Their dataset which is labeled for named entity recognition task contains 8,257 tweets. There are 12,784 entities in total in this dataset. Table TABREF19 shows statistics related to each named entity in training, development and test sets."
+    ],
+    [
+      "In order to obtain the best results in tab. TABREF20 for our first model (CWI), we have used the following setup in tables TABREF22, TABREF23, TABREF24 and TABREF25. For the second proposed method, the same parameter settings have been used with an additional attention layer. This additional layer has been added after layer 31 in table TABREF25 and before the final CRF layer, indexed as 32. $Adam$ optimizer with $8\\times 10^{-5}$ has been used in training phase with 10 epochs."
+    ],
+    [
+      "Table TABREF20 presents evaluation results of our proposed models. Compared to other state of the art methods, our first proposed model shows $1\\%$ improvement on f1 score. The effect of different word embedding sizes on our proposed method is presented in TABREF26. Sensitivity to TD+SineRelu+GN is presented in tab. TABREF28."
+    ],
+    [
+      "In this article we have proposed a novel named entity recognizer based on multimodal deep learning. In our proposed model, we have used a new architecture in character feature extraction that has helped our model to overcome the issue of noise. Instead of using direct image features from near last layers of image feature extractors such as Inception, we have used the direct output of the last layer. This last layer which is 1000 classes of diverse objects that is result of InceptionV3 trained on ImageNet dataset. We used top 5 classes out of these and converted them to one-hot vectors. The resulting image feature embedding out of these high probability one-hot vectors helped our model to overcome the issue of noise in images posted by social media users. Evaluation results of our proposed model compared to other state of the art methods show its superiority to these methods in overall while in two categories (Person and Miscellaneous) our model outperformed others."
+    ]
+  ]
+}
+```

qasper-0940/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Measuring Compositional Generalization: A Comprehensive Method on Realistic Data
+
+Question: How strong is negative correlation between compound divergence and accuracy in performed experiment?

qasper-0947/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Prototypical Metric Transfer Learning for Continuous Speech Keyword Spotting With Limited Training Data
+
+Question: What are the baselines?

qasper-0948/instruction.md

@@ -0,0 +1,53 @@
+Name of Paper: Prototypical Metric Transfer Learning for Continuous Speech Keyword Spotting With Limited Training Data
+
+Question: What languages are considered?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related work",
+    "Dataset",
+    "Data Preprocessing",
+    "Feature Engineering",
+    "Deep Learning Architectures",
+    "Experiments, Results and Discussion"
+  ],
+  "paragraphs": [
+    [
+      "Continuous Speech Keyword Spotting (CSKS) aims to detect embedded keywords in audio recordings. These spotted keyword frequencies can then be used to analyze theme of communication, creating temporal visualizations and word clouds BIBREF0 . Another use case is to detect domain specific keywords which ASR (Automatic Speech Recognition) systems trained on public data cannot detect. For example, to detect a TV model number \u201cW884\u201d being mentioned in a recording, we might not have a large number of training sentences containing the model number of a newly launched TV to finetune a speech recognition (ASR) algorithm. A trained CSKS algorithm can be used to quickly extract out all instances of such keywords.",
+      "We train CSKS algorithms like other Keyword Spotting algorithms by classifying small fragments of audio in running speech. This requires the classifier model to have a formalized process to reject unseen instances (everything not a keyword, henceforth referred to as background) apart from ability to differentiate between classes (keywords). Another real world constraint that needs to be addressed while training such an algorithm is the availability of small amount of labeled keyword instances. We combine practices from fields of transfer learning, few-shot learning and metric learning to get better performance on this low training data imbalanced classification task.",
+      "Our work involves :",
+      "Our baselines, Honk( UID9 ), DeepSpeech-finetune( UID10 ), had comparatively both lower recall and precision. We noticed an improvement when fine tuning DeepSpeech model with prototypical loss (DeepSpeech-finetune-prototypical ( UID11 )). While analysing the false positives of this model, it was observed that the model gets confused between the keywords and it also wrongly classifies background noise as a keyword. To improve this, we combined prototypical loss with a metric loss to reject background (DeepSpeech-finetune-prototypical+metric( UID14 )). This model gave us the best results."
+    ],
+    [
+      "In the past, Hidden Markov Models (HMM) BIBREF6 , BIBREF7 , BIBREF8 have been used to solve the CSKS problem. But since the HMM techniques use Viterbi algorithms(computationally expensive) a faster approach is required.",
+      "Owning to the popularity of deep learning, many recent works such as BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 have used deep learning techniques for many speech processing tasks. In tasks such as ASR, Hannun et al. BIBREF3 proposed a RNN based model to transcribe speech into text. Even for plain keyword spotting, BIBREF1 , BIBREF2 , BIBREF14 , BIBREF15 , BIBREF16 , BIBREF17 have proposed various deep learning architectures to solve the task. But to the best of our knowledge, no past work has deployed deep learning for spotting keywords in continuous speech.",
+      "Recently, a lot of work is being done on training deep learning models with limited training data. Out of them, few-shot techniques as proposed by BIBREF18 , BIBREF4 have become really popular. Pons et al. BIBREF16 proposed a few-shot technique using prototypical networks BIBREF4 and transfer leaning BIBREF19 , BIBREF20 to solve a different audio task.",
+      "We took inspiration from these works to design our experiments to solve the CSKS task."
+    ],
+    [
+      "Our learning data, which was created in-house, has 20 keywords to be spotted about television models of a consumer electronics brand. It was collected by making 40 participants utter each keyword 3 times. Each participant recorded in normal ambient noise conditions. As a result, after collection of learning data we have 120 (3 x 40) instances of each of the 20 keywords. We split the learning data 80:20 into train and validation sets. Train/Validation split was done on speaker level, so as to make sure that all occurrences of a particular speaker is present only on either of two sets. For testing, we used 10 different 5 minutes long simulated conversational recordings of television salesmen and customers from a shopping mall in India. These recordings contain background noise (as is expected in a mall) and have different languages (Indians speak a mixture of English and Hindi). The CSKS algorithm trained on instances of keywords in learning data is supposed to detect keywords embedded in conversations of test set."
+    ],
+    [
+      "Our dataset consisted of keyword instances but the algorithm trained using this data needs to classify keywords in fragments of running conversations. To address this, we simulate the continuous speech scenario, both for keyword containing audio and background fragments, by using publicly available audio data which consisted of podcasts audio, songs, and audio narration files. For simulating fragments with keywords, we extract two random contiguous chunks from these publicly available audio files and insert the keyword either in the beginning, in the middle or in the end of the chunks, thus creating an audio segment of 2 seconds. Random 2 second segments taken from publicly available audio are used to simulate segments with no keywords(also referred to as background elsewhere in the paper). These artificially simulated audio chunks from train/validation set of pure keyword utterances were used to train/validate the model. Since the test data is quite noisy, we further used various kinds of techniques such as time-shift, pitch-shift and intensity variation to augment the data. Furthermore we used the same strategy as Tang et al. BIBREF2 of caching the data while training deep neural network on batches and artificially generating only 30% data which goes into a batch. By following these techniques, we could increase the data by many folds which not only helped the model to generalise better but also helped reduce the data preparation time during every epoch."
+    ],
+    [
+      "For all the experiments using Honk architecture, MFCC features were used. To extract these features, 20Hz/4kHz band pass filters was used to reduce the random noise. Mel-Frequency Cepstrum Coefficient (MFCC) of forty dimension were constructed and stacked using 20 milliseconds window size with 10 miliseconds overlap. For all the experiments using deep speech architecture, we have extracted spectrograms of audio files using 20 milliseconds window size with 10 milliseconds overlap and 480 nfft value."
+    ],
+    [
+      "Honk is a baseline Neural Network architecture we used to address the problem. Honk has shown good performance on normal Keyword Spotting and thus was our choice as the first baseline. The neural network is a Deep Residual Convolutional Neural Network BIBREF21 which has number of feature maps fixed for all residual blocks. The python code of the model was taken from the open source repository BIBREF22 . We tried changing training strategies of Honk architecture by the methods we will describe later for DeepSpeech, but this did not improve the accuracy.",
+      "DeepSpeech-finetune is fine tuning the weights of openly available DeepSpeech BIBREF3 model (initial feature extraction layers and not the final ASR layer) for CSKS task. The architecture consists of pretrained initial layers of DeepSpeech followed by a set of LSTM layers and a Fully Connected layer (initialized randomly) for classification. Pretrained layers taken from DeepSpeech are the initial 2D convolution layers and the GRU layers which process the output of the 2D convolutions. The output of Fully Connected layer is fed into a softmax and then a cross entropy loss for classification is used to train the algorithm. Please note that the finetune trains for 21 classes (20 keywords + 1 background) as in aforementioned Honk model. The architecture can be seen in Fig. FIGREF6 .",
+      "The next model we try is fine tuning DeepSpeech model but with a different loss function. This loss function is taken from BIBREF4 . Prototypical loss works by concentrating embeddings of all data points of a class around the class prototype. This is done by putting a softmax on the negative distances from different prototypes to determine the probability to belong to corresponding classes. The architecture FIGREF7 is same as DeepSpeech-finetune, except output of pre-final layer is taken as embedding rather than applying a Fully Connected layer for classification. These embeddings are then used to calculate euclidean distances between datapoints and prototypes, represented as INLINEFORM0 in formulae. The softmax over negative distances from prototypes is used to train cross-entropy loss. During training, examples of each class are divided into support and query embeddings. The support embeddings are used to determine prototypes of the class. Equation EQREF12 shows derivation of prototype of INLINEFORM1 class where INLINEFORM2 is the neural network yielding the embedding and INLINEFORM3 is the set of support vectors for the class. The distance of query vectors from the prototypes of the class they belong to are minimized and prototypes of other classes is maximized when training the prototypical loss. The negative distances from the prototypes of each class are passed into softmax to get the probability of belonging in a class as shown in equation EQREF13 . We see better results when we train the algorithm using prototypical loss than normal cross entropy. On qualitatively observing the output from DeepSpeech-finetune-prototypical we see that the mistakes involving confusion between keywords are very less compared to datapoints of the class background being classified as one of the keywords. We hypothesize that this might be due to treating the entire background data as one class. The variance of background is very high and treating it as one class (a unimodal class in case of prototypes) might not be the best approach. To address this, we propose the next method where we use prototypes for classification within keywords and an additional metric loss component to keep distances of background datapoints from each prototype high. DISPLAYFORM0 DISPLAYFORM1 ",
+      "We hypothesize the components of loss function of this variant from failures of prototypical loss as stated earlier. The architecture is same as in FIGREF7 , but the loss function is different from DeepSpeech-finetune-prototypical. While in DeepSpeech-finetune-prototypical, we trained prototype loss with 21 classes(20 keywords + 1 background), in DeepSpeech-finetune-prototypical+metric prototype loss is trained only amongst the 20 keywords and a new additional metric loss component inspired from BIBREF5 is added to loss function. This metric loss component aims to bring datapoints of same class together and datapoints of different class further. Datapoints belonging to background are treated as different class objects for all other datapoints in a batch. So for each object in a batch, we add a loss component like equation EQREF15 to prototypical loss. INLINEFORM0 is all datapoints in the batch belonging to the same class as INLINEFORM1 and INLINEFORM2 is all datapoints belonging to different classes than INLINEFORM3 (including background). This architecture gets the best results. DISPLAYFORM0 "
+    ],
+    [
+      "While testing, the distance of a datapoint is checked with all the prototypes to determine its predicted class. Overlapping chunks of running audio are sent to the classifier to get classified for presence of a keyword.",
+      "Train set numbers corresponding to all the models have shown in Table TABREF16 . DeepSpeech-finetune-prototypical+metric clearly beats the baselines in terms of both precision and recall. Honk is a respectable baseline and gets second best results after DeepSpeech-finetune-prototypical+metric, however, attempts to better Honk's performance using prototype loss and metric loss did not work at all.",
+      "Our method to combine prototypical loss with metric learning can be used for any classification problem which has a set of classes and a large background class, but its effectiveness needs to be tested on other datasets."
+    ]
+  ]
+}
+```

qasper-0949/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow
+
+Question: Does this model train faster than state of the art models?

qasper-0976/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: MIT-QCRI Arabic Dialect Identification System for the 2017 Multi-Genre Broadcast Challenge
+
+Question: How do they explore domain mismatch?

qasper-0977/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: MIT-QCRI Arabic Dialect Identification System for the 2017 Multi-Genre Broadcast Challenge
+
+Question: How do they explore dialect variability?

qasper-0978/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: MIT-QCRI Arabic Dialect Identification System for the 2017 Multi-Genre Broadcast Challenge
+
+Question: Which are the four Arabic dialects?

qasper-0982/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: A Swiss German Dictionary: Variation in Speech and Writing
+
+Question: Is the model evaluated on the graphemes-to-phonemes task?

qasper-0983/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: QuaRel: A Dataset and Models for Answering Questions about Qualitative Relationships
+
+Question: How does the QuaSP+Zero model work?

qasper-1036/instruction.md

@@ -0,0 +1,184 @@
+Name of Paper: NumNet: Machine Reading Comprehension with Numerical Reasoning
+
+Question: what are the existing models they compared with?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work ::: Machine Reading Comprehension",
+    "Related Work ::: Arithmetic Word Problem Solving",
+    "Methodology",
+    "Methodology ::: Framework",
+    "Methodology ::: Framework ::: Encoding Module",
+    "Methodology ::: Framework ::: Reasoning Module",
+    "Methodology ::: Framework ::: Prediction Module",
+    "Methodology ::: Framework ::: Comparison with NAQANet",
+    "Methodology ::: Numerically-aware Graph Construction",
+    "Methodology ::: Numerical Reasoning",
+    "Methodology ::: Numerical Reasoning ::: Initialization",
+    "Methodology ::: Numerical Reasoning ::: One-step Reasoning",
+    "Methodology ::: Numerical Reasoning ::: Multi-step Reasoning",
+    "Experiments ::: Dataset and Evaluation Metrics",
+    "Experiments ::: Baselines",
+    "Experiments ::: Experimental Settings",
+    "Experiments ::: Overall Results",
+    "Experiments ::: Effect of GNN Structure",
+    "Experiments ::: Effect of GNN Layer Number",
+    "Experiments ::: Case Study",
+    "Experiments ::: Error Analysis",
+    "Experiments ::: Discussion",
+    "Conclusion and Future Work",
+    "Acknowledgments",
+    "Appendix: Baseline Enhancements"
+  ],
+  "paragraphs": [
+    [
+      "Machine reading comprehension (MRC) aims to infer the answer to a question given the document. In recent years, researchers have proposed lots of MRC models BIBREF0, BIBREF1, BIBREF2, BIBREF3 and these models have achieved remarkable results in various public benchmarks such as SQuAD BIBREF4 and RACE BIBREF5. The success of these models is due to two reasons: (1) Multi-layer architectures which allow these models to read the document and the question iteratively for reasoning; (2) Attention mechanisms which would enable these models to focus on the part related to the question in the document.",
+      "However, most of existing MRC models are still weak in numerical reasoning such as addition, subtraction, sorting and counting BIBREF6, which are naturally required when reading financial news, scientific articles, etc. BIBREF6 proposed a numerically-aware QANet (NAQANet) model, which divides the answer generation for numerical MRC into three types: (1) extracting spans; (2) counting; (3) addition or subtraction over numbers. NAQANet makes a pioneering attempt to answer numerical questions but still does not explicitly consider numerical reasoning.",
+      "To tackle this problem, we introduce a novel model NumNet that integrates numerical reasoning into existing MRC models. A key problem to answer questions requiring numerical reasoning is how to perform numerical comparison in MRC systems, which is crucial for two common types of questions:",
+      "(1) Numerical Comparison: The answers of the questions can be directly obtained via performing numerical comparison, such as sorting and comparison, in the documents. For example, in Table TABREF1, for the first question, if the MRC system knows the fact that \u201c$49>47>36>31>22$\u201d, it could easily extract that the second longest field goal is 47-yard.",
+      "(2) Numerical Condition: The answers of the questions cannot be directly obtained through simple numerical comparison in the documents, but often require numerical comparison for understanding the text. For example, for the second question in Table TABREF1, an MRC system needs to know which age group made up more than 7% of the population to count the group number.",
+      "Hence, our NumNet model considers numerical comparing information among numbers when answering numerical questions. As shown in Figure FIGREF3, NumNet first encodes both the question and passages through an encoding module consisting of convolution layers, self-attention layers and feed-forward layers as well as a passage-question attention layer. After that, we feed the question and passage representations into a numerically-aware graph neural network (NumGNN) to further integrate the comparison information among numbers into their representations. Finally, we utilize the numerically-aware representation of passages to infer the answer to the question.",
+      "The experimental results on a public numerical MRC dataset DROP BIBREF6 show that our NumNet model achieves significant and consistent improvement as compared to all baseline methods by explicitly performing numerical reasoning over numbers in the question and passage. In particular, we show that our model could effectively deal with questions requiring sorting with multi-layer NumGNN. The source code of our paper is available at https://github.com/ranqiu92/NumNet."
+    ],
+    [
+      "Machine reading comprehension (MRC) has become an important research area in NLP. In recent years, researchers have published a large number of annotated MRC datasets such as CNN/Daily Mail BIBREF7, SQuAD BIBREF4, RACE BIBREF5, TriviaQA BIBREF8 and so on. With the blooming of available large-scale MRC datasets, a great number of neural network-based MRC models have been proposed to answer questions for a given document including Attentive Reader BIBREF9, BiDAF BIBREF3, Interactive AoA Reader BIBREF2, Gated Attention Reader BIBREF1, R-Net BIBREF10, DCN BIBREF11, QANet BIBREF12, and achieve promising results in most existing public MRC datasets.",
+      "Despite the success of neural network-based MRC models, researchers began to analyze the data and rethink to what extent we have solved the problem of MRC. Some works BIBREF0, BIBREF13, BIBREF14 classify the reasoning skills required to answer the questions into the following types: (1) Exact matching/Paraphrasing; (2) Summary; (3) Logic reasoning; (4) Utilizing external knowledge; (5) Numerical reasoning. They found that most existing MRC models are focusing on dealing with the first three types of questions. However, all these models suffer from problems when answering the questions requiring numerical reasoning. To the best of our knowledge, our work is the first one that explicitly incorporates numerical reasoning into the MRC system. The most relevant work to ours is NAQANet BIBREF6, which adapts the output layer of QANet BIBREF12 to support predicting answers based on counting and addition/subtraction over numbers. However, it does not consider numerical reasoning explicitly during encoding or inference."
+    ],
+    [
+      "Recently, understanding and solving arithmetic word problems (AWP) has attracted the growing interest of NLP researchers. BIBREF15 proposed a simple method to address arithmetic word problems, but mostly focusing on subsets of problems which only require addition and subtraction. After that, BIBREF16 proposed an algorithmic approach which could handle arithmetic word problems with multiple steps and operations. BIBREF17 further formalized the AWP problem as that of generating and scoring equation trees via integer linear programming. BIBREF18 and BIBREF19 proposed sequence to sequence solvers for the AWP problems, which are capable of generating unseen expressions and do not rely on sophisticated manual features. BIBREF20 leveraged deep Q-network to solve the AWP problems, achieving a good balance between effectiveness and efficiency. However, all the existing AWP systems are only trained and validated on small benchmark datasets. BIBREF21 found that the performance of these AWP systems sharply degrades on larger datasets. Moreover, from the perspective of NLP, MRC problems are more challenging than AWP since the passages in MRC are mostly real-world texts which require more complex skills to be understood. Above all, it is nontrivial to adapt most existing AWP models to the MRC scenario. Therefore, we focus on enhancing MRC models with numerical reasoning abilities in this work."
+    ],
+    [
+      "In this section, we will introduce the framework of our model NumNet and provide the details of the proposed numerically-aware graph neural network (NumGNN) for numerical reasoning."
+    ],
+    [
+      "An overview of our model NumNet is shown in Figure FIGREF3. We compose our model with encoding module, reasoning module and prediction module. Our major contribution is the reasoning module, which leverages a NumGNN between the encoding module and prediction module to explicitly consider the numerical comparison information and perform numerical reasoning. As NAQANet has been shown effective for handling numerical MRC problem BIBREF6, we leverage it as our base model and mainly focus on the design and integration of the NumGNN in this work."
+    ],
+    [
+      "Without loss of generality, we use the encoding components of QANet and NAQANet to encode the question and passage into vector-space representations. Formally, the question $Q$ and passage $P$ are first encoded as:",
+      "and then the passage-aware question representation and the question-aware passage representation are computed as:",
+      "where $\\texttt {QANet-Emb-Enc}(\\cdot )$ and $\\texttt {QANet-Att}(\\cdot )$ denote the \u201cstacked embedding encoder layer\u201d and \u201ccontext-query attention layer\u201d of QANet respectively. The former consists of convolution, self-attention and feed-forward layers. The latter is a passage-question attention layer. $\\bar{\\mathbf {Q}}$ and $\\bar{\\mathbf {P}}$ are used by the following components."
+    ],
+    [
+      "First we build a heterogeneous directed graph $\\mathcal {G}=(\\mathbf {V};\\mathbf {E})$, whose nodes ($\\mathbf {V}$) are corresponding to the numbers in the question and passage, and edges ($\\mathbf {E}$) are used to encode numerical relationships among the numbers. The details will be explained in Sec. SECREF19.",
+      "Then we perform reasoning on the graph based on a graph neural network, which can be formally denoted as:",
+      "where $\\mathbf {W}^M$ is a shared weight matrix, $\\mathbf {U}$ is the representations of the nodes corresponding to the numbers, $\\texttt {QANet-Mod-Enc}(\\cdot )$ is the \u201cmodel encoder layer\u201d defined in QANet which is similar to $\\texttt {QANet-Emb-Enc}(\\cdot )$, and the definition of $\\texttt {Reasoning}(\\cdot )$ will be given in Sec. SECREF23.",
+      "Finally, as $\\mathbf {U}$ only contains the representations of numbers, to tackle span-style answers containing non-numerical words, we concatenate $\\mathbf {U}$ with $\\mathbf {M}^P$ to produce numerically-aware passage representation $\\mathbf {M}_0$. Formally,",
+      "where $[\\cdot ;\\cdot ]$ denotes matrix concatenation, $\\mathbf {W}[k]$ denotes the $k$-th column of a matrix $\\mathbf {W}$, $\\mathbf {0}$ is a zero vector, $I(i)$ denotes the node index corresponding to the passage word $w_i^p$ which is a number, $\\mathbf {W}_0$ is a weight matrix, and $\\mathbf {b}_0$ is a bias vector."
+    ],
+    [
+      "Following NAQANet BIBREF6, we divide the answers into four types and use a unique output layer to calculate the conditional answer probability $\\Pr (\\text{answer}|\\text{type})$ for each type :",
+      "Passage span: The answer is a span of the passage, and the answer probability is defined as the product of the probabilities of the start and end positions.",
+      "Question span: The answer is a span of the question, and the answer probability is also defined as the product of the probabilities of the start and end positions.",
+      "Count: The answer is obtained by counting, and it is treated as a multi-class classification problem over ten numbers (0-9), which covers most of the Count type answers in the DROP dataset.",
+      "Arithmetic expression: The answer is the result of an arithmetic expression. The expression is obtained in three steps: (1) extract all numbers from the passage; (2) assign a sign (plus, minus or zero) for each number; (3) sum the signed numbers .",
+      "Meanwhile, an extra output layer is also used to predict the probability $\\Pr (\\text{type})$ of each answer type. At training time, the final answer probability is defined as the joint probability over all feasible answer types, i.e., $\\sum _{\\text{type}}\\Pr (\\text{type})\\Pr (\\text{answer}|\\text{type})$. Here, the answer type annotation is not required and the probability $\\Pr (\\text{type})$ is learnt by the model. At test time, the model first selects the most probable answer type greedily and then predicts the best answer accordingly.",
+      "Without loss of generality, we leverage the definition of the five output layers in BIBREF6, with $\\mathbf {M_0}$ and $\\mathbf {Q}$ as inputs. Please refer to the paper for more details due to space limitation."
+    ],
+    [
+      "The major difference between our model and NAQANet is that NAQANet does not have the reasoning module, i.e., $\\mathbf {M}_0$ is simply set as $\\mathbf {M}^P$. As a result, numbers are treated as common words in NAQANet except in the prediction module, thus NAQANet may struggle to learn the numerical relationships between numbers, and potentially cannot well generalize to unseen numbers. However, as discussed in Sec. SECREF1, the numerical comparison is essential for answering questions requiring numerical reasoning. In our model, the numerical relationships are explicitly represented with the topology of the graph and a NumGNN is used to perform numerical reasoning. Therefore, our NumNet model can handle questions requiring numerical reasoning more effectively, which is verified by the experiments in Sec. SECREF4."
+    ],
+    [
+      "We regard all numbers from the question and passage as nodes in the graph for reasoning . The set of nodes corresponding to the numbers occurring in question and passage are denoted as $\\mathbf {V}^Q$ and $\\mathbf {V}^P$ respectively. And we denote all the nodes as $\\mathbf {V}=\\mathbf {V}^Q\\cup \\mathbf {V}^P$, and the number corresponding to a node $v\\in \\mathbf {V}$ as $n(v)$.",
+      "Two sets of edges are considered in this work:",
+      "Greater Relation Edge ($\\overrightarrow{\\mathbf {E}}$): For two nodes $v_i, v_j\\in \\mathbf {V}$, a directed edge $\\overrightarrow{e}_{ij}=(v_i, v_j)$ pointing from $v_i$ to $v_j$ will be added to the graph if $n(v_i)>n(v_j)$, which is denoted as solid arrow in Figure FIGREF3.",
+      "Lower or Equal Relation Edge ($\\overleftarrow{\\mathbf {E}}$): For two nodes $v_i, v_j\\in \\mathbf {V}$, a directed edge $\\overleftarrow{e}_{ij}=(v_j, v_i)$ will be added to the graph if $n(v_i)\\le n(v_j)$, which is denoted as dashed arrow in Figure FIGREF3.",
+      "Theoretically, $\\overrightarrow{\\mathbf {E}}$ and $\\overleftarrow{\\mathbf {E}}$ are complement to each other . However, as a number may occur several times and represent different facts in a document, we add a distinct node for each occurrence in the graph to prevent potential ambiguity. Therefore, it is more reasonable to use both $\\overrightarrow{\\mathbf {E}}$ and $\\overleftarrow{\\mathbf {E}}$ in order to encode the equal information among nodes."
+    ],
+    [
+      "As we built the graph $\\mathcal {G}=(\\mathbf {V},\\mathbf {E})$, we leverage NumGNN to perform reasoning, which is corresponding to the function $\\texttt {Reasoning}(\\cdot )$ in Eq. DISPLAY_FORM10. The reasoning process is as follows:"
+    ],
+    [
+      "For each node $v^P_i\\in \\mathbf {V}^P$, its representation is initialized as the corresponding column vector of $\\mathbf {M}^P$. Formally, the initial representation is $\\mathbf {v}_i^P=\\mathbf {M}^P[I^P(v_i^P)]$, where $I^P(v^P_i)$ denotes the word index corresponding to $v_i^P$. Similarly, the initial representation $\\mathbf {v}_j^Q$ for a node $v^Q_j\\in \\mathbf {V}^Q$ is set as the corresponding column vector of $\\mathbf {M}^Q$. We denote all the initial node representations as $\\mathbf {v}^0=\\lbrace \\mathbf {v}_i^P\\rbrace \\cup \\lbrace \\mathbf {v}_j^Q\\rbrace $."
+    ],
+    [
+      "Given the graph $\\mathcal {G}$ and the node representations $\\mathbf {v}$, we use a GNN to perform reasoning in three steps:",
+      "(1) Node Relatedness Measure: As only a few numbers are relevant for answering a question generally, we compute a weight for each node to by-pass irrelevant numbers in reasoning. Formally, the weight for node $v_i$ is computed as:",
+      "where $\\mathbf {W}_v$ is a weight matrix, and $b_v$ is a bias.",
+      "(2) Message Propagation: As the role a number plays in reasoning is not only decided by itself, but also related to the context, we propagate messages from each node to its neighbors to help to perform reasoning. As numbers in question and passage may play different roles in reasoning and edges corresponding to different numerical relations should be distinguished, we use relation-specific transform matrices in the message propagation. Formally, we define the following propagation function for calculating the forward-pass update of a node:",
+      "where $\\widetilde{\\mathbf {v}}^{\\prime }_i$ is the message representation of node $v_i$, $\\texttt {r}_{ji}$ is the relation assigned to edge $e_{ji}$, $\\mathbf {W}^{\\texttt {r}_{ji}}$ are relation-specific transform matrices, and $\\mathcal {N}_i=\\lbrace j|(v_j,v_i)\\in \\mathbf {E}\\rbrace $ is the neighbors of node $v_i$.",
+      "For each edge $e_{ji}$, $\\texttt {r}_{ji}$ is determined by the following two attributes:",
+      "Number relation: $>$ or $\\le $;",
+      "Node types: the two nodes of the edge corresponding to two numbers that: (1) both from the question ($\\text{q-q}$); (2) both from the passage ($\\text{p-p}$); (3) from the question and the passage respectively ($\\text{q-p}$); (4) from the passage and the question respectively ($\\text{p-q}$).",
+      "Formally, $\\texttt {r}_{ij}\\in \\lbrace >,\\le \\rbrace \\times \\lbrace \\text{q-q},\\text{p-p},\\text{q-p},\\text{p-q}\\rbrace $.",
+      "(3) Node Representation Update: As the message representation obtained in the previous step only contains information from the neighbors, it needs to be fused with the node representation to combine with the information carried by the node itself, which is performed as:",
+      "where $\\mathbf {W}_f$ is a weight matrix, and $\\mathbf {b}_f$ is a bias vector.",
+      "We denote the entire one-step reasoning process (Eq. DISPLAY_FORM26-DISPLAY_FORM30) as a single function",
+      "As the graph $\\mathcal {G}$ constructed in Sec. SECREF19 has encoded the numerical relations via its topology, the reasoning process is numerically-aware."
+    ],
+    [
+      "By single-step reasoning, we can only infer relations between adjacent nodes. However, relations between multiple nodes may be required for certain tasks, e.g., sorting. Therefore, it is essential to perform multi-step reasoning, which can be done as follows\uff1a",
+      "where $t\\ge 1$. Suppose we perform $K$ steps of reasoning, $\\mathbf {v}^K$ is used as $\\mathbf {U}$ in Eq. DISPLAY_FORM10."
+    ],
+    [
+      "We evaluate our proposed model on DROP dataset BIBREF6, which is a public numerical MRC dataset. The DROP dataset is constructed by crowd-sourcing, which asks the annotators to generate question-answer pairs according to the given Wikipedia passages, which require numerical reasoning such as addition, counting, or sorting over numbers in the passages. There are $77,409$ training samples, $9,536$ development samples and $9,622$ testing samples in the dataset.",
+      "In this paper, we adopt two metrics including Exact Match (EM) and numerically-focused F1 scores to evaluate our model following BIBREF6. The numerically-focused F1 is set to be 0 when the predicted answer is mismatched for those questions with the numeric golden answer."
+    ],
+    [
+      "For comparison, we select several public models as baselines including semantic parsing models:",
+      "[topsep=2pt, itemsep=0pt]",
+      "Syn Dep BIBREF6, the neural semantic parsing model (KDG) BIBREF22 with Stanford dependencies based sentence representations;",
+      "OpenIE BIBREF6, KDG with open information extraction based sentence representations;",
+      "SRL BIBREF6, KDG with semantic role labeling based sentence representations;",
+      "and traditional MRC models:",
+      "[topsep=2pt, itemsep=0pt]",
+      "BiDAF BIBREF3, an MRC model which utilizes a bi-directional attention flow network to encode the question and passage;",
+      "QANet BIBREF12, which utilizes convolutions and self-attentions as the building blocks of encoders to represent the question and passage;",
+      "BERT BIBREF23, a pre-trained bidirectional Transformer-based language model which achieves state-of-the-art performance on lots of public MRC datasets recently;",
+      "and numerical MRC models:",
+      "[topsep=2pt, itemsep=0pt]",
+      "NAQANet BIBREF6, a numerical version of QANet model.",
+      "NAQANet+, an enhanced version of NAQANet implemented by ourselves, which further considers real number (e.g. \u201c2.5\u201d), richer arithmetic expression, data augmentation, etc. The enhancements are also used in our NumNet model and the details are given in the Appendix."
+    ],
+    [
+      "In this paper, we tune our model on the development set and use a grid search to determine the optimal parameters. The dimensions of all the representations (e.g., $\\mathbf {Q}$, $\\mathbf {P}$, $\\mathbf {M}^Q$, $\\mathbf {M}^P$, $\\mathbf {U}$, $\\mathbf {M}_0^{\\prime }$, $\\mathbf {M}_0$ and $\\mathbf {v}$) are set to 128. If not specified, the reasoning step $K$ is set to 3. Since other parameters have little effect on the results, we simply follow the settings used in BIBREF6.",
+      "We use the Adam optimizer BIBREF24 with $\\beta _1=0.8$, $\\beta _2=0.999$, $\\epsilon =10^{-7}$ to minimize the objective function. The learning rate is $5 \\times 10^{-4}$, L2 weight decay $\\lambda $ is $10^{-7}$ and the maximum norm value of gradient clipping is 5. We also apply exponential moving average with a decay rate $0.9999$ on all trainable variables. The model is trained with a batch size of 16 for 40 epochs. Passages and questions are trimmed to 400 and 50 tokens respectively during training, and trimmed to $1,000$ and 100 tokens respectively during prediction ."
+    ],
+    [
+      "The performance of our NumNet model and other baselines on DROP dataset are shown in Table TABREF47. From the results, we can observe that:",
+      "(1) Our NumNet model achieves better results on both the development and testing sets on DROP dataset as compared to semantic parsing-based models, traditional MRC models and even numerical MRC models NAQANet and NAQANet+. The reason is that our NumNet model can make full use of the numerical comparison information over numbers in both question and passage via the proposed NumGNN module.",
+      "(2) Our implemented NAQANet+ has a much better performance compared to the original version of NAQANet. It verifies the effectiveness of our proposed enhancements for baseline."
+    ],
+    [
+      "In this part, we investigate the effect of different GNN structures on the DROP development set. The results are shown in Table TABREF51. The \u201cComparison\u201d, \u201cNumber\u201d and \u201cALL\u201d are corresponding to the comparing question subset , the number-type answer subset, and the entire development set, respectively . If we replace the proposed numerically-aware graph (Sec. SECREF19) with a fully connected graph, our model fallbacks to a traditional GNN, denoted as \u201cGNN\u201d in the table. Moreover, \u201c- question num\u201d denotes the numbers in the question is not included in the graph, and \u201c- $\\le $ type edge\u201d and \u201c- $>$ type edge\u201d denote edges of $\\le $ and $>$ types are not adopted respectively.",
+      "As shown in Table TABREF51, our proposed NumGNN leads to statistically significant improvements compared to traditional GNN on both EM and F1 scores especially for comparing questions. It indicates that considering the comparing information over numbers could effectively help the numerical reasoning for comparing questions. Moreover, we find that the numbers in the question are often related to the numerical reasoning for answering the question, thus considering numbers in questions in NumGNN achieves better performance. And the results also justify that encoding \u201cgreater relation\u201d and \u201clower or equal relation\u201d simultaneously in the graph also benefits our model."
+    ],
+    [
+      "The number of NumGNN layers represents the numerical reasoning ability of our models. A $K$-layer version has the ability for $K$-step numerical inference. In this part, we additionally perform experiments to understand the values of the numbers of NumGNN layers. From Figure FIGREF52, we could observe that:",
+      "(1) The 2-layer version of NumNet achieves the best performance for the comparing questions. From careful analysis, we find that most comparing questions only require at most 2-step reasoning (e.g., \u201cWho was the second oldest player in the MLB, Clemens or Franco?\u201d), and therefore the 3-layer version of NumNet is more complex but brings no gains for these questions.",
+      "(2) The performance of our NumNet model on the overall development set is improved consistently as the number of GNN layers increases. The reason is that some of the numerical questions require reasoning over many numbers in the passage, which could benefit from the multi-step reasoning ability of multi-layer GNN. However, further investigation shows that the performance gain is not stable when $K\\ge 4$. We believe it is due to the intrinsic over smoothing problem of GNNs BIBREF25."
+    ],
+    [
+      "We further give some examples to show why incorporating comparing information over numbers in the passage could help numerical reasoning in MRC in Table TABREF53. For the first case, we observe that NAQANet+ gives a wrong prediction, and we find that NAQANet+ will give the same prediction for the question \u201cWhich age group is smaller: under the age of 18 or 18 and 24?\u201d. The reason is that NAQANet+ cannot distinguish which one is larger for $10.1\\%$ and $56.2\\%$. For the second case, NAQANet+ cannot recognize the second longest field goal is 22-yard and also gives a wrong prediction. For these two cases, our NumNet model could give the correct answer through the numeric reasoning, which indicates the effectiveness of our NumNet model."
+    ],
+    [
+      "To investigate how well our NumNet model handles sorting/comparison questions and better understand the remaining challenges, we perform an error analysis on a random sample of NumNet predictions. We find that:",
+      "(1) Our NumNet model can answer about 76% of sorting/comparison questions correctly, which indicates that our NumNet model has achieved numerical reasoning ability to some extend.",
+      "(2) Among the incorrectly answered sorting/comparison questions, the most ones (26%) are those whose golden answers are multiple nonadjacent spans (row 1 in Table TABREF54), and the second most ones (19%) are those involving comparison with an intermediate number that does not literally occur in the document/question but has to be derived from counting or arithmetic operation (row 1 in Table TABREF54)."
+    ],
+    [
+      "By combining the numerically-aware graph and the NumGNN together, our NumNet model achieves the numerical reasoning ability. On one hand, the numerically-aware graph encodes numbers as nodes and relationships between them as the edges, which is required for numerical comparison. On the other hand, through one-step reasoning, our NumGNN could perform comparison and identify the numerical condition. After multiple-step reasoning, our NumGNN could further perform sorting.",
+      "However, since the numerically-aware graph is pre-defined, our NumNet is not applicable to the case where an intermediate number has to be derived (e.g., from arithmetic operation) in the reasoning process, which is a major limitation of our model."
+    ],
+    [
+      "Numerical reasoning skills such as addition, subtraction, sorting and counting are naturally required by machine reading comprehension (MRC) problems in practice. Nevertheless, these skills are not taken into account explicitly for most existing MRC models. In this work, we propose a numerical MRC model named NumNet which performs explicit numerical reasoning while reading the passages. To be specific, NumNet encodes the numerical relations among numbers in the question and passage into a graph as its topology, and leverages a numerically-aware graph neural network to perform numerical reasoning on the graph. Our NumNet model outperforms strong baselines with a large margin on the DROP dataset. In the future, we will explore the following directions: (1)As we use a pre-defined reasoning graph in our model, it is incapable of handling reasoning process which involves intermediate numbers that not presented in the graph. How to incorporate dynamic graph into our model is an interesting problem. (2) Compared with methods proposed for arithmetic word problems (AWPs), our model has better natural language understanding ability. However, the methods for AWPs can handle much richer arithmetic expressions. Therefore, how to combine both of their abilities to develop a more powerful numerical MRC model is an interesting future direction. (3) Symbolic reasoning plays a crucial role in human reading comprehension. Our work integrates numerical reasoning, which is a special case of symbolic reasoning, into traditional MRC systems. How to incorporate more sophisticated symbolic reasoning abilities into MRC systems is also a valuable future direction."
+    ],
+    [
+      "We would like to thank all anonymous reviewers for their insightful comments, and thank Yan Zhang for her help on improving the presentation of Figure FIGREF3."
+    ],
+    [
+      "The major enhancements leveraged by our implemented NAQANet+ model include:",
+      "(1) \u201creal number\u201d: Unlike NAQANet only considers integer numbers, we also consider real numbers.",
+      "(2) \u201cricher arithmetic expression\u201d: We conceptually append an extra number \u201c100\u201d to the passage to support arithmetic expressions like \u201c100-25\u201d, which is required for answering questions such as \u201cHow many percent were not American?\u201d.",
+      "(3) \u201cpassage-preferred\u201d: If an answer is both a span of the question and the passage, we only propagate gradients through the output layer for processing \u201cPassage span\u201d type answers.",
+      "(4) \u201cdata augmentation\u201d: The original questions in the DROP dataset are generated by crowdsourced workers. For the comparing questions which contain answer candidates, we observe that the workers frequently only change the incorrect answer candidate to generate a new question. For example, \u201cHow many from the census is bigger: Germans or English?\u201d whose golden answer is \u201cGermans\u201d is modified to \u201cHow many from the census is bigger: Germans or Irish?\u201d. This may introduce undesired inductive bias to the model. Therefore, we propose to augment the training dataset with new questions automatically generated by swapping the candidate answers, e.g., \u201cHow many from the census is bigger: English or Germans?\u201d is added to the training dataset.",
+      "We further conduct ablation studies on the enhancements. And the validation scores on the development set are shown in Table TABREF59. As can be seen from Table TABREF59:",
+      "(1) The uses of real number and richer arithmetic expression are crucial for answering numerical questions: both EM and F1 drop drastically by up to $15-21$ points if they are removed.",
+      "(2) The passage-preferred strategy and data augmentation are also necessary components that contribute significant improvements for those comparing questions."
+    ]
+  ]
+}
+```

qasper-1052/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Kurdish (Sorani) Speech to Text: Presenting an Experimental Dataset
+
+Question: How many different subjects does the dataset contain?

qasper-1053/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Kurdish (Sorani) Speech to Text: Presenting an Experimental Dataset
+
+Question: How many annotators participated?

qasper-1062/instruction.md

@@ -0,0 +1,109 @@
+Name of Paper: Neural Language Modeling by Jointly Learning Syntax and Lexicon
+
+Question: Which dataset do they experiment with?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Work",
+    "Motivation",
+    "Modeling Local Structure",
+    "Parsing Network",
+    "Reading Network",
+    "Predict Network",
+    "Experiments",
+    "Character-level Language Model",
+    "Word-level Language Model",
+    "Unsupervised Constituency Parsing",
+    "Conclusion",
+    "Acknowledgement"
+  ],
+  "paragraphs": [
+    [
+      "Linguistic theories generally regard natural language as consisting of two part: a lexicon, the complete set of all possible words in a language; and a syntax, the set of rules, principles, and processes that govern the structure of sentences BIBREF0 . To generate a proper sentence, tokens are put together with a specific syntactic structure. Understanding a sentence also requires lexical information to provide meanings, and syntactical knowledge to correctly combine meanings. Current neural language models can provide meaningful word represent BIBREF1 , BIBREF2 , BIBREF3 . However, standard recurrent neural networks only implicitly model syntax, thus fail to efficiently use structure information BIBREF4 .",
+      "Developing a deep neural network that can leverage syntactic knowledge to form a better semantic representation has received a great deal of attention in recent years BIBREF5 , BIBREF4 , BIBREF6 . Integrating syntactic structure into a language model is important for different reasons: 1) to obtain a hierarchical representation with increasing levels of abstraction, which is a key feature of deep neural networks and of the human brain BIBREF7 , BIBREF8 , BIBREF9 ; 2) to capture complex linguistic phenomena, like long-term dependency problem BIBREF4 and the compositional effects BIBREF5 ; 3) to provide shortcut for gradient back-propagation BIBREF6 .",
+      "A syntactic parser is the most common source for structure information. Supervised parsers can achieve very high performance on well constructed sentences. Hence, parsers can provide accurate information about how to compose word semantics into sentence semantics BIBREF5 , or how to generate the next word given previous words BIBREF10 . However, only major languages have treebank data for training parsers, and it request expensive human expert annotation. People also tend to break language rules in many circumstances (such as writing a tweet). These defects limit the generalization capability of supervised parsers.",
+      "Unsupervised syntactic structure induction has been among the longstanding challenges of computational linguistic BIBREF11 , BIBREF12 , BIBREF13 . Researchers are interested in this problem for a variety of reasons: to be able to parse languages for which no annotated treebanks exist BIBREF14 ; to create a dependency structure to better suit a particular NLP application BIBREF10 ; to empirically argue for or against the poverty of the stimulus BIBREF15 , BIBREF16 ; and to examine cognitive issues in language learning BIBREF17 .",
+      "In this paper, we propose a novel neural language model: Parsing-Reading-Predict Networks (PRPN), which can simultaneously induce the syntactic structure from unannotated sentences and leverage the inferred structure to form a better language model. With our model, we assume that language can be naturally represented as a tree-structured graph. The model is composed of three parts:",
+      "We evaluate our model on three tasks: word-level language modeling, character-level language modeling, and unsupervised constituency parsing. The proposed model achieves (or is close to) the state-of-the-art on both word-level and character-level language modeling. The model's unsupervised parsing outperforms some strong baseline models, demonstrating that the structure found by our model is similar to the intrinsic structure provided by human experts."
+    ],
+    [
+      "The idea of introducing some structures, especially trees, into language understanding to help a downstream task has been explored in various ways. For example, BIBREF5 , BIBREF4 learn a bottom-up encoder, taking as an input a parse tree supplied from an external parser. There are models that are able to infer a tree during test time, while still need supervised signal on tree structure during training. For example, BIBREF18 , BIBREF19 , BIBREF20 , BIBREF21 , etc. Moreover, BIBREF22 did an in-depth analysis of recursive models that are able to learn tree structure without being exposed to any grammar trees. Our model is also able to infer tree structure in an unsupervised setting, but different from theirs, it is a recurrent network that implicitly models tree structure through attention.",
+      "Apart from the approach of using recursive networks to capture structures, there is another line of research which try to learn recurrent features at multiple scales, which can be dated back to 1990s (e.g. BIBREF23 , BIBREF24 , BIBREF25 ). The NARX RNN BIBREF25 is another example which used a feed forward net taking different inputs with predefined time delays to model long-term dependencies. More recently, BIBREF26 also used multiple layers of recurrent networks with different pre-defined updating frequencies. Instead, our model tries to learn the structure from data, rather than predefining it. In that respect, BIBREF6 relates to our model since it proposes a hierarchical multi-scale structure with binary gates controlling intra-layer connections, and the gating mechanism is learned from data too. The difference is that their gating mechanism controls the updates of higher layers directly, while ours control it softly through an attention mechanism.",
+      "In terms of language modeling, syntactic language modeling can be dated back to BIBREF27 . BIBREF28 , BIBREF29 have also proposed language models with a top-down parsing mechanism. Recently BIBREF30 , BIBREF31 have introduced neural networks into this space. It learns both a discriminative and a generative model with top-down parsing, trained with a supervision signal from parsed sentences in the corpus. There are also dependency-based approaches using neural networks, including BIBREF32 , BIBREF33 , BIBREF34 .",
+      "Parsers are also related to our work since they are all inferring grammatical tree structure given a sentence. For example, SPINN BIBREF35 is a shift-reduce parser that uses an LSTM as its composition function. The transition classifier in SPINN is supervisedly trained on the Stanford PCFG Parser BIBREF36 output. Unsupervised parsers are more aligned with what our model is doing. BIBREF12 presented a generative model for the unsupervised learning of dependency structures. BIBREF11 is a generative distributional model for the unsupervised induction of natural language syntax which explicitly models constituent yields and contexts. We compare our parsing quality with the aforementioned two papers in Section SECREF43 ."
+    ],
+    [
+      "Suppose we have a sequence of tokens INLINEFORM0 governed by the tree structure showed in Figure FIGREF4 . The leafs INLINEFORM1 are observed tokens. Node INLINEFORM2 represents the meaning of the constituent formed by its leaves INLINEFORM3 , where INLINEFORM4 and INLINEFORM5 stands for the leftmost child and right most child. Root INLINEFORM6 represents the meaning of the whole sequence. Arrows represent the dependency relations between nodes. The underlying assumption is that each node depends only on its parent and its left siblings.",
+      "Directly modeling the tree structure is a challenging task, usually requiring supervision to learn BIBREF4 . In addition, relying on tree structures can result in a model that is not sufficiently robust to face ungrammatical sentences BIBREF37 . In contrast, recurrent models provide a convenient way to model sequential data, with the current hidden state only depends on the last hidden state. This makes models more robust when facing nonconforming sequential data, but it suffers from neglecting the real dependency relation that dominates the structure of natural language sentences.",
+      "In this paper, we use skip-connection to integrate structured dependency relations with recurrent neural network. In other words, the current hidden state does not only depend on the last hidden state, but also on previous hidden states that have a direct syntactic relation to the current one.",
+      "Figure FIGREF5 shows the structure of our model. The non-leaf node INLINEFORM0 is represented by a set of hidden states INLINEFORM1 , where INLINEFORM2 is the left most descendant leaf and INLINEFORM3 is the right most one. Arrows shows skip connections built by our model according to the latent structure. Skip connections are controlled by gates INLINEFORM4 . In order to define INLINEFORM5 , we introduce a latent variable INLINEFORM6 to represent local structural context of INLINEFORM7 :",
+      "and gates are defined as: DISPLAYFORM0 ",
+      "Given this architecture, the siblings dependency relation is modeled by at least one skip-connect. The skip connection will directly feed information forward, and pass gradient backward. The parent-to-child relation will be implicitly modeled by skip-connect relation between nodes.",
+      "The model recurrently updates the hidden states according to: DISPLAYFORM0 ",
+      "and the probability distribution for next word is approximated by: DISPLAYFORM0 ",
+      " where INLINEFORM0 are gates that control skip-connections. Both INLINEFORM1 and INLINEFORM2 have a structured attention mechanism that takes INLINEFORM3 as input and forces the model to focus on the most related information. Since INLINEFORM4 is an unobserved latent variable, We explain an approximation for INLINEFORM5 in the next section. The structured attention mechanism is explained in section SECREF21 ."
+    ],
+    [
+      "In this section we give a probabilistic view on how to model the local structure of language. A detailed elaboration for this section is given in Appendix . At time step INLINEFORM0 , INLINEFORM1 represents the probability of choosing one out of INLINEFORM2 possible local structures. We propose to model the distribution by the Stick-Breaking Process: DISPLAYFORM0 ",
+      "The formula can be understood by noting that after the time step INLINEFORM0 have their probabilities assigned, INLINEFORM1 is remaining probability, INLINEFORM2 is the portion of remaining probability that we assign to time step INLINEFORM3 . Variable INLINEFORM4 is parametrized in the next section.",
+      "As shown in Appendix , the expectation of gate value INLINEFORM0 is the Cumulative Distribution Function (CDF) of INLINEFORM1 . Thus, we can replace the discrete gate value by its expectation: DISPLAYFORM0 ",
+      "With these relaxations, Eq. EQREF9 and EQREF10 can be approximated by using a soft gating vector to update the hidden state and predict the next token."
+    ],
+    [
+      "In Eq. EQREF12 , INLINEFORM0 is the portion of the remaining probability that we assign to position INLINEFORM1 . Because the stick-breaking process should assign high probability to INLINEFORM2 , which is the closest constituent-beginning word. The model should assign large INLINEFORM3 to words beginning new constituents. While INLINEFORM4 itself is a constituent-beginning word, the model should assign large INLINEFORM5 to words beginning larger constituents. In other words, the model will consider longer dependency relations for the first word in constituent. Given the sentence in Figure FIGREF4 , at time step INLINEFORM6 , both INLINEFORM7 and INLINEFORM8 should be close to 1, and all other INLINEFORM9 should be close to 0.",
+      "In order to parametrize INLINEFORM0 , our basic hypothesis is that words in the same constituent should have a closer syntactic relation within themselves, and that this syntactical proximity can be represented by a scalar value. From the tree structure point of view, the shortest path between leafs in same subtree is shorter than the one between leafs in different subtree.",
+      "To model syntactical proximity, we introduce a new feature Syntactic Distance. For a sentence with length INLINEFORM0 , we define a set of INLINEFORM1 real valued scalar variables INLINEFORM2 , with INLINEFORM3 representing a measure of the syntactic relation between the pair of adjacent words INLINEFORM4 . INLINEFORM5 could be the last word in previous sentence or a padding token. For time step INLINEFORM6 , we want to find the closest words INLINEFORM7 , that have larger syntactic distance than INLINEFORM8 . Thus INLINEFORM9 can be defined as: DISPLAYFORM0 ",
+      "where INLINEFORM0 . INLINEFORM1 is the temperature parameter that controls the sensitivity of INLINEFORM2 to the differences between distances.",
+      "The Syntactic Distance has some nice properties that both allow us to infer a tree structure from it and be robust to intermediate non-valid tree structures that the model may encounter during learning. In Appendix and we list these properties and further explain the meanings of their values.",
+      " BIBREF38 shows that it's possible to identify the beginning and ending words of a constituent using local information. In our model, the syntactic distance between a given token (which is usually represented as a vector word embedding INLINEFORM0 ) and its previous token INLINEFORM1 , is provided by a convolutional kernel over a set of consecutive previous tokens INLINEFORM2 . This convolution is depicted as the gray triangles shown in Figure FIGREF20 . Each triangle here represent 2 layers of convolution. Formally, the syntactic distance INLINEFORM3 between token INLINEFORM4 and INLINEFORM5 is computed by DISPLAYFORM0 DISPLAYFORM1 ",
+      "where INLINEFORM0 , INLINEFORM1 are the kernel parameters. INLINEFORM2 and INLINEFORM3 can be seen as another convolutional kernel with window size 1, convolved over INLINEFORM4 's. Here the kernel window size INLINEFORM5 determines how far back into the history node INLINEFORM6 can reach while computing its syntactic distance INLINEFORM7 . Thus we call it the look-back range.",
+      "Convolving INLINEFORM0 and INLINEFORM1 on the whole sequence with length INLINEFORM2 yields a set of distances. For the tokens in the beginning of the sequence, we simply pad INLINEFORM3 zero vectors to the front of the sequence in order to get INLINEFORM4 outputs."
+    ],
+    [
+      "The Reading Network generate new states INLINEFORM0 considering on input INLINEFORM1 , previous memory states INLINEFORM2 , and gates INLINEFORM3 , as shown in Eq. EQREF9 .",
+      "Similar to Long Short-Term Memory-Network (LSTMN) BIBREF39 , the Reading Network maintains the memory states by maintaining two sets of vectors: a hidden tape INLINEFORM0 , and a memory tape INLINEFORM1 , where INLINEFORM2 is the upper bound for the memory span. Hidden states INLINEFORM3 is now represented by a tuple of two vectors INLINEFORM4 . The Reading Network captures the dependency relation by a modified attention mechanism: structured attention. At each step of recurrence, the model summarizes the previous recurrent states via the structured attention mechanism, then performs a normal LSTM update, with hidden and cell states output by the attention mechanism.",
+      "At each time step INLINEFORM0 , the read operation attentively links the current token to previous memories with a structured attention layer: DISPLAYFORM0 ",
+      " where, INLINEFORM0 is the dimension of the hidden state. Modulated by the gates in Eq. EQREF13 , the structured intra-attention weight is defined as: DISPLAYFORM0 ",
+      " This yields a probability distribution over the hidden state vectors of previous tokens. We can then compute an adaptive summary vector for the previous hidden tape and memory denoting by INLINEFORM0 and INLINEFORM1 : DISPLAYFORM0 ",
+      "Structured attention provides a way to model the dependency relations shown in Figure FIGREF4 .",
+      "The Reading Network takes INLINEFORM0 , INLINEFORM1 and INLINEFORM2 as input, computes the values of INLINEFORM3 and INLINEFORM4 by the LSTM recurrent update BIBREF40 . Then the write operation concatenates INLINEFORM5 and INLINEFORM6 to the end of hidden and memory tape."
+    ],
+    [
+      "Predict Network models the probability distribution of next word INLINEFORM0 , considering on hidden states INLINEFORM1 , and gates INLINEFORM2 . Note that, at time step INLINEFORM3 , the model cannot observe INLINEFORM4 , a temporary estimation of INLINEFORM5 is computed considering on INLINEFORM6 : DISPLAYFORM0 ",
+      "From there we compute its corresponding INLINEFORM0 and INLINEFORM1 for Eq. EQREF10 . We parametrize INLINEFORM2 function as: DISPLAYFORM0 ",
+      " where INLINEFORM0 is an adaptive summary of INLINEFORM1 , output by structured attention controlled by INLINEFORM2 . INLINEFORM3 could be a simple feed-forward MLP, or more complex architecture, like ResNet, to add more depth to the model."
+    ],
+    [
+      "We evaluate the proposed model on three tasks, character-level language modeling, word-level language modeling, and unsupervised constituency parsing."
+    ],
+    [
+      "From a character-level view, natural language is a discrete sequence of data, where discrete symbols form a distinct and shallow tree structure: the sentence is the root, words are children of the root, and characters are leafs. However, compared to word-level language modeling, character-level language modeling requires the model to handle longer-term dependencies. We evaluate a character-level variant of our proposed language model over a preprocessed version of the Penn Treebank (PTB) and Text8 datasets.",
+      "When training, we use truncated back-propagation, and feed the final memory position from the previous batch as the initial memory of next one. At the beginning of training and test time, the model initial hidden states are filled with zero. Optimization is performed with Adam using learning rate INLINEFORM0 , weight decay INLINEFORM1 , INLINEFORM2 , INLINEFORM3 and INLINEFORM4 . We carry out gradient clipping with maximum norm 1.0. The learning rate is multiplied by 0.1 whenever validation performance does not improve during 2 checkpoints. These checkpoints are performed at the end of each epoch. We also apply layer normalization BIBREF41 to the Reading Network and batch normalization to the Predict Network and parsing network. For all of the character-level language modeling experiments, we apply the same procedure, varying only the number of hidden units, mini-batch size and dropout rate.",
+      "we process the Penn Treebank dataset BIBREF42 by following the procedure introduced in BIBREF43 . For character-level PTB, Reading Network has two recurrent layers, Predict Network has one residual block. Hidden state size is 1024 units. The input and output embedding size are 128, and not shared. Look-back range INLINEFORM0 , temperature parameter INLINEFORM1 , upper band of memory span INLINEFORM2 . We use a batch size of 64, truncated back-propagation with 100 timesteps. The values used of dropout on input/output embeddings, between recurrent layers, and on recurrent states were (0, 0.25, 0.1) respectively.",
+      "In Figure FIGREF32 , we visualize the syntactic distance estimated by the Parsing Network, while reading three different sequences from the PTB test set. We observe that the syntactic distance tends to be higher between the last character of a word and a space, which is a reasonable breakpoint to separate between words. In other words, if the model sees a space, it will attend on all previous step. If the model sees a letter, it will attend no further then the last space step. The model autonomously discovered to avoid inter-word attention connection, and use the hidden states of space (separator) tokens to summarize previous information. This is strong proof that the model can understand the latent structure of data. As a result our model achieve state-of-the-art performance and significantly outperform baseline models. It is worth noting that HM-LSTM BIBREF6 also unsupervisedly induce similar structure from data. But discrete operations in HM-LSTM make their training procedure more complicated then ours."
+    ],
+    [
+      "Comparing to character-level language modeling, word-level language modeling needs to deal with complex syntactic structure and various linguistic phenomena. But it has less long-term dependencies. We evaluate the word-level variant of our language model on a preprocessed version of the Penn Treebank (PTB) BIBREF42 and Text8 BIBREF49 dataset.",
+      "We apply the same procedure and hyper-parameters as in character-level language model. Except optimization is performed with Adam with INLINEFORM0 . This turns off the exponential moving average for estimates of the means of the gradients BIBREF50 . We also adapt the number of hidden units, mini-batch size and the dropout rate according to the different tasks.",
+      "we process the Penn Treebank dataset BIBREF43 by following the procedure introduced in BIBREF51 . For word-level PTB, the Reading Network has two recurrent layers and the Predict Network do not have residual block. The hidden state size is 1200 units and the input and output embedding sizes are 800, and shared BIBREF52 , BIBREF53 . Look-back range INLINEFORM0 , temperature parameter INLINEFORM1 and the upper band of memory span INLINEFORM2 . We use a batch size of 64, truncated back-propagation with 35 time-steps. The values used of dropout on input/output embeddings, between recurrent layers, and on recurrent states were (0.7, 0.5, 0.5) respectively.",
+      "dataset contains 17M training tokens and has a vocabulary size of 44k words. The dataset is partitioned into a training set (first 99M characters) and a development set (last 1M characters) that is used to report performance. As this dataset contains various articles from Wikipedia, the longer term information (such as current topic) plays a bigger role than in the PTB experiments BIBREF61 . We apply the same procedure and hyper-parameters as in character-level PTB, except we use a batch size of 128. The values used of dropout on input/output embeddings, between Recurrent Layers and on recurrent states were (0.4, 0.2, 0.2) respectively.",
+      "In Table TABREF39 , our results are comparable to the state-of-the-art methods. Since we do not have the same computational resource used in BIBREF50 to tune hyper-parameters at large scale, we expect that our model could achieve better performance after an aggressive hyperparameter tuning process. As shown in Table TABREF42 , our method outperform baseline methods. It is worth noticing that the continuous cache pointer can also be applied to output of our Predict Network without modification. Visualizations of tree structure generated from learned PTB language model are included in Appendix . In Table TABREF40 , we show the value of test perplexity for different variants of PRPN, each variant remove part of the model. By removing Parsing Network, we observe a significant drop of performance. This stands as empirical evidence regarding the benefit of having structure information to control attention."
+    ],
+    [
+      "The unsupervised constituency parsing task compares hte tree structure inferred by the model with those annotated by human experts. The experiment is performed on WSJ10 dataset. WSJ10 is the 7422 sentences in the Penn Treebank Wall Street Journal section which contained 10 words or less after the removal of punctuation and null elements. Evaluation was done by seeing whether proposed constituent spans are also in the Treebank parse, measuring unlabeled F1 ( INLINEFORM0 ) of unlabeled constituent precision and recall. Constituents which could not be gotten wrong (those of span one and those spanning entire sentences) were discarded. Given the mechanism discussed in Section SECREF14 , our model generates a binary tree. Although standard constituency parsing tree is not limited to binary tree. Previous unsupervised constituency parsing model also generate binary trees BIBREF11 , BIBREF13 . Our model is compared with the several baseline methods, that are explained in Appendix .",
+      "Different from the previous experiment setting, the model treat each sentence independently during train and test time. When training, we feed one batch of sentences at each iteration. In a batch, shorter sentences are padded with 0. At the beginning of the iteration, the model's initial hidden states are filled with zero. When testing, we feed on sentence one by one to the model, then use the gate value output by the model to recursively combine tokens into constituents, as described in Appendix .",
+      "Table TABREF44 summarizes the results. Our model significantly outperform the RANDOM baseline indicate a high consistency with human annotation. Our model also shows a comparable performance with CCM model. In fact our parsing network and CCM both focus on the relation between successive tokens. As described in Section SECREF14 , our model computes syntactic distance between all successive pair of tokens, then our parsing algorithm recursively assemble tokens into constituents according to the learned distance. CCM also recursively model the probability whether a contiguous subsequences of a sentence is a constituent. Thus, one can understand how our model is outperformed by DMV+CCM and UML-DOP models. The DMV+CCM model has extra information from a dependency parser. The UML-DOP approach captures both contiguous and non-contiguous lexical dependencies BIBREF13 ."
+    ],
+    [
+      "In this paper, we propose a novel neural language model that can simultaneously induce the syntactic structure from unannotated sentences and leverage the inferred structure to learn a better language model. We introduce a new neural parsing network: Parsing-Reading-Predict Network, that can make differentiable parsing decisions. We use a new structured attention mechanism to control skip connections in a recurrent neural network. Hence induced syntactic structure information can be used to improve the model's performance. Via this mechanism, the gradient can be directly back-propagated from the language model loss function into the neural Parsing Network. The proposed model achieve (or is close to) the state-of-the-art on both word/character-level language modeling tasks. Experiment also shows that the inferred syntactic structure highly correlated to human expert annotation."
+    ],
+    [
+      "The authors would like to thank Timothy J. O'Donnell and Chris Dyer for the helpful discussions."
+    ]
+  ]
+}
+```

qasper-1065/instruction.md

@@ -0,0 +1,153 @@
+Name of Paper: Extracting information from free text through unsupervised graph-based clustering: an application to patient incident records
+
+Question: What cluster identification method is used in this paper?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Introduction ::: Data description",
+    "Graph-based framework for text analysis and clustering",
+    "Graph-based framework for text analysis and clustering ::: Text Preprocessing",
+    "Graph-based framework for text analysis and clustering ::: Text Vector Embedding",
+    "Graph-based framework for text analysis and clustering ::: Similarity graph of documents from text similarities",
+    "Graph-based framework for text analysis and clustering ::: Multiscale Graph Partitioning",
+    "Graph-based framework for text analysis and clustering ::: Visualisation and interpretation of the results",
+    "Graph-based framework for text analysis and clustering ::: Quantitative benchmarking of topic clusters",
+    "Graph-based framework for text analysis and clustering ::: Supervised Classification for Degree of Harm",
+    "Application to the clustering of hospital incident text reports",
+    "Application to the clustering of hospital incident text reports ::: Markov Stability extracts content clusters at different levels of granularity",
+    "Application to the clustering of hospital incident text reports ::: Robustness of the results and comparison with other methods",
+    "Using free-text descriptions to predict the degree of harm of patient safety incidents with a supervised classifier",
+    "Using free-text descriptions to predict the degree of harm of patient safety incidents with a supervised classifier ::: Supervised classification of degree of harm",
+    "Discussion"
+  ],
+  "paragraphs": [
+    [
+      "",
+      "The vast amounts of data collected by healthcare providers in conjunction with modern data analytics present a unique opportunity to improve the quality and safety of medical care for patient benefit BIBREF1. Much recent research in this area has been on personalised medicine, with the aim to deliver improved diagnostic and treatment through the synergistic integration of datasets at the level of the individual. A different source of healthcare data pertains to organisational matters. In the United Kingdom, the National Health Service (NHS) has a long history of documenting the different aspects of healthcare provision, and is currently in the process of making available properly anonymised datasets, with the aim of leveraging advanced analytics to improve NHS services.",
+      "One such database is the National Reporting and Learning System (NRLS), a repository of patient safety incident reports from the NHS in England and Wales set up in 2003, which now contains over 13 million records. The incidents are reported under standardised categories and contain both organisational and spatio-temporal information (structured data) and a substantial component of free text (unstructured data) where incidents are described in the `voice' of the person reporting. The incidents are wide ranging: from patient accidents to lost forms or referrals; from delays in admission or discharge to serious untoward incidents, such as retained foreign objects after operations. The review and analysis of such data provides critical insight into complex processes in healthcare with a view towards service improvement.",
+      "Although statistical analyses are routinely performed on the structured data (dates, locations, hand-coded categories, etc), free text is typically read manually and often ignored in practice, unless a detailed review of a case is undertaken because of the severity of harm that resulted. These limitations are due to a lack of methodologies that can provide content-based groupings across the large volume of reports submitted nationally for organisational learning. Automatic categorisation of incidents from free text would sidestep human error and difficulties in assigning incidents to a priori pre-defined lists in the reporting system. Such tools can also offer unbiased insight into the root cause analysis of incidents that could improve the safety and quality of care and efficiency of healthcare services.",
+      "In this work, we showcase an algorithmic methodology that detects content-based groups of records in an unsupervised manner, based only on the free (unstructured) textual descriptions of the incidents. To do so, we combine deep neural-network high-dimensional text-embedding algorithms with graph-theoretical methods for multiscale clustering. Specifically, we apply the framework of Markov Stability (MS), a multiscale community detection algorithm, to sparsified graphs of documents obtained from text vector similarities. Our method departs both from traditional natural language processing tools, which have generally used bag-of-words (BoW) representation of documents and statistical methods based on Latent Dirichlet Allocation (LDA) to cluster documents BIBREF2, and from more recent approaches that have used deep neural network based language models, but have used k-means clustering without a graph-based analysis BIBREF3. Previous applications of network theory to text analysis have included the work of Lanchichinetti and co-workers BIBREF4, who proposed a probabilistic graph construction analysed with the InfoMap algorithm BIBREF5; however, their community detection was carried out at a single-scale and the BoW representation of text lacks the power of text embeddings. The application of multiscale community detection allows us to find groups of records with consistent content at different levels of resolution; hence the content categories emerge from the textual data, rather than from pre-designed classifications. The obtained results can help mitigate human error or effort in finding the right category in complex classification trees. We illustrate in our analysis the insight gained from this unsupervised, multi-resolution approach in this specialised corpus of medical records.",
+      "As an additional application, we use machine learning methods for the prediction of the degree of harm of incidents directly from the text in the NRLS incident reports. Although the degree of harm is recorded by the reporting person for every event, this information can be unreliable as reporters have been known to game the system, or to give different answers depending on their professional status BIBREF6. Previous work on predicting the severity of adverse events BIBREF7, BIBREF8 used reports submitted to the Advanced Incident Management System by Australian public hospitals, and used BoW and Support Vector Machines (SVMs) to detect extreme-risk events. Here we demonstrate that publicly reported measures derived from NHS Staff Surveys can help select ground truth labels that allow supervised training of machine learning classifiers to predict the degree of harm directly from text embeddings. Further, we show that the unsupervised clusters of content derived with our method improve the classification results significantly.",
+      "An a posteriori manual labelling by three clinicians agree with our predictions based purely on text almost as much as with the original hand-coded labels. These results indicate that incidents can be automatically classified according to their degree of harm based only on their textual descriptions, and underlines the potential of automatic document analysis to help reduce human workload."
+    ],
+    [
+      "The full dataset includes more than 13 million confidential reports of patient safety incidents reported to the National Reporting and Learning System (NRLS) between 2004 and 2016 from NHS trusts and hospitals in England and Wales. Each record has more than 170 features, including organisational details (e.g., time, trust code and location), anonymised patient information, medication and medical devices, among many other details. In most records, there is also a detailed description of the incident in free text, although the quality of the text is highly variable.",
+      "The records are manually classified by operators according to a two-level system of incident types. The top level contains 15 categories including general classes such as `Patient accident', `Medication', `Clinical assessment', `Documentation', `Admissions/Transfer' or `Infrastructure', alongside more specific groups such as `Aggressive behaviour', `Patient abuse', `Self-harm' or `Infection control'.",
+      "Each record is also labelled based on the degree of harm to the patients as one of: `No Harm', `Low Harm', `Moderate Harm', `Severe Harm' or `Death'. These degrees are precisely defined by the WHO BIBREF9 and the NHS BIBREF10."
+    ],
+    [
+      "Our framework combines text-embedding, geometric graph construction and multi-resolution community detection to identify, rather than impose, content-based clusters from free, unstructured text in an unsupervised manner.",
+      "Figure FIGREF2 shows a summary of our pipeline. First, we pre-process each document to transform text into consecutive word tokens, with words in their most normalised forms and some words removed if they have no distinctive meaning when used out of context BIBREF11, BIBREF12. We then train a paragraph vector model using the Document to Vector (Doc2Vec) framework BIBREF13 on the full set (13 million) of pre-processed text records. (Training a vector model on smaller sets of 1 million records also produces good results as seen in Table TABREF5). This training step of the text model is only done once.",
+      "The trained Doc2Vec model is subsequently used to infer high-dimensional vector descriptions for the text of each document in our target analysis set. We then compute a matrix containing all the pairwise (cosine) similarities between the Doc2Vec document vectors. This similarity matrix can be thought of as the adjacency matrix of a full, weighted graph with documents as nodes and edges weighted by their similarity. We sparsify this graph to the union of a minimum spanning tree and a k-Nearest Neighbors (MST-kNN) graph BIBREF14, a geometric construction that removes less important similarities but preserves global connectivity for the graph and, hence, for the dataset. The MST-kNN graph is then analysed with Markov Stability BIBREF15, BIBREF16, BIBREF17, BIBREF18, a multi-resolution graph partitioning method that identifies relevant subgraphs (i.e., clusters of documents) at different levels of granularity. MS uses a diffusive process on the graph to reveal the multiscale organisation at different resolutions without the need to choose a priori the number or type of clusters.",
+      "The partitions found by MS across levels of resolution are analysed a posteriori through visualisations and quantitative scores. The visualisations include: (i) word clouds to summarise the main content; (ii) graph layouts; and (iii) Sankey diagrams and contingency tables that capture correspondences between partitions. The quantitative scores include: (i) the intrinsic topic coherence (measured by the pairwise mutual information BIBREF19, BIBREF20); and (ii) the similarity to hand-coded categories (measured by the normalised mutual information BIBREF21).",
+      "Our framework also covers prediction of the degree of harm (DoH) caused to the patient usig text embeddings and the unsupervised cluster assignments obtaind from our multiscale graph partitioning. To perform this task, we use the hand-coded DoH from the NRLS to train three commonly used classifiers BIBREF22, BIBREF23 (Ridge, Support Vector Machine with a linear kernel, Random Forest) to predict the DoH using TF-iDF and Doc2Vec embeddings of the text and our MS cluster assignments. The classifiers are then evaluated in predicting the DoH using cross-validation.",
+      "We now explain the steps of the methodological pipeline in more detail."
+    ],
+    [
+      "Text preprocessing is important to enhance the performance of text embedding techniques. We applied standard preprocessing to the raw text of all 13 million records in our corpus, as follows. We divide our documents into iterative word tokens using the NLTK library BIBREF11 and remove punctuation and digit-only tokens. We then apply word stemming using the Porter algorithm BIBREF12, BIBREF24. If the Porter method cannot find a stemmed version for a token, we apply the Snowball algorithm BIBREF25. Finally, we remove any stop-words (repeat words with low content) using NLTK's stop-word list. Although pre-processing reduces some of the syntactic information, it consolidates the semantic information of the vocabulary. We note that the incident descriptions contain typos and acronyms, which have been left uncorrected to avoid manual intervention or the use of spell checkers, so as to mimic as closely as possible a realistic scenario."
+    ],
+    [
+      "Computational text analysis relies on a mathematical representation of the base units of text (character $n$-grams, words or documents). Since our methodology is unsupervised, we avoid the use of labelled data, in contrast to supervised or semi-supervised classification methods BIBREF26, BIBREF27. In our work, we use a representation of text documents as vectors following recent developments in the field.",
+      "Traditionally, bag-of-words (BoW) methods represented documents as vectors of word frequencies weighted by inverse document frequency (TF-iDF). Such methods provide a statistical description of documents but they do not carry information about the order or proximity of words to each other and hence disregard semantic or syntactic relationships between words. In addition, BoW representations carry little information content as they tend to be high-dimensional and very sparse, due to the large size of word dictionaries and low frequencies of many terms.",
+      "Recently, deep neural network language models have successfully overcome the limitations of BoW methods by incorporating neighbourhoods in the mathematical description of each term. Distributed Bag of Words (DBOW), better known as Doc2Vec BIBREF13, is a form of Paragraph Vectors (PV) which creates a model that represents any word sequence (i.e. sentences, paragraphs, documents) as $d$-dimensional vectors, where $d$ is user-defined (typically $d=300$). Training a Doc2Vec model starts with a random $d$-dimensional vector assignment for each document in the corpus. A stochastic gradient descent algorithm iterates over the corpus with the objective of predicting a randomly sampled set of words from each document by using only the document's $d$-dimensional vector BIBREF13. The objective function being optimised by PV-DBOW is similar to the skip-gram model in Refs. BIBREF28, BIBREF29. Doc2Vec has been shown BIBREF30 to capture both semantic and syntactic characterisations of the input text, and outperforms BoW-based models such as LDA BIBREF2.",
+      "Benchmarking the Doc2Vec training: Here, we use the Gensim Python library BIBREF31 to train the PV-DBOW model. The Doc2Vec training was repeated several times with a variety of training hyper-parameters (chosen based on our own numerical experiments and the general guidelines provided by BIBREF32) in order to optimise the output. To characterise the usability and quality of models, we trained Doc2Vec models using text corpora of different sizes and content with different sets of hyper-parameters. . In particular, we checked the effect of corpus size by training Doc2Vec models on the full 13 million NRLS records and on randomly sampled subsets of 1 million and 2 million records.",
+      "Since our target analysis has heavy medical content and specific use of words, we also tested the importance of the training corpus by generating an additional Doc2Vec model using a set of 5 million articles from the English Wikipedia representing standard, generic English usage, which works well in the analysis of news articles BIBREF33.",
+      "The results in Table TABREF5 show that training on the highly specific text from the NRLS records is an important ingredient in the successful vectorisation of the documents, as shown by the degraded performance for the Wikipedia model across a variety of training hyper-parameters. On the other hand, reducing the size of the corpus from 13 million to 1 million records did not affect the benchmarking dramatically. This robustness of the results to the size of the training corpus was confirmed further with the use of more detailed metrics, as discussed below in Section SECREF27 (see e.g., Figure FIGREF29).",
+      "Based on our benchmarking, henceforth we use the Doc2Vec model trained on the 13+ million NRLS records with the following hyper-parameters: {training method = dbow, number of dimensions for feature vectors size = 300, number of epochs = 10, window size = 15, minimum count = 5, number of negative samples = 5, random down-sampling threshold for frequent words = 0.001 }. As an indication of computational cost, the training of this model takes approximately 11 hours (run in parallel with 7 threads) on shared servers."
+    ],
+    [
+      "Once the Doc2Vec model is trained, we use it to infer a vector for each record in our analysis subset and construct $\\hat{S}$, a similarity matrix between the vectors by: computing the matrix of cosine similarities between all pairs of records, $S_\\text{cos}$; transforming it into a distance matrix $D_{cos} = 1-S_{cos}$; applying element-wise max norm to obtain $\\hat{D}=\\Vert D_{cos}\\Vert _{max}$; and normalising the similarity matrix $\\hat{S} = 1-\\hat{D}$ which has elements in the interval $[0,1]$.",
+      "This similarity matrix can be thought of as the adjacency matrix of a fully connected weighted graph. However, such a graph contains many edges with small weights reflecting the fact that in high-dimensional noisy data even the least similar nodes present a substantial degree of similarity. Indeed, such weak similarities are in most cases redundant and can be explained through stronger pairwise similarities. These weak, redundant edges obscure the graph structure, as shown by the diffuse visualisation in Figure FIGREF7A.",
+      "To reveal the graph structure, we sparsify the similarity matrix to obtain a MST-kNN graph BIBREF14 based on a geometric heuristic that preserves the global connectivity of the graph while retaining details about the local geometry of the dataset. The MST-kNN algorithm starts by computing the minimum spanning tree (MST) of the full matrix $\\hat{D}$, i.e., the tree with $(N-1)$ edges connecting all nodes in the graph with minimal sum of edge weights (distances). The MST is computed using the Kruskal algorithm implemented in SciPy BIBREF34. To this MST, we add edges connecting each node to its $k$ nearest nodes (kNN) if they are not already in the MST. Here $k$ is an user-defined parameter that regulates the sparsity of the resulting graph. The binary adjacency matrix of the MST-kNN graph is Hadamard-multiplied with $\\hat{S}$ to give the adjacency matrix $A$ of the weighted, undirected sparsified graph.",
+      "The network visualisations in Figure FIGREF7 give an intuitive picture of the effect of sparsification as $k$ is decreased. If $k$ is very small, the graph is very sparse but not robust to noise. As $k$ is increased, the local similarities between documents induce the formation of dense subgraphs (which appear closer in the graph visualisation layout). When the number of neighbours becomes too large, the local structure becomes diffuse and the subgraphs lose coherence, signalling the degradation of the local graph structure. Relatively sparse graphs that preserve important edges and global connectivity of the dataset (guaranteed here by the MST) have computational advantages when using community detection algorithms.",
+      "Although we use here the MST-kNN construction due to its simplicity and robustness, network inference, graph sparsification and graph construction from data is an active area of research, and several alternatives exist based on different heuristics, e.g., Graphical Lasso BIBREF35, Planar Maximally Filtered Graph BIBREF36, spectral sparsification BIBREF37, or the Relaxed Minimum Spanning Tree (RMST) BIBREF38. We have experimented with some of those methods and obtained comparable results. A detailed comparison of sparsification methods as well as the choice of distance in defining the similarity matrix $\\hat{S}$ is left for future work."
+    ],
+    [
+      "Community detection encompasses various graph partitioning approaches which aim to find `good' partitions into subgraphs (or communities) according to different cost functions, without imposing the number of communities a priori BIBREF39. The notion of community depends on the choice of cost function. Commonly, communities are taken to be subgraphs whose nodes are connected strongly within the community with relatively weak inter-community edges. Such structural notion is related to balanced cuts. Other cost functions are posed in terms of transitions inside and outside of the communities, usually as one-step processes BIBREF5. When transition paths of all lengths are considered, the concept of community becomes intrinsically multi-scale, i.e., different partitions are relevant at different time scales leading to a multi-level description dictated by the transition dynamics BIBREF15, BIBREF40, BIBREF16. This leads to the framework of Markov Stability (MS), a dynamics-based, multi-scale community detection methodology, which recovers several well-known heuristics as particular cases BIBREF15, BIBREF17, BIBREF18.",
+      "MS is an unsupervised community detection method that finds robust and stable partitions of a graph (and the associated communities) under the evolution of a continuous-time diffusion process without a priori choice of the number or type of communities or their relative relationships BIBREF15, BIBREF40, BIBREF16, BIBREF41 . In simple terms, MS can be understood by analogy to a drop of ink diffusing on the graph: the ink diffuses homogeneously unless the graph has intrinsic sub-structures, in which case the ink gets transiently contained, over particular time scales, within groups of nodes. The existence of such transients indicates a natural scale to partition the graph along the subgraphs (or communities) where the diffusion is transiently trapped. As the process continues to evolve, the ink diffuses out of those communities but might get transiently contained in other, larger subgraphs, if such multi-level structure exists. By analysing the Markov dynamics over time, MS detects the structure of the graph across scales. If a graph has no natural scales for partitioning, then MS returns no communities. The Markov time $t$ thus acts as a resolution parameter that allows us to extract robust partitions that persist over particular time scales, in an unsupervised manner.",
+      "Mathematically, given the adjacency matrix $A_{N \\times N}$ of the graph obtained as described previously, let us define the diagonal matrix $D=\\text{diag}(\\mathbf {d})$, where $\\mathbf {d}=A \\mathbf {1}$ is the degree vector. The random walk Laplacian matrix is defined as $L_\\text{RW}=I_N-D^{-1}A$, where $I_N$ is the identity matrix of size $N$ and the transition matrix (or kernel) of the associated continuous-time Markov process is $P(t)=e^{-t L_\\text{RW}}, \\, t>0$ BIBREF16. Any partition $\\mathcal {H}$ into $C$ clusters is associated with a binary membership matrix $H_{N \\times C}$ that maps the $N$ nodes into the clusters. Below, we will use the matrix $H$ to denote the corresponding partition $\\mathcal {H}$. We can then compute the $C\\times C$ clustered autocovariance matrix:",
+      "where $\\pi $ is the steady-state distribution of the process and $\\Pi =\\text{diag}(\\pi )$. The element $[R(t,H)]_{\\alpha \\beta }$ quantifies the probability that a random walker starting from community $\\alpha $ at $t=0$ will be in community $\\beta $ at time $t$, minus the probability that this event occurs by chance at stationarity.",
+      "The above definitions allow us to introduce our cost function measuring the goodness of a partition over time $t$, termed the Markov Stability of partition $H$:",
+      "A partition $H$ that maximises $r(t,H)$ is comprised of communities that preserve the flow within themselves over time $t$, since in that case the diagonal elements of $R(t,H)$ will be large and the off-diagonal elements will be small. For details, see BIBREF15, BIBREF40, BIBREF16, BIBREF42.",
+      "Our computational algorithm thus searches for partitions at each Markov time $t$ that maximise $r(t,H)$. Although the maximisation of (DISPLAY_FORM11) is an NP-hard problem (hence with no guarantees for global optimality), there are efficient optimisation methods that work well in practice. Our implementation here uses the Louvain Algorithm BIBREF43, BIBREF18 which is efficient and known to give good results when applied to benchmarks. To obtain robust partitions, we run the Louvain algorithm 500 times with different initialisations at each Markov time and pick the best 50 with the highest Markov Stability value $r(t,H)$. We then compute the variation of information BIBREF44 of this ensemble of solutions $VI(t)$, as a measure of the reproducibility of the result under the optimisation. In addition, we search for partitions that are persistent across time $t$, as given by low values of the variation of information between optimised partitions across time $VI(t,t^{\\prime })$. Robust partitions are therefore indicated by Markov times where $VI(t)$ shows a dip and $VI(t,t^{\\prime })$ has an extended plateau with low values, indicating consistency under the optimisation and validity over extended scales BIBREF42, BIBREF16. Below, we apply MS to find partitions across scales of the similarity graph of documents, $A$. The communities detected correspond to groups of documents with similar content at different levels of granularity."
+    ],
+    [
+      "Graph layouts: We use the ForceAtlas2 BIBREF45 layout algorithm to represent graphs on the plane. This layout assigns a harmonic spring to each edge and finds through iterative rearrangements finds an arrangement on the plane that balances attractive and repulsive forces between nodes. Hence similar nodes tend to appear close together on this layout. We colour the nodes by either hand-coded categories (Figure FIGREF7) or multiscale MS communities (Figure FIGREF21). Spatially coherent colourings on this layout imply good clusters in terms of the similarity graph.",
+      "Tracking membership through Sankey diagrams: Sankey diagrams allow us to visualise the relationship of node membership across different partitions and with respect to the hand-coded categories. Two-layer Sankey diagrams (e.g., Fig. FIGREF22) reflect the correspondence between MS clusters and the hand-coded external categories, whereas we use a multilayer Sankey diagram in Fig. FIGREF21 to present the multi-resolution MS community detection across scales.",
+      "Normalised contingency tables: To capture the relationship between our MS clusters and the hand-coded categories, we also provide a complementary visualisation as z-score heatmaps of normalised contingency tables, e.g., Fig. FIGREF22. This allows us to compare the relative association of content clusters to the external categories at different resolution levels. A quantification of the overall correspondence is also provided by the $NMI$ score in Eq. (DISPLAY_FORM17).",
+      "Word clouds of increased intelligibility through lemmatisation: Our method clusters text documents according to their intrinsic content. This can be understood as a type of topic detection. To visualise the content of clusters, we use Word Clouds as basic, yet intuitive, summaries of information to extract insights and compare a posteriori with hand-coded categories. They can also provide an aid for monitoring results when used by practitioners.",
+      "The stemming methods described in Section SECREF3 truncate words severely to enhance the power of the language processing computational methods by reducing the redundancy in the word corpus. Yet when presenting the results back to a human observer, it is desirable to report the cluster content with words that are readily comprehensible. To generate comprehensible word clouds in our a posteriori analyses, we use a text processing method similar to the one described in BIBREF46. Specifically, we use the part of speech (POS) tagging module from NLTK to leave out sentence parts except the adjectives, nouns, and verbs. We also remove less meaningful common verbs such as `be', `have', and `do' and their variations. The remaining words are then lemmatised in order to normalise variations of the same word. Finally, we use the Python library wordcloud to create word clouds with 2 or 3-gram frequency list of common word groups."
+    ],
+    [
+      "Although our dataset has a classification hand-coded by a human operator, we do not use it in our analysis. Indeed, one of our aims is to explore the relevance of the fixed external classes as compared to content-driven groupings obtained in an unsupervised manner. Therefore we provide a double route to quantify the quality of the clusters by computing two complementary measures: (i) an intrinsic measure of topic coherence, and (ii) a measure of similarity to the external hand-coded categories.",
+      "Topic coherence of text: As an intrinsic measure of consistency of word association, we use the pointwise mutual information ($PMI$) BIBREF19, BIBREF47. The $PMI$ is an information-theoretical score that captures the probability of words being used together in the same group of documents. The $PMI$ score for a pair of words $(w_1,w_2)$ is:",
+      "where the probabilities of the words $P(w_1)$, $P(w_2)$, and of their co-occurrence $P(w_1 w_2)$ are obtained from the corpus. We obtain an aggregate $\\widehat{PMI}$ for the graph partition $C=\\lbrace c_i\\rbrace $ by computing the $PMI$ for each cluster, as the median $PMI$ between its 10 most common words (changing the number of words gives similar results), and computing the weighted average of the $PMI$ cluster scores:",
+      "where $c_i$ denotes the clusters in partition $C$, each with size $n_i$, so that $N=\\sum _{c_i \\in C} n_i$ is the total number of nodes. Here $S_i$ denotes the set of top 10 words for cluster $c_i$.",
+      "The $PMI$ score has been shown to perform well BIBREF19, BIBREF47 when compared to human interpretation of topics on different corpora BIBREF48, BIBREF49, and is designed to evaluate topical coherence for groups of documents, in contrast to other tools aimed at short forms of text. See BIBREF26, BIBREF27, BIBREF50, BIBREF51 for other examples.",
+      "Here, we use the $\\widehat{PMI}$ score to evaluate partitions without any reference to an externally labelled `ground truth'.",
+      "Similarity between the obtained partitions and the hand-coded categories: To quantify how our content-driven unsupervised clusters compare against the external classification, we use the normalised mutual information ($NMI$), a well-known information-theoretical score that quantifies the similarity between clusterings considering correct and incorrect assignments in terms of the information between the clusterings. The NMI between two partitions $C$ and $D$ of the same graph is:",
+      "where $I(C,D)$ is the Mutual Information and $H(C)$ and $H(D)$ are the entropies of the two partitions.",
+      "The $NMI$ is bounded ($0 \\le NMI \\le 1$) and a higher value corresponds to higher similarity of the partitions (i.e., $NMI=1$ when there is perfect agreement between partitions $C$ and $D$). The $NMI$ score is directly related to the V-measure in the computer science literature BIBREF52."
+    ],
+    [
+      "As a further application of our work, we have carried out a supervised classification task aimed at predicting the degree of harm of an incident directly from the text and the hand-coded features (e.g., external category, medical specialty, location). A one-hot encoding is applied to turn these categorical values into numerical ones. We also checked if using our unsupervised content-driven cluster labels as additional features can improve the performance of the supervised classification.",
+      "The supervised classification was carried out by training on features and text three classifiers commonly applied to text classification tasks BIBREF22, BIBREF23: a Ridge classifier, Support Vector Machines with a linear kernel, and Random Forests. The goal is to predict the degree of harm (DoH) among five possible values (1-5). The classification is carried out with five-fold cross validation, using 80% of the data to train the model and the remaining 20% to test it. As a measure of performance of the classifiers and models, we use the weighted average of the F1 score for all levels of DoH, which takes into account both precision and recall, i.e., both the exactness and completeness of the model."
+    ],
+    [
+      "We showcase our methodology through the analysis of the text from NRLS patient incident reports. In addition to textual descriptions, the reports are hand-coded upon reporting with up to 170 features per case, including a two-level manual classification of the incidents.",
+      "Here, we only use the text component and apply our graph-based text clustering to a set of 3229 reports from St Mary's Hospital, London (Imperial College Healthcare NHS Trust) over three months in 2014. As summarised in Figure FIGREF2, we start by training our Doc2Vec text embedding using the full 13+ million records collected by the NRLS since 2004 (although, as discussed above, a much smaller corpus of NRLS documents can be used). We then infer vectors for our 3229 records, compute the cosine similarity matrix and construct an MST-kNN graph with $k=13$ for our graph-based clustering. (We have confirmed the robustness of the MST-kNN construction in our data for $k>13$ by scanning values of $k \\in [1,50]$, see Section SECREF27). We then applied Markov Stability, a multi-resolution graph partitioning algorithm to the MST-kNN graph. We scan across Markov time ($t \\in [0.01, 100]$ in steps of 0.01). At each $t$, we run 500 independent Louvain optimisations to select the optimal partition found, as well as quantifying the robustness to optimisation by computing the average variation of information $VI(t)$ between the top 50 partitions. Once the full scan across $t$ is finalised, we compute $VI(t,t^{\\prime })$, the variation of information between the optimised partitions found across the scan in Markov time, to select partitions that are robust across scales."
+    ],
+    [
+      "Figure FIGREF21 presents a summary of our MS analysis. We plot the number of clusters of the optimal partition and the two metrics of variation of information across all Markov times. The existence of a long plateau in $VI(t,t^{\\prime })$ coupled to a dip in $VI(t)$ implies the presence of a partition that is robust both to the optimisation and across Markov time. To illustrate the multi-scale features of the method, we choose several of these robust partitions, from finer (44 communities) to coarser (3 communities), obtained at five Markov times and examine their structure and content. The multi-level Sankey diagram summarises the relationship of the partitions across levels.",
+      "The MS analysis of the graph reveals a multi-level structure of partitions, with a strong quasi-hierarchical organisation. We remark that our optimisation does not impose any hierarchical structure a priori, so that the observed consistency of communities across levels is intrinsic to the data and suggests the existence of sub-themes that integrate into larger thematic categories. The unsupervised detection of intrinsic scales by MS enables us to obtain groups of records with high content similarity at different levels of granularity. This capability can be used by practitioners to tune the level of description to their specific needs, and is used below as an aid in our supervised classification task in Section SECREF4.",
+      "To ascertain the relevance of the layers of content found by MS, we examined the five levels of resolution in Figure FIGREF21. For each level, we produced lemmatised word clouds, which we used to generate descriptive content labels for the communities. We then compared a posteriori the content clusters with the hand-coded categories through a Sankey diagram and a contingency table. The results are shown in Figures FIGREF22\u2013FIGREF25 for each of the levels.",
+      "The partition into 44 communities presents content clusters with well-defined characterisations, as shown by the Sankey diagram and the highly clustered structure of the contingency table (Figure FIGREF22). Compared to the 15 hand-coded categories, this 44-community partition provides finer groupings corresponding to specific sub-themes within the generic hand-coded categories. This is apparent in the hand-coded classes `Accidents', `Medication', `Clinical assessment', `Documentation' and `Infrastructure', where a variety of meaningful subtopics are identified (see Fig. FIGREF23 for details). In other cases, however, the content clusters cut across the external categories, e.g., the clusters on labour ward, chemotherapy, radiotherapy and infection control are coherent in content but can belong to several of the external classes. At this level of resolution, our algorithm also identified highly specific topics as separate content clusters, including blood transfusions, pressure ulcer, consent, mental health, and child protection, which have no direct relationship with the external classes provided to the operator.",
+      "Figure FIGREF24A and FIGREF24B present the results for two partitions at medium level of resolution, where the number of communities (12 and 17) is close to that of hand-coded categories (15). As expected from the quasi-hierarchy detected by our multi-resolution analysis, we find that the communities in the 17-way and 12-way partitions emerge from consistent aggregation of the smaller communities in the 44-way partition in Figure FIGREF22. Focussing on the 12-way partition, we see that some of the sub-themes in Figure FIGREF23 are merged into more general topics. An example is Accidents (community 2 in Fig. FIGREF24A), a merger of seven finer communities, which corresponds well with the external category `Patient accidents'. A similar phenomenon is seen for the Nursing cluster (community 1), which falls completely under the external category `Infrastructure'. The clusters related to `Medication' similarly aggregate into a larger community (community 3), yet there still remains a smaller, specific community related to Homecare medication (community 12) with distinct content. Other communities, on the other hand, still strand across external categories. This is clearly observable in communities 10 and 11 (Samples/ lab tests/forms and Referrals/appointments), which fall naturally across the `Documentation' and `Clinical Assessment'. Similarly, community 9 (Patient transfers) sits across the `Admission/Transfer' and `Infrastructure' external categories, due to its relation to nursing and hospital constraints. A substantial proportion of records was hand-coded under the generic `Treatment/Procedure' class, yet MS splits into into content clusters that retain medical coherence, e.g., Radiotherapy (Comm. 4), Blood transfusions (Comm. 7), IV/cannula (Comm. 5), Pressure ulcer (Comm. 8), and the large community Labour ward (Comm. 6).",
+      "The medical specificity of the Radiotherapy, Pressure ulcer and Labour ward clusters means that they are still preserved as separate groups to the next level of coarseness in the 7-way partition (Figure FIGREF25A). The mergers in this case lead to a larger communities referring to Medication, Referrals/Forms and Staffing/Patient transfers. Figure FIGREF25B shows the final level of agglomeration into 3 content clusters: records referring to Accidents; a group broadly referring to matters Procedural (referrals, forms, staffing, medical procedures) cutting across external categories; and the Labour ward cluster, still on its own as a subgroup with distinctive content.",
+      "This process of agglomeration of content, from sub-themes into larger themes, as a result of the multi-scale hierarchy of MS graph partitions is shown explicitly with word clouds in Figure FIGREF26 for the 17-, 12- and 7-way partitions. Our results show good overall correspondence with the hand-coded categories across resolutions, yet our results also reveal complementary categories of incidents not defined in the external classification. The possibility of tuning the granularity afforded by our method can be used to provide a distinct level of resolution in certain areas corresponding to specialised or particular sub-themes."
+    ],
+    [
+      "We have examined quantitatively the robustness of the results to parametric and methodological choices in different steps of our framework. Specifically, we evaluate the effect of: (i) using Doc2Vec embeddings instead of BoW vectors; (ii) the size of corpus for training Doc2Vec; (iii) the sparsity of the MST-kNN graph construction. We have also carried out quantitative comparisons to other methods for topic detection and clustering: (i) LDA-BoW, and (ii) several standard clustering methods.",
+      "Doc2Vec provides improved clusters compared to BoW: As compared to standard bag of words (BoW), fixed-sized vector embeddings (Doc2Vec) produces lower dimensional vector representations with higher semantic and syntactic content. Doc2Vec outperforms BoW representations in practical benchmarks of semantic similarity and is less sensitive to hyper-parameters BIBREF30. To quantify the improvement provided by Doc2Vec, we constructed a MST-kNN graph from TF-iDF vectors and ran MS on this TF-iDF similarity graph. Figure FIGREF28 shows that Doc2Vec outperforms BoW across all resolutions in terms of both $NMI$ and $\\widehat{PMI}$ scores.",
+      "Robustness to the size of the Doc2Vec training dataset : Table TABREF5 indicates a small effect of the size of the training corpus on the Doc2Vec model. To confirm this, we trained two additional Doc2Vec models on sets of 1 million and 2 million records (randomly chosen from the full 13+ million records) and followed the same procedure to construct the MST-kNN graph and carry out the MS analysis. Figure FIGREF29 shows that the performance is affected only mildly by the size of the Doc2Vec training set.",
+      "Robustness to the level of graph sparsification:",
+      "We sparsify the matrix of cosine similarities using the MST-kNN graph construction. The smaller the value of $k$, the sparser the graph. Sparser graphs have computational advantages for community detection algorithms, but too much sparsification degrades the results. Figure FIGREF30 shows the effect of sparsification in the graph construction on the performance of MS clusters. Our results are robust to the choice of $k$, provided it is not too small: both the $NMI$ and $\\widehat{PMI}$ scores reach a similar level for values of $k$ above 13-16. Due to computational efficiency, we favour a relatively small value of $k=13$.",
+      "Comparison of MS partitions to Latent Dirichlet Allocation with Bag-of-Words (LDA-BoW): We have compared the MS results to LDA, a widely used methodology for text analysis. A key difference in LDA is that a different model needs to be trained when the number of topics changes, whereas our MS method produces clusterings at all levels of resolution in one go. To compare the outcomes, we trained five LDA models corresponding to the five MS levels in Figure FIGREF21. Table TABREF31 shows that MS and LDA give partitions that are comparably similar to the hand-coded categories (as measured with $NMI$), with some differences depending on the scale, whereas the MS clusters have higher topic coherence (as given by $\\widehat{PMI}$) across all scales.",
+      "To give an indication of computational cost, we ran both methods on the same servers. Our method takes approximately 13 hours in total (11 hours to train the Doc2Vec model on 13 million records and 2 hours to produce the full MS scan with 400 partitions across all resolutions). The time required to train just the 5 LDA models on the same corpus amounts to 30 hours (with timings ranging from $\\sim $2 hours for the 3 topic LDA model to 12.5 hours for the 44 topic LDA model). This comparison also highlights the conceptual difference between our multi-scale methodology and LDA topic modelling. While LDA computes topics at a pre-determined level of resolution, our method obtains partitions at all resolutions in one sweep of the Markov time, from which relevant partitions are chosen based on their robustness. The MS partitions at all resolutions are available for further investigation if so needed.",
+      "Comparison of MS to other partitioning and community detection algorithms: We have partitioned the same kNN-MST graph using several well-known algorithms readily available in code libraries (i.e., the iGraph module for Python): Modularity Optimisation BIBREF53, InfoMap BIBREF5, Walktrap BIBREF54, Label Propagation BIBREF55, and Multi-resolution Louvain BIBREF43. Note that, in contrast with our multiscale MS analysis, these methods give just one partition at a particular resolution (or two for the Louvain implementation in iGraph). Figure FIGREF32 shows that MS provides improved or equal results to all those other graph partitioning methods for both $NMI$ and $\\widehat{PMI}$ across all scales. Only for very fine resolution (more than 50 clusters) does Infomap, which partitions graphs into small clique-like subgraphs BIBREF40, BIBREF56, provide a slightly improved $NMI$. Therefore, MS finds both relevant and high quality clusterings across all scales by sweeping the Markov time parameter."
+    ],
+    [
+      "Here we approach the task of training a supervised classifier that predicts the degree of harm of an incident based on other features of the record (such as location, external category, and medical specialty) and on the textual component of the report. To this end, we use the embedded text vectors and MS cluster labels of the records as features to predict the degree of harm to the patient.",
+      "Each NRLS record has more than 170 features filled manually by healthcare staff, including the degree of harm (DoH) to the patient, a crucial assessment of the reported incident. The incident is classified into five levels: 'No harm', 'Low', 'Moderate', 'Severe', and 'Death'. However, the reported DoH is not consistent across hospitals and can be unreliable BIBREF6.",
+      "The lack of reliability of the recorded DoH poses a challenge when training supervised models. Given the size of the dataset, it is not realistic to ask medics to re-evaluate incidents manually. Instead, we use the publicly available `Learning from mistakes league table' based on NHS staff survey data to identify organisations (NHS Trusts) with `outstanding' (O) and `poor reporting culture' (PRC). Our hypothesis is that training our classifiers on records from organisations with better rankings in the league table should lead to improved prediction. If there is a real disparity in the manual classification among organisations, only incidents labelled by O-ranked Trusts should be regarded as a `ground truth'."
+    ],
+    [
+      "We study NRLS incidents reported between 2015 and 2017 from O-ranked and PRC-ranked Trusts. The 2015-17 NRLS dataset is very unbalanced: there are 2,038,889 \u201cNo harm\u201d incidents against only 6,754 \u201cDeath\u201d incidents. To tackle this issue, we sample our dataset as recommended by BIBREF8, and randomly select 1,016 records each of `No harm' , `Low', and `Moderate', and 508 records each of `Severe' and `Death' incidents, from each type of Trust. We thus obtain two datasets (O and PRC) consisting of a total of 4,064 incidents each.",
+      "For each dataset (O and PRC), we train three classifiers (Ridge, Support Vector Machine with a linear kernel, and Random Forest) with five-fold cross validation, and we compute the F-1 scores of each fold to evaluate the model performance. We first train models using three categories from the reports: location (L), external hand-coded category (C), and medical specialty (S). We also compute the performance of models trained on text features, both TF-iDF and Doc2Vec. We also study models trained on a mixture of text and categories. Finally, we run Markov Stability as described above to obtain cluster labels for each dataset (O and PRC) at different resolutions (70, 45, 30 and 13 communities). We then evaluate if it is advantageous to include the labels of the MS clusters as additional features.",
+      "Table TABREF34 presents the results of our numerical experiments. Our first observation is that, for this data, SVM with linear kernel has the best performance (similar to Ridge), and Random Forests perform poorly in general. There are several conclusions from our study. First, there is a consistent difference between the scores of the O and PRC datasets (ranging from 1.7% to 11.2% for an average of 5.6%), thus confirming our hypothesis that automated classification performs better when training with data from organizations with better rankings in the league table. Second, using text features is highly advantageous in predicting the degree of harm compared to category alone: there is a substantial increase of up to 100% in the F1 score between column 1 (all three categories) and column 2 (Tf-iDF). Furthermore, adding categorical features (L, C, or S) to the TF-iDF text features improves the scores only marginally (around 2%), as seen by comparing columns 3\u20136 with column 2.",
+      "Given the demonstrated importance of text, we studied the effect of using more refined textual features for classification. In columns 7-10, we considered the effect of adding to TF-iDF the MS labels extracted from our text analysis (as described above), and we find a larger improvement of around 7% with respect to mere TF-iDF (column 2). The improvement is larger for finer clusterings into 70 and 45 communities, which contain enough detail that can be associated with levels of risk (e.g., type of accident). This supports the value of the multi-resolution groupings we have extracted through our analysis.",
+      "We also studied the impact of using Doc2Vec vectors as features. Interestingly, the comparison between columns 2 and 11 shows that there is only a slight improvement of 2% when using Doc2Vec instead of TF-iDF features for the case of records from O-ranked institutions, but the improvement is of 12% for the records from PRC Trusts. This differences suggests that the usage of terms is more precise in O-ranked hospitals so that the differences between TF-iDF are minimised, while the advantages of the syntactic and semantic reconstruction of the Doc2Vec embedding becomes more important in the case of PRC Trusts.",
+      "Based on these findings, we build our final model that uses a Support Vector Machine classifier with both Doc2Vec embeddings and the MS labels for 30 content clusters (encoded via a One-Hot encoder) as features. We choose to keep only 30 communities as this performs well when combined with the Doc2Vec embedding (without slowing too much the classifier). We performed a grid search to optimise the hyperparameters of our model (penalty = 10, tolerance for stopping criterion = 0.0001, linear kernel). For the O-ranked records, our model achieves a weighted F1 score of 0.657, with a 19% improvement with respect to TF-iDF text features and a 107% improvement with respect to categorical features. (For the PRC records, the corresponding improvements are 33% and 215%, respectively.) Note that similar improvements are also obtained for the other classifiers when using Doc2Vec and MS labels as features. It is also worth noting that the differences in the prediction of DoH between PRC and O-ranked records is reduced when using text tools and, specifically, the F1-score of the SVM classifier based on Doc2Vec with MS is almost the same for both datasets. Hence the difference in the quality of the reporting categories can be ameliorated by the use of the textual content of the reports. We summarise the main comparison of the performance of the SVM classifier based on categorical, raw text, and text with content for both datasets in Figure FIGREF35.",
+      "Examination of the types of errors and ex novo re-classification by clinicians:",
+      "A further analysis of the confusion matrices used to compute the F1 score reveals that most of the errors of our model are concentrated in the `No harm', `Low harm' and `Moderate harm' categories, whereas fewer errors are incurred in the `Severe harm' and `Death' categories. Therefore, our method is more likely to return false alarms rather than missing important and harmful incidents.",
+      "In order to have a further evaluation of our results, we asked three clinicians to analyse ex novo a randomly chosen sample of 135 descriptions of incidents, and to determine their degree of harm based on the information in the incident report. The sample was selected from the O-ranked dataset and no extra information apart from the text was provided. We then compared the DoH assigned by the clinicians with both the results of our classifier and the recorded DoH in the dataset.",
+      "Remarkably, the agreement rate of the clinicians' assessment with the recorded DoH was surprisingly low. For example, the agreement in the `No Harm' incidents was only 38%, and in the `Severe' incidents only 49%. In most cases, though, the disparities amounted to switching the DoH by one degree above or below. To reduce this variability, we analysed the outcomes in terms of three larger groups: `No Harm' and `Low Harm' incidents were considered as one outcome; `Moderate Harm' was kept separate; and `Severe Harm' and `Death' were grouped as one outcome, since they both need to be notified to NHS safety managers.",
+      "The results are presented in Table TABREF36. Our classification agrees as well as the pre-existing DoH in the dataset with the ex novo assessment of the clinicians, but our method has higher agreement in the severe and deadly incidents. These results confirm that our method performs as well as the original annotators but is better at identifying risky events."
+    ],
+    [
+      "We have applied a multiscale graph partitioning algorithm (Markov Stability) to extract content-based clusters of documents from a textual dataset of incident reports in an unsupervised manner at different levels of resolution. The method uses paragraph vectors to represent the records and analyses the ensuing similarity graph of documents through multi-resolution capabilities to capture clusters without imposing a priori their number or structure. The different levels of resolution found to be relevant can be chosen by the practitioner to suit the requirements of detail for each specific task. For example, the top level categories of the pre-defined classification hierarchy are highly diverse in size, with large groups such as `Patient accident', `Medication', `Clinical assessment', `Documentation', `Admissions/Transfer' or `Infrastructure' alongside small, specific groups such as `Aggressive behaviour', `Patient abuse', `Self-harm' or `Infection control'. Our multi-scale partitioning finds additional subcategories with medical detail within some of the large categories (Fig. FIGREF22 and FIGREF23).",
+      "Our a posteriori analysis showed that the method recovers meaningful clusters of content as measured by the similarity of the groups against the hand-coded categories and by the intrinsic topic coherence of the clusters. The clusters have high medical content, thus providing complementary information to the externally imposed classification categories. Indeed, some of the most relevant and persistent communities emerge because of their highly homogeneous medical content, even if they cannot be mapped to standardised external categories.",
+      "An area of future research will be to confirm if the finer unsupervised cluster found by our analysis are consistent with a second level in the hierarchy of external categories (Level 2, around 100 categories), which is used less consistently in hospital settings. The use of content-driven classification of reports could also be important within current efforts by the World Health Organisation (WHO) under the framework for the International Classification for Patient Safety (ICPS) BIBREF9 to establish a set of conceptual categories to monitor, analyse and interpret information to improve patient care.",
+      "We have used our clusters within a supervised classifier to predict the degree of harm of an incident based only on free-text descriptions. The degree of harm is an important measure in hospital evaluation and has been shown to depend on the reporting culture of the particular organisation. Overall, our method shows that text description complemented by the topic labels extracted by our method show improved performance in this task. The use of such enhanced NLP tools could help improve reporting frequency and quality, in addition to reducing burden to staff, since most of the necessary information can be retrieved automatically from text descriptions. Further work, would aim to add interpretability to the supervised classification BIBREF57, so as to provide medical staff with a clearer view of the outcomes of our method and to encourage its uptake.",
+      "One of the advantages of a free text analytical approach is the provision, in a timely manner, of an intelligible description of incident report categories derived directly from the 'words' of the reporters themselves. Insights from the analysis of such free text entries can add rich information than would have not otherwise been obtained from pre-defined classes. Not only could this improve the current state of play where much of the free text of these reports goes unused, but by avoiding the strict assignment to pre-defined categories of fixed granularity free text analysis could open an opportunity for feedback and learning through more nuanced classifications as a complementary axis to existing approaches.",
+      "Currently, local incident reporting systems used by hospitals to submit reports to the NRLS require risk managers to improve data quality, due to errors or uncertainty in categorisation. The application of free text analytical approaches has the potential to free up time from this labour-intensive task, focussing instead in quality improvement derived from the content of the data itself. Additionally, the method allows for the discovery of emerging topics or classes of incidents directly from the data when such events do not fit existing categories by using methods for anomaly detection to decide whether new topic clusters should be created. This is a direction of future work.",
+      "Further work also includes the use of our method to enable comparisons across healthcare organisations and also to monitor changes in their incident reports over time. Another interesting direction is to provide online classification suggestions to users based on the text they input as an aid with decision support and data collection, which can also help fine-tune the predefined categories. Finally, it would be interesting to test if the use of deep learning algorithms can improve our classification scores.",
+      "We thank Elias Bamis, Zijing Liu and Michael Schaub for helpful discussions. This research was supported by the National Institute for Health Research (NIHR) Imperial Patient Safety Translational Research Centre and NIHR Imperial Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health. All authors acknowledge support from the EPSRC through award EP/N014529/1 funding the EPSRC Centre for Mathematics of Precision Healthcare."
+    ]
+  ]
+}
+```

qasper-1090/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Nefnir: A high accuracy lemmatizer for Icelandic
+
+Question: How are the substitution rules built?

qasper-1091/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Nefnir: A high accuracy lemmatizer for Icelandic
+
+Question: Which dataset do they use?

qasper-1096/instruction.md

@@ -0,0 +1,91 @@
+Name of Paper: Efficient Vector Representation for Documents through Corruption
+
+Question: Is their approach similar to making an averaged weighted sum of word vectors, where weights reflect word frequencies?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "Related Works and Notations",
+    "Method",
+    "Corruption as data-dependent regularization",
+    "Experiments",
+    "Baselines",
+    "Sentiment analysis",
+    "Word analogy",
+    "Document Classification",
+    "Semantic relatedness",
+    "Conclusion"
+  ],
+  "paragraphs": [
+    [
+      "Text understanding starts with the challenge of finding machine-understandable representation that captures the semantics of texts. Bag-of-words (BoW) and its N-gram extensions are arguably the most commonly used document representations. Despite its simplicity, BoW works surprisingly well for many tasks BIBREF0 . However, by treating words and phrases as unique and discrete symbols, BoW often fails to capture the similarity between words or phrases and also suffers from sparsity and high dimensionality.",
+      "Recent works on using neural networks to learn distributed vector representations of words have gained great popularity. The well celebrated Word2Vec BIBREF1 , by learning to predict the target word using its neighboring words, maps words of similar meanings to nearby points in the continuous vector space. The surprisingly simple model has succeeded in generating high-quality word embeddings for tasks such as language modeling, text understanding and machine translation. Word2Vec naturally scales to large datasets thanks to its simple model architecture. It can be trained on billions of words per hour on a single machine.",
+      "Paragraph Vectors BIBREF2 generalize the idea to learn vector representation for documents. A target word is predicted by the word embeddings of its neighbors in together with a unique document vector learned for each document. It outperforms established document representations, such as BoW and Latent Dirichlet Allocation BIBREF3 , on various text understanding tasks BIBREF4 . However, two caveats come with this approach: 1) the number of parameters grows with the size of the training corpus, which can easily go to billions; and 2) it is expensive to generate vector representations for unseen documents at test time.",
+      "We propose an efficient model architecture, referred to as Document Vector through Corruption (Doc2VecC), to learn vector representations for documents. It is motivated by the observation that linear operations on the word embeddings learned by Word2Vec can sustain substantial amount of syntactic and semantic meanings of a phrase or a sentence BIBREF5 . For example, vec(\u201cRussia\u201d) + vec(\u201criver\u201d) is close to vec(\u201cVolga River\u201d) BIBREF6 , and vec(\u201cking\u201d) - vec(\u201cman\u201d) + vec(\u201cwomen\u201d) is close to vec(\u201cqueen\u201d) BIBREF5 . In Doc2VecC, we represent each document as a simple average of the word embeddings of all the words in the document. In contrast to existing approaches which post-process learned word embeddings to form document representation BIBREF7 , BIBREF8 , Doc2VecC enforces a meaningful document representation can be formed by averaging the word embeddings during learning. Furthermore, we include a corruption model that randomly remove words from a document during learning, a mechanism that is critical to the performance and learning speed of our algorithm.",
+      "Doc2VecC has several desirable properties: 1. The model complexity of Doc2VecC is decoupled from the size of the training corpus, depending only on the size of the vocabulary; 2. The model architecture of Doc2VecC resembles that of Word2Vec, and can be trained very efficiently; 3. The new framework implicitly introduces a data-dependent regularization, which favors rare or informative words and suppresses words that are common but not discriminative; 4. Vector representation of a document can be generated by simply averaging the learned word embeddings of all the words in the document, which significantly boost test efficiency; 5. The vector representation generated by Doc2VecC matches or beats the state-of-the-art for sentiment analysis, document classification as well as semantic relatedness tasks."
+    ],
+    [
+      "Text representation learning has been extensively studied. Popular representations range from the simplest BoW and its term-frequency based variants BIBREF9 , language model based methods BIBREF10 , BIBREF11 , BIBREF12 , topic models BIBREF13 , BIBREF3 , Denoising Autoencoders and its variants BIBREF14 , BIBREF15 , and distributed vector representations BIBREF8 , BIBREF2 , BIBREF16 . Another prominent line of work includes learning task-specific document representation with deep neural networks, such as CNN BIBREF17 or LSTM based approaches BIBREF18 , BIBREF19 .",
+      "In this section, we briefly introduce Word2Vec and Paragraph Vectors, the two approaches that are most similar to ours. There are two well-know model architectures used for both methods, referred to as Continuous Bag-of-Words (CBoW) and Skipgram models BIBREF1 . In this work, we focus on CBoW. Extending to Skipgram is straightforward. Here are the notations we are going to use throughout the paper:"
+    ],
+    [
+      "Several works BIBREF6 , BIBREF5 showcased that syntactic and semantic regularities of phrases and sentences are reasonably well preserved by adding or subtracting word embeddings learned through Word2Vec. It prompts us to explore the option of simply representing a document as an average of word embeddings. Figure FIGREF9 illustrates the new model architecture.",
+      "Similar to Word2Vec or Paragraph Vectors, Doc2VecC consists of an input layer, a projection layer as well as an output layer to predict the target word, \u201cceremony\u201d in this example. The embeddings of neighboring words (\u201copening\u201d, \u201cfor\u201d, \u201cthe\u201d) provide local context while the vector representation of the entire document (shown in grey) serves as the global context. In contrast to Paragraph Vectors, which directly learns a unique vector for each document, Doc2VecC represents each document as an average of the embeddings of words randomly sampled from the document (\u201cperformance\u201d at position INLINEFORM0 , \u201cpraised\u201d at position INLINEFORM1 , and \u201cbrazil\u201d at position INLINEFORM2 ). BIBREF25 also proposed the idea of using average of word embeddings to represent the global context of a document. Different from their work, we choose to corrupt the original document by randomly removing significant portion of words, and represent the document using only the embeddings of the words remained. This corruption mechanism offers us great speedup during training as it significantly reduces the number of parameters to update in back propagation. At the same time, as we are going to detail in the next section, it introduces a special form of regularization, which brings great performance improvement.",
+      "Here we describe the stochastic process we used to generate a global context at each update. The global context, which we denote as INLINEFORM0 , is generated through a unbiased mask-out/drop-out corruption, in which we randomly overwrites each dimension of the original document INLINEFORM1 with probability INLINEFORM2 . To make the corruption unbiased, we set the uncorrupted dimensions to INLINEFORM3 times its original value. Formally, DISPLAYFORM0 ",
+      "Doc2VecC then defines the probability of observing a target word INLINEFORM0 given its local context INLINEFORM1 as well as the global context INLINEFORM2 as DISPLAYFORM0 ",
+      "Here INLINEFORM0 is the length of the document. Exactly computing the probability is impractical, instead we approximate it with negative sampling BIBREF1 . DISPLAYFORM0 ",
+      "here INLINEFORM0 stands for a uniform distribution over the terms in the vocabulary. The two projection matrices INLINEFORM1 and INLINEFORM2 are then learned to minimize the loss: DISPLAYFORM0 ",
+      "Given the learned projection matrix INLINEFORM0 , we then represent each document simply as an average of the embeddings of the words in the document, DISPLAYFORM0 ",
+      "We are going to elaborate next why we choose to corrupt the original document with the corruption model in eq.( EQREF10 ) during learning, and how it enables us to simply use the average word embeddings as the vector representation for documents at test time."
+    ],
+    [
+      "We approximate the log likelihood for each instance INLINEFORM0 in eq.( EQREF13 ) with its Taylor expansion with respect to INLINEFORM1 up to the second-order BIBREF26 , BIBREF27 , BIBREF28 . Concretely, we choose to expand at the mean of the corruption INLINEFORM2 : INLINEFORM3 ",
+      "where INLINEFORM0 and INLINEFORM1 are the first-order (i.e., gradient) and second-order (i.e., Hessian) of the log likelihood with respect to INLINEFORM2 . Expansion at the mean INLINEFORM3 is crucial as shown in the following steps. Let us assume that for each instance, we are going to sample the global context INLINEFORM4 infinitely many times, and thus compute the expected log likelihood with respect to the corrupted INLINEFORM5 . INLINEFORM6 ",
+      "The linear term disappears as INLINEFORM0 . We substitute in INLINEFORM1 for the mean INLINEFORM2 of the corrupting distribution (unbiased corruption) and the matrix INLINEFORM3 for the variance, and obtain DISPLAYFORM0 ",
+      "As each word in a document is corrupted independently of others, the variance matrix INLINEFORM0 is simplified to a diagonal matrix with INLINEFORM1 element equals INLINEFORM2 . As a result, we only need to compute the diagonal terms of the Hessian matrix INLINEFORM3 .",
+      "The INLINEFORM0 dimension of the Hessian's diagonal evaluated at the mean INLINEFORM1 is given by INLINEFORM2 ",
+      "Plug the Hessian matrix and the variance matrix back into eq.( EQREF16 ), and then back to the loss defined in eq.( EQREF13 ), we can see that Doc2VecC intrinsically minimizes DISPLAYFORM0 ",
+      "Each INLINEFORM0 in the first term measures the log likelihood of observing the target word INLINEFORM1 given its local context INLINEFORM2 and the document vector INLINEFORM3 . As such, Doc2VecC enforces that a document vector generated by averaging word embeddings can capture the global semantics of the document, and fill in information missed in the local context. The second term here is a data-dependent regularization. The regularization on the embedding INLINEFORM4 of each word INLINEFORM5 takes the following form, INLINEFORM6 ",
+      "where INLINEFORM0 prescribes the confidence of predicting the target word INLINEFORM1 given its neighboring context INLINEFORM2 as well as the document vector INLINEFORM3 .",
+      "Closely examining INLINEFORM0 leads to several interesting findings: 1. the regularizer penalizes more on the embeddings of common words. A word INLINEFORM1 that frequently appears across the training corpus, i.e, INLINEFORM2 often, will have a bigger regularization than a rare word; 2. on the other hand, the regularization is modulated by INLINEFORM3 , which is small if INLINEFORM4 . In other words, if INLINEFORM5 is critical to a confident prediction INLINEFORM6 when it is active, then the regularization is diminished. Similar effect was observed for dropout training for logistic regression model BIBREF27 and denoising autoencoders BIBREF28 ."
+    ],
+    [
+      "We evaluate Doc2VecC on a sentiment analysis task, a document classification task and a semantic relatedness task, along with several document representation learning algorithms. All experiments can be reproduced using the code available at https://github.com/mchen24/iclr2017"
+    ],
+    [
+      "We compare against the following document representation baselines: bag-of-words (BoW); Denoising Autoencoders (DEA) BIBREF14 , a representation learned from reconstructing original document INLINEFORM0 using corrupted one INLINEFORM1 . SDAs have been shown to be the state-of-the-art for sentiment analysis tasks BIBREF29 . We used Kullback-Liebler divergence as the reconstruction error and an affine encoder. To scale up the algorithm to large vocabulary, we only take into account the non-zero elements of INLINEFORM2 in the reconstruction error and employed negative sampling for the remainings; Word2Vec BIBREF1 +IDF, a representation generated through weighted average of word vectors learned using Word2Vec; Doc2Vec BIBREF2 ; Skip-thought Vectors BIBREF16 , a generic, distributed sentence encoder that extends the Word2Vec skip-gram model to sentence level. It has been shown to produce highly generic sentence representations that apply to various natural language processing tasks. We also include RNNLM BIBREF11 , a recurrent neural network based language model in the comparison. In the semantic relatedness task, we further compare to LSTM-based methods BIBREF18 that have been reported on this dataset."
+    ],
+    [
+      "For sentiment analysis, we use the IMDB movie review dataset. It contains 100,000 movies reviews categorized as either positive or negative. It comes with predefined train/test split BIBREF30 : 25,000 reviews are used for training, 25,000 for testing, and the rest as unlabeled data. The two classes are balanced in the training and testing sets. We remove words that appear less than 10 times in the training set, resulting in a vocabulary of 43,375 distinct words and symbols.",
+      "Setup. We test the various representation learning algorithms under two settings: one follows the same protocol proposed in BIBREF8 , where representation is learned using all the available data, including the test set; another one where the representation is learned using training and unlabeled set only. For both settings, a linear support vector machine (SVM) BIBREF31 is trained afterwards on the learned representation for classification. For Skip-thought Vectors, we used the generic model trained on a much bigger book corpus to encode the documents. A vector of 4800 dimensions, first 2400 from the uni-skip model, and the last 2400 from the bi-skip model, are generated for each document. In comparison, all the other algorithms produce a vector representation of size 100. The supervised RNN-LM is learned on the training set only. The hyper-parameters are tuned on a validation set subsampled from the training set.",
+      "Accuracy. Comparing the two columns in Table TABREF20 , we can see that all the representation learning algorithms benefits from including the testing data during the representation learning phrase. Doc2VecC achieved similar or even better performance than Paragraph Vectors. Both methods outperforms the other baselines, beating the BOW representation by 15%. In comparison with Word2Vec+IDF, which applies post-processing on learned word embeddings to form document representation, Doc2VecC naturally enforces document semantics to be captured by averaged word embeddings during training. This leads to better performance. Doc2VecC reduces to Denoising Autoencoders (DEA) if the local context words are removed from the paradigm shown in Figure FIGREF9 . By including the context words, Doc2VecC allows the document vector to focus more on capturing the global context. Skip-thought vectors perform surprisingly poor on this dataset comparing to other methods. We hypothesized that it is due to the length of paragraphs in this dataset. The average length of paragraphs in the IMDB movie review dataset is INLINEFORM0 , much longer than the ones used for training and testing in the original paper, which is in the order of 10. As noted in BIBREF18 , the performance of LSTM based method (similarly, the gated RNN used in Skip-thought vectors) drops significantly with increasing paragraph length, as it is hard to preserve state over long sequences of words.",
+      "Time. Table TABREF22 summarizes the time required by these algorithms to learn and generate the document representation. Word2Vec is the fastest one to train. Denoising Autoencoders and Doc2VecC second that. The number of parameters that needs to be back-propagated in each update was increased by the number of surviving words in INLINEFORM0 . We found that both models are not sensitive to the corruption rate INLINEFORM1 in the noise model. Since the learning time decreases with higher corruption rate, we used INLINEFORM2 throughout the experiments. Paragraph Vectors takes longer time to train as there are more parameters (linear to the number of document in the learning set) to learn. At test time, Word2Vec+IDF, DEA and Doc2VecC all use (weighted) averaging of word embeddings as document representation. Paragraph Vectors, on the other hand, requires another round of inference to produce the vector representation of unseen test documents. It takes Paragraph Vectors 4 minutes and 17 seconds to infer the vector representations for the 25,000 test documents, in comparison to 7 seconds for the other methods. As we did not re-train the Skip-thought vector models on this dataset, the training time reported in the table is the time it takes to generate the embeddings for the 25,000 training documents. Due to repeated high-dimensional matrix operations required for encoding long paragraphs, it takes fairly long time to generate the representations for these documents. Similarly for testing. The experiments were conducted on a desktop with Intel i7 2.2Ghz cpu.",
+      "Data dependent regularization. As explained in Section SECREF15 , the corruption introduced in Doc2VecC acts as a data-dependent regularization that suppresses the embeddings of frequent but uninformative words. Here we conduct an experiment to exam the effect. We used a cutoff of 100 in this experiment. Table TABREF24 lists the words having the smallest INLINEFORM0 norm of embeddings found by different algorithms. The number inside the parenthesis after each word is the number of times this word appears in the learning set. In word2Vec or Paragraph Vectors, the least frequent words have embeddings that are close to zero, despite some of them being indicative of sentiment such as debacle, bliss and shabby. In contrast, Doc2VecC manages to clamp down the representation of words frequently appear in the training set, but are uninformative, such as symbols and stop words.",
+      "Subsampling frequent words. Note that for all the numbers reported, we applied the trick of subsampling of frequent words introduced in BIBREF6 to counter the imbalance between frequent and rare words. It is critical to the performance of simple Word2Vec+AVG as the sole remedy to diminish the contribution of common words in the final document representation. If we were to remove this step, the error rate of Word2Vec+AVG will increases from INLINEFORM0 to INLINEFORM1 . Doc2VecC on the other hand naturally exerts a stronger regularization toward embeddings of words that are frequent but uninformative, therefore does not rely on this trick."
+    ],
+    [
+      "In table TABREF24 , we demonstrated that the corruption model introduced in Doc2VecC dampens the embeddings of words which are common and non-discriminative (stop words). In this experiment, we are going to quantatively compare the word embeddings generated by Doc2VecC to the ones generated by Word2Vec, or Paragraph Vectors on the word analogy task introduced by BIBREF1 . The dataset contains five types of semantic questions, and nine types of syntactic questions, with a total of 8,869 semantic and 10,675 syntactic questions. The questions are answered through simple linear algebraic operations on the word embeddings generated by different methods. Please refer to the original paper for more details on the evaluation protocol.",
+      "We trained the word embeddings of different methods using the English news dataset released under the ACL workshop on statistical machine translation. The training set includes close to 15M paragraphs with 355M tokens. We compare the performance of word embeddings trained by different methods with increasing embedding dimensionality as well as increasing training data.",
+      "We observe similar trends as in BIBREF1 . Increasing embedding dimensionality as well as training data size improves performance of the word embeddings on this task. However, the improvement is diminishing. Doc2VecC produces word embeddings which performs significantly better than the ones generated by Word2Vec. We observe close to INLINEFORM0 uplift when we train on the full training corpus. Paragraph vectors on the other hand performs surprisingly bad on this dataset. Our hypothesis is that due to the large capacity of the model architecture, Paragraph Vectors relies mostly on the unique document vectors to capture the information in a text document instead of learning the word semantic or syntactic similarities. This also explains why the PV-DBOW BIBREF2 model architecture proposed in the original work, which completely removes word embedding layers, performs comparable to the distributed memory version.",
+      "In table 5, we list a detailed comparison of the performance of word embeddings generated by Word2Vec and Doc2VecC on the 14 subtasks, when trained on the full dataset with embedding of size 100. We can see that Doc2VecC significantly outperforms the word embeddings produced by Word2Vec across almost all the subtasks."
+    ],
+    [
+      "For the document classification task, we use a subset of the wikipedia dump, which contains over 300,000 wikipedia pages in 100 categories. The 100 categories includes categories under sports, entertainment, literature, and politics etc. Examples of categories include American drama films, Directorial debut films, Major League Baseball pitchers and Sydney Swans players. Body texts (the second paragraph) were extracted for each page as a document. For each category, we select 1,000 documents with unique category label, and 100 documents were used for training and 900 documents for testing. The remaining documents are used as unlabeled data. The 100 classes are balanced in the training and testing sets. For this data set, we learn the word embedding and document representation for all the algorithms using all the available data. We apply a cutoff of 10, resulting in a vocabulary of size INLINEFORM0 .",
+      "Table TABREF29 summarizes the classification error of a linear SVM trained on representations of different sizes. We can see that most of the algorithms are not sensitive to the size of the vector representation. Doc2Vec benefits most from increasing representation size. Across all sizes of representations, Doc2VecC outperform the existing algorithms by a significant margin. In fact, Doc2VecC can achieve same or better performance with a much smaller representation vector.",
+      "Figure FIGREF30 visualizes the document representations learned by Doc2Vec (left) and Doc2VecC (right) using t-SNE BIBREF32 . We can see that documents from the same category are nicely clustered using the representation generated by Doc2VecC. Doc2Vec, on the other hand, does not produce a clear separation between different categories, which explains its worse performance reported in Table TABREF29 .",
+      "Figure FIGREF31 visualizes the vector representation generated by Doc2VecC w.r.t. coarser categorization. we manually grouped the 100 categories into 7 coarse categories, television, albums, writers, musicians, athletes, species and actors. Categories that do no belong to any of these 7 groups are not included in the figure. We can see that documents belonging to a coarser category are grouped together. This subset includes is a wide range of sports descriptions, ranging from football, crickets, baseball, and cycling etc., which explains why the athletes category are less concentrated. In the projection, we can see documents belonging to the musician category are closer to those belonging to albums category than those of athletes or species."
+    ],
+    [
+      "We test Doc2VecC on the SemEval 2014 Task 1: semantic relatedness SICK dataset BIBREF33 . Given two sentences, the task is to determine how closely they are semantically related. The set contains 9,927 pairs of sentences with human annotated relatedness score, ranging from 1 to 5. A score of 1 indicates that the two sentences are not related, while 5 indicates high relatedness. The set is splitted into a training set of 4,500 instances, a validation set of 500, and a test set of 4,927.",
+      "We compare Doc2VecC with several winning solutions of the competition as well as several more recent techniques reported on this dataset, including bi-directional LSTM and Tree-LSTM trained from scratch on this dataset, Skip-thought vectors learned a large book corpus BIBREF34 and produced sentence embeddings of 4,800 dimensions on this dataset. We follow the same protocol as in skip-thought vectors, and train Doc2VecC on the larger book corpus dataset. Contrary to the vocabulary expansion technique used in BIBREF16 to handle out-of-vocabulary words, we extend the vocabulary of the learned model directly on the target dataset in the following way: we use the pre-trained word embedding as an initialization, and fine-tune the word and sentence representation on the SICK dataset. Notice that the fine-tuning is done for sentence representation learning only, and we did not use the relatedness score in the learning. This step brings small improvement to the performance of our algorithm. Given the sentence embeddings, we used the exact same training and testing protocol as in BIBREF16 to score each pair of sentences: with two sentence embedding INLINEFORM0 and INLINEFORM1 , we concatenate their component-wise product, INLINEFORM2 and their absolute difference, INLINEFORM3 as the feature representation.",
+      "Table TABREF35 summarizes the performance of various algorithms on this dataset. Despite its simplicity, Doc2VecC significantly out-performs the winning solutions of the competition, which are heavily feature engineered toward this dataset and several baseline methods, noticeably the dependency-tree RNNs introduced in BIBREF35 , which relies on expensive dependency parsers to compose sentence vectors from word embeddings. The performance of Doc2VecC is slightly worse than the LSTM based methods or skip-thought vectors on this dataset, while it significantly outperforms skip-thought vectors on the IMDB movie review dataset ( INLINEFORM0 error rate vs INLINEFORM1 ). As we hypothesized in previous section, while Doc2VecC is better at handling longer paragraphs, LSTM-based methods are superior for relatively short sentences (of length in the order of 10s). We would like to point out that Doc2VecC is much faster to train and test comparing to skip-thought vectors. It takes less than 2 hours to learn the embeddings on the large book corpus for Doc2VecC on a desktop with Intel i7 2.2Ghz cpu, in comparison to the 2 weeks on GPU required by skip-thought vectors."
+    ],
+    [
+      "We introduce a new model architecture Doc2VecC for document representation learning. It is very efficient to train and test thanks to its simple model architecture. Doc2VecC intrinsically makes sure document representation generated by averaging word embeddings capture semantics of document during learning. It also introduces a data-dependent regularization which favors informative or rare words while dampening the embeddings of common and non-discriminative words. As such, each document can be efficiently represented as a simple average of the learned word embeddings. In comparison to several existing document representation learning algorithms, Doc2VecC outperforms not only in testing efficiency, but also in the expressiveness of the generated representations."
+    ]
+  ]
+}
+```

qasper-1098/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Microsoft Research Asia's Systems for WMT19
+
+Question: What is their best performance on the largest language direction dataset?

qasper-1099/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Microsoft Research Asia's Systems for WMT19
+
+Question: How does soft contextual data augmentation work?

qasper-1204/instruction.md

@@ -0,0 +1,66 @@
+Name of Paper: Harry Potter and the Action Prediction Challenge from Natural Language
+
+Question: Isn't simple word association enough to predict the next spell?
+
+## Full Paper Text (JSON)
+
+```json
+{
+  "section_name": [
+    "Introduction",
+    "HPAC: The Harry Potter's Action prediction Corpus",
+    "Domain motivation",
+    "Data crawling",
+    "Models",
+    "Machine learning models",
+    "Sequential models",
+    "Conclusion",
+    "Acknowlegments",
+    "Corpus distribution"
+  ],
+  "paragraphs": [
+    [
+      "Natural language processing (nlp) has achieved significant advances in reading comprehension tasks BIBREF0 , BIBREF1 . These are partially due to embedding methods BIBREF2 , BIBREF3 and neural networks BIBREF4 , BIBREF5 , BIBREF6 , but also to the availability of new resources and challenges. For instance, in cloze-form tasks BIBREF7 , BIBREF8 , the goal is to predict the missing word given a short context. weston2015towards presented baBI, a set of proxy tasks for reading comprenhension. In the SQuAD corpus BIBREF9 , the aim is to answer questions given a Wikipedia passage. 2017arXiv171207040K introduce NarrativeQA, where answering the questions requires to process entire stories. In a related line, 2017arXiv171011601F use fictional crime scene investigation data, from the CSI series, to define a task where the models try to answer the question: \u2018who committed the crime?\u2019.",
+      "In an alternative line of work, script induction BIBREF10 has been also a useful approach to evaluate inference and semantic capabilities of nlp systems. Here, a model processes a document to infer new sequences that reflect events that are statistically probable (e.g. go to a restaurant, be seated, check the menu, ...). For example, chambers2008unsupervised introduce narrative event chains, a representation of structured knowledge of a set of events occurring around a protagonist. They then propose a method to learn statistical scripts, and also introduce two different evaluation strategies. With a related aim, Pichotta2014Statistical propose a multi-event representation of statistical scripts to be able to consider multiple entities. These same authors BIBREF11 have also studied the abilities of recurrent neural networks for learning scripts, generating upcoming events given a raw sequence of tokens, using bleu BIBREF12 for evaluation.",
+      "This paper explores instead a new task: action prediction from natural language descriptions of scenes. The challenge is addressed as follows: given a natural language input sequence describing the scene, such as a piece of a story coming from a transcript, the goal is to infer which action is most likely to happen next."
+    ],
+    [
+      "To build an action prediction corpus, we need to: (1) consider the set of actions, and (2) collect data where these occur. Data should come from different users, to approximate a real natural language task. Also, it needs to be annotated, determining that a piece of text ends up triggering an action. These tasks are however time consuming, as they require annotators to read vast amounts of large texts. In this context, machine comprehension resources usually establish a compromise between their complexity and the costs of building them BIBREF7 , BIBREF13 ."
+    ],
+    [
+      "We rely on an intuitive idea that uses transcripts from the Harry Potter world to build up a corpus for textual action prediction. The domain has a set of desirable properties to evaluate reading comprehension systems, which we now review.",
+      "Harry Potter novels define a variety of spells. These are keywords cast by witches and wizards to achieve purposes, such as turning on a light (\u2018Lumos\u2019), unlocking a door (\u2018Alohomora\u2019) or killing (\u2018Avada Kedavra\u2019). They abstract complex and non-ambiguous actions. Their use also makes it possible to build an automatic and self-annotated corpus for action prediction. The moment a spell occurs in a text represents a response to the environment, and hence, it can be used to label the preceding text fragment as a scene description that ends up triggering that action. Table 1 illustrates it with some examples from the original books.",
+      "This makes it possible to consider texts from the magic world of Harry Potter as the domain for the action prediction corpus, and the spells as the set of eligible actions. Determining the length of the preceding context, namely snippet, that has to be considered as the scene description is however not trivial. This paper considers experiments (\u00a7 \"Experiments\" ) using snippets with the 32, 64, 96 and 128 previous tokens to an action. We provide the needed scripts to rebuild the corpus using arbitrary lengths."
+    ],
+    [
+      "The number of occurrences of spells in the original Harry Potter books is small (432 occurrences), which makes it difficult to train and test a machine learning model. However, the amount of available fan fiction for this saga allows to create a large corpus. For hpac, we used fan fiction (and only fan fiction texts) from https://www.fanfiction.net/book/Harry-Potter/ and a version of the crawler by milli2016beyond. We collected Harry Potter stories written in English and marked with the status \u2018completed\u2019. From these we extracted a total of 82 836 spell occurrences, that we used to obtain the scene descriptions. Table 2 details the statistics of the corpus (see also Appendix \"Corpus distribution\" ). Note that similar to Twitter corpora, fan fiction stories can be deleted over time by users or admins, causing losses in the dataset.",
+      "We tokenized the samples with BIBREF14 and merged the occurrences of multi-word spells into a single token."
+    ],
+    [
+      "This work addresses the task as a classification problem, and in particular as a sequence to label classification problem. For this reason, we rely on standard models used for this type of task: multinomial logistic regression, a multi-layered perceptron, convolutional neural networks and long short-term memory networks. We outline the essentials of each of these models, but will treat them as black boxes. In a related line, kaushik2018much discuss the need of providing rigorous baselines that help better understand the improvement coming from future and complex models, and also the need of not demanding architectural novelty when introducing new datasets.",
+      "Although not done in this work, an alternative (but also natural) way to address the task is as a special case of language modelling, where the output vocabulary is restricted to the size of the `action' vocabulary. Also, note that the performance for this task is not expected to achieve a perfect accuracy, as there may be situations where more than one action is reasonable, and also because writers tell a story playing with elements such as surprise or uncertainty.",
+      "The source code for the models can be found in the GitHub repository mentioned above."
+    ],
+    [
+      "The input sentence $w_{1:n}$ is encoded as a one-hot vector, $\\mathbf {v}$ (total occurrence weighting scheme).",
+      "Let mlr $_\\theta (\\mathbf {v})$ be an abstraction of a multinomial logistic regression parametrized by $\\theta $ , the output for an input $\\mathbf {v}$ is computed as the $\\operatornamewithlimits{arg\\,max}_{a \\in A}$ $P(y=a|\\mathbf {v})$ , where $P(y=a|\\mathbf {v})$ is a $softmax$ function, i.e, $P(y=a|\\mathbf {v}) = \\frac{e^{W_{a} \\cdot \\mathbf {v}}}{\\sum _{a^{\\prime }}^{A} e^{W_{a^{\\prime }} \\cdot \\mathbf {v}}}$ .",
+      "We use one hidden layer with a rectifier activation function ( $relu(x)$ = $max(0,x)$ ). The output is computed as mlp $_\\theta (\\mathbf {v})$ = $softmax(W_2 \\cdot relu(W \\cdot \\mathbf {v} + \\mathbf {b}) + \\mathbf {b_2})$ ."
+    ],
+    [
+      "The input sequence is represented as a sequence of word embeddings, $\\mathbf {w}_{1:n}$ , where $\\mathbf {w}_i$ is a concatenation of an internal embedding learned during the training process for the word $w_i$ , and a pre-trained embedding extracted from GloVe BIBREF15 , that is further fine-tuned.",
+      " BIBREF5 : The output for an element $\\mathbf {w}_i$ also depends on the output of $\\mathbf {w}_{i-1}$ . The lstm $_\\theta (\\mathbf {w}_{1:n})$ takes as input a sequence of word embeddings and produces a sequence of hidden outputs, $\\mathbf {h}_{1:n}$ ( $\\mathbf {h}_{i}$ size set to 128). The last output of the lstm $_\\theta $ , $\\mathbf {h}_n$ , is fed to a mlp $_\\theta $ .",
+      " BIBREF16 , BIBREF17 . It captures local properties over continuous slices of text by applying a convolution layer made of different filters. We use a wide convolution, with a window slice size of length 3 and 250 different filters. The convolutional layer uses a $\\mathit {relu}$ as the activation function. The output is fed to a max pooling layer, whose output vector is passed again as input to a mlp $_\\theta $ ."
+    ],
+    [
+      "We explored action prediction from written stories. We first introduced a corpus set in the world of Harry Potter's literature. Spells in these novels act as keywords that abstract actions. This idea was used to label a collection of fan fiction. We then evaluated standard nlp approaches, from logistic regression to sequential models such as lstms. The latter performed better in general, although vanilla models achieved a higher performance for actions that occurred a few times in the training set. An analysis over the output of the lstm approach also revealed difficulties to discriminate among semantically related actions.",
+      "The challenge here proposed corresponded to a fictional domain. A future line of work we are interested in is to test whether the knowledge learned with this dataset could be transferred to real-word actions (i.e. real-domain setups), or if such transfer is not possible and a model needs to be trained from scratch."
+    ],
+    [
+      "This work has received support from the TELEPARES-UDC project (FFI2014-51978-C2-2-R) and the ANSWER-ASAP project (TIN2017-85160-C2-1-R) from MINECO, and from Xunta de Galicia (ED431B 2017/01), and from the European Research Council (ERC), under the European Union's Horizon 2020 research and innovation programme (FASTPARSE, grant agreement No 714150)."
+    ],
+    [
+      "Table 6 summarizes the label distribution across the training, development and test sets of the hpac corpus."
+    ]
+  ]
+}
+```

qasper-1250/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Dreaddit: A Reddit Dataset for Stress Analysis in Social Media
+
+Question: Is the dataset balanced across categories?