Datasets:

irenepap

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qasper

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[ "a vocabulary of positive and negative predicates that helps determine the polarity score of an event", "seed lexicon consists of positive and negative predicates" ]

[ [ "The seed lexicon consists of positive and negative predicates. If the predicate of an extracted event is in the seed lexicon and does not involve complex phenomena like negation, we assign the corresponding polarity score ($+1$ for positive events and $-1$ for negative events) to the event. We expect the model to automatically learn complex phenomena through label propagation. Based on the availability of scores and the types of discourse relations, we classify the extracted event pairs into the following three types." ], [ "The seed lexicon consists of positive and negative predicates. If the predicate of an extracted event is in the seed lexicon and does not involve complex phenomena like negation, we assign the corresponding polarity score ($+1$ for positive events and $-1$ for negative events) to the event. We expect the model to automatically learn complex phenomena through label propagation. Based on the availability of scores and the types of discourse relations, we classify the extracted event pairs into the following three types." ] ]

[ "Using all data to train: AL -- BiGRU achieved 0.843 accuracy, AL -- BERT achieved 0.863 accuracy, AL+CA+CO -- BiGRU achieved 0.866 accuracy, AL+CA+CO -- BERT achieved 0.835, accuracy, ACP -- BiGRU achieved 0.919 accuracy, ACP -- BERT achived 0.933, accuracy, ACP+AL+CA+CO -- BiGRU achieved 0.917 accuracy, ACP+AL+CA+CO -- BERT achieved 0.913 accuracy. \nUsing a subset to train: BERT achieved 0.876 accuracy using ACP (6K), BERT achieved 0.886 accuracy using ACP (6K) + AL, BiGRU achieved 0.830 accuracy using ACP (6K), BiGRU achieved 0.879 accuracy using ACP (6K) + AL + CA + CO." ]

[ [ "As for ${\\rm Encoder}$, we compared two types of neural networks: BiGRU and BERT. GRU BIBREF16 is a recurrent neural network sequence encoder. BiGRU reads an input sequence forward and backward and the output is the concatenation of the final forward and backward hidden states.", "We trained the model with the following four combinations of the datasets: AL, AL+CA+CO (two proposed models), ACP (supervised), and ACP+AL+CA+CO (semi-supervised). The corresponding objective functions were: $\\mathcal {L}_{\\rm AL}$, $\\mathcal {L}_{\\rm AL} + \\mathcal {L}_{\\rm CA} + \\mathcal {L}_{\\rm CO}$, $\\mathcal {L}_{\\rm ACP}$, and $\\mathcal {L}_{\\rm ACP} + \\mathcal {L}_{\\rm AL} + \\mathcal {L}_{\\rm CA} + \\mathcal {L}_{\\rm CO}$." ] ]

[ "based on the relation between events, the suggested polarity of one event can determine the possible polarity of the other event ", "cause relation: both events in the relation should have the same polarity; concession relation: events should have opposite polarity" ]

[ [ "In this paper, we propose a simple and effective method for learning affective events that only requires a very small seed lexicon and a large raw corpus. As illustrated in Figure FIGREF1, our key idea is that we can exploit discourse relations BIBREF4 to efficiently propagate polarity from seed predicates that directly report one's emotions (e.g., “to be glad” is positive). Suppose that events $x_1$ are $x_2$ are in the discourse relation of Cause (i.e., $x_1$ causes $x_2$). If the seed lexicon suggests $x_2$ is positive, $x_1$ is also likely to be positive because it triggers the positive emotion. The fact that $x_2$ is known to be negative indicates the negative polarity of $x_1$. Similarly, if $x_1$ and $x_2$ are in the discourse relation of Concession (i.e., $x_2$ in spite of $x_1$), the reverse of $x_2$'s polarity can be propagated to $x_1$. Even if $x_2$'s polarity is not known in advance, we can exploit the tendency of $x_1$ and $x_2$ to be of the same polarity (for Cause) or of the reverse polarity (for Concession) although the heuristic is not exempt from counterexamples. We transform this idea into objective functions and train neural network models that predict the polarity of a given event." ], [ "In this paper, we propose a simple and effective method for learning affective events that only requires a very small seed lexicon and a large raw corpus. As illustrated in Figure FIGREF1, our key idea is that we can exploit discourse relations BIBREF4 to efficiently propagate polarity from seed predicates that directly report one's emotions (e.g., “to be glad” is positive). Suppose that events $x_1$ are $x_2$ are in the discourse relation of Cause (i.e., $x_1$ causes $x_2$). If the seed lexicon suggests $x_2$ is positive, $x_1$ is also likely to be positive because it triggers the positive emotion. The fact that $x_2$ is known to be negative indicates the negative polarity of $x_1$. Similarly, if $x_1$ and $x_2$ are in the discourse relation of Concession (i.e., $x_2$ in spite of $x_1$), the reverse of $x_2$'s polarity can be propagated to $x_1$. Even if $x_2$'s polarity is not known in advance, we can exploit the tendency of $x_1$ and $x_2$ to be of the same polarity (for Cause) or of the reverse polarity (for Concession) although the heuristic is not exempt from counterexamples. We transform this idea into objective functions and train neural network models that predict the polarity of a given event.", "The seed lexicon consists of positive and negative predicates. If the predicate of an extracted event is in the seed lexicon and does not involve complex phenomena like negation, we assign the corresponding polarity score ($+1$ for positive events and $-1$ for negative events) to the event. We expect the model to automatically learn complex phenomena through label propagation. Based on the availability of scores and the types of discourse relations, we classify the extracted event pairs into the following three types." ] ]

[ "7000000 pairs of events were extracted from the Japanese Web corpus, 529850 pairs of events were extracted from the ACP corpus", "The ACP corpus has around 700k events split into positive and negative polarity " ]

[ [ "As a raw corpus, we used a Japanese web corpus that was compiled through the procedures proposed by BIBREF13. To extract event pairs tagged with discourse relations, we used the Japanese dependency parser KNP and in-house postprocessing scripts BIBREF14. KNP used hand-written rules to segment each sentence into what we conventionally called clauses (mostly consecutive text chunks), each of which contained one main predicate. KNP also identified the discourse relations of event pairs if explicit discourse connectives BIBREF4 such as “ので” (because) and “のに” (in spite of) were present. We treated Cause/Reason (原因・理由) and Condition (条件) in the original tagset BIBREF15 as Cause and Concession (逆接) as Concession, respectively. Here is an example of event pair extraction.", "We constructed our seed lexicon consisting of 15 positive words and 15 negative words, as shown in Section SECREF27. From the corpus of about 100 million sentences, we obtained 1.4 millions event pairs for AL, 41 millions for CA, and 6 millions for CO. We randomly selected subsets of AL event pairs such that positive and negative latter events were equal in size. We also sampled event pairs for each of CA and CO such that it was five times larger than AL. The results are shown in Table TABREF16.", "We used the latest version of the ACP Corpus BIBREF12 for evaluation. It was used for (semi-)supervised training as well. Extracted from Japanese websites using HTML layouts and linguistic patterns, the dataset covered various genres. For example, the following two sentences were labeled positive and negative, respectively:", "Although the ACP corpus was originally constructed in the context of sentiment analysis, we found that it could roughly be regarded as a collection of affective events. We parsed each sentence and extracted the last clause in it. The train/dev/test split of the data is shown in Table TABREF19." ], [] ]

[ "negative, positive" ]

[ [ "Affective events BIBREF0 are events that typically affect people in positive or negative ways. For example, getting money and playing sports are usually positive to the experiencers; catching cold and losing one's wallet are negative. Understanding affective events is important to various natural language processing (NLP) applications such as dialogue systems BIBREF1, question-answering systems BIBREF2, and humor recognition BIBREF3. In this paper, we work on recognizing the polarity of an affective event that is represented by a score ranging from $-1$ (negative) to 1 (positive)." ] ]

[ "3%" ]

[ [] ]

[ "by exploiting discourse relations to propagate polarity from seed predicates to final sentiment polarity" ]

[ [ "In this paper, we propose a simple and effective method for learning affective events that only requires a very small seed lexicon and a large raw corpus. As illustrated in Figure FIGREF1, our key idea is that we can exploit discourse relations BIBREF4 to efficiently propagate polarity from seed predicates that directly report one's emotions (e.g., “to be glad” is positive). Suppose that events $x_1$ are $x_2$ are in the discourse relation of Cause (i.e., $x_1$ causes $x_2$). If the seed lexicon suggests $x_2$ is positive, $x_1$ is also likely to be positive because it triggers the positive emotion. The fact that $x_2$ is known to be negative indicates the negative polarity of $x_1$. Similarly, if $x_1$ and $x_2$ are in the discourse relation of Concession (i.e., $x_2$ in spite of $x_1$), the reverse of $x_2$'s polarity can be propagated to $x_1$. Even if $x_2$'s polarity is not known in advance, we can exploit the tendency of $x_1$ and $x_2$ to be of the same polarity (for Cause) or of the reverse polarity (for Concession) although the heuristic is not exempt from counterexamples. We transform this idea into objective functions and train neural network models that predict the polarity of a given event." ] ]

[ "30 words" ]

[ [ "We constructed our seed lexicon consisting of 15 positive words and 15 negative words, as shown in Section SECREF27. From the corpus of about 100 million sentences, we obtained 1.4 millions event pairs for AL, 41 millions for CA, and 6 millions for CO. We randomly selected subsets of AL event pairs such that positive and negative latter events were equal in size. We also sampled event pairs for each of CA and CO such that it was five times larger than AL. The results are shown in Table TABREF16." ] ]

[ "100 million sentences" ]

[ [ "As a raw corpus, we used a Japanese web corpus that was compiled through the procedures proposed by BIBREF13. To extract event pairs tagged with discourse relations, we used the Japanese dependency parser KNP and in-house postprocessing scripts BIBREF14. KNP used hand-written rules to segment each sentence into what we conventionally called clauses (mostly consecutive text chunks), each of which contained one main predicate. KNP also identified the discourse relations of event pairs if explicit discourse connectives BIBREF4 such as “ので” (because) and “のに” (in spite of) were present. We treated Cause/Reason (原因・理由) and Condition (条件) in the original tagset BIBREF15 as Cause and Concession (逆接) as Concession, respectively. Here is an example of event pair extraction.", "We constructed our seed lexicon consisting of 15 positive words and 15 negative words, as shown in Section SECREF27. From the corpus of about 100 million sentences, we obtained 1.4 millions event pairs for AL, 41 millions for CA, and 6 millions for CO. We randomly selected subsets of AL event pairs such that positive and negative latter events were equal in size. We also sampled event pairs for each of CA and CO such that it was five times larger than AL. The results are shown in Table TABREF16." ] ]

[ "Yes", "Yes" ]

[ [], [ "We find that the multilingual model cannot handle infrequent categories, i.e., Awe/Sublime, Suspense and Humor. However, increasing the dataset with English data improves the results, suggesting that the classification would largely benefit from more annotated data. The best model overall is DBMDZ (.520), showing a balanced response on both validation and test set. See Table TABREF37 for a breakdown of all emotions as predicted by the this model. Precision is mostly higher than recall. The labels Awe/Sublime, Suspense and Humor are harder to predict than the other labels." ] ]

[ "confusion matrices of labels between annotators" ]

[ [ "We find that Cohen $\\kappa $ agreement ranges from .84 for Uneasiness in the English data, .81 for Humor and Nostalgia, down to German Suspense (.65), Awe/Sublime (.61) and Vitality for both languages (.50 English, .63 German). Both annotators have a similar emotion frequency profile, where the ranking is almost identical, especially for German. However, for English, Annotator 2 annotates more Vitality than Uneasiness. Figure FIGREF18 shows the confusion matrices of labels between annotators as heatmaps. Notably, Beauty/Joy and Sadness are confused across annotators more often than other labels. This is topical for poetry, and therefore not surprising: One might argue that the beauty of beings and situations is only beautiful because it is not enduring and therefore not to divorce from the sadness of the vanishing of beauty BIBREF48. We also find considerable confusion of Sadness with Awe/Sublime and Vitality, while the latter is also regularly confused with Beauty/Joy." ] ]

[ "feelings of suspense experienced in narratives not only respond to the trajectory of the plot's content, but are also directly predictive of aesthetic liking (or disliking), Emotions that exhibit this dual capacity have been defined as “aesthetic emotions”" ]

[ [ "To emotionally move readers is considered a prime goal of literature since Latin antiquity BIBREF1, BIBREF2, BIBREF3. Deeply moved readers shed tears or get chills and goosebumps even in lab settings BIBREF4. In cases like these, the emotional response actually implies an aesthetic evaluation: narratives that have the capacity to move readers are evaluated as good and powerful texts for this very reason. Similarly, feelings of suspense experienced in narratives not only respond to the trajectory of the plot's content, but are also directly predictive of aesthetic liking (or disliking). Emotions that exhibit this dual capacity have been defined as “aesthetic emotions” BIBREF2. Contrary to the negativity bias of classical emotion catalogues, emotion terms used for aesthetic evaluation purposes include far more positive than negative emotions. At the same time, many overall positive aesthetic emotions encompass negative or mixed emotional ingredients BIBREF2, e.g., feelings of suspense include both hopeful and fearful anticipations." ] ]

[ "No", "Unanswerable" ]

[ [ "Our full dataset consists of all subreddits on Reddit from January 2013 to December 2014, for which there are at least 500 words in the vocabulary used to estimate our measures, in at least 4 months of the subreddit's history. We compute our measures over the comments written by users in a community in time windows of months, for each sufficiently active month, and manually remove communities where the bulk of the contributions are in a foreign language. This results in 283 communities ( INLINEFORM0 ), for a total of 4,872 community-months ( INLINEFORM1 )." ], [] ]

[ "Dynamic communities have substantially higher rates of monthly user retention than more stable communities. More distinctive communities exhibit moderately higher monthly retention rates than more generic communities. There is also a strong positive relationship between a community's dynamicity and the average number of months that a user will stay in that community - a short-term trend observed for monthly retention translates into longer-term engagement and suggests that long-term user retention might be strongly driven by the extent to which a community continually provides novel content.\n" ]

[ [ "We find that dynamic communities, such as Seahawks or starcraft, have substantially higher rates of monthly user retention than more stable communities (Spearman's INLINEFORM0 = 0.70, INLINEFORM1 0.001, computed with community points averaged over months; Figure FIGREF11 .A, left). Similarly, more distinctive communities, like Cooking and Naruto, exhibit moderately higher monthly retention rates than more generic communities (Spearman's INLINEFORM2 = 0.33, INLINEFORM3 0.001; Figure FIGREF11 .A, right).", "As with monthly retention, we find a strong positive relationship between a community's dynamicity and the average number of months that a user will stay in that community (Spearman's INLINEFORM0 = 0.41, INLINEFORM1 0.001, computed over all community points; Figure FIGREF11 .B, left). This verifies that the short-term trend observed for monthly retention translates into longer-term engagement and suggests that long-term user retention might be strongly driven by the extent to which a community continually provides novel content. Interestingly, there is no significant relationship between distinctiveness and long-term engagement (Spearman's INLINEFORM2 = 0.03, INLINEFORM3 0.77; Figure FIGREF11 .B, right). Thus, while highly distinctive communities like RandomActsOfMakeup may generate focused commitment from users over a short period of time, such communities are unlikely to retain long-term users unless they also have sufficiently dynamic content." ] ]

[ "communities that are characterized by specialized, constantly-updating content have higher user retention rates, but also exhibit larger linguistic gaps that separate newcomers from established members, within distinctive communities, established users have an increased propensity to engage with the community's specialized content, compared to newcomers " ]

[ [ "Engagement and community identity. We apply our framework to understand how two important aspects of user engagement in a community—the community's propensity to retain its users (Section SECREF3 ), and its permeability to new members (Section SECREF4 )—vary according to the type of collective identity it fosters. We find that communities that are characterized by specialized, constantly-updating content have higher user retention rates, but also exhibit larger linguistic gaps that separate newcomers from established members.", "More closely examining factors that could contribute to this linguistic gap, we find that especially within distinctive communities, established users have an increased propensity to engage with the community's specialized content, compared to newcomers (Section SECREF5 ). Interestingly, while established members of distinctive communities more avidly respond to temporal updates than newcomers, in more generic communities it is the outsiders who engage more with volatile content, perhaps suggesting that such content may serve as an entry-point to the community (but not necessarily a reason to stay). Such insights into the relation between collective identity and user engagement can be informative to community maintainers seeking to better understand growth patterns within their online communities." ] ]

[ "They selected all the subreddits from January 2013 to December 2014 with at least 500 words in the vocabulary and at least 4 months of the subreddit's history. They also removed communities with the bulk of the contributions are in foreign language.", "They collect subreddits from January 2013 to December 2014,2 for which there are at\nleast 500 words in the vocabulary used to estimate the measures,\nin at least 4 months of the subreddit’s history. They compute our measures over the comments written by users in a community in time windows of months, for each sufficiently active month, and manually remove communities where the bulk of the contributions are in a foreign language." ]

[ [ "Our full dataset consists of all subreddits on Reddit from January 2013 to December 2014, for which there are at least 500 words in the vocabulary used to estimate our measures, in at least 4 months of the subreddit's history. We compute our measures over the comments written by users in a community in time windows of months, for each sufficiently active month, and manually remove communities where the bulk of the contributions are in a foreign language. This results in 283 communities ( INLINEFORM0 ), for a total of 4,872 community-months ( INLINEFORM1 )." ], [ "Our full dataset consists of all subreddits on Reddit from January 2013 to December 2014, for which there are at least 500 words in the vocabulary used to estimate our measures, in at least 4 months of the subreddit's history. We compute our measures over the comments written by users in a community in time windows of months, for each sufficiently active month, and manually remove communities where the bulk of the contributions are in a foreign language. This results in 283 communities ( INLINEFORM0 ), for a total of 4,872 community-months ( INLINEFORM1 )." ] ]

[ "the average volatility of all utterances" ]

[ [ "Dynamicity. A highly dynamic community constantly shifts interests from one time window to another, and these temporal variations are reflected in its use of volatile language. Formally, we define the dynamicity of a community INLINEFORM0 as the average volatility of all utterances in INLINEFORM1 . We refer to a community whose language is relatively consistent throughout time as being stable." ] ]

[ " the average specificity of all utterances" ]

[ [ "Distinctiveness. A community with a very distinctive identity will tend to have distinctive interests, expressed through specialized language. Formally, we define the distinctiveness of a community INLINEFORM0 as the average specificity of all utterances in INLINEFORM1 . We refer to a community with a less distinctive identity as being generic." ] ]

[ "Chinese general corpus", "Unanswerable" ]

[ [ "To implement deep neural network models, we utilize the Keras library BIBREF36 with TensorFlow BIBREF37 backend. Each model is run on a single NVIDIA GeForce GTX 1080 Ti GPU. The models are trained by Adam optimization algorithm BIBREF38 whose parameters are the same as the default settings except for learning rate set to $5\\times 10^{-5}$. Batch size is set to 3 or 4 due to the lack of graphical memory. We select BERT-base as the pre-trained language model in this paper. Due to the high cost of pre-training BERT language model, we directly adopt parameters pre-trained by Google in Chinese general corpus. The named entity recognition is applied on both pathology report texts and query texts." ], [] ]

[ "BERT-Base, QANet", "QANet BIBREF39, BERT-Base BIBREF26" ]

[ [ "Experimental Studies ::: Comparison with State-of-the-art Methods", "Since BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26. As BERT has two versions: BERT-Base and BERT-Large, due to the lack of computational resource, we can only compare with BERT-Base model instead of BERT-Large. Prediction layer is attached at the end of the original BERT-Base model and we fine tune it on our dataset. In this section, the named entity integration method is chosen to pure concatenation (Concatenate the named entity information on pathology report text and query text first and then concatenate contextualized representation and concatenated named entity information). Comparative results are summarized in Table TABREF23." ], [ "Since BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26. As BERT has two versions: BERT-Base and BERT-Large, due to the lack of computational resource, we can only compare with BERT-Base model instead of BERT-Large. Prediction layer is attached at the end of the original BERT-Base model and we fine tune it on our dataset. In this section, the named entity integration method is chosen to pure concatenation (Concatenate the named entity information on pathology report text and query text first and then concatenate contextualized representation and concatenated named entity information). Comparative results are summarized in Table TABREF23." ] ]

[ "Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surgery, or what the specific laboratory test result is, are obtained., Unlike the traditional CTS task, our QA-CTS task aims to discover the most related text from original paragraph text. ", "CTS is extracting structural data from medical research data (unstructured). Authors define QA-CTS task that aims to discover most related text from original text." ]

[ [ "Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surgery, or what the specific laboratory test result is, are obtained. It is important to extract structured data from clinical text because bio-medical systems or bio-medical researches greatly rely on structured data but they cannot obtain them directly. In addition, clinical text often contains abundant healthcare information. CTS is able to provide large-scale extracted structured data for enormous down-stream clinical researches.", "To reduce the pipeline depth and break the barrier of non-uniform output formats, we present a question answering based clinical text structuring (QA-CTS) task (see Fig. FIGREF1). Unlike the traditional CTS task, our QA-CTS task aims to discover the most related text from original paragraph text. For some cases, it is already the final answer in deed (e.g., extracting sub-string). While for other cases, it needs several steps to obtain the final answer, such as entity names conversion and negative words recognition. Our presented QA-CTS task unifies the output format of the traditional CTS task and make the training data shareable, thus enriching the training data. The main contributions of this work can be summarized as follows." ], [ "Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surgery, or what the specific laboratory test result is, are obtained. It is important to extract structured data from clinical text because bio-medical systems or bio-medical researches greatly rely on structured data but they cannot obtain them directly. In addition, clinical text often contains abundant healthcare information. CTS is able to provide large-scale extracted structured data for enormous down-stream clinical researches.", "However, end-to-end CTS is a very challenging task. Different CTS tasks often have non-uniform output formats, such as specific-class classifications (e.g. tumor stage), strings in the original text (e.g. result for a laboratory test) and inferred values from part of the original text (e.g. calculated tumor size). Researchers have to construct different models for it, which is already costly, and hence it calls for a lot of labeled data for each model. Moreover, labeling necessary amount of data for training neural network requires expensive labor cost. To handle it, researchers turn to some rule-based structuring methods which often have lower labor cost.", "To reduce the pipeline depth and break the barrier of non-uniform output formats, we present a question answering based clinical text structuring (QA-CTS) task (see Fig. FIGREF1). Unlike the traditional CTS task, our QA-CTS task aims to discover the most related text from original paragraph text. For some cases, it is already the final answer in deed (e.g., extracting sub-string). While for other cases, it needs several steps to obtain the final answer, such as entity names conversion and negative words recognition. Our presented QA-CTS task unifies the output format of the traditional CTS task and make the training data shareable, thus enriching the training data. The main contributions of this work can be summarized as follows." ] ]

[ " three types of questions, namely tumor size, proximal resection margin and distal resection margin", "Unanswerable" ]

[ [ "To reduce the pipeline depth and break the barrier of non-uniform output formats, we present a question answering based clinical text structuring (QA-CTS) task (see Fig. FIGREF1). Unlike the traditional CTS task, our QA-CTS task aims to discover the most related text from original paragraph text. For some cases, it is already the final answer in deed (e.g., extracting sub-string). While for other cases, it needs several steps to obtain the final answer, such as entity names conversion and negative words recognition. Our presented QA-CTS task unifies the output format of the traditional CTS task and make the training data shareable, thus enriching the training data. The main contributions of this work can be summarized as follows.", "Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. These annotated instances have been partitioned into 1,899 training instances (12,412 sentences) and 815 test instances (5,421 sentences). Each instance has one or several sentences. Detailed statistics of different types of entities are listed in Table TABREF20.", "In this paper, we present a question answering based clinical text structuring (QA-CTS) task, which unifies different clinical text structuring tasks and utilize different datasets. A novel model is also proposed to integrate named entity information into a pre-trained language model and adapt it to QA-CTS task. Initially, sequential results of named entity recognition on both paragraph and query texts are integrated together. Contextualized representation on both paragraph and query texts are transformed by a pre-trained language model. Then, the integrated named entity information and contextualized representation are integrated together and fed into a feed forward network for final prediction. Experimental results on real-world dataset demonstrate that our proposed model competes favorably with strong baseline models in all three specific tasks. The shared task and shared model introduced by QA-CTS task has also been proved to be useful for improving the performance on most of the task-specific datasets. In conclusion, the best way to achieve the best performance for a specific dataset is to pre-train the model in multiple datasets and then fine tune it on the specific dataset." ], [] ]

[ "the dataset consists of pathology reports including sentences and questions and answers about tumor size and resection margins so it does include additional sentences " ]

[ [ "Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. These annotated instances have been partitioned into 1,899 training instances (12,412 sentences) and 815 test instances (5,421 sentences). Each instance has one or several sentences. Detailed statistics of different types of entities are listed in Table TABREF20." ] ]

[ "2,714 " ]

[ [ "Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. These annotated instances have been partitioned into 1,899 training instances (12,412 sentences) and 815 test instances (5,421 sentences). Each instance has one or several sentences. Detailed statistics of different types of entities are listed in Table TABREF20." ] ]

[ "Unanswerable" ]

[ [] ]

[ "Unanswerable" ]

[ [] ]

[ "integrate clinical named entity information into pre-trained language model" ]

[ [ "We first present a question answering based clinical text structuring (QA-CTS) task, which unifies different specific tasks and make dataset shareable. We also propose an effective model to integrate clinical named entity information into pre-trained language model.", "In this section, we present an effective model for the question answering based clinical text structuring (QA-CTS). As shown in Fig. FIGREF8, paragraph text $X$ is first passed to a clinical named entity recognition (CNER) model BIBREF12 to capture named entity information and obtain one-hot CNER output tagging sequence for query text $I_{nq}$ and paragraph text $I_{nt}$ with BIEOS (Begin, Inside, End, Outside, Single) tag scheme. $I_{nq}$ and $I_{nt}$ are then integrated together into $I_n$. Meanwhile, the paragraph text $X$ and query text $Q$ are organized and passed to contextualized representation model which is pre-trained language model BERT BIBREF26 here to obtain the contextualized representation vector $V_s$ of both text and query. Afterwards, $V_s$ and $I_n$ are integrated together and fed into a feed forward network to calculate the start and end index of answer-related text. Here we define this calculation problem as a classification for each word to be the start or end word." ] ]

[ "17,833 sentences, 826,987 characters and 2,714 question-answer pairs" ]

[ [ "Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. These annotated instances have been partitioned into 1,899 training instances (12,412 sentences) and 815 test instances (5,421 sentences). Each instance has one or several sentences. Detailed statistics of different types of entities are listed in Table TABREF20." ] ]

[ "17,833 sentences, 826,987 characters and 2,714 question-answer pairs" ]

[ [ "Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. These annotated instances have been partitioned into 1,899 training instances (12,412 sentences) and 815 test instances (5,421 sentences). Each instance has one or several sentences. Detailed statistics of different types of entities are listed in Table TABREF20." ] ]

[ "state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26" ]

[ [ "Since BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26. As BERT has two versions: BERT-Base and BERT-Large, due to the lack of computational resource, we can only compare with BERT-Base model instead of BERT-Large. Prediction layer is attached at the end of the original BERT-Base model and we fine tune it on our dataset. In this section, the named entity integration method is chosen to pure concatenation (Concatenate the named entity information on pathology report text and query text first and then concatenate contextualized representation and concatenated named entity information). Comparative results are summarized in Table TABREF23." ] ]

[ "Quality measures using perplexity and recall, and performance measured using latency and energy usage. " ]

[ [ "For each model, we examined word-level perplexity, R@3 in next-word prediction, latency (ms/q), and energy usage (mJ/q). To explore the perplexity–recall relationship, we collected individual perplexity and recall statistics for each sentence in the test set." ] ]

[ "Kneser–Ney smoothing" ]

[ [ "In this paper, we examine the quality–performance tradeoff in the shift from non-neural to neural language models. In particular, we compare Kneser–Ney smoothing, widely accepted as the state of the art prior to NLMs, to the best NLMs today. The decrease in perplexity on standard datasets has been well documented BIBREF3 , but to our knowledge no one has examined the performances tradeoffs. With deployment on a mobile device in mind, we evaluate energy usage and inference latency on a Raspberry Pi (which shares the same ARM architecture as nearly all smartphones today). We find that a 2.5 $\\times $ reduction in perplexity on PTB comes at a staggering cost in terms of performance: inference with NLMs takes 49 $\\times $ longer and requires 32 $\\times $ more energy. Furthermore, we find that impressive reductions in perplexity translate into at best modest improvements in next-word prediction, which is arguable a better metric for evaluating software keyboards on a smartphone. The contribution of this paper is the first known elucidation of this quality–performance tradeoff. Note that we refrain from prescriptive recommendations: whether or not a tradeoff is worthwhile depends on the application. Nevertheless, NLP engineers should arguably keep these tradeoffs in mind when selecting a particular operating point." ] ]

[ "perplexity", "perplexity" ]

[ [ "Deep learning has unquestionably advanced the state of the art in many natural language processing tasks, from syntactic dependency parsing BIBREF0 to named-entity recognition BIBREF1 to machine translation BIBREF2 . The same certainly applies to language modeling, where recent advances in neural language models (NLMs) have led to dramatically better approaches as measured using standard metrics such as perplexity BIBREF3 , BIBREF4 ." ], [ "Deep learning has unquestionably advanced the state of the art in many natural language processing tasks, from syntactic dependency parsing BIBREF0 to named-entity recognition BIBREF1 to machine translation BIBREF2 . The same certainly applies to language modeling, where recent advances in neural language models (NLMs) have led to dramatically better approaches as measured using standard metrics such as perplexity BIBREF3 , BIBREF4 ." ] ]

[ "the Yelp Challenge dataset", "Yelp Challenge dataset BIBREF2" ]

[ [ "We use the Yelp Challenge dataset BIBREF2 for our fake review generation. The dataset (Aug 2017) contains 2.9 million 1 –5 star restaurant reviews. We treat all reviews as genuine human-written reviews for the purpose of this work, since wide-scale deployment of machine-generated review attacks are not yet reported (Sep 2017) BIBREF19 . As preprocessing, we remove non-printable (non-ASCII) characters and excessive white-space. We separate punctuation from words. We reserve 15,000 reviews for validation and 3,000 for testing, and the rest we use for training. NMT models require a parallel corpus of source and target sentences, i.e. a large set of (source, target)-pairs. We set up a parallel corpus by constructing (context, review)-pairs from the dataset. Next, we describe how we created our input context." ], [ "We use the Yelp Challenge dataset BIBREF2 for our fake review generation. The dataset (Aug 2017) contains 2.9 million 1 –5 star restaurant reviews. We treat all reviews as genuine human-written reviews for the purpose of this work, since wide-scale deployment of machine-generated review attacks are not yet reported (Sep 2017) BIBREF19 . As preprocessing, we remove non-printable (non-ASCII) characters and excessive white-space. We separate punctuation from words. We reserve 15,000 reviews for validation and 3,000 for testing, and the rest we use for training. NMT models require a parallel corpus of source and target sentences, i.e. a large set of (source, target)-pairs. We set up a parallel corpus by constructing (context, review)-pairs from the dataset. Next, we describe how we created our input context." ] ]

[ "No", "No" ]

[ [], [] ]

[ "Unanswerable" ]

[ [] ]

[ "AdaBoost-based classifier" ]

[ [ "We developed an AdaBoost-based classifier to detect our new fake reviews, consisting of 200 shallow decision trees (depth 2). The features we used are recorded in Table~\\ref{table:features_adaboost} (Appendix)." ] ]

[ "Yes" ]

[ [ "We noticed some variation in the detection of different fake review categories. The respondents in our MTurk survey had most difficulties recognizing reviews of category $(b=0.3, \\lambda=-5)$, where true positive rate was $40.4\\%$, while the true negative rate of the real class was $62.7\\%$. The precision were $16\\%$ and $86\\%$, respectively. The class-averaged F-score is $47.6\\%$, which is close to random. Detailed classification reports are shown in Table~\\ref{table:MTurk_sub} in Appendix. Our MTurk-study shows that \\emph{our NMT-Fake reviews pose a significant threat to review systems}, since \\emph{ordinary native English-speakers have very big difficulties in separating real reviews from fake reviews}. We use the review category $(b=0.3, \\lambda=-5)$ for future user tests in this paper, since MTurk participants had most difficulties detecting these reviews. We refer to this category as NMT-Fake* in this paper.", "Figure~\\ref{fig:adaboost_matrix_b_lambda} shows our AdaBoost classifier's class-averaged F-score at detecting different kind of fake reviews. The classifier is very effective in detecting reviews that humans have difficulties detecting. For example, the fake reviews MTurk users had most difficulty detecting ($b=0.3, \\lambda=-5$) are detected with an excellent 97\\% F-score." ] ]

[ "1,006 fake reviews and 994 real reviews" ]

[ [ "We first investigated overall detection of any NMT-Fake reviews (1,006 fake reviews and 994 real reviews). We found that the participants had big difficulties in detecting our fake reviews. In average, the reviews were detected with class-averaged \\emph{F-score of only 56\\%}, with 53\\% F-score for fake review detection and 59\\% F-score for real review detection. The results are very close to \\emph{random detection}, where precision, recall and F-score would each be 50\\%. Results are recorded in Table~\\ref{table:MTurk_super}. Overall, the fake review generation is very successful, since human detection rate across categories is close to random." ] ]

[ "The baseline model is a deep sequence-to-sequence encoder/decoder model with attention. The encoder is a bidirectional Long-Short Term Memory(LSTM) cell BIBREF14 and the decoder a single LSTM cell with attention mechanism. The attention mechanism is computed as in BIBREF9 and we use a greedy search for decoding. We train end-to-end including the words embeddings. The embedding size used is of 128 and the hidden state size of the LSTM cells is of 254.", "The baseline model is a deep sequence-to-sequence encoder/decoder model with attention. The encoder is a bidirectional Long-Short Term Memory(LSTM) cell BIBREF14 and the decoder a single LSTM cell with attention mechanism. The attention mechanism is computed as in BIBREF9 and we use a greedy search for decoding. We train end-to-end including the words embeddings. The embedding size used is of 128 and the hidden state size of the LSTM cells is of 254." ]

[ [ "We present in this section the baseline model from See et al. See2017 trained on the CNN/Daily Mail dataset. We reproduce the results from See et al. See2017 to then apply LRP on it.", "The baseline model is a deep sequence-to-sequence encoder/decoder model with attention. The encoder is a bidirectional Long-Short Term Memory(LSTM) cell BIBREF14 and the decoder a single LSTM cell with attention mechanism. The attention mechanism is computed as in BIBREF9 and we use a greedy search for decoding. We train end-to-end including the words embeddings. The embedding size used is of 128 and the hidden state size of the LSTM cells is of 254." ], [ "The baseline model is a deep sequence-to-sequence encoder/decoder model with attention. The encoder is a bidirectional Long-Short Term Memory(LSTM) cell BIBREF14 and the decoder a single LSTM cell with attention mechanism. The attention mechanism is computed as in BIBREF9 and we use a greedy search for decoding. We train end-to-end including the words embeddings. The embedding size used is of 128 and the hidden state size of the LSTM cells is of 254." ] ]

[ "one" ]

[ [ "The baseline model is a deep sequence-to-sequence encoder/decoder model with attention. The encoder is a bidirectional Long-Short Term Memory(LSTM) cell BIBREF14 and the decoder a single LSTM cell with attention mechanism. The attention mechanism is computed as in BIBREF9 and we use a greedy search for decoding. We train end-to-end including the words embeddings. The embedding size used is of 128 and the hidden state size of the LSTM cells is of 254." ] ]

[ "No" ]

[ [ "We showed that in some cases the saliency maps are truthful to the network's computation, meaning that they do highlight the input features that the network focused on. But we also showed that in some cases the saliency maps seem to not capture the important input features. This brought us to discuss the fact that these attributions are not sufficient by themselves, and that we need to define the counter-factual case and test it to measure how truthful the saliency maps are.", "The second observation we can make is that the saliency map doesn't seem to highlight the right things in the input for the summary it generates. The saliency maps on Figure 3 correspond to the summary from Figure 1 , and we don't see the word “video\" highlighted in the input text, which seems to be important for the output." ] ]

[ "We compare this method of bias mitigation with the no bias mitigation (\"Orig\"), geometric bias mitigation (\"Geo\"), the two pieces of our method alone (\"Prob\" and \"KNN\") and the composite method (\"KNN+Prob\"). We note that the composite method performs reasonably well according the the RIPA metric, and much better than traditional geometric bias mitigation according to the neighborhood metric, without significant performance loss according to the accepted benchmarks. To our knowledge this is the first bias mitigation method to perform reasonably both on both metrics.", "Unanswerable" ]

[ [ "We evaluate our framework on fastText embeddings trained on Wikipedia (2017), UMBC webbase corpus and statmt.org news dataset (16B tokens) BIBREF11. For simplicity, only the first 22000 words are used in all embeddings, though preliminary results indicate the findings extend to the full corpus. For our novel methods of mitigating bias, a shallow neural network is used to adjust the embedding. The single layer of the model is an embedding layer with weights initialized to those of the original embedding. For the composite method, these weights are initialized to those of the embedding after probabilistic bias mitigation. A batch of word indices is fed into the model, which are then embedded and for which a loss value is calculated, allowing back-propagation to adjust the embeddings. For each of the models, a fixed number of iterations is used to prevent overfitting, which can eventually hurt performance on the embedding benchmarks (See Figure FIGREF12). We evaluated the embedding after 1000 iterations, and stopped training if performance on a benchmark decreased significantly.", "We construct a list of candidate words to debias, taken from the words used in the WEAT gender bias statistics. Words in this list should be gender neutral, and are related to the topics of career, arts, science, math, family and professions (see appendix). We note that this list can easily be expanded to include a greater proportion of words in the corpus. For example, BIBREF4 suggested a method for identifying inappropriately gendered words using unsupervised learning.", "We compare this method of bias mitigation with the no bias mitigation (\"Orig\"), geometric bias mitigation (\"Geo\"), the two pieces of our method alone (\"Prob\" and \"KNN\") and the composite method (\"KNN+Prob\"). We note that the composite method performs reasonably well according the the RIPA metric, and much better than traditional geometric bias mitigation according to the neighborhood metric, without significant performance loss according to the accepted benchmarks. To our knowledge this is the first bias mitigation method to perform reasonably both on both metrics." ], [] ]

[ "RIPA, Neighborhood Metric, WEAT" ]

[ [ "Geometric bias mitigation uses the cosine distances between words to both measure and remove gender bias BIBREF0. This method implicitly defines bias as a geometric asymmetry between words when projected onto a subspace, such as the gender subspace constructed from a set of gender pairs such as $\\mathcal {P} = \\lbrace (he,she),(man,woman),(king,queen)...\\rbrace $. The projection of a vector $v$ onto $B$ (the subspace) is defined by $v_B = \\sum _{j=1}^{k} (v \\cdot b_j) b_j$ where a subspace $B$ is defined by k orthogonal unit vectors $B = {b_1,...,b_k}$.", "The WEAT statistic BIBREF1 demonstrates the presence of biases in word embeddings with an effect size defined as the mean test statistic across the two word sets:", "Where $s$, the test statistic, is defined as: $s(w,A,B) = mean_{a \\in A} cos(w,a) - mean_{b \\in B} cos(w,a)$, and $X$,$Y$,$A$, and $B$ are groups of words for which the association is measured. Possible values range from $-2$ to 2 depending on the association of the words groups, and a value of zero indicates $X$ and $Y$ are equally associated with $A$ and $B$. See BIBREF4 for further details on WEAT.", "The RIPA (relational inner product association) metric was developed as an alternative to WEAT, with the critique that WEAT is likely to overestimate the bias of a target attribute BIBREF4. The RIPA metric formalizes the measure of bias used in geometric bias mitigation as the inner product association of a word vector $v$ with respect to a relation vector $b$. The relation vector is constructed from the first principal component of the differences between gender word pairs. We report the absolute value of the RIPA metric as the value can be positive or negative according to the direction of the bias. A value of zero indicates a lack of bias, and the value is bound by $[-||w||,||w||]$.", "The neighborhood bias metric proposed by BIBREF5 quantifies bias as the proportion of male socially-biased words among the $k$ nearest socially-biased male and female neighboring words, whereby biased words are obtained by projecting neutral words onto a gender relation vector. As we only examine the target word among the 1000 most socially-biased words in the vocabulary (500 male and 500 female), a word’s bias is measured as the ratio of its neighborhood of socially-biased male and socially-biased female words, so that a value of 0.5 in this metric would indicate a perfectly unbiased word, and values closer to 0 and 1 indicate stronger bias." ] ]

[ "Unanswerable" ]

[ [] ]

[ "only high-quality data helps", "high-quality" ]

[ [ "The Spearman $\\rho $ correlation for fastText models on the curated small dataset (clean), C1, improves the baselines by a large margin ($\\rho =0.354$ for Twi and 0.322 for Yorùbá) even with a small dataset. The improvement could be justified just by the larger vocabulary in Twi, but in the case of Yorùbá the enhancement is there with almost half of the vocabulary size. We found out that adding some noisy texts (C2 dataset) slightly improves the correlation for Twi language but not for the Yorùbá language. The Twi language benefits from Wikipedia articles because its inclusion doubles the vocabulary and reduces the bias of the model towards religious texts. However, for Yorùbá, noisy texts often ignore diacritics or tonal marks which increases the vocabulary size at the cost of an increment in the ambiguity too. As a result, the correlation is slightly hurt. One would expect that training with more data would improve the quality of the embeddings, but we found out with the results obtained with the C3 dataset, that only high-quality data helps. The addition of JW300 boosts the vocabulary in both cases, but whereas for Twi the corpus mixes dialects and is noisy, for Yorùbá it is very clean and with full diacritics. Consequently, the best embeddings for Yorùbá are obtained when training with the C3 dataset, whereas for Twi, C2 is the best option. In both cases, the curated embeddings improve the correlation with human judgements on the similarity task a $\\Delta \\rho =+0.25$ or, equivalently, by an increment on $\\rho $ of 170% (Twi) and 180% (Yorùbá)." ], [ "The Spearman $\\rho $ correlation for fastText models on the curated small dataset (clean), C1, improves the baselines by a large margin ($\\rho =0.354$ for Twi and 0.322 for Yorùbá) even with a small dataset. The improvement could be justified just by the larger vocabulary in Twi, but in the case of Yorùbá the enhancement is there with almost half of the vocabulary size. We found out that adding some noisy texts (C2 dataset) slightly improves the correlation for Twi language but not for the Yorùbá language. The Twi language benefits from Wikipedia articles because its inclusion doubles the vocabulary and reduces the bias of the model towards religious texts. However, for Yorùbá, noisy texts often ignore diacritics or tonal marks which increases the vocabulary size at the cost of an increment in the ambiguity too. As a result, the correlation is slightly hurt. One would expect that training with more data would improve the quality of the embeddings, but we found out with the results obtained with the C3 dataset, that only high-quality data helps. The addition of JW300 boosts the vocabulary in both cases, but whereas for Twi the corpus mixes dialects and is noisy, for Yorùbá it is very clean and with full diacritics. Consequently, the best embeddings for Yorùbá are obtained when training with the C3 dataset, whereas for Twi, C2 is the best option. In both cases, the curated embeddings improve the correlation with human judgements on the similarity task a $\\Delta \\rho =+0.25$ or, equivalently, by an increment on $\\rho $ of 170% (Twi) and 180% (Yorùbá)." ] ]

[ "Unanswerable" ]

[ [] ]

[ "fastText, CWE-LP" ]

[ [ "As a first experiment, we compare the quality of fastText embeddings trained on (high-quality) curated data and (low-quality) massively extracted data for Twi and Yorùbá languages.", "The huge ambiguity in the written Twi language motivates the exploration of different approaches to word embedding estimations. In this work, we compare the standard fastText methodology to include sub-word information with the character-enhanced approach with position-based clustered embeddings (CWE-LP as introduced in Section SECREF17). With the latter, we expect to specifically address the ambiguity present in a language that does not translate the different oral tones on vowels into the written language." ] ]

[ "Unanswerable", "Unanswerable" ]

[ [], [] ]

[ "large Portuguese corpus" ]

[ [ "In BIBREF14 , several word embedding models trained in a large Portuguese corpus are evaluated. Within the Word2Vec model, two training strategies were used. In the first, namely Skip-Gram, the model is given the word and attempts to predict its neighboring words. The second, Continuous Bag-of-Words (CBOW), the model is given the sequence of words without the middle one and attempts to predict this omitted word. The latter was chosen for application in the present proposal.", "Using the word2vec model available in a public repository BIBREF14 , the proposal involves the analysis of the most similar analogies generated before and after the application of the BIBREF3 . The work is focused on the analysis of gender bias associated with professions in word embeddings. So therefore into the evaluation of the accuracy of the associations generated, aiming at achieving results as good as possible without prejudicing the evaluation metrics." ] ]

[ "Continuous Bag-of-Words (CBOW)" ]

[ [ "In BIBREF14 , several word embedding models trained in a large Portuguese corpus are evaluated. Within the Word2Vec model, two training strategies were used. In the first, namely Skip-Gram, the model is given the word and attempts to predict its neighboring words. The second, Continuous Bag-of-Words (CBOW), the model is given the sequence of words without the middle one and attempts to predict this omitted word. The latter was chosen for application in the present proposal." ] ]

[ "No", "Yes" ]

[ [], [ "In order to obtain the citation data of the Czech apex courts, it was necessary to recognize and extract the references from the CzCDC 1.0. Given that training data for both the reference recognition model BIBREF13, BIBREF34 and the text segmentation model BIBREF33 are publicly available, we were able to conduct extensive error analysis and put together a pipeline to arguably achieve the maximum efficiency in the task. The pipeline described in this part is graphically represented in Figure FIGREF10.", "At this point, it is necessary to evaluate the performance of the above mentioned part of the pipeline before proceeding further. The evaluation of the performance is summarised in Table TABREF11. It shows that organising the two models into the pipeline boosted the performance of the reference recognition model, leading to a higher F1 measure in the initial recognition of the text spans and their classification." ] ]

[ "it is necessary to evaluate the performance of the above mentioned part of the pipeline before proceeding further. The evaluation of the performance is summarised in Table TABREF11. It shows that organising the two models into the pipeline boosted the performance of the reference recognition model, leading to a higher F1 measure in the initial recognition of the text spans and their classification." ]

[ [ "In order to obtain the citation data of the Czech apex courts, it was necessary to recognize and extract the references from the CzCDC 1.0. Given that training data for both the reference recognition model BIBREF13, BIBREF34 and the text segmentation model BIBREF33 are publicly available, we were able to conduct extensive error analysis and put together a pipeline to arguably achieve the maximum efficiency in the task. The pipeline described in this part is graphically represented in Figure FIGREF10.", "At this point, it is necessary to evaluate the performance of the above mentioned part of the pipeline before proceeding further. The evaluation of the performance is summarised in Table TABREF11. It shows that organising the two models into the pipeline boosted the performance of the reference recognition model, leading to a higher F1 measure in the initial recognition of the text spans and their classification." ] ]

[ "903019 references" ]

[ [ "Overall, through the process described in Section SECREF3, we have retrieved three datasets of extracted references - one dataset per each of the apex courts. These datasets consist of the individual pairs containing the identification of the decision from which the reference was retrieved, and the identification of the referred documents. As we only extracted references to other judicial decisions, we obtained 471,319 references from Supreme Court decisions, 167,237 references from Supreme Administrative Court decisions and 264,463 references from Constitutional Court Decisions. These are numbers of text spans identified as references prior the further processing described in Section SECREF3." ] ]

[ "Unanswerable" ]

[ [] ]

[ "No" ]

[ [] ]

[ "Given we have four intensity, No PTSD, Low Risk PTSD, Moderate Risk PTSD and High Risk PTSD with a score of 0, 1, 2 and 3 respectively, the estimated intensity is established as mean squared error.", "defined into four categories from high risk, moderate risk, to low risk" ]

[ [ "To provide an initial results, we take 50% of users' last week's (the week they responded of having PTSD) data to develop PTSD Linguistic dictionary and apply LAXARY framework to fill up surveys on rest of 50% dataset. The distribution of this training-test dataset segmentation followed a 50% distribution of PTSD and No PTSD from the original dataset. Our final survey based classification results showed an accuracy of 96% in detecting PTSD and mean squared error of 1.2 in estimating its intensity given we have four intensity, No PTSD, Low Risk PTSD, Moderate Risk PTSD and High Risk PTSD with a score of 0, 1, 2 and 3 respectively. Table TABREF29 shows the classification details of our experiment which provide the very good accuracy of our classification. To compare the outperformance of our method, we also implemented Coppersmith et. al. proposed method and achieved an 86% overall accuracy of detecting PTSD users BIBREF11 following the same training-test dataset distribution. Fig FIGREF28 illustrates the comparisons between LAXARY and Coppersmith et. al. proposed method. Here we can see, the outperformance of our proposed method as well as the importance of $s-score$ estimation. We also illustrates the importance of $\\alpha -score$ and $S-score$ in Fig FIGREF30. Fig FIGREF30 illustrates that if we change the number of training samples (%), LAXARY models outperforms Coppersmith et. al. proposed model under any condition. In terms of intensity, Coppersmith et. al. totally fails to provide any idea however LAXARY provides extremely accurate measures of intensity estimation for PTSD sufferers (as shown in Fig FIGREF31) which can be explained simply providing LAXARY model filled out survey details. Table TABREF29 shows the details of accuracies of both PTSD detection and intensity estimation. Fig FIGREF32 shows the classification accuracy changes over the training sample sizes for each survey which shows that DOSPERT scale outperform other surveys. Fig FIGREF33 shows that if we take previous weeks (instead of only the week diagnosis of PTSD was taken), there are no significant patterns of PTSD detection." ], [ "There are many clinically validated PTSD assessment tools that are being used both to detect the prevalence of PTSD and its intensity among sufferers. Among all of the tools, the most popular and well accepted one is Domain-Specific Risk-Taking (DOSPERT) Scale BIBREF15. This is a psychometric scale that assesses risk taking in five content domains: financial decisions (separately for investing versus gambling), health/safety, recreational, ethical, and social decisions. Respondents rate the likelihood that they would engage in domain-specific risky activities (Part I). An optional Part II assesses respondents' perceptions of the magnitude of the risks and expected benefits of the activities judged in Part I. There are more scales that are used in risky behavior analysis of individual's daily activities such as, The Berlin Social Support Scales (BSSS) BIBREF16 and Values In Action Scale (VIAS) BIBREF17. Dryhootch America BIBREF18, BIBREF19, a veteran peer support community organization, chooses 5, 6 and 5 questions respectively from the above mentioned survey systems to assess the PTSD among war veterans and consider rest of them as irrelevant to PTSD. The details of dryhootch chosen survey scale are stated in Table TABREF13. Table!TABREF14 shows a sample DOSPERT scale demographic chosen by dryhootch. The threshold (in Table TABREF13) is used to calculate the risky behavior limits. For example, if one individual's weekly DOSPERT score goes over 28, he is in critical situation in terms of risk taking symptoms of PTSD. Dryhootch defines the intensity of PTSD into four categories based on the weekly survey results of all three clinical survey tools (DOSPERT, BSSS and VIAS )", "High risk PTSD: If one individual veteran's weekly PTSD assessment scores go above the threshold for all three PTSD assessment tools i.e. DOSPERT, BSSS and VIAS, then he/she is in high risk situation which needs immediate mental support to avoid catastrophic effect of individual's health or surrounding people's life.", "Moderate risk PTSD: If one individual veteran's weekly PTSD assessment scores go above the threshold for any two of the three PTSD assessment tools, then he/she is in moderate risk situation which needs close observation and peer mentoring to avoid their risk progression.", "Low risk PTSD: If one individual veteran's weekly PTSD assessment scores go above the threshold for any one of the three PTSD assessment tools, then he/she has light symptoms of PTSD.", "No PTSD: If one individual veteran's weekly PTSD assessment scores go below the threshold for all three PTSD assessment tools, then he/she has no PTSD." ] ]

[ " For each user, we calculate the proportion of tweets scored positively by each LIWC category.", "to calculate the possible scores of each survey question using PTSD Linguistic Dictionary " ]

[ [ "A threshold of 1 for $s-score$ divides scores into positive and negative classes. In a multi-class setting, the algorithm minimizes the cross entropy, selecting the model with the highest probability. For each user, we calculate the proportion of tweets scored positively by each LIWC category. These proportions are used as a feature vector in a loglinear regression model BIBREF20. Prior to training, we preprocess the text of each tweet: we replace all usernames with a single token (USER), lowercase all text, and remove extraneous whitespace. We also exclude any tweet that contained a URL, as these often pertain to events external to the user." ], [ "LAXARY includes a modified LIWC model to calculate the possible scores of each survey question using PTSD Linguistic Dictionary to fill out the PTSD assessment surveys which provides a practical way not only to determine fine-grained discrimination of physiological and psychological health markers of PTSD without incurring the expensive and laborious in-situ laboratory testing or surveys, but also obtain trusts of clinicians who are expected to see traditional survey results of the PTSD assessment." ] ]

[ "210" ]

[ [ "We download 210 users' all twitter posts who are war veterans and clinically diagnosed with PTSD sufferers as well which resulted a total 12,385 tweets. Fig FIGREF16 shows each of the 210 veteran twitter users' monthly average tweets. We categorize these Tweets into two groups: Tweets related to work and Tweets not related to work. That is, only the Tweets that use a form of the word “work*” (e.g. work,worked, working, worker, etc.) or “job*” (e.g. job, jobs, jobless, etc.) are identified as work-related Tweets, with the remaining categorized as non-work-related Tweets. This categorization method increases the likelihood that most Tweets in the work group are indeed talking about work or job; for instance, “Back to work. Projects are firing back up and moving ahead now that baseball is done.” This categorization results in 456 work-related Tweets, about 5.4% of all Tweets written in English (and 75 unique Twitter users). To conduct weekly-level analysis, we consider three categorizations of Tweets (i.e. overall Tweets, work-related Tweets, and non work-related Tweets) on a daily basis, and create a text file for each week for each group." ] ]

[ "DOSPERT, BSSS and VIAS" ]

[ [ "We use an automated regular expression based searching to find potential veterans with PTSD in twitter, and then refine the list manually. First, we select different keywords to search twitter users of different categories. For example, to search self-claimed diagnosed PTSD sufferers, we select keywords related to PTSD for example, post trauma, post traumatic disorder, PTSD etc. We use a regular expression to search for statements where the user self-identifies as being diagnosed with PTSD. For example, Table TABREF27 shows a self-identified tweet posts. To search veterans, we mostly visit to different twitter accounts of veterans organizations such as \"MA Women Veterans @WomenVeterans\", \"Illinois Veterans @ILVetsAffairs\", \"Veterans Benefits @VAVetBenefits\" etc. We define an inclusion criteria as follows: one twitter user will be part of this study if he/she describes himself/herself as a veteran in the introduction and have at least 25 tweets in last week. After choosing the initial twitter users, we search for self-identified PTSD sufferers who claim to be diagnosed with PTSD in their twitter posts. We find 685 matching tweets which are manually reviewed to determine if they indicate a genuine statement of a diagnosis for PTSD. Next, we select the username that authored each of these tweets and retrieve last week's tweets via the Twitter API. We then filtered out users with less than 25 tweets and those whose tweets were not at least 75% in English (measured using an automated language ID system.) This filtering left us with 305 users as positive examples. We repeated this process for a group of randomly selected users. We randomly selected 3,000 twitter users who are veterans as per their introduction and have at least 25 tweets in last one week. After filtering (as above) in total 2,423 users remain, whose tweets are used as negative examples developing a 2,728 user's entire weeks' twitter posts where 305 users are self-claimed PTSD sufferers. We distributed Dryhootch chosen surveys among 1,200 users (305 users are self claimed PTSD sufferers and rest of them are randomly chosen from previous 2,423 users) and received 210 successful responses. Among these responses, 92 users were diagnosed as PTSD by any of the three surveys and rest of the 118 users are diagnosed with NO PTSD. Among the clinically diagnosed PTSD sufferers, 17 of them were not self-identified before. However, 7 of the self-identified PTSD sufferers are assessed with no PTSD by PTSD assessment tools. The response rates of PTSD and NO PTSD users are 27% and 12%. In summary, we have collected one week of tweets from 2,728 veterans where 305 users claimed to have diagnosed with PTSD. After distributing Dryhootch surveys, we have a dataset of 210 veteran twitter users among them 92 users are assessed with PTSD and 118 users are diagnosed with no PTSD using clinically validated surveys. The severity of the PTSD are estimated as Non-existent, light, moderate and high PTSD based on how many surveys support the existence of PTSD among the participants according to dryhootch manual BIBREF18, BIBREF19.", "There are many clinically validated PTSD assessment tools that are being used both to detect the prevalence of PTSD and its intensity among sufferers. Among all of the tools, the most popular and well accepted one is Domain-Specific Risk-Taking (DOSPERT) Scale BIBREF15. This is a psychometric scale that assesses risk taking in five content domains: financial decisions (separately for investing versus gambling), health/safety, recreational, ethical, and social decisions. Respondents rate the likelihood that they would engage in domain-specific risky activities (Part I). An optional Part II assesses respondents' perceptions of the magnitude of the risks and expected benefits of the activities judged in Part I. There are more scales that are used in risky behavior analysis of individual's daily activities such as, The Berlin Social Support Scales (BSSS) BIBREF16 and Values In Action Scale (VIAS) BIBREF17. Dryhootch America BIBREF18, BIBREF19, a veteran peer support community organization, chooses 5, 6 and 5 questions respectively from the above mentioned survey systems to assess the PTSD among war veterans and consider rest of them as irrelevant to PTSD. The details of dryhootch chosen survey scale are stated in Table TABREF13. Table!TABREF14 shows a sample DOSPERT scale demographic chosen by dryhootch. The threshold (in Table TABREF13) is used to calculate the risky behavior limits. For example, if one individual's weekly DOSPERT score goes over 28, he is in critical situation in terms of risk taking symptoms of PTSD. Dryhootch defines the intensity of PTSD into four categories based on the weekly survey results of all three clinical survey tools (DOSPERT, BSSS and VIAS )" ] ]

[ "Yes" ]

[ [ "In Figure FIGREF28, we show some examples of the annotation results in CORD-19-NER. We can see that our distantly- or weakly supervised methods achieve high quality recognizing the new entity types, requiring only several seed examples as the input. For example, we recognized “SARS-CoV-2\" as the “CORONAVIRUS\" type, “bat\" and “pangolins\" as the “WILDLIFE\" type and “Van der Waals forces\" as the “PHYSICAL_SCIENCE\" type. This NER annotation results help downstream text mining tasks in discovering the origin and the physical nature of the virus. Our NER methods are domain-independent that can be applied to corpus in different domains. In addition, we show another example of NER annotation on New York Times with our system in Figure FIGREF29.", "In Figure FIGREF30, we show the comparison of our annotation results with existing NER/BioNER systems. In Figure FIGREF30, we can see that only our method can identify “SARS-CoV-2\" as a coronavirus. In Figure FIGREF30, we can see that our method can identify many more entities such as “pylogenetic\" as a evolution term and “bat\" as a wildlife. In Figure FIGREF30, we can also see that our method can identify many more entities such as “racism\" as a social behavior. In summary, our distantly- and weakly-supervised NER methods are reliable for high-quality entity recognition without requiring human effort for training data annotation." ] ]

[ "29,500 documents", "29,500 documents in the CORD-19 corpus (2020-03-13)" ]

[ [ "Named entity recognition (NER) is a fundamental step in text mining system development to facilitate the COVID-19 studies. There is critical need for NER methods that can quickly adapt to all the COVID-19 related new types without much human effort for training data annotation. We created this CORD-19-NER dataset with comprehensive named entity annotation on the CORD-19 corpus (2020-03-13). This dataset covers 75 fine-grained named entity types. CORD-19-NER is automatically generated by combining the annotation results from four sources. In the following sections, we introduce the details of CORD-19-NER dataset construction. We also show some NER annotation results in this dataset.", "The corpus is generated from the 29,500 documents in the CORD-19 corpus (2020-03-13). We first merge all the meta-data (all_sources_metadata_2020-03-13.csv) with their corresponding full-text papers. Then we create a tokenized corpus (CORD-19-corpus.json) for further NER annotations." ], [ "The corpus is generated from the 29,500 documents in the CORD-19 corpus (2020-03-13). We first merge all the meta-data (all_sources_metadata_2020-03-13.csv) with their corresponding full-text papers. Then we create a tokenized corpus (CORD-19-corpus.json) for further NER annotations." ] ]

[ "No" ]

[ [] ]

[ "Accuracy and the macro-F1 (averaged F1 over positive and negative classes) are used as a measure of quality.", "Unanswerable" ]

[ [], [] ]

[ "Unanswerable" ]

[ [] ]

[ "manually created lexicon in Czech BIBREF11 , German BIBREF12 , French BIBREF13 , Macedonian BIBREF14 , and Spanish BIBREF15" ]

[ [ "As the gold standard sentiment lexica, we chose manually created lexicon in Czech BIBREF11 , German BIBREF12 , French BIBREF13 , Macedonian BIBREF14 , and Spanish BIBREF15 . These lexica contain general domain words (as opposed to Twitter or Bible). As gold standard for twitter domain we use emoticon dataset and perform emoticon sentiment prediction BIBREF16 , BIBREF17 ." ] ]

[ "Yes", "Yes" ]

[ [ "The task of Word Sense Induction (WSI) can be seen as an unsupervised version of WSD. WSI aims at clustering word senses and does not require to map each cluster to a predefined sense. Instead of that, word sense inventories are induced automatically from the clusters, treating each cluster as a single sense of a word. WSI approaches fall into three main groups: context clustering, word ego-network clustering and synonyms (or substitute) clustering.", "We suggest a more advanced procedure of graph construction that uses the interpretability of vector addition and subtraction operations in word embedding space BIBREF6 while the previous algorithm only relies on the list of nearest neighbours in word embedding space. The key innovation of our algorithm is the use of vector subtraction to find pairs of most dissimilar graph nodes and construct the graph only from the nodes included in such “anti-edges”. Thus, our algorithm is based on graph-based word sense induction, but it also relies on vector-based operations between word embeddings to perform filtering of graph nodes. Analogously to the work of Pelevina:16, we construct a semantic relatedness graph from a list of nearest neighbours, but we filter this list using the following procedure:" ], [ "We suggest a more advanced procedure of graph construction that uses the interpretability of vector addition and subtraction operations in word embedding space BIBREF6 while the previous algorithm only relies on the list of nearest neighbours in word embedding space. The key innovation of our algorithm is the use of vector subtraction to find pairs of most dissimilar graph nodes and construct the graph only from the nodes included in such “anti-edges”. Thus, our algorithm is based on graph-based word sense induction, but it also relies on vector-based operations between word embeddings to perform filtering of graph nodes. Analogously to the work of Pelevina:16, we construct a semantic relatedness graph from a list of nearest neighbours, but we filter this list using the following procedure:" ] ]

[ "The contexts are manually labelled with WordNet senses of the target words" ]

[ [ "The dataset consists of a set of polysemous words: 20 nouns, 20 verbs, and 10 adjectives and specifies 20 to 100 contexts per word, with the total of 4,664 contexts, drawn from the Open American National Corpus. Given a set of contexts of a polysemous word, the participants of the competition had to divide them into clusters by sense of the word. The contexts are manually labelled with WordNet senses of the target words, the gold standard clustering is generated from this labelling.", "The task allows two setups: graded WSI where participants can submit multiple senses per word and provide the probability of each sense in a particular context, and non-graded WSI where a model determines a single sense for a word in context. In our experiments we performed non-graded WSI. We considered the most suitable sense as the one with the highest cosine similarity with embeddings of the context, as described in Section SECREF9." ] ]

[ "Yes" ]

[ [ "We first evaluate our converted embedding models on multi-language lexical similarity and relatedness tasks, as a sanity check, to make sure the word sense induction process did not hurt the general performance of the embeddings. Then, we test the sense embeddings on WSD task." ] ]

[ "No" ]

[ [] ]

[ "Yes", "No" ]

[ [ "In Table TABREF1, we summarize the quantitative results of the above previous studies. It includes the model basis, feature description, languages classified and the used dataset along with accuracy obtained. The table also lists the overall results of our proposed models (at the top). The languages used by various authors along with their acronyms are English (En), Spanish (Es), French (Fr), German (De), Russian (Ru), Italian (It), Bengali (Ben), Hindi (Hi) and Telegu (Tel)." ], [] ]

[ "Answer with content missing: (Table 1)\nPrevious state-of-the art on same dataset: ResNet50 89% (6 languages), SVM-HMM 70% (4 languages)" ]

[ [ "In Table TABREF1, we summarize the quantitative results of the above previous studies. It includes the model basis, feature description, languages classified and the used dataset along with accuracy obtained. The table also lists the overall results of our proposed models (at the top). The languages used by various authors along with their acronyms are English (En), Spanish (Es), French (Fr), German (De), Russian (Ru), Italian (It), Bengali (Ben), Hindi (Hi) and Telegu (Tel)." ] ]

[ "CNN-mean, CNN-avgmax" ]

[ [ "We compare our approach with two baseline vision-based methods proposed in BIBREF6 , BIBREF7 , which measure the similarity of two sets of global visual features for bilingual lexicon induction:", "CNN-mean: taking the similarity score of the averaged feature of the two image sets.", "CNN-avgmax: taking the average of the maximum similarity scores of two image sets." ] ]

[ "CNN-mean, CNN-avgmax" ]

[ [ "We compare our approach with two baseline vision-based methods proposed in BIBREF6 , BIBREF7 , which measure the similarity of two sets of global visual features for bilingual lexicon induction:", "CNN-mean: taking the similarity score of the averaged feature of the two image sets.", "CNN-avgmax: taking the average of the maximum similarity scores of two image sets." ] ]

[ "German-English, French-English, and Japanese-English", "multiple language pairs including German-English, French-English, and Japanese-English." ]

[ [ "We carry out experiments on multiple language pairs including German-English, French-English, and Japanese-English. The experimental results show that the proposed multi-lingual caption model not only achieves better caption performance than independent mono-lingual models for data-scarce languages, but also can induce the two types of features, linguistic and visual features, for different languages in joint spaces. Our proposed method consistently outperforms previous state-of-the-art vision-based bilingual word induction approaches on different languages. The contributions of this paper are as follows:" ], [ "We carry out experiments on multiple language pairs including German-English, French-English, and Japanese-English. The experimental results show that the proposed multi-lingual caption model not only achieves better caption performance than independent mono-lingual models for data-scarce languages, but also can induce the two types of features, linguistic and visual features, for different languages in joint spaces. Our proposed method consistently outperforms previous state-of-the-art vision-based bilingual word induction approaches on different languages. The contributions of this paper are as follows:" ] ]

[ "Yes" ]

[ [ "Implementation & Models Parameters. For all our tasks, we use the BERT-Base Multilingual Cased model released by the authors . The model is trained on 104 languages (including Arabic) with 12 layer, 768 hidden units each, 12 attention heads, and has 110M parameters in entire model. The model has 119,547 shared WordPieces vocabulary, and was pre-trained on the entire Wikipedia for each language. For fine-tuning, we use a maximum sequence size of 50 tokens and a batch size of 32. We set the learning rate to $2e-5$ and train for 15 epochs and choose the best model based on performance on a development set. We use the same hyper-parameters in all of our BERT models. We fine-tune BERT on each respective labeled dataset for each task. For BERT input, we apply WordPiece tokenization, setting the maximal sequence length to 50 words/WordPieces. For all tasks, we use a TensorFlow implementation. An exception is the sentiment analysis task, where we used a PyTorch implementation with the same hyper-parameters but with a learning rate $2e-6$.", "We presented AraNet, a deep learning toolkit for a host of Arabic social media processing. AraNet predicts age, dialect, gender, emotion, irony, and sentiment from social media posts. It delivers state-of-the-art and competitive performance on these tasks and has the advantage of using a unified, simple framework based on the recently-developed BERT model. AraNet has the potential to alleviate issues related to comparing across different Arabic social media NLP tasks, by providing one way to test new models against AraNet predictions. Our toolkit can be used to make important discoveries on the wide region of the Arab world, and can enhance our understating of Arab online communication. AraNet will be publicly available upon acceptance." ] ]

[ " we do not explicitly compare to previous research since most existing works either exploit smaller data (and so it will not be a fair comparison), use methods pre-dating BERT (and so will likely be outperformed by our models)" ]

[ [ "Although we create new models for tasks such as sentiment analysis and gender detection as part of AraNet, our focus is more on putting together the toolkit itself and providing strong baselines that can be compared to. Hence, although we provide some baseline models for some of the tasks, we do not explicitly compare to previous research since most existing works either exploit smaller data (and so it will not be a fair comparison), use methods pre-dating BERT (and so will likely be outperformed by our models) . For many of the tasks we model, there have not been standard benchmarks for comparisons across models. This makes it difficult to measure progress and identify areas worthy of allocating efforts and budgets. As such, by publishing our toolkit models, we believe model-based comparisons will be one way to relieve this bottleneck. For these reasons, we also package models from our recent works on dialect BIBREF12 and irony BIBREF14 as part of AraNet ." ] ]

[ "Arap-Tweet BIBREF19 , an in-house Twitter dataset for gender, the MADAR shared task 2 BIBREF20, the LAMA-DINA dataset from BIBREF22, LAMA-DIST, Arabic tweets released by IDAT@FIRE2019 shared-task BIBREF24, BIBREF25, BIBREF26, BIBREF27, BIBREF1, BIBREF28, BIBREF29, BIBREF30, BIBREF31, BIBREF32, BIBREF33, BIBREF34", " Arap-Tweet , UBC Twitter Gender Dataset, MADAR , LAMA-DINA , IDAT@FIRE2019, 15 datasets related to sentiment analysis of Arabic, including MSA and dialects" ]

[ [ "Arab-Tweet. For modeling age and gender, we use Arap-Tweet BIBREF19 , which we will refer to as Arab-Tweet. Arab-tweet is a tweet dataset of 11 Arabic regions from 17 different countries. For each region, data from 100 Twitter users were crawled. Users needed to have posted at least 2,000 and were selected based on an initial list of seed words characteristic of each region. The seed list included words such as <برشة> /barsha/ ‘many’ for Tunisian Arabic and <وايد> /wayed/ ‘many’ for Gulf Arabic. BIBREF19 employed human annotators to verify that users do belong to each respective region. Annotators also assigned gender labels from the set male, female and age group labels from the set under-25, 25-to34, above-35 at the user-level, which in turn is assigned at tweet level. Tweets with less than 3 words and re-tweets were removed. Refer to BIBREF19 for details about how annotation was carried out. We provide a description of the data in Table TABREF10. Table TABREF10 also provides class breakdown across our splits.We note that BIBREF19 do not report classification models exploiting the data.", "UBC Twitter Gender Dataset. We also develop an in-house Twitter dataset for gender. We manually labeled 1,989 users from each of the 21 Arab countries. The data had 1,246 “male\", 528 “female\", and 215 unknown users. We remove the “unknown\" category and balance the dataset to have 528 from each of the two `male\" and “female\" categories. We ended with 69,509 tweets for `male\" and 67,511 tweets for “female\". We split the users into 80% TRAIN set (110,750 tweets for 845 users), 10% DEV set (14,158 tweets for 106 users), and 10% TEST set (12,112 tweets for 105 users). We, then, model this dataset with BERT-Base, Multilingual Cased model and evaluate on development and test sets. Table TABREF15 shows that fine-tuned model obtains 62.42% acc on DEV and 60.54% acc on TEST.", "The dialect identification model in AraNet is based on our winning system in the MADAR shared task 2 BIBREF20 as described in BIBREF12. The corpus is divided into train, dev and test, and the organizers masked test set labels. We lost some tweets from training data when we crawled using tweet ids, ultimately acquiring 2,036 (TRAIN-A), 281 (DEV) and 466 (TEST). We also make use of the task 1 corpus (95,000 sentences BIBREF21). More specifically, we concatenate the task 1 data to the training data of task 2, to create TRAIN-B. Again, note that TEST labels were only released to participants after the official task evaluation. Table TABREF17 shows statistics of the data. We used tweets from 21 Arab countries as distributed by task organizers, except that we lost some tweets when we crawled using tweet ids. We had 2,036 (TRAIN-A), 281 (DEV) and 466 (TEST). For our experiments, we also make use of the task 1 corpus (95,000 sentences BIBREF21). More specifically, we concatenate the task 1 data to the training data of task 2, to create TRAIN-B. Note that both DEV and TEST across our experiments are exclusively the data released in task 2, as described above. TEST labels were only released to participants after the official task evaluation. Table TABREF17 shows statistics of the data. More information about the data is in BIBREF21. We use TRAIN-A to perform supervised modeling with BERT and TRAIN-B for self training, under various conditions. We refer the reader to BIBREF12 for more information about our different experimental settings on dialect id. We acquire our best results with self-training, with a classification accuracy of 49.39% and F1 score at 35.44. This is the winning system model in the MADAR shared task and we showed in BIBREF12 that our tweet-level predictions can be ported to user-level prediction. On user-level detection, our models perform superbly, with 77.40% acc and 71.70% F1 score on unseen MADAR blind test data.", "We make use of two datasets, the LAMA-DINA dataset from BIBREF22, a Twitter dataset with a combination of gold labels from BIBREF23 and distant supervision labels. The tweets are labeled with the Plutchik 8 primary emotions from the set: {anger, anticipation, disgust, fear, joy, sadness, surprise, trust}. The distant supervision approach depends on use of seed phrases with the Arabic first person pronoun انا> (Eng. “I\") + a seed word expressing an emotion, e.g., فرحان> (Eng. “happy\"). The manually labeled part of the data comprises tweets carrying the seed phrases verified by human annotators $9,064$ tweets for inclusion of the respective emotion. The rest of the dataset is only labeled using distant supervision (LAMA-DIST) ($182,605$ tweets) . For more information about the dataset, readers are referred to BIBREF22. The data distribution over the emotion classes is in Table TABREF20. We combine LAMA+DINA and LAMA-DIST training set and refer to this new training set as LAMA-D2 (189,903 tweets). We fine-tune BERT-Based, Multilingual Cased on the LAMA-D2 and evaluate the model with same DEV and TEST sets from LAMA+DINA. On DEV set, the fine-tuned BERT model obtains 61.43% on accuracy and 58.83 on $F_1$ score. On TEST set, we acquire 62.38% acc and 60.32% $F_1$ score.", "We use the dataset for irony identification on Arabic tweets released by IDAT@FIRE2019 shared-task BIBREF24. The shared task dataset contains 5,030 tweets related to different political issues and events in the Middle East taking place between 2011 and 2018. Tweets are collected using pre-defined keywords (i.e., targeted political figures or events) and the positive class involves ironic hashtags such as #sokhria, #tahakoum, and #maskhara (Arabic variants for “irony\"). Duplicates, retweets, and non-intelligible tweets are removed by organizers. Tweets involve both MSA as well as dialects at various degrees of granularity such as Egyptian, Gulf, and Levantine.", "We collect 15 datasets related to sentiment analysis of Arabic, including MSA and dialects BIBREF25, BIBREF26, BIBREF27, BIBREF1, BIBREF28, BIBREF29, BIBREF30, BIBREF31, BIBREF32, BIBREF33, BIBREF34. Table TABREF28 shows all the corpora we use. These datasets involve different types of sentiment analysis tasks such as binary classification (i.e., negative or positive), 3-way classification (i.e., negative, neutral, or positive), and subjective language detection. To combine these datasets for binary sentiment classification, we normalize different types of label to binary labels in the set $\\lbrace `positive^{\\prime }, `negative^{\\prime }\\rbrace $ by following rules:" ], [ "Arab-Tweet. For modeling age and gender, we use Arap-Tweet BIBREF19 , which we will refer to as Arab-Tweet. Arab-tweet is a tweet dataset of 11 Arabic regions from 17 different countries. For each region, data from 100 Twitter users were crawled. Users needed to have posted at least 2,000 and were selected based on an initial list of seed words characteristic of each region. The seed list included words such as <برشة> /barsha/ ‘many’ for Tunisian Arabic and <وايد> /wayed/ ‘many’ for Gulf Arabic. BIBREF19 employed human annotators to verify that users do belong to each respective region. Annotators also assigned gender labels from the set male, female and age group labels from the set under-25, 25-to34, above-35 at the user-level, which in turn is assigned at tweet level. Tweets with less than 3 words and re-tweets were removed. Refer to BIBREF19 for details about how annotation was carried out. We provide a description of the data in Table TABREF10. Table TABREF10 also provides class breakdown across our splits.We note that BIBREF19 do not report classification models exploiting the data.", "UBC Twitter Gender Dataset. We also develop an in-house Twitter dataset for gender. We manually labeled 1,989 users from each of the 21 Arab countries. The data had 1,246 “male\", 528 “female\", and 215 unknown users. We remove the “unknown\" category and balance the dataset to have 528 from each of the two `male\" and “female\" categories. We ended with 69,509 tweets for `male\" and 67,511 tweets for “female\". We split the users into 80% TRAIN set (110,750 tweets for 845 users), 10% DEV set (14,158 tweets for 106 users), and 10% TEST set (12,112 tweets for 105 users). We, then, model this dataset with BERT-Base, Multilingual Cased model and evaluate on development and test sets. Table TABREF15 shows that fine-tuned model obtains 62.42% acc on DEV and 60.54% acc on TEST.", "The dialect identification model in AraNet is based on our winning system in the MADAR shared task 2 BIBREF20 as described in BIBREF12. The corpus is divided into train, dev and test, and the organizers masked test set labels. We lost some tweets from training data when we crawled using tweet ids, ultimately acquiring 2,036 (TRAIN-A), 281 (DEV) and 466 (TEST). We also make use of the task 1 corpus (95,000 sentences BIBREF21). More specifically, we concatenate the task 1 data to the training data of task 2, to create TRAIN-B. Again, note that TEST labels were only released to participants after the official task evaluation. Table TABREF17 shows statistics of the data. We used tweets from 21 Arab countries as distributed by task organizers, except that we lost some tweets when we crawled using tweet ids. We had 2,036 (TRAIN-A), 281 (DEV) and 466 (TEST). For our experiments, we also make use of the task 1 corpus (95,000 sentences BIBREF21). More specifically, we concatenate the task 1 data to the training data of task 2, to create TRAIN-B. Note that both DEV and TEST across our experiments are exclusively the data released in task 2, as described above. TEST labels were only released to participants after the official task evaluation. Table TABREF17 shows statistics of the data. More information about the data is in BIBREF21. We use TRAIN-A to perform supervised modeling with BERT and TRAIN-B for self training, under various conditions. We refer the reader to BIBREF12 for more information about our different experimental settings on dialect id. We acquire our best results with self-training, with a classification accuracy of 49.39% and F1 score at 35.44. This is the winning system model in the MADAR shared task and we showed in BIBREF12 that our tweet-level predictions can be ported to user-level prediction. On user-level detection, our models perform superbly, with 77.40% acc and 71.70% F1 score on unseen MADAR blind test data.", "Data and Models ::: Emotion", "We make use of two datasets, the LAMA-DINA dataset from BIBREF22, a Twitter dataset with a combination of gold labels from BIBREF23 and distant supervision labels. The tweets are labeled with the Plutchik 8 primary emotions from the set: {anger, anticipation, disgust, fear, joy, sadness, surprise, trust}. The distant supervision approach depends on use of seed phrases with the Arabic first person pronoun انا> (Eng. “I\") + a seed word expressing an emotion, e.g., فرحان> (Eng. “happy\"). The manually labeled part of the data comprises tweets carrying the seed phrases verified by human annotators $9,064$ tweets for inclusion of the respective emotion. The rest of the dataset is only labeled using distant supervision (LAMA-DIST) ($182,605$ tweets) . For more information about the dataset, readers are referred to BIBREF22. The data distribution over the emotion classes is in Table TABREF20. We combine LAMA+DINA and LAMA-DIST training set and refer to this new training set as LAMA-D2 (189,903 tweets). We fine-tune BERT-Based, Multilingual Cased on the LAMA-D2 and evaluate the model with same DEV and TEST sets from LAMA+DINA. On DEV set, the fine-tuned BERT model obtains 61.43% on accuracy and 58.83 on $F_1$ score. On TEST set, we acquire 62.38% acc and 60.32% $F_1$ score.", "We use the dataset for irony identification on Arabic tweets released by IDAT@FIRE2019 shared-task BIBREF24. The shared task dataset contains 5,030 tweets related to different political issues and events in the Middle East taking place between 2011 and 2018. Tweets are collected using pre-defined keywords (i.e., targeted political figures or events) and the positive class involves ironic hashtags such as #sokhria, #tahakoum, and #maskhara (Arabic variants for “irony\"). Duplicates, retweets, and non-intelligible tweets are removed by organizers. Tweets involve both MSA as well as dialects at various degrees of granularity such as Egyptian, Gulf, and Levantine.", "We collect 15 datasets related to sentiment analysis of Arabic, including MSA and dialects BIBREF25, BIBREF26, BIBREF27, BIBREF1, BIBREF28, BIBREF29, BIBREF30, BIBREF31, BIBREF32, BIBREF33, BIBREF34. Table TABREF28 shows all the corpora we use. These datasets involve different types of sentiment analysis tasks such as binary classification (i.e., negative or positive), 3-way classification (i.e., negative, neutral, or positive), and subjective language detection. To combine these datasets for binary sentiment classification, we normalize different types of label to binary labels in the set $\\lbrace `positive^{\\prime }, `negative^{\\prime }\\rbrace $ by following rules:" ] ]

[ "We construct a GAN model which combines different sets of word embeddings INLINEFORM4 , INLINEFORM5 , into a single set of word embeddings INLINEFORM6 . ", "weGAN, deGAN" ]

[ [ "We assume that for each corpora INLINEFORM0 , we are given word embeddings for each word INLINEFORM1 , where INLINEFORM2 is the dimension of each word embedding. We are also given a classification task on documents that is represented by a parametric model INLINEFORM3 taking document embeddings as feature vectors. We construct a GAN model which combines different sets of word embeddings INLINEFORM4 , INLINEFORM5 , into a single set of word embeddings INLINEFORM6 . Note that INLINEFORM7 are given but INLINEFORM8 is trained. Here we consider INLINEFORM9 as the generator, and the goal of the discriminator is to distinguish documents represented by the original embeddings INLINEFORM10 and the same documents represented by the new embeddings INLINEFORM11 ." ], [ "Suppose we have a number of different corpora INLINEFORM0 , which for example can be based on different categories or sentiments of text documents. We suppose that INLINEFORM1 , INLINEFORM2 , where each INLINEFORM3 represents a document. The words in all corpora are collected in a dictionary, and indexed from 1 to INLINEFORM4 . We name the GAN model to train cross-corpus word embeddings as “weGAN,” where “we” stands for “word embeddings,” and the GAN model to generate document embeddings for multiple corpora as “deGAN,” where “de” stands for “document embeddings.”" ] ]

[ "No" ]

[ [ "We hypothesize that because weGAN takes into account document labels in a semi-supervised way, the embeddings trained from weGAN can better incorporate the labeling information and therefore, produce document embeddings which are better separated. The results are shown in Table 1 and averaged over 5 randomized runs. Performing the Welch's t-test, both changes after weGAN training are statistically significant at a INLINEFORM0 significance level. Because the Rand index captures matching accuracy, we observe from the Table 1 that weGAN tends to improve both metrics." ] ]

[ "CNN, TIME, 20 Newsgroups, and Reuters-21578" ]

[ [ "In the experiments, we consider four data sets, two of them newly created and the remaining two already public: CNN, TIME, 20 Newsgroups, and Reuters-21578. The code and the two new data sets are available at github.com/baiyangwang/emgan. For the pre-processing of all the documents, we transformed all characters to lower case, stemmed the documents, and ran the word2vec model on each corpora to obtain word embeddings with a size of 300. In all subsequent models, we only consider the most frequent INLINEFORM0 words across all corpora in a data set." ] ]

[ "Yes" ]

[ [ "Even though this corpus has incorrect sentences and their emotional labels, they lack their respective corrected sentences, necessary for the training of our model. In order to obtain this missing information, we outsource native English speakers from an unbiased and anonymous platform, called Amazon Mechanical Turk (MTurk) BIBREF19, which is a paid marketplace for Human Intelligence Tasks (HITs). We use this platform to create tasks for native English speakers to format the original incorrect tweets into correct sentences. Some examples are shown in Table TABREF12.", "The dataset used to evaluate the models' performance is the Chatbot Natural Language Unerstanding (NLU) Evaluation Corpus, introduced by Braun et al. BIBREF20 to test NLU services. It is a publicly available benchmark and is composed of sentences obtained from a German Telegram chatbot used to answer questions about public transport connections. The dataset has two intents, namely Departure Time and Find Connection with 100 train and 106 test samples, shown in Table TABREF18. Even though English is the main language of the benchmark, this dataset contains a few German station and street names." ] ]

[ "typos in spellings or ungrammatical words" ]

[ [ "Understanding a user's intent and sentiment is of utmost importance for current intelligent chatbots to respond appropriately to human requests. However, current systems are not able to perform to their best capacity when presented with incomplete data, meaning sentences with missing or incorrect words. This scenario is likely to happen when one considers human error done in writing. In fact, it is rather naive to assume that users will always type fully grammatically correct sentences. Panko BIBREF0 goes as far as claiming that human accuracy regarding research paper writing is none when considering the entire document. This has been aggravated with the advent of internet and social networks, which allowed language and modern communication to be been rapidly transformed BIBREF1, BIBREF2. Take Twitter for instance, where information is expected to be readily communicated in short and concise sentences with little to no regard to correct sentence grammar or word spelling BIBREF3." ] ]

[ "Unanswerable" ]

[ [] ]

[ "No", "No" ]

[ [ "The incomplete dataset used for training is composed of lower-cased incomplete data obtained by manipulating the original corpora. The incomplete sentences with STT error are obtained in a 2-step process shown in Fig. FIGREF22. The first step is to apply a TTS module to the available complete sentence. Here, we apply gtts , a Google Text-to-Speech python library, and macsay , a terminal command available in Mac OS as say. The second step consists of applying an STT module to the obtained audio files in order to obtain text containing STT errors. The STT module used here was witai , freely available and maintained by Wit.ai. The mentioned TTS and STT modules were chosen according to code availability and whether it's freely available or has high daily usage limitations." ], [ "In order to evaluate the performance of our model, we need access to a naturally noisy dataset with real human errors. Poor quality texts obtained from Twitter, called tweets, are then ideal for our task. For this reason, we choose Kaggle's two-class Sentiment140 dataset BIBREF18, which consists of spoken text being used in writing and without strong consideration for grammar or sentence correctness. Thus, it has many mistakes, as specified in Table TABREF11.", "In the intent classification task, we are presented with a corpus that suffers from the opposite problem of the Twitter sentiment classification corpus. In the intent classification corpus, we have the complete sentences and intent labels but lack their corresponding incomplete sentences, and since our task revolves around text classification in incomplete or incorrect data, it is essential that we obtain this information. To remedy this issue, we apply a Text-to-Speech (TTS) module followed by a Speech-to-Text (STT) module to the complete sentences in order to obtain incomplete sentences with STT error. Due to TTS and STT modules available being imperfect, the resulting sentences have a reasonable level of noise in the form of missing or incorrectly transcribed words. Analysis on this dataset adds value to our work by enabling evaluation of our model's robustness to different rates of data incompleteness." ] ]

[ "No", "No" ]

[ [ "Experimental results for the Twitter Sentiment Classification task on Kaggle's Sentiment140 Corpus dataset, displayed in Table TABREF37, show that our model has better F1-micros scores, outperforming the baseline models by 6$\\%$ to 8$\\%$. We evaluate our model and baseline models on three versions of the dataset. The first one (Inc) only considers the original data, containing naturally incorrect tweets, and achieves accuracy of 80$\\%$ against BERT's 72$\\%$. The second version (Corr) considers the corrected tweets, and shows higher accuracy given that it is less noisy. In that version, Stacked DeBERT achieves 82$\\%$ accuracy against BERT's 76$\\%$, an improvement of 6$\\%$. In the last case (Inc+Corr), we consider both incorrect and correct tweets as input to the models in hopes of improving performance. However, the accuracy was similar to the first aforementioned version, 80$\\%$ for our model and 74$\\%$ for the second highest performing model. Since the first and last corpus gave similar performances with our model, we conclude that the Twitter dataset does not require complete sentences to be given as training input, in addition to the original naturally incorrect tweets, in order to better model the noisy sentences." ], [ "We propose Stacked Denoising BERT (DeBERT) as a novel encoding scheming for the task of incomplete intent classification and sentiment classification from incorrect sentences, such as tweets and text with STT error. The proposed model, illustrated in Fig. FIGREF4, is structured as a stacking of embedding layers and vanilla transformer layers, similarly to the conventional BERT BIBREF10, followed by layers of novel denoising transformers. The main purpose of this model is to improve the robustness and efficiency of BERT when applied to incomplete data by reconstructing hidden embeddings from sentences with missing words. By reconstructing these hidden embeddings, we are able to improve the encoding scheme in BERT." ] ]

[ "In the sentiment classification task by 6% to 8% and in the intent classification task by 0.94% on average" ]

[ [ "Experimental results for the Twitter Sentiment Classification task on Kaggle's Sentiment140 Corpus dataset, displayed in Table TABREF37, show that our model has better F1-micros scores, outperforming the baseline models by 6$\\%$ to 8$\\%$. We evaluate our model and baseline models on three versions of the dataset. The first one (Inc) only considers the original data, containing naturally incorrect tweets, and achieves accuracy of 80$\\%$ against BERT's 72$\\%$. The second version (Corr) considers the corrected tweets, and shows higher accuracy given that it is less noisy. In that version, Stacked DeBERT achieves 82$\\%$ accuracy against BERT's 76$\\%$, an improvement of 6$\\%$. In the last case (Inc+Corr), we consider both incorrect and correct tweets as input to the models in hopes of improving performance. However, the accuracy was similar to the first aforementioned version, 80$\\%$ for our model and 74$\\%$ for the second highest performing model. Since the first and last corpus gave similar performances with our model, we conclude that the Twitter dataset does not require complete sentences to be given as training input, in addition to the original naturally incorrect tweets, in order to better model the noisy sentences.", "Experimental results for the Intent Classification task on the Chatbot NLU Corpus with STT error can be seen in Table TABREF40. When presented with data containing STT error, our model outperforms all baseline models in both combinations of TTS-STT: gtts-witai outperforms the second placing baseline model by 0.94% with F1-score of 97.17%, and macsay-witai outperforms the next highest achieving model by 1.89% with F1-score of 96.23%." ] ]

[ "34,432 user conversations", "34,432 " ]

[ [ "From January 5, 2019 to March 5, 2019, we collected conversational data for Gunrock. During this time, no other code updates occurred. We analyzed conversations for Gunrock with at least 3 user turns to avoid conversations triggered by accident. Overall, this resulted in a total of 34,432 user conversations. Together, these users gave Gunrock an average rating of 3.65 (median: 4.0), which was elicited at the end of the conversation (“On a scale from 1 to 5 stars, how do you feel about talking to this socialbot again?\"). Users engaged with Gunrock for an average of 20.92 overall turns (median 13.0), with an average of 6.98 words per utterance, and had an average conversation time of 7.33 minutes (median: 2.87 min.). We conducted three principal analyses: users' response depth (wordcount), backstory queries (backstorypersona), and interleaving of personal and factual responses (pets)." ], [ "Amazon Alexa Prize BIBREF0 provides a platform to collect real human-machine conversation data and evaluate performance on speech-based social conversational systems. Our system, Gunrock BIBREF1 addresses several limitations of prior chatbots BIBREF2, BIBREF3, BIBREF4 including inconsistency and difficulty in complex sentence understanding (e.g., long utterances) and provides several contributions: First, Gunrock's multi-step language understanding modules enable the system to provide more useful information to the dialog manager, including a novel dialog act scheme. Additionally, the natural language understanding (NLU) module can handle more complex sentences, including those with coreference. Second, Gunrock interleaves actions to elicit users' opinions and provide responses to create an in-depth, engaging conversation; while a related strategy to interleave task- and non-task functions in chatbots has been proposed BIBREF5, no chatbots to our knowledge have employed a fact/opinion interleaving strategy. Finally, we use an extensive persona database to provide coherent profile information, a critical challenge in building social chatbots BIBREF3. Compared to previous systems BIBREF4, Gunrock generates more balanced conversations between human and machine by encouraging and understanding more human inputs (see Table TABREF2 for an example).", "From January 5, 2019 to March 5, 2019, we collected conversational data for Gunrock. During this time, no other code updates occurred. We analyzed conversations for Gunrock with at least 3 user turns to avoid conversations triggered by accident. Overall, this resulted in a total of 34,432 user conversations. Together, these users gave Gunrock an average rating of 3.65 (median: 4.0), which was elicited at the end of the conversation (“On a scale from 1 to 5 stars, how do you feel about talking to this socialbot again?\"). Users engaged with Gunrock for an average of 20.92 overall turns (median 13.0), with an average of 6.98 words per utterance, and had an average conversation time of 7.33 minutes (median: 2.87 min.). We conducted three principal analyses: users' response depth (wordcount), backstory queries (backstorypersona), and interleaving of personal and factual responses (pets)." ] ]

[ "overall rating, mean number of turns", "overall rating, mean number of turns" ]

[ [ "We assessed the degree of conversational depth by measuring users' mean word count. Prior work has found that an increase in word count has been linked to improved user engagement (e.g., in a social dialog system BIBREF13). For each user conversation, we extracted the overall rating, the number of turns of the interaction, and the user's per-utterance word count (averaged across all utterances). We modeled the relationship between word count and the two metrics of user engagement (overall rating, mean number of turns) in separate linear regressions." ], [ "We assessed the degree of conversational depth by measuring users' mean word count. Prior work has found that an increase in word count has been linked to improved user engagement (e.g., in a social dialog system BIBREF13). For each user conversation, we extracted the overall rating, the number of turns of the interaction, and the user's per-utterance word count (averaged across all utterances). We modeled the relationship between word count and the two metrics of user engagement (overall rating, mean number of turns) in separate linear regressions." ] ]

[ "Amazon Conversational Bot Toolkit, natural language understanding (NLU) (nlu) module, dialog manager, knowledge bases, natural language generation (NLG) (nlg) module, text to speech (TTS) (tts)" ]

[ [ "Figure FIGREF3 provides an overview of Gunrock's architecture. We extend the Amazon Conversational Bot Toolkit (CoBot) BIBREF6 which is a flexible event-driven framework. CoBot provides ASR results and natural language processing pipelines through the Alexa Skills Kit (ASK) BIBREF7. Gunrock corrects ASR according to the context (asr) and creates a natural language understanding (NLU) (nlu) module where multiple components analyze the user utterances. A dialog manager (DM) (dm) uses features from NLU to select topic dialog modules and defines an individual dialog flow. Each dialog module leverages several knowledge bases (knowledge). Then a natural language generation (NLG) (nlg) module generates a corresponding response. Finally, we markup the synthesized responses and return to the users through text to speech (TTS) (tts). While we provide an overview of the system in the following sections, for detailed system implementation details, please see the technical report BIBREF1." ] ]

[ "No" ]

[ [ "Figure FIGREF3 provides an overview of Gunrock's architecture. We extend the Amazon Conversational Bot Toolkit (CoBot) BIBREF6 which is a flexible event-driven framework. CoBot provides ASR results and natural language processing pipelines through the Alexa Skills Kit (ASK) BIBREF7. Gunrock corrects ASR according to the context (asr) and creates a natural language understanding (NLU) (nlu) module where multiple components analyze the user utterances. A dialog manager (DM) (dm) uses features from NLU to select topic dialog modules and defines an individual dialog flow. Each dialog module leverages several knowledge bases (knowledge). Then a natural language generation (NLG) (nlg) module generates a corresponding response. Finally, we markup the synthesized responses and return to the users through text to speech (TTS) (tts). While we provide an overview of the system in the following sections, for detailed system implementation details, please see the technical report BIBREF1." ] ]

[ "Yes" ]

[ [ "From January 5, 2019 to March 5, 2019, we collected conversational data for Gunrock. During this time, no other code updates occurred. We analyzed conversations for Gunrock with at least 3 user turns to avoid conversations triggered by accident. Overall, this resulted in a total of 34,432 user conversations. Together, these users gave Gunrock an average rating of 3.65 (median: 4.0), which was elicited at the end of the conversation (“On a scale from 1 to 5 stars, how do you feel about talking to this socialbot again?\"). Users engaged with Gunrock for an average of 20.92 overall turns (median 13.0), with an average of 6.98 words per utterance, and had an average conversation time of 7.33 minutes (median: 2.87 min.). We conducted three principal analyses: users' response depth (wordcount), backstory queries (backstorypersona), and interleaving of personal and factual responses (pets)." ] ]

[ "modeled the relationship between word count and the two metrics of user engagement (overall rating, mean number of turns) in separate linear regressions" ]

[ [ "We assessed the degree of conversational depth by measuring users' mean word count. Prior work has found that an increase in word count has been linked to improved user engagement (e.g., in a social dialog system BIBREF13). For each user conversation, we extracted the overall rating, the number of turns of the interaction, and the user's per-utterance word count (averaged across all utterances). We modeled the relationship between word count and the two metrics of user engagement (overall rating, mean number of turns) in separate linear regressions.", "Results showed that users who, on average, produced utterances with more words gave significantly higher ratings ($\\beta $=0.01, SE=0.002, t=4.79, p$<$0.001)(see Figure 2) and engaged with Gunrock for significantly greater number of turns ($\\beta $=1.85, SE=0.05, t=35.58, p$<$0.001) (see Figure 2). These results can be interpreted as evidence for Gunrock's ability to handle complex sentences, where users are not constrained to simple responses to be understood and feel engaged in the conversation – and evidence that individuals are more satisfied with the conversation when they take a more active role, rather than the system dominating the dialog. On the other hand, another interpretation is that users who are more talkative may enjoy talking to the bot in general, and thus give higher ratings in tandem with higher average word counts." ] ]

[ "Unanswerable" ]

[ [] ]

[ "FastText, BiLSTM, BERT", "FastText, BERT , two-layer BiLSTM architecture with GloVe word embeddings" ]

[ [ "FastTextBIBREF4: It uses bag of words and bag of n-grams as features for text classification, capturing partial information about the local word order efficiently.", "BiLSTM: Unlike feedforward neural networks, recurrent neural networks like BiLSTMs use memory based on history information to learn long-distance features and then predict the output. We use a two-layer BiLSTM architecture with GloVe word embeddings as a strong RNN baseline.", "BERT BIBREF5: It is a contextualized word representation model that uses bidirectional transformers, pretrained on a large $3.3B$ word corpus. We use the $BERT_{large}$ model finetuned on the training dataset." ], [ "Baselines and Approach", "In this section, we outline baseline models like $BERT_{large}$. We further propose three approaches: optimized BERT-based models, distilled pretrained models, and the use of ensemble methods for the task of subjectivity detection.", "Baselines and Approach ::: Baselines", "FastTextBIBREF4: It uses bag of words and bag of n-grams as features for text classification, capturing partial information about the local word order efficiently.", "BiLSTM: Unlike feedforward neural networks, recurrent neural networks like BiLSTMs use memory based on history information to learn long-distance features and then predict the output. We use a two-layer BiLSTM architecture with GloVe word embeddings as a strong RNN baseline.", "BERT BIBREF5: It is a contextualized word representation model that uses bidirectional transformers, pretrained on a large $3.3B$ word corpus. We use the $BERT_{large}$ model finetuned on the training dataset." ] ]

[ "They used BERT-based models to detect subjective language in the WNC corpus" ]

[ [ "In natural language, subjectivity refers to the aspects of communication used to express opinions, evaluations, and speculationsBIBREF0, often influenced by one's emotional state and viewpoints. Writers and editors of texts like news and textbooks try to avoid the use of biased language, yet subjective bias is pervasive in these texts. More than $56\\%$ of Americans believe that news sources do not report the news objectively , thus implying the prevalence of the bias. Therefore, when presenting factual information, it becomes necessary to differentiate subjective language from objective language.", "In this work, we investigate the application of BERT-based models for the task of subjective language detection. We explore various BERT-based models, including BERT, RoBERTa, ALBERT, with their base and large specifications along with their native classifiers. We propose an ensemble model exploiting predictions from these models using multiple ensembling techniques. We show that our model outperforms the baselines by a margin of $5.6$ of F1 score and $5.95\\%$ of Accuracy.", "Experiments ::: Dataset and Experimental Settings", "We perform our experiments on the WNC dataset open-sourced by the authors of BIBREF2. It consists of aligned pre and post neutralized sentences made by Wikipedia editors under the neutral point of view. It contains $180k$ biased sentences, and their neutral counterparts crawled from $423,823$ Wikipedia revisions between 2004 and 2019. We randomly shuffled these sentences and split this dataset into two parts in a $90:10$ Train-Test split and perform the evaluation on the held-out test dataset." ] ]

[ "No", "No, other baseline metrics they use besides ROUGE-L are n-gram overlap, negative cross-entropy, perplexity, and BLEU." ]

[ [ "Our first baseline is ROUGE-L BIBREF1 , since it is the most commonly used metric for compression tasks. ROUGE-L measures the similarity of two sentences based on their longest common subsequence. Generated and reference compressions are tokenized and lowercased. For multiple references, we only make use of the one with the highest score for each example.", "We compare to the best n-gram-overlap metrics from toutanova2016dataset; combinations of linguistic units (bi-grams (LR2) and tri-grams (LR3)) and scoring measures (recall (R) and F-score (F)). With multiple references, we consider the union of the sets of n-grams. Again, generated and reference compressions are tokenized and lowercased.", "We further compare to the negative LM cross-entropy, i.e., the log-probability which is only normalized by sentence length. The score of a sentence $S$ is calculated as", "Our next baseline is perplexity, which corresponds to the exponentiated cross-entropy:", "Due to its popularity, we also performed initial experiments with BLEU BIBREF17 . Its correlation with human scores was so low that we do not consider it in our final experiments." ], [ "Our first baseline is ROUGE-L BIBREF1 , since it is the most commonly used metric for compression tasks. ROUGE-L measures the similarity of two sentences based on their longest common subsequence. Generated and reference compressions are tokenized and lowercased. For multiple references, we only make use of the one with the highest score for each example.", "We compare to the best n-gram-overlap metrics from toutanova2016dataset; combinations of linguistic units (bi-grams (LR2) and tri-grams (LR3)) and scoring measures (recall (R) and F-score (F)). With multiple references, we consider the union of the sets of n-grams. Again, generated and reference compressions are tokenized and lowercased.", "We further compare to the negative LM cross-entropy, i.e., the log-probability which is only normalized by sentence length. The score of a sentence $S$ is calculated as", "Our next baseline is perplexity, which corresponds to the exponentiated cross-entropy:", "Due to its popularity, we also performed initial experiments with BLEU BIBREF17 . Its correlation with human scores was so low that we do not consider it in our final experiments." ] ]

[ "LSTM LMs" ]

[ [ "We calculate the probability of a sentence with a long-short term memory (LSTM, hochreiter1997long) LM, i.e., a special type of RNN LM, which has been trained on a large corpus. More details on LSTM LMs can be found, e.g., in sundermeyer2012lstm. The unigram probabilities for SLOR are estimated using the same corpus.", "We train our LSTM LMs on the English Gigaword corpus BIBREF15 , which consists of news data." ] ]

[ "Unanswerable" ]

[ [] ]

[ "if the attention loose track of the objects in the picture and \"gets lost\", the model still takes it into account and somehow overrides the information brought by the text-based annotations", "if the attention loose track of the objects in the picture and \"gets lost\", the model still takes it into account and somehow overrides the information brought by the text-based annotations" ]

[ [ "It is also worth to mention that we use a ResNet trained on 1.28 million images for a classification tasks. The features used by the attention mechanism are strongly object-oriented and the machine could miss important information for a multimodal translation task. We believe that the robust architecture of both encoders INLINEFORM0 combined with a GRU layer and word-embeddings took care of the right translation for relationships between objects and time-dependencies. Yet, we noticed a common misbehavior for all our multimodal models: if the attention loose track of the objects in the picture and \"gets lost\", the model still takes it into account and somehow overrides the information brought by the text-based annotations. The translation is then totally mislead. We illustrate with an example:" ], [ "It is also worth to mention that we use a ResNet trained on 1.28 million images for a classification tasks. The features used by the attention mechanism are strongly object-oriented and the machine could miss important information for a multimodal translation task. We believe that the robust architecture of both encoders INLINEFORM0 combined with a GRU layer and word-embeddings took care of the right translation for relationships between objects and time-dependencies. Yet, we noticed a common misbehavior for all our multimodal models: if the attention loose track of the objects in the picture and \"gets lost\", the model still takes it into account and somehow overrides the information brought by the text-based annotations. The translation is then totally mislead. We illustrate with an example:" ] ]