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id stringlengths 10 10	title stringlengths 19 145	abstract stringlengths 273 1.91k	full_text dict	qas dict	figures_and_tables dict	question list	retrieval_gt list	answer_gt list	__index_level_0__ int64 0 887
1909.00694	Minimally Supervised Learning of Affective Events Using Discourse Relations	Recognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event is not necessarily predictable from its constituent words. In this paper, we propose to propagate affective pola...	{ "paragraphs": [ [ "Affective events BIBREF0 are events that typically affect people in positive or negative ways. For example, getting money and playing sports are usually positive to the experiencers; catching cold and losing one's wallet are negative. Understanding affective events is important to vario...	{ "answers": [ { "annotation_id": [ "31e85022a847f37c15fd0415f3c450c74c8e4755", "95da0a6e1b08db74a405c6a71067c9b272a50ff5" ], "answer": [ { "evidence": [ "The seed lexicon consists of positive and negative predicates. If the predicate of an extracted...	{ "caption": [ "Figure 1: An overview of our method. We focus on pairs of events, the former events and the latter events, which are connected with a discourse relation, CAUSE or CONCESSION. Dropped pronouns are indicated by brackets in English translations. We divide the event pairs into three types: AL, CA, and...	[ "What is the seed lexicon?", "What are the results?", "How are relations used to propagate polarity?", "How big is the Japanese data?", "How big are improvements of supervszed learning results trained on smalled labeled data enhanced with proposed approach copared to basic approach?", "How does their mode...	[ [ "1909.00694-Proposed Method ::: Discourse Relation-Based Event Pairs-1" ], [ "1909.00694-Experiments ::: Model Configurations-2", "1909.00694-5-Table4-1.png", "1909.00694-Experiments ::: Model Configurations-0", "1909.00694-5-Table3-1.png" ], [ "1909.00694-Proposed Method ::: Disco...	[ "a vocabulary of positive and negative predicates that helps determine the polarity score of an event", "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 0.9...	0
1705.09665	Community Identity and User Engagement in a Multi-Community Landscape	A community's identity defines and shapes its internal dynamics. Our current understanding of this interplay is mostly limited to glimpses gathered from isolated studies of individual communities. In this work we provide a systematic exploration of the nature of this relation across a wide variety of online communities...	{ "paragraphs": [ [ "“If each city is like a game of chess, the day when I have learned the rules, I shall finally possess my empire, even if I shall never succeed in knowing all the cities it contains.”", "", "— Italo Calvino, Invisible Cities", "A community's identity—defined through the...	{ "answers": [ { "annotation_id": [ "04ae0cc420f69540ca11707ab8ecc07a89f803f7", "31d8f8ed7ba40b27c480f7caf7cfb48fba47bb07" ], "answer": [ { "evidence": [ "Our full dataset consists of all subreddits on Reddit from January 2013 to December 2014, for w...	{ "caption": [ "Figure 1: A: Within a community certain words are more community-specific and temporally volatile than others. For instance, words like onesies are highly specific to the BabyBumps community (top left corner), while words like easter are temporally ephemeral. B: Extending these word-level measures...	[ "How do the various social phenomena examined manifest in different types of communities?", "How did the select the 300 Reddit communities for comparison?" ]	[ [ "1705.09665-Community-type and user tenure-0", "1705.09665-Community-type and monthly retention-0" ], [ "1705.09665-Applying the typology to Reddit-3" ] ]	[ "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 num...	2
1908.06606	Question Answering based Clinical Text Structuring Using Pre-trained Language Model	Clinical text structuring is a critical and fundamental task for clinical research. Traditional methods such as taskspecific end-to-end models and pipeline models usually suffer from the lack of dataset and error propagation. In this paper, we present a question answering based clinical text structuring (QA-CTS) task t...	{ "paragraphs": [ [ "Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at du...	{ "answers": [ { "annotation_id": [ "0ab604dbe114dba174da645cc06a713e12a1fd9d", "1f1495d06d0abe86ee52124ec9f2f0b25a536147" ], "answer": [ { "evidence": [ "To implement deep neural network models, we utilize the Keras library BIBREF36 with TensorFlow ...	{ "caption": [ "Fig. 1. An illustrative example of QA-CTS task.", "TABLE I AN ILLUSTRATIVE EXAMPLE OF NAMED ENTITY FEATURE TAGS", "Fig. 2. The architecture of our proposed model for QA-CTS task", "TABLE II STATISTICS OF DIFFERENT TYPES OF QUESTION ANSWER INSTANCES", "TABLE V COMPARATIVE RESULTS FO...	[ "How is the clinical text structuring task defined?", "Is all text in this dataset a question, or are there unrelated sentences in between questions?", "How many questions are in the dataset?" ]	[ [ "1908.06606-Introduction-0", "1908.06606-1-Figure1-1.png", "1908.06606-Introduction-1", "1908.06606-Introduction-3" ], [ "1908.06606-Experimental Studies ::: Dataset and Evaluation Metrics-0" ], [ "1908.06606-Experimental Studies ::: Dataset and Evaluation Metrics-0" ] ]	[ "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.", "the dataset consists of pathology reports including sentences and questions and answers about tumor size and resection margins so it does include ...	3
1811.00942	Progress and Tradeoffs in Neural Language Models	In recent years, we have witnessed a dramatic shift towards techniques driven by neural networks for a variety of NLP tasks. Undoubtedly, neural language models (NLMs) have reduced perplexity by impressive amounts. This progress, however, comes at a substantial cost in performance, in terms of inference latency and ene...	{ "paragraphs": [ [ "Deep learning has unquestionably advanced the state of the art in many natural language processing tasks, from syntactic dependency parsing BIBREF0 to named-entity recognition BIBREF1 to machine translation BIBREF2 . The same certainly applies to language modeling, where recent advances...	{ "answers": [ { "annotation_id": [ "c17796e0bd3bfcc64d5a8e844d23d8d39274af6b" ], "answer": [ { "evidence": [ "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–...	{ "caption": [ "Table 1: Comparison of neural language models on Penn Treebank and WikiText-103.", "Figure 1: Log perplexity–recall error with KN-5.", "Figure 2: Log perplexity–recall error with QRNN.", "Table 2: Language modeling results on performance and model quality." ], "file": [ "3-Tabl...	[ "What aspects have been compared between various language models?" ]	[ [ "1811.00942-Experimental Setup-2" ] ]	[ "Quality measures using perplexity and recall, and performance measured using latency and energy usage. " ]	4
1907.05664	Saliency Maps Generation for Automatic Text Summarization	Saliency map generation techniques are at the forefront of explainable AI literature for a broad range of machine learning applications. Our goal is to question the limits of these approaches on more complex tasks. In this paper we apply Layer-Wise Relevance Propagation (LRP) to a sequence-to-sequence attention model t...	{ "paragraphs": [ [ "Ever since the LIME algorithm BIBREF0 , \"explanation\" techniques focusing on finding the importance of input features in regard of a specific prediction have soared and we now have many ways of finding saliency maps (also called heat-maps because of the way we like to visualize them)....	{ "answers": [ { "annotation_id": [ "0850b7c0555801d057062480de6bb88adb81cae3", "93216bca45711b73083372495d9a2667736fbac9" ], "answer": [ { "evidence": [ "We present in this section the baseline model from See et al. See2017 trained on the CNN/Daily ...	{ "caption": [ "Figure 2: Representation of the propagation of the relevance from the output to the input. It passes through the decoder and attention mechanism for each previous decoding time-step, then is passed onto the encoder which takes into account the relevance transiting in both direction due to the bidi...	[ "How many attention layers are there in their model?" ]	[ [ "1907.05664-The Model-0" ] ]	[ "one" ]	6
1910.14497	Probabilistic Bias Mitigation in Word Embeddings	It has been shown that word embeddings derived from large corpora tend to incorporate biases present in their training data. Various methods for mitigating these biases have been proposed, but recent work has demonstrated that these methods hide but fail to truly remove the biases, which can still be observed in word n...	{ "paragraphs": [ [ "Word embeddings, or vector representations of words, are an important component of Natural Language Processing (NLP) models and necessary for many downstream tasks. However, word embeddings, including embeddings commonly deployed for public use, have been shown to exhibit unwanted socie...	{ "answers": [ { "annotation_id": [ "50e0354ccb4d7d6fda33c34e69133daaa8978a2f", "eb66f1f7e89eca5dcf2ae6ef450b1693a43f4e69" ], "answer": [ { "evidence": [ "We evaluate our framework on fastText embeddings trained on Wikipedia (2017), UMBC webbase corp...	{ "caption": [ "Figure 1: Word embedding semantic quality benchmarks for each bias mitigation method (higher is better). See Jastrzkebski et al. [11] for details of each metric.", "Table 1: Remaining Bias (as measured by RIPA and Neighborhood metrics) in fastText embeddings for baseline (top two rows) and our...	[ "What are the three measures of bias which are reduced in experiments?" ]	[ [ "1910.14497-Background ::: Geometric Bias Mitigation ::: RIPA-0", "1910.14497-4-Table1-1.png", "1910.14497-Background ::: Geometric Bias Mitigation ::: Neighborhood Metric-0", "1910.14497-Background ::: Geometric Bias Mitigation ::: WEAT-1", "1910.14497-Background ::: Geometric Bias Mitigation...	[ "RIPA, Neighborhood Metric, WEAT" ]	7
2002.02224	Citation Data of Czech Apex Courts	In this paper, we introduce the citation data of the Czech apex courts (Supreme Court, Supreme Administrative Court and Constitutional Court). This dataset was automatically extracted from the corpus of texts of Czech court decisions - CzCDC 1.0. We obtained the citation data by building the natural language processing...	{ "paragraphs": [ [ "Analysis of the way court decisions refer to each other provides us with important insights into the decision-making process at courts. This is true both for the common law courts and for their counterparts in the countries belonging to the continental legal system. Citation data can be...	{ "answers": [ { "annotation_id": [ "3bf5c275ced328b66fd9a07b30a4155fa476d779", "ae80f5c5b782ad02d1dde21b7384bc63472f5796" ], "answer": [ { "evidence": [], "extractive_spans": [], "free_form_answer": "", "highlighted_evidence": [], ...	{ "caption": [ "Figure 1: NLP pipeline including the text segmentation, reference recognition and parsing of references to the specific document", "Table 1: Model performance", "Table 2: References sorted by categories, unlinked", "Table 3: References linked with texts in CzCDC" ], "file": [ "...	[ "How big is the dataset?" ]	[ [ "2002.02224-Results-0" ] ]	[ "903019 references" ]	10
2003.07433	LAXARY: A Trustworthy Explainable Twitter Analysis Model for Post-Traumatic Stress Disorder Assessment	Veteran mental health is a significant national problem as large number of veterans are returning from the recent war in Iraq and continued military presence in Afghanistan. While significant existing works have investigated twitter posts-based Post Traumatic Stress Disorder (PTSD) assessment using blackbox machine lea...	{ "paragraphs": [ [ "Combat veterans diagnosed with PTSD are substantially more likely to engage in a number of high risk activities including engaging in interpersonal violence, attempting suicide, committing suicide, binge drinking, and drug abuse BIBREF0. Despite improved diagnostic screening, outpatient...	{ "answers": [ { "annotation_id": [ "4e3a79dc56c6f39d1bec7bac257c57f279431967" ], "answer": [ { "evidence": [], "extractive_spans": [], "free_form_answer": "", "highlighted_evidence": [], "unanswerable": true, "yes_no": ...	{ "caption": [ "Fig. 1. Overview of our framework", "Fig. 2. WordStat dictionary sample", "TABLE I DRYHOOTCH CHOSEN PTSD ASSESSMENT SURVEYS (D: DOSPERT, B: BSSS AND V: VIAS) DEMOGRAPHICS", "TABLE II SAMPLE DRYHOOTCH CHOSEN QUESTIONS FROM DOSPERT", "Fig. 3. Each 210 users’ average tweets per month"...	[ "How is the intensity of the PTSD established?" ]	[ [ "2003.07433-Demographics of Clinically Validated PTSD Assessment Tools-4", "2003.07433-Demographics of Clinically Validated PTSD Assessment Tools-0", "2003.07433-Demographics of Clinically Validated PTSD Assessment Tools-3", "2003.07433-Demographics of Clinically Validated PTSD Assessment Tools-1"...	[ "defined into four categories from high risk, moderate risk, to low risk" ]	11
1904.09678	UniSent: Universal Adaptable Sentiment Lexica for 1000+ Languages	In this paper, we introduce UniSent a universal sentiment lexica for 1000 languages created using an English sentiment lexicon and a massively parallel corpus in the Bible domain. To the best of our knowledge, UniSent is the largest sentiment resource to date in terms of number of covered languages, including many low ...	{ "paragraphs": [ [ "Sentiment classification is an important task which requires either word level or document level sentiment annotations. Such resources are available for at most 136 languages BIBREF0 , preventing accurate sentiment classification in a low resource setup. Recent research efforts on cross...	{ "answers": [ { "annotation_id": [ "97009bed24107de806232d7cf069f51053d7ba5e", "e38ed05ec140abd97006a8fa7af9a7b4930247df" ], "answer": [ { "evidence": [ "FLOAT SELECTED: Table 1: Comparison of manually created lexicon performance with UniSent in Cze...	{ "caption": [ "Figure 1: Neighbors of word ’sensual’ in Spanish, in bible embedding graph (a) and twitter embedding graph (b). Our unsupervised drift weighting method found this word in Spanish to be the most changing word from bible context to the twitter context. Looking more closely at the neighbors, the word...	[ "how is quality measured?" ]	[ [ "1904.09678-4-Table1-1.png" ] ]	[ "Accuracy and the macro-F1 (averaged F1 over positive and negative classes) are used as a measure of quality." ]	13
1910.04269	Spoken Language Identification using ConvNets	Language Identification (LI) is an important first step in several speech processing systems. With a growing number of voice-based assistants, speech LI has emerged as a widely researched field. To approach the problem of identifying languages, we can either adopt an implicit approach where only the speech for a langua...	{ "paragraphs": [ [ "Language Identification (LI) is a problem which involves classifying the language being spoken by a speaker. LI systems can be used in call centers to route international calls to an operator who is fluent in that identified language BIBREF0. In speech-based assistants, LI acts as the f...	{ "answers": [ { "annotation_id": [ "32dee5de8cb44c67deef309c16e14e0634a7a95e" ], "answer": [ { "evidence": [ "FLOAT SELECTED: Table 4: Results of the two models and all its variations" ], "extractive_spans": [], "free_form_answ...	{ "caption": [ "Table 2: Architecture of the 1D-ConvNet model", "Fig. 1: Effect of hyperparameter variation of the hyperparameter on the classification accuracy for the case of 1D-ConvNet. Orange colored violin plots show the most favored choice of the hyperparameter and blue shows otherwise. One dot represen...	[ "What is the accuracy reported by state-of-the-art methods?" ]	[ [ "1910.04269-Related Work-6" ] ]	[ "Answer with content missing: (Table 1)\nPrevious state-of-the art on same dataset: ResNet50 89% (6 languages), SVM-HMM 70% (4 languages)" ]	15
2001.00137	Stacked DeBERT: All Attention in Incomplete Data for Text Classification	In this paper, we propose Stacked DeBERT, short for Stacked Denoising Bidirectional Encoder Representations from Transformers. This novel model improves robustness in incomplete data, when compared to existing systems, by designing a novel encoding scheme in BERT, a powerful language representation model solely based o...	{ "paragraphs": [ [ "Understanding a user's intent and sentiment is of utmost importance for current intelligent chatbots to respond appropriately to human requests. However, current systems are not able to perform to their best capacity when presented with incomplete data, meaning sentences with missing or...	{ "answers": [ { "annotation_id": [ "c7a83f3225e54b6306ef3372507539e471c155d0" ], "answer": [ { "evidence": [ "Even though this corpus has incorrect sentences and their emotional labels, they lack their respective corrected sentences, necessary for the train...	{ "caption": [ "Figure 1: The proposed model Stacked DeBERT is organized in three layers: embedding, conventional bidirectional transformers and denoising bidirectional transformer.", "Table 1: Types of mistakes on the Twitter dataset.", "Table 2: Examples of original tweets and their corrected version.",...	[ "How do the authors define or exemplify 'incorrect words'?", "By how much do they outperform other models in the sentiment in intent classification tasks?" ]	[ [ "2001.00137-Introduction-0" ], [ "2001.00137-Experiments ::: Results on Sentiment Classification from Incorrect Text-0", "2001.00137-Experiments ::: Results on Intent Classification from Text with STT Error-0", "2001.00137-16-Table7-1.png" ] ]	[ "typos in spellings or ungrammatical words", "In the sentiment classification task by 6% to 8% and in the intent classification task by 0.94% on average" ]	19
2002.06644	Towards Detection of Subjective Bias using Contextualized Word Embeddings	Subjective bias detection is critical for applications like propaganda detection, content recommendation, sentiment analysis, and bias neutralization. This bias is introduced in natural language via inflammatory words and phrases, casting doubt over facts, and presupposing the truth. In this work, we perform comprehens...	{ "paragraphs": [ [ "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...	{ "answers": [ { "annotation_id": [ "dfc487e35ee5131bc5054463ace009e6bd8fc671" ], "answer": [ { "evidence": [], "extractive_spans": [], "free_form_answer": "", "highlighted_evidence": [], "unanswerable": true, "yes_no": ...	{ "caption": [ "Table 1: Experimental Results for the Subjectivity Detection Task" ], "file": [ "2-Table1-1.png" ] }	[ "Which experiments are perfomed?" ]	[ [ "2002.06644-Introduction-3", "2002.06644-Experiments ::: Dataset and Experimental Settings-0", "2002.06644-Introduction-0" ] ]	[ "They used BERT-based models to detect subjective language in the WNC corpus" ]	21
1809.08731	Sentence-Level Fluency Evaluation: References Help, But Can Be Spared!	Motivated by recent findings on the probabilistic modeling of acceptability judgments, we propose syntactic log-odds ratio (SLOR), a normalized language model score, as a metric for referenceless fluency evaluation of natural language generation output at the sentence level. We further introduce WPSLOR, a novel WordPie...	{ "paragraphs": [ [ "Producing sentences which are perceived as natural by a human addressee—a property which we will denote as fluency throughout this paper —is a crucial goal of all natural language generation (NLG) systems: it makes interactions more natural, avoids misunderstandings and, overall, leads ...	{ "answers": [ { "annotation_id": [ "24ebf6cd50b3f873f013cd206aa999a4aa841317", "d04c757c5a09e8a9f537d15bdd93ac4043c7a3e9" ], "answer": [ { "evidence": [ "Our first baseline is ROUGE-L BIBREF1 , since it is the most commonly used metric for compressi...	{ "caption": [ "Table 1: Example compressions from our dataset with their fluency scores; scores in [1, 3], higher is better.", "Table 2: Average fluency ratings for each compression system in the dataset by Toutanova et al. (2016).", "Table 3: Pearson correlation (higher is better) and MSE (lower is bett...	[ "Is ROUGE their only baseline?" ]	[ [ "1809.08731-Baseline Metrics-1", "1809.08731-Baseline Metrics-7", "1809.08731-Baseline Metrics-0" ] ]	[ "No, other baseline metrics they use besides ROUGE-L are n-gram overlap, negative cross-entropy, perplexity, and BLEU." ]	22
1809.04960	Unsupervised Machine Commenting with Neural Variational Topic Model	Article comments can provide supplementary opinions and facts for readers, thereby increase the attraction and engagement of articles. Therefore, automatically commenting is helpful in improving the activeness of the community, such as online forums and news websites. Previous work shows that training an automatic comm...	{ "paragraphs": [ [ "Making article comments is a fundamental ability for an intelligent machine to understand the article and interact with humans. It provides more challenges because commenting requires the abilities of comprehending the article, summarizing the main ideas, mining the opinions, and genera...	{ "answers": [ { "annotation_id": [ "4cab4c27ed7f23d35b539bb3b1c7380ef603afe7", "a951e1f37364826ddf170c9076b0d647f29db95a" ], "answer": [ { "evidence": [ "In addition to the unsupervised training, we explore a semi-supervised training framework to co...	{ "caption": [ "Table 2: The performance of the unsupervised models and supervised models under the retrieval evaluation settings. (Recall@k, MRR: higher is better; MR: lower is better.)", "Table 3: The performance of the unsupervised models and supervised models under the generative evaluation settings. (MET...	[ "By how much does their system outperform the lexicon-based models?" ]	[ [ "1809.04960-Baselines-2", "1809.04960-Generative Evaluation-1", "1809.04960-Retrieval Evaluation-10", "1809.04960-Baselines-4", "1809.04960-5-Table2-1.png", "1809.04960-5-Table3-1.png" ] ]	[ "Proposed model is better than both lexical based models by significan margin in all metrics: BLEU 0.261 vs 0.250, ROUGLE 0.162 vs 0.155 etc." ]	24
1708.05873	What Drives the International Development Agenda? An NLP Analysis of the United Nations General Debate 1970-2016	There is surprisingly little known about agenda setting for international development in the United Nations (UN) despite it having a significant influence on the process and outcomes of development efforts. This paper addresses this shortcoming using a novel approach that applies natural language processing techniques ...	{ "paragraphs": [ [ "Decisions made in international organisations are fundamental to international development efforts and initiatives. It is in these global governance arenas that the rules of the global economic system, which have a huge impact on development outcomes are agreed on; decisions are made ab...	{ "answers": [ { "annotation_id": [ "9a8d3b251090979a6b4c6d04ed95386a881bbd1c" ], "answer": [ { "evidence": [ "Yet surprisingly little is known about the agenda-setting process for international development in global governance institutions. This is perhaps ...	{ "caption": [ "Fig. 1. Optimal model search. Semantic coherence and exclusivity results for a model search from 3 to 50 topics. Models above the regression line provide a better trade off. Largest positive residual is a 16-topic model.", "Fig. 2. Topic quality. 20 highest probability words for the 16-topic m...	[ "How are the main international development topics that states raise identified?" ]	[ [ "1708.05873-Estimation of topic models-0", "1708.05873-Estimation of topic models-1" ] ]	[ " They focus on exclusivity and semantic coherence measures: Highly frequent words in a given topic that do not appear very often in other topics are viewed as making that topic exclusive. They select select the 16-topic model, which has the largest positive residual in the regression fit, and provides higher exclu...	29
1909.12140	DisSim: A Discourse-Aware Syntactic Text Simplification Frameworkfor English and German	We introduce DisSim, a discourse-aware sentence splitting framework for English and German whose goal is to transform syntactically complex sentences into an intermediate representation that presents a simple and more regular structure which is easier to process for downstream semantic applications. For this purpose, w...	{ "paragraphs": [ [ "We developed a syntactic text simplification (TS) approach that can be used as a preprocessing step to facilitate and improve the performance of a wide range of artificial intelligence (AI) tasks, such as Machine Translation, Information Extraction (IE) or Text Summarization. Since shor...	{ "answers": [ { "annotation_id": [ "4083f879cdc02cfa51c88a45ce16e30707a8a63e", "d12ac9d62a47d355ba1fdd0799c58e59877d5eb8" ], "answer": [ { "evidence": [ "An extrinsic evaluation was carried out on the task of Open IE BIBREF7. It revealed that when a...	{ "caption": [ "Figure 1: DISSIM’s browser-based user interface. The simplified output is displayed in the form of a directed graph where the split sentences are connected by arrows whose labels denote the semantic relationship that holds between a pair of simplified sentences and whose direction indicates their ...	[ "Is the semantic hierarchy representation used for any task?", "Is the model evaluated?" ]	[ [ "1909.12140-Application in Downstream Tasks-0", "1909.12140-Application in Downstream Tasks-1" ], [ "1909.12140-Experiments-0" ] ]	[ "Yes, Open IE", "the English version is evaluated. The German version evaluation is in progress " ]	33
1909.08859	Procedural Reasoning Networks for Understanding Multimodal Procedures	This paper addresses the problem of comprehending procedural commonsense knowledge. This is a challenging task as it requires identifying key entities, keeping track of their state changes, and understanding temporal and causal relations. Contrary to most of the previous work, in this study, we do not rely on strong in...	{ "paragraphs": [ [ "A great deal of commonsense knowledge about the world we live is procedural in nature and involves steps that show ways to achieve specific goals. Understanding and reasoning about procedural texts (e.g. cooking recipes, how-to guides, scientific processes) are very hard for machines as...	{ "answers": [ { "annotation_id": [ "4e5d6e5c9fcd614bd589bc0ea42cc2997bcf28eb", "9a39d77579baa6cde733cb84ad043de21ec9d0d5" ], "answer": [ { "evidence": [ "In the following, we explain our Procedural Reasoning Networks model. Its architecture is based...	{ "caption": [ "Figure 1: A recipe for preparing a cheeseburger (adapted from the cooking instructions available at https: //www.instructables.com/id/In-N-Out-Double-Double-Cheeseburger-Copycat). Each basic ingredient (entity) is highlighted by a different color in the text and with bounding boxes on the accompan...	[ "How better is accuracy of new model compared to previously reported models?" ]	[ [ "1909.08859-Experiments ::: Results-0", "1909.08859-7-Table1-1.png" ] ]	[ "Average accuracy of proposed model vs best prevous result:\nSingle-task Training: 57.57 vs 55.06\nMulti-task Training: 50.17 vs 50.59" ]	35
1908.08419	Active Learning for Chinese Word Segmentation in Medical Text	Electronic health records (EHRs) stored in hospital information systems completely reflect the patients' diagnosis and treatment processes, which are essential to clinical data mining. Chinese word segmentation (CWS) is a fundamental and important task for Chinese natural language processing. Currently, most state-of-t...	{ "paragraphs": [ [ "Electronic health records (EHRs) systematically collect patients' clinical information, such as health profiles, histories of present illness, past medical histories, examination results and treatment plans BIBREF0 . By analyzing EHRs, many useful information, closely related to patient...	{ "answers": [ { "annotation_id": [ "7f52a42b5c714e3a236ad19e17d6118d7150020d", "dfd42925ad6801aefc716d18331afc2671840e52" ], "answer": [ { "evidence": [ "To select the most appropriate sentences in a large number of unlabeled corpora, we propose a s...	{ "caption": [ "Fig. 1. The diagram of active learning for the Chinese word segmentation.", "Fig. 2. The architecture of the information entropy based scoring model, where ‘/’ represents candidate word separator, xi represents the one-hot encoding of the i-th character, cj represents the j-th character embedd...	[ "How does the active learning model work?" ]	[ [ "1908.08419-Active Learning for Chinese Word Segmentation-0" ] ]	[ "Active learning methods has a learning engine (mainly used for training of classification problems) and the selection engine (which chooses samples that need to be relabeled by annotators from unlabeled data). Then, relabeled samples are added to training set for classifier to re-train, thus continuously improving...	36
1703.05260	InScript: Narrative texts annotated with script information	This paper presents the InScript corpus (Narrative Texts Instantiating Script structure). InScript is a corpus of 1,000 stories centered around 10 different scenarios. Verbs and noun phrases are annotated with event and participant types, respectively. Additionally, the text is annotated with coreference information. T...	{ "paragraphs": [ [ "A script is “a standardized sequence of events that describes some stereotypical human activity such as going to a restaurant or visiting a doctor” BIBREF0 . Script events describe an action/activity along with the involved participants. For example, in the script describing a visit to ...	{ "answers": [ { "annotation_id": [ "697e318cbd3c0685caf6f8670044f74eeca2dd29" ], "answer": [ { "evidence": [], "extractive_spans": [], "free_form_answer": "", "highlighted_evidence": [], "unanswerable": true, "yes_no": ...	{ "caption": [ "Figure 1: An excerpt from a story on the TAKING A BATH script.", "Figure 2: Connecting DeScript and InScript: an example from the BAKING A CAKE scenario (InScript participant annotation is omitted for better readability).", "Table 1: Bath scenario template (labels added in the second phase...	[ "Did the annotators agreed and how much?" ]	[ [ "1703.05260-Inter-Annotator Agreement-1", "1703.05260-6-Figure4-1.png", "1703.05260-Inter-Annotator Agreement-0" ] ]	[ "Moderate agreement of 0.64-0.68 Fleiss’ Kappa over event type labels, 0.77 Fleiss’ Kappa over participant labels, and good agreement of 90.5% over coreference information." ]	37
1905.00563	Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications	Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data. Existing approaches, however, primarily focus on improving accuracy and overlook other aspects such as robustness and interpretability. In this paper, we propose adversarial modifications for li...	{ "paragraphs": [ [ "Knowledge graphs (KG) play a critical role in many real-world applications such as search, structured data management, recommendations, and question answering. Since KGs often suffer from incompleteness and noise in their facts (links), a number of recent techniques have proposed models...	{ "answers": [ { "annotation_id": [ "8f1f61837454d9f482cd81ea51f1eabd07870b6f", "a922089b7e48e898c731a414d8b871e45fc72666" ], "answer": [ { "evidence": [ "FLOAT SELECTED: Table 2: Data Statistics of the benchmarks." ], "extractive...	{ "caption": [ "Figure 1: Completion Robustness and Interpretability via Adversarial Graph Edits (CRIAGE): Change in the graph structure that changes the prediction of the retrained model, where (a) is the original sub-graph of the KG, (b) removes a neighboring link of the target, resulting in a change in the pre...	[ "What datasets are used to evaluate this approach?" ]	[ [ "1905.00563-5-Table2-1.png", "1905.00563-Introduction-1" ] ]	[ " Kinship and Nations knowledge graphs, YAGO3-10 and WN18KGs knowledge graphs " ]	38
2002.11893	CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset	To advance multi-domain (cross-domain) dialogue modeling as well as alleviate the shortage of Chinese task-oriented datasets, we propose CrossWOZ, the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restauran...	{ "paragraphs": [ [ "Recently, there have been a variety of task-oriented dialogue models thanks to the prosperity of neural architectures BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5. However, the research is still largely limited by the availability of large-scale high-quality dialogue data. Many ...	{ "answers": [ { "annotation_id": [ "d1dbe98f982bef1faf43aa1d472c8ed9ffd763fd", "ff705c27c283670b07e788139cc9e91baa6f328d" ], "answer": [ { "evidence": [ "Database Construction: we crawled travel information in Beijing from the Web, including Hotel, ...	{ "caption": [], "file": [] }	[ "How was the dataset collected?" ]	[ [ "2002.11893-Data Collection-1", "2002.11893-Data Collection-3", "2002.11893-Data Collection-4", "2002.11893-Data Collection-2", "2002.11893-Data Collection-0" ] ]	[ "They crawled travel information from the Web to build a database, created a multi-domain goal generator from the database, collected dialogue between workers an automatically annotated dialogue acts. " ]	40
1910.07181	BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance	Pretraining deep contextualized representations using an unsupervised language modeling objective has led to large performance gains for a variety of NLP tasks. Despite this success, recent work by Schick and Schutze (2019) suggests that these architectures struggle to understand rare words. For context-independent wor...	{ "paragraphs": [ [ "As traditional word embedding algorithms BIBREF1 are known to struggle with rare words, several techniques for improving their representations have been proposed over the last few years. These approaches exploit either the contexts in which rare words occur BIBREF2, BIBREF3, BIBREF4, BI...	{ "answers": [ { "annotation_id": [ "d01e0f2398f8229187e2e368b2b09229b352b9a7" ], "answer": [ { "evidence": [ "Results on WNLaMPro rare and medium are shown in Table TABREF34, where the mean reciprocal rank (MRR) is reported for BERT, Attentive Mimicking and...	{ "caption": [ "Figure 1: Schematic representation of BERTRAM in the add-gated configuration processing the input word w = “washables” given a single context C1 = “other washables such as trousers . . .” (left) and given multiple contexts C = {C1, . . . , Cm} (right)", "Table 1: Results on WNLaMPro test for b...	[ "What models other than standalone BERT is new model compared to?" ]	[ [ "1910.07181-Evaluation ::: WNLaMPro-2" ] ]	[ "Only Bert base and Bert large are compared to proposed approach." ]	41
1902.00330	Joint Entity Linking with Deep Reinforcement Learning	Entity linking is the task of aligning mentions to corresponding entities in a given knowledge base. Previous studies have highlighted the necessity for entity linking systems to capture the global coherence. However, there are two common weaknesses in previous global models. First, most of them calculate the pairwise ...	{ "paragraphs": [ [ "Entity Linking (EL), which is also called Entity Disambiguation (ED), is the task of mapping mentions in text to corresponding entities in a given knowledge Base (KB). This task is an important and challenging stage in text understanding because mentions are usually ambiguous, i.e., dif...	{ "answers": [ { "annotation_id": [ "42325ec6f5639d307e01d65ebd24c589954df837" ], "answer": [ { "evidence": [], "extractive_spans": [], "free_form_answer": "", "highlighted_evidence": [], "unanswerable": true, "yes_no": ...	{ "caption": [ "Figure 1: Illustration of mentions in the free text and their candidate entities in the knowledge base. Solid black lines point to the correct target entities corresponding to the mentions and to the descriptions of these correct target entities. Solid red lines indicate the consistency between co...	[ "How big is the performance difference between this method and the baseline?" ]	[ [ "1902.00330-7-Table3-1.png" ] ]	[ "Comparing with the highest performing baseline: 1.3 points on ACE2004 dataset, 0.6 points on CWEB dataset, and 0.86 points in the average of all scores." ]	42
1909.00542	Classification Betters Regression in Query-based Multi-document Summarisation Techniques for Question Answering: Macquarie University at BioASQ7b	Task B Phase B of the 2019 BioASQ challenge focuses on biomedical question answering. Macquarie University's participation applies query-based multi-document extractive summarisation techniques to generate a multi-sentence answer given the question and the set of relevant snippets. In past participation we explored the...	{ "paragraphs": [ [ "The BioASQ Challenge includes a question answering task (Phase B, part B) where the aim is to find the “ideal answer” — that is, an answer that would normally be given by a person BIBREF0. This is in contrast with most other question answering challenges where the aim is normally to giv...	{ "answers": [ { "annotation_id": [ "be76304cc653b787c5b7c0d4f88dbfbafd20e537" ], "answer": [ { "evidence": [], "extractive_spans": [], "free_form_answer": "", "highlighted_evidence": [], "unanswerable": true, "yes_no": ...	{ "caption": [ "Table 1. Summarisation techniques used in BioASQ 6b for the generation of ideal answers. The evaluation result is the human evaluation of the best run.", "Fig. 2. Architecture of the neural classification and regression systems. A matrix of pre-trained word embeddings (same pre-trained vectors...	[ "What approaches without reinforcement learning have been tried?" ]	[ [ "1909.00542-Classification vs. Regression Experiments-11", "1909.00542-Deep Learning Models-0", "1909.00542-Deep Learning Models-1" ] ]	[ "classification, regression, neural methods" ]	43
1810.06743	Marrying Universal Dependencies and Universal Morphology	The Universal Dependencies (UD) and Universal Morphology (UniMorph) projects each present schemata for annotating the morphosyntactic details of language. Each project also provides corpora of annotated text in many languages - UD at the token level and UniMorph at the type level. As each corpus is built by different a...	{ "paragraphs": [ [ "The two largest standardized, cross-lingual datasets for morphological annotation are provided by the Universal Dependencies BIBREF1 and Universal Morphology BIBREF2 , BIBREF3 projects. Each project's data are annotated according to its own cross-lingual schema, prescribing how features...	{ "answers": [ { "annotation_id": [ "020ac14a36ff656cccfafcb0e6e869f98de7a78e" ], "answer": [ { "evidence": [ "We present the intrinsic task's recall scores in tab:recall. Bear in mind that due to annotation errors in the original corpora (like the vas examp...	{ "caption": [ "Figure 1: Example of annotation disagreement in UD between two languages on translations of one phrase, reproduced from Malaviya et al. (2018). The final word in each, “refrescante”, is not inflected for gender: It has the same surface form whether masculine or feminine. Only in Portuguese, it is ...	[ "Which languages do they validate on?" ]	[ [ "1810.06743-Results-1", "1810.06743-Introduction-1", "1810.06743-Results-0", "1810.06743-Results-2", "1810.06743-8-Table3-1.png", "1810.06743-8-Table4-1.png" ] ]	[ "Ar, Bg, Ca, Cs, Da, De, En, Es, Eu, Fa, Fi, Fr, Ga, He, Hi, Hu, It, La, Lt, Lv, Nb, Nl, Nn, PL, Pt, Ro, Ru, Sl, Sv, Tr, Uk, Ur" ]	44
1909.02764	Towards Multimodal Emotion Recognition in German Speech Events in Cars using Transfer Learning	The recognition of emotions by humans is a complex process which considers multiple interacting signals such as facial expressions and both prosody and semantic content of utterances. Commonly, research on automatic recognition of emotions is, with few exceptions, limited to one modality. We describe an in-car experime...	{ "paragraphs": [ [ "Automatic emotion recognition is commonly understood as the task of assigning an emotion to a predefined instance, for example an utterance (as audio signal), an image (for instance with a depicted face), or a textual unit (e.g., a transcribed utterance, a sentence, or a Tweet). The set...	{ "answers": [ { "annotation_id": [ "600f0c923d0043277bfac1962a398d487bdca7fa" ], "answer": [ { "evidence": [], "extractive_spans": [], "free_form_answer": "", "highlighted_evidence": [], "unanswerable": false, "yes_no":...	{ "caption": [ "Figure 1: The setup of the driving simulator.", "Table 1: Examples for triggered interactions with translations to English. (D: Driver, A: Agent, Co: Co-Driver)", "Table 2: Examples from the collected data set (with translation to English). E: Emotion, IT: interaction type with agent (A) a...	[ "What is the baseline method for the task?" ]	[ [ "1909.02764-Methods ::: Emotion Recognition from Transcribed Utterances-0" ] ]	[ "For the emotion recognition from text they use described neural network as baseline.\nFor audio and face there is no baseline." ]	45
1905.11901	Revisiting Low-Resource Neural Machine Translation: A Case Study	It has been shown that the performance of neural machine translation (NMT) drops starkly in low-resource conditions, underperforming phrase-based statistical machine translation (PBSMT) and requiring large amounts of auxiliary data to achieve competitive results. In this paper, we re-assess the validity of these result...	{ "paragraphs": [ [ "While neural machine translation (NMT) has achieved impressive performance in high-resource data conditions, becoming dominant in the field BIBREF0 , BIBREF1 , BIBREF2 , recent research has argued that these models are highly data-inefficient, and underperform phrase-based statistical m...	{ "answers": [ { "annotation_id": [ "073418dd5dee73e79f085f846b12ab2255d1fba9", "8ebf6954a9db622ffa0e1a1a578dc757efb66253" ], "answer": [ { "evidence": [ "We use the TED data from the IWSLT 2014 German INLINEFORM0 English shared translation task BIBR...	{ "caption": [ "Figure 4: Translations of the first sentence of the test set using NMT system trained on varying amounts of training data. Under low resource conditions, NMT produces fluent output unrelated to the input.", "Table 1: Training corpus size and subword vocabulary size for different subsets of IWS...	[ "what amounts of size were used on german-english?" ]	[ [ "1905.11901-Data and Preprocessing-3", "1905.11901-3-Table1-1.png", "1905.11901-4-Table2-1.png", "1905.11901-Data and Preprocessing-0", "1905.11901-Results-1", "1905.11901-Results-0" ] ]	[ "Training data with 159000, 80000, 40000, 20000, 10000 and 5000 sentences, and 7584 sentences for development" ]	46
1912.13109	"Hinglish"Language -- Modeling a Messy Code-Mixed Language	With a sharp rise in fluency and users of "Hinglish" in linguistically diverse country, India, it has increasingly become important to analyze social content written in this language in platforms such as Twitter, Reddit, Facebook. This project focuses on using deep learning techniques to tackle a classification problem...	{ "paragraphs": [ [ "Hinglish is a linguistic blend of Hindi (very widely spoken language in India) and English (an associate language of urban areas) and is spoken by upwards of 350 million people in India. While the name is based on the Hindi language, it does not refer exclusively to Hindi, but is used i...	{ "answers": [ { "annotation_id": [ "7011aa54bc26a8fc6341a2dcdb252137b10afb54" ], "answer": [ { "evidence": [ "Mathur et al. in their paper for detecting offensive tweets proposed a Ternary Trans-CNN model where they train a model architecture comprising of ...	{ "caption": [ "Table 1: Annotated Data set", "Table 2: Examples in the dataset", "Table 3: Train-test split", "Figure 1: Deep learning network used for the modeling", "Figure 2: Results of various experiments" ], "file": [ "2-Table1-1.png", "3-Table2-1.png", "4-Table3-1.png", ...	[ "How big is the dataset?" ]	[ [ "1912.13109-Dataset and Features ::: Challenges-12", "1912.13109-Introduction ::: Modeling challenges-5", "1912.13109-4-Table3-1.png" ] ]	[ "Resulting dataset was 7934 messages for train and 700 messages for test." ]	48
1703.04617	Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering	The last several years have seen intensive interest in exploring neural-network-based models for machine comprehension (MC) and question answering (QA). In this paper, we approach the problems by closely modelling questions in a neural network framework. We first introduce syntactic information to help encode questions...	{ "paragraphs": [ [ "Enabling computers to understand given documents and answer questions about their content has recently attracted intensive interest, including but not limited to the efforts as in BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 . Many specific problems such as machine comprehe...	{ "answers": [ { "annotation_id": [ "3723cd0588687070d28ed836a630db0991b52dd6" ], "answer": [ { "evidence": [ "Enabling computers to understand given documents and answer questions about their content has recently attracted intensive interest, including but ...	{ "caption": [ "Figure 1: A high level view of our basic model.", "Figure 2: The inference layer implemented with a residual network.", "Figure 3: The discriminative block for question discrimination and adaptation.", "Table 1: The official leaderboard of single models on SQuAD test set as we submitte...	[ "What MC abbreviate for?" ]	[ [ "1703.04617-Introduction-0" ] ]	[ "machine comprehension" ]	53
1909.00578	SUM-QE: a BERT-based Summary Quality Estimation Model	We propose SumQE, a novel Quality Estimation model for summarization based on BERT. The model addresses linguistic quality aspects that are only indirectly captured by content-based approaches to summary evaluation, without involving comparison with human references. SumQE achieves very high correlations with human rat...	{ "paragraphs": [ [ "Quality Estimation (QE) is a term used in machine translation (MT) to refer to methods that measure the quality of automatically translated text without relying on human references BIBREF0, BIBREF1. In this study, we address QE for summarization. Our proposed model, Sum-QE, successfully...	{ "answers": [ { "annotation_id": [ "8498b608303a9387fdac2f1ac707b9a33a37fd3a" ], "answer": [ { "evidence": [ "FLOAT SELECTED: Table 1: Spearman’s ρ, Kendall’s τ and Pearson’s r correlations on DUC-05, DUC-06 and DUC-07 for Q1–Q5. BEST-ROUGE refers to the ve...	{ "caption": [ "Figure 1: SUM-QE rates summaries with respect to five linguistic qualities (Dang, 2006a). The datasets we use for tuning and evaluation contain human assigned scores (from 1 to 5) for each of these categories.", "Figure 2: Illustration of different flavors of the investigated neural QE methods...	[ "What are their correlation results?", "What simpler models do they look at?", "What linguistic quality aspects are addressed?" ]	[ [ "1909.00578-4-Table1-1.png" ], [ "1909.00578-Methods ::: Baselines ::: BiGRU s with attention:-0", "1909.00578-Methods ::: Baselines ::: Next sentence prediction:-0", "1909.00578-Methods ::: Baselines ::: Next sentence prediction:-1", "1909.00578-Methods ::: Baselines ::: Language model (L...	[ "High correlation results range from 0.472 to 0.936", "BiGRUs with attention, ROUGE, Language model, and next sentence prediction ", "Grammaticality, non-redundancy, referential clarity, focus, structure & coherence" ]	54
1910.11471	Machine Translation from Natural Language to Code using Long-Short Term Memory	Making computer programming language more understandable and easy for the human is a longstanding problem. From assembly language to present day’s object-oriented programming, concepts came to make programming easier so that a programmer can focus on the logic and the architecture rather than the code and language itse...	{ "paragraphs": [ [ "Removing computer-human language barrier is an inevitable advancement researchers are thriving to achieve for decades. One of the stages of this advancement will be coding through natural human language instead of traditional programming language. On naturalness of computer programming ...	{ "answers": [ { "annotation_id": [ "712162ee41fcd33e17f5974b52db5ef08caa28ef", "ca3b72709cbea8e97d402eef60ef949c8818ae6f" ], "answer": [ { "evidence": [], "extractive_spans": [], "free_form_answer": "", "highlighted_evidence": [], ...	{ "caption": [ "Fig. 1. Text-Code bi-lingual corpus", "Fig. 2. Neural training model architecture of Text-To-Code", "Fig. 3. Accuracy gain in progress of training the RNN" ], "file": [ "4-Figure1-1.png", "5-Figure2-1.png", "6-Figure3-1.png" ] }	[ "What dataset do they use?" ]	[ [ "1910.11471-Proposed Methodology ::: Statistical Machine Translation ::: Data Preparation-0" ] ]	[ "A parallel corpus where the source is an English expression of code and the target is Python code." ]	56
1910.09399	A Survey and Taxonomy of Adversarial Neural Networks for Text-to-Image Synthesis	Text-to-image synthesis refers to computational methods which translate human written textual descriptions, in the form of keywords or sentences, into images with similar semantic meaning to the text. In earlier research, image synthesis relied mainly on word to image correlation analysis combined with supervised metho...	{ "paragraphs": [ [ "“ (GANs), and the variations that are now being proposed is the most interesting idea in the last 10 years in ML, in my opinion.” (2016)", "– Yann LeCun", "A picture is worth a thousand words! While written text provide efficient, effective, and concise ways for communicatio...	{ "answers": [ { "annotation_id": [ "45a2b7dc749c642c3ed415dd5a44202ad8b6ac61", "b4fc38fa3c0347286c4cae9d60f5bb527cf6ae85" ], "answer": [ { "evidence": [ "Following the above definition, the $\\min \\max $ objective function in Eq. (DISPLAY_FORM10) a...	{ "caption": [ "Figure 1. Early research on text-to-image synthesis (Zhu et al., 2007). The system uses correlation between keywords (or keyphrase) and images and identifies informative and “picturable” text units, then searches for the most likely image parts conditioned on the text, and eventually optimizes the...	[ "What is typical GAN architecture for each text-to-image synhesis group?" ]	[ [ "1910.09399-Text-to-Image Synthesis Taxonomy and Categorization-0", "1910.09399-12-Figure9-1.png" ] ]	[ "Semantic Enhancement GANs: DC-GANs, MC-GAN\nResolution Enhancement GANs: StackGANs, AttnGAN, HDGAN\nDiversity Enhancement GANs: AC-GAN, TAC-GAN etc.\nMotion Enhancement GAGs: T2S, T2V, StoryGAN" ]	57
1807.03367	Talk the Walk: Navigating New York City through Grounded Dialogue	We introduce"Talk The Walk", the first large-scale dialogue dataset grounded in action and perception. The task involves two agents (a"guide"and a"tourist") that communicate via natural language in order to achieve a common goal: having the tourist navigate to a given target location. The task and dataset, which are de...	{ "paragraphs": [ [ "0pt0.03.03 ", "0pt0.030.03 ", "0pt0.030.03", "We introduce “Talk The Walk”, the first large-scale dialogue dataset grounded in action and perception. The task involves two agents (a “guide” and a “tourist”) that communicate via natural language in order to achieve a ...	{ "answers": [ { "annotation_id": [ "2e3c476fd6c267447136656da446e9bb41953f03", "83b6b215aff8b6d9e9fa3308c962e0a916725a78" ], "answer": [ { "evidence": [ "We crowd-sourced the collection of the dataset on Amazon Mechanical Turk (MTurk). We use the MT...	{ "caption": [ "Figure 1: Example of the Talk The Walk task: two agents, a “tourist” and a “guide”, interact with each other via natural language in order to have the tourist navigate towards the correct location. The guide has access to a map and knows the target location but not the tourist location, while the ...	[ "What language do the agents talk in?" ]	[ [ "1807.03367-Dataset Details-40", "1807.03367-Dataset Details-25", "1807.03367-Dataset Details-39", "1807.03367-Dataset Details-2", "1807.03367-Dataset Details-20", "1807.03367-Dataset Details-4", "1807.03367-Dataset Details-38", "1807.03367-Dataset Details-27", "1807.03367-Data...	[ "English" ]	62
1910.03891	Learning High-order Structural and Attribute information by Knowledge Graph Attention Networks for Enhancing Knowledge Graph Embedding	The goal of representation learning of knowledge graph is to encode both entities and relations into a low-dimensional embedding spaces. Many recent works have demonstrated the benefits of knowledge graph embedding on knowledge graph completion task, such as relation extraction. However, we observe that: 1) existing me...	{ "paragraphs": [ [ "In the past decade, many large-scale Knowledge Graphs (KGs), such as Freebase BIBREF0, DBpedia BIBREF1 and YAGO BIBREF2 have been built to represent human complex knowledge about the real-world in the machine-readable format. The facts in KGs are usually encoded in the form of triples $...	{ "answers": [ { "annotation_id": [ "b27e860ab0d3f3d3c9f7fe0a2f8907d38965d7a2" ], "answer": [ { "evidence": [ "Experimental results of entity classification on the test sets of all the datasets is shown in Table TABREF25. The results is clearly demonstrate t...	{ "caption": [ "Figure 1: Subgraph of a knowledge graph contains entities, relations and attributes.", "Figure 2: Illustration of the KANE architecture.", "Table 1: The statistics of datasets.", "Table 2: Entity classification results in accuracy. We run all models 10 times and report mean ± standard ...	[ "How much better is performance of proposed method than state-of-the-art methods in experiments?" ]	[ [ "1910.03891-Experiments ::: Entity Classification ::: Test Performance.-0", "1910.03891-6-Table2-1.png" ] ]	[ "Accuracy of best proposed method KANE (LSTM+Concatenation) are 0.8011, 0.8592, 0.8605 compared to best state-of-the art method R-GCN + LR 0.7721, 0.8193, 0.8229 on three datasets respectively." ]	66
1610.00879	A Computational Approach to Automatic Prediction of Drunk Texting	Alcohol abuse may lead to unsociable behavior such as crime, drunk driving, or privacy leaks. We introduce automatic drunk-texting prediction as the task of identifying whether a text was written when under the influence of alcohol. We experiment with tweets labeled using hashtags as distant supervision. Our classifier...	{ "paragraphs": [ [ "The ubiquity of communication devices has made social media highly accessible. The content on these media reflects a user's day-to-day activities. This includes content created under the influence of alcohol. In popular culture, this has been referred to as `drunk-texting'. In this pape...	{ "answers": [ { "annotation_id": [ "9673c8660ce783e03520c8e10c5ec0167cb2bce2" ], "answer": [ { "evidence": [ "FLOAT SELECTED: Figure 1: Word cloud for drunk tweets" ], "extractive_spans": [], "free_form_answer": "", "...	{ "caption": [ "Figure 1: Word cloud for drunk tweets", "Table 1: Our Feature Set for Drunk-texting Prediction", "Table 2: Performance of our features on Datasets 1 and 2", "Table 4: Cohen’s Kappa for three annotators (A1A3)", "Table 3: Top stylistic features for Datasets 1 and 2 obtained using Ch...	[ "What baseline model is used?", "What stylistic features are used to detect drunk texts?" ]	[ [ "1610.00879-4-Table5-1.png" ], [ "1610.00879-3-Table1-1.png" ] ]	[ "Human evaluators", "LDA unigrams (Presence/Count), POS Ratio, #Named Entity Mentions, #Discourse Connectors, Spelling errors, Repeated characters, Capitalization, Length, Emoticon (Presence/Count), Sentiment Ratio." ]	67
1704.05572	Answering Complex Questions Using Open Information Extraction	While there has been substantial progress in factoid question-answering (QA), answering complex questions remains challenging, typically requiring both a large body of knowledge and inference techniques. Open Information Extraction (Open IE) provides a way to generate semi-structured knowledge for QA, but to date such ...	{ "paragraphs": [ [ "Effective question answering (QA) systems have been a long-standing quest of AI research. Structured curated KBs have been used successfully for this task BIBREF0 , BIBREF1 . However, these KBs are expensive to build and typically domain-specific. Automatically constructed open vocabula...	{ "answers": [ { "annotation_id": [ "3dc26c840c9d93a07e7cfd50dae2ec9e454e39e4", "b66d581a485f807a457f36777a1ab22dbf849998" ], "answer": [ { "evidence": [ "We use the text corpora (S) from BIBREF6 aristo2016:combining to build our tuple KB. For each t...	{ "caption": [ "Figure 1: An example support graph linking a question (top), two tuples from the KB (colored) and an answer option (nitrogen).", "Table 2: TUPLEINF is significantly better at structured reasoning than TABLEILP.9", "Table 1: High-level ILP constraints; we report results for ~w = (2, 4, 4, 4...	[ "What is the accuracy of the proposed technique?" ]	[ [ "1704.05572-4-Table2-1.png" ] ]	[ "51.7 and 51.6 on 4th and 8th grade question sets with no curated knowledge. 47.5 and 48.0 on 4th and 8th grade question sets when both solvers are given the same knowledge" ]	68
1707.03904	Quasar: Datasets for Question Answering by Search and Reading	We present two new large-scale datasets aimed at evaluating systems designed to comprehend a natural language query and extract its answer from a large corpus of text. The Quasar-S dataset consists of 37000 cloze-style (fill-in-the-gap) queries constructed from definitions of software entity tags on the popular website...	{ "paragraphs": [ [ "Factoid Question Answering (QA) aims to extract answers, from an underlying knowledge source, to information seeking questions posed in natural language. Depending on the knowledge source available there are two main approaches for factoid QA. Structured sources, including Knowledge Bas...	{ "answers": [ { "annotation_id": [ "00112b6bc9f87e8d1943add164637a03ebc74336" ], "answer": [ { "evidence": [ "Each dataset consists of a collection of records with one QA problem per record. For each record, we include some question text, a context document...	{ "caption": [ "Figure 1: Example short-document instances from QUASAR-S (top) and QUASAR-T (bottom)", "Figure 2: Cloze generation", "Table 1: Dataset Statistics. Single-Token refers to the questions whose answer is a single token (for QUASAR-S all answers come from a fixed vocabulary). Answer in Short (L...	[ "Which retrieval system was used for baselines?" ]	[ [ "1707.03904-Context Retrieval-0", "1707.03904-Dataset Construction-0", "1707.03904-Results-0" ] ]	[ "The dataset comes with a ranked set of relevant documents. Hence the baselines do not use a retrieval system." ]	70
1911.07228	Error Analysis for Vietnamese Named Entity Recognition on Deep Neural Network Models	In recent years, Vietnamese Named Entity Recognition (NER) systems have had a great breakthrough when using Deep Neural Network methods. This paper describes the primary errors of the state-of-the-art NER systems on Vietnamese language. After conducting experiments on BLSTM-CNN-CRF and BLSTM-CRF models with different w...	{ "paragraphs": [ [ "Named Entity Recognition (NER) is one of information extraction subtasks that is responsible for detecting entity elements from raw text and can determine the category in which the element belongs, these categories include the names of persons, organizations, locations, expressions of t...	{ "answers": [ { "annotation_id": [ "20b9bd9b3d0d70cf39bfdd986a5fd5d78f702e0f" ], "answer": [ { "evidence": [ "We use the word embeddings for Vietnamese that created by Kyubyong Park and Edouard Grave at al:", "Kyubyong Park: In his project, he u...	{ "caption": [ "Fig. 1. Chart flow to analyze errors based on gold labels", "Fig. 2. Chart flow to analyze errors based on predicted labels", "Table 1. Number type of each tags in the corpus", "Table 2. F1 score of two models with different pre-trained word embeddings", "Table 3. Performances of L...	[ "How much better was the BLSTM-CNN-CRF than the BLSTM-CRF?" ]	[ [ "1911.07228-Experiment and Results-0", "1911.07228-7-Table2-1.png" ] ]	[ "Best BLSTM-CNN-CRF had F1 score 86.87 vs 86.69 of best BLSTM-CRF " ]	71
1603.07044	Recurrent Neural Network Encoder with Attention for Community Question Answering	We apply a general recurrent neural network (RNN) encoder framework to community question answering (cQA) tasks. Our approach does not rely on any linguistic processing, and can be applied to different languages or domains. Further improvements are observed when we extend the RNN encoders with a neural attention mechan...	{ "paragraphs": [ [ "Community question answering (cQA) is a paradigm that provides forums for users to ask or answer questions on any topic with barely any restrictions. In the past decade, these websites have attracted a great number of users, and have accumulated a large collection of question-comment th...	{ "answers": [ { "annotation_id": [ "005fda1710dc27880d84605c9bb3971e626fda3b" ], "answer": [ { "evidence": [ "Automation of cQA forums can be divided into three tasks: question-comment relevance (Task A), question-question relevance (Task B), and question-e...	{ "caption": [ "Figure 1: RNN encoder for related question/comment selection.", "Figure 2: Neural attention model for related question/comment selection.", "Figure 3: Joint learning for external comment selection.", "Figure 4: IR-based system and feature-rich based system.", "Table 2: The RNN enco...	[ "What supplemental tasks are used for multitask learning?", "How much performance gap between their approach and the strong handcrafted method?" ]	[ [ "1603.07044-Predicting Relationships of Object Pairs with an Attention Model-0", "1603.07044-Modeling Question-External Comments-0", "1603.07044-Introduction-1" ], [ "1603.07044-7-Table4-1.png" ] ]	[ "Multitask learning is used for the task of predicting relevance of a comment on a different question to a given question, where the supplemental tasks are predicting relevance between the questions, and between the comment and the corresponding question", "0.007 MAP on Task A, 0.032 MAP on Task B, 0.055 MAP on T...	72
1902.09314	Attentional Encoder Network for Targeted Sentiment Classification	Targeted sentiment classification aims at determining the sentimental tendency towards specific targets. Most of the previous approaches model context and target words with RNN and attention. However, RNNs are difficult to parallelize and truncated backpropagation through time brings difficulty in remembering long-term...	{ "paragraphs": [ [ "Targeted sentiment classification is a fine-grained sentiment analysis task, which aims at determining the sentiment polarities (e.g., negative, neutral, or positive) of a sentence over “opinion targets” that explicitly appear in the sentence. For example, given a sentence “I hated thei...	{ "answers": [ { "annotation_id": [ "0064ff0d9e06a701f36bb4baabb7d086c3311fd6" ], "answer": [ { "evidence": [ "The attentional encoder layer is a parallelizable and interactive alternative of LSTM and is applied to compute the hidden states of the input embe...	{ "caption": [ "Figure 1: Overall architecture of the proposed AEN.", "Table 1: Statistics of the datasets.", "Table 2: Main results. The results of baseline models are retrieved from published papers. Top 2 scores are in bold.", "Table 3: Model sizes. Memory footprints are evaluated on the Restaurant...	[ "How big is their model?" ]	[ [ "1902.09314-Model Analysis-1", "1902.09314-7-Table3-1.png" ] ]	[ "Proposed model has 1.16 million parameters and 11.04 MB." ]	73
1910.11769	DENS: A Dataset for Multi-class Emotion Analysis	We introduce a new dataset for multi-class emotion analysis from long-form narratives in English. The Dataset for Emotions of Narrative Sequences (DENS) was collected from both classic literature available on Project Gutenberg and modern online narratives available on Wattpad, annotated using Amazon Mechanical Turk. A ...	{ "paragraphs": [ [ "Humans experience a variety of complex emotions in daily life. These emotions are heavily reflected in our language, in both spoken and written forms.", "Many recent advances in natural language processing on emotions have focused on product reviews BIBREF0 and tweets BIBREF1, BIB...	{ "answers": [ { "annotation_id": [ "42eb0c70a3fc181f2418a7a3d55c836817cc4d8b" ], "answer": [ { "evidence": [ "FLOAT SELECTED: Table 4: Benchmark results (averaged 5-fold cross validation)", "We computed bag-of-words-based benchmarks using the fo...	{ "caption": [ "Figure 1: Plutchik’s wheel of emotions (Wikimedia, 2011)", "Table 1: Genre distribution of the modern narratives", "Table 4: Benchmark results (averaged 5-fold cross validation)", "Table 2: Dataset label distribution" ], "file": [ "2-Figure1-1.png", "3-Table1-1.png", "4...	[ "How many emotions do they look at?" ]	[ [ "1910.11769-Dataset ::: Plutchik’s Wheel of Emotions-5" ] ]	[ "9" ]	75
1909.13375	Tag-based Multi-Span Extraction in Reading Comprehension	With models reaching human performance on many popular reading comprehension datasets in recent years, a new dataset, DROP, introduced questions that were expected to present a harder challenge for reading comprehension models. Among these new types of questions were "multi-span questions", questions whose answers cons...	{ "paragraphs": [ [ "The task of reading comprehension, where systems must understand a single passage of text well enough to answer arbitrary questions about it, has seen significant progress in the last few years. With models reaching human performance on the popular SQuAD dataset BIBREF0, and with much o...	{ "answers": [ { "annotation_id": [ "eb32830971e006411f8136f81ff218c63213dc22" ], "answer": [ { "evidence": [ "MTMSN BIBREF4 is the first, and only model so far, that specifically tried to tackle the multi-span questions of DROP. Their approach consisted of ...	{ "caption": [ "Table 1. Examples of faulty answers for multi-span questions in the training dataset, with their perfect clean answers, and answers generated by our cleaning method", "Table 2. Performance of different models on DROP’s development set in terms of Exact Match (EM) and F1.", "Table 3. Compar...	[ "What approach did previous models use for multi-span questions?", "What is difference in peformance between proposed model and state-of-the art on other question types?", "What is the performance of proposed model on entire DROP dataset?" ]	[ [ "1909.13375-Related Work-2" ], [ "1909.13375-6-Table2-1.png" ], [ "1909.13375-Results and Discussion ::: Performance on DROP's Test Set-0", "1909.13375-6-Table3-1.png" ] ]	[ "Only MTMSM specifically tried to tackle the multi-span questions. Their approach consisted of two parts: first train a dedicated categorical variable to predict the number of spans to extract and the second was to generalize the single-span head method of extracting a span", "For single-span questions, the propo...	79
1909.00430	Transfer Learning Between Related Tasks Using Expected Label Proportions	Deep learning systems thrive on abundance of labeled training data but such data is not always available, calling for alternative methods of supervision. One such method is expectation regularization (XR) (Mann and McCallum, 2007), where models are trained based on expected label proportions. We propose a novel applica...	{ "paragraphs": [ [ "Data annotation is a key bottleneck in many data driven algorithms. Specifically, deep learning models, which became a prominent tool in many data driven tasks in recent years, require large datasets to work well. However, many tasks require manual annotations which are relatively hard ...	{ "answers": [ { "annotation_id": [ "8f217f179202ac3fbdd22ceb878a60b4ca2b14c8" ], "answer": [ { "evidence": [], "extractive_spans": [], "free_form_answer": "", "highlighted_evidence": [], "unanswerable": true, "yes_no": ...	{ "caption": [ "Figure 1: Illustration of the algorithm. Cs is applied to Du resulting in ỹ for each sentence, Uj is built according with the fragments of the same labelled sentences, the probabilities for each fragment in Uj are summed and normalized, the XR loss in equation (4) is calculated and the network is...	[ "How accurate is the aspect based sentiment classifier trained only using the XR loss?" ]	[ [ "1909.00430-7-Table1-1.png" ] ]	[ "BiLSTM-XR-Dev Estimation accuracy is 83.31 for SemEval-15 and 87.68 for SemEval-16.\nBiLSTM-XR accuracy is 83.31 for SemEval-15 and 88.12 for SemEval-16.\n" ]	80
1910.11493	The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection	The SIGMORPHON 2019 shared task on cross-lingual transfer and contextual analysis in morphology examined transfer learning of inflection between 100 language pairs, as well as contextual lemmatization and morphosyntactic description in 66 languages. The first task evolves past years' inflection tasks by examining trans...	{ "paragraphs": [ [ "While producing a sentence, humans combine various types of knowledge to produce fluent output—various shades of meaning are expressed through word selection and tone, while the language is made to conform to underlying structural rules via syntax and morphology. Native speakers are oft...	{ "answers": [ { "annotation_id": [ "012a77e1bbdaa410ad83a28a87526db74bd1e353" ], "answer": [ { "evidence": [ "BIBREF17: The Lemming model is a log-linear model that performs joint morphological tagging and lemmatization. The model is globally normalized wit...	{ "caption": [ "Table 1: Sample language pair and data format for Task 1", "Table 2: Task 1 Team Scores, averaged across all Languages; * indicates submissions were only applied to a subset of languages, making scores incomparable. † indicates that additional resources were used for training.", "Table 3: ...	[ "What were the non-neural baselines used for the task?" ]	[ [ "1910.11493-Baselines ::: Task 2 Baselines ::: Non-neural-0" ] ]	[ "The Lemming model in BIBREF17" ]	81
1908.10449	Interactive Machine Comprehension with Information Seeking Agents	Existing machine reading comprehension (MRC) models do not scale effectively to real-world applications like web-level information retrieval and question answering (QA). We argue that this stems from the nature of MRC datasets: most of these are static environments wherein the supporting documents and all necessary inf...	{ "paragraphs": [ [ "Many machine reading comprehension (MRC) datasets have been released in recent years BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4 to benchmark a system's ability to understand and reason over natural language. Typically, these datasets require an MRC model to read through a document to a...	{ "answers": [ { "annotation_id": [ "6704ca0608ed345578616637b277f39d9fff4c98" ], "answer": [ { "evidence": [ "The key idea behind our proposed interactive MRC (iMRC) is to restrict the document context that a model observes at one time. Concretely, we split...	{ "caption": [ "Table 1: Examples of interactive machine reading comprehension behavior. In the upper example, the agent has no memory of past observations, and thus it answers questions only with observation string at current step. In the lower example, the agent is able to use its memory to find answers.", ...	[ "What are the models evaluated on?" ]	[ [ "1908.10449-iMRC: Making MRC Interactive-0", "1908.10449-iMRC: Making MRC Interactive ::: Evaluation Metric-0" ] ]	[ "They evaluate F1 score and agent's test performance on their own built interactive datasets (iSQuAD and iNewsQA)" ]	83
1910.03814	Exploring Hate Speech Detection in Multimodal Publications	In this work we target the problem of hate speech detection in multimodal publications formed by a text and an image. We gather and annotate a large scale dataset from Twitter, MMHS150K, and propose different models that jointly analyze textual and visual information for hate speech detection, comparing them with unimo...	{ "paragraphs": [ [ "Social Media platforms such as Facebook, Twitter or Reddit have empowered individuals' voices and facilitated freedom of expression. However they have also been a breeding ground for hate speech and other types of online harassment. Hate speech is defined in legal literature as speech (...	{ "answers": [ { "annotation_id": [ "e759a4245a5ac52632d3fbc424192e9e72b16350" ], "answer": [ { "evidence": [ "The objective of this work is to build a hate speech detector that leverages both textual and visual data and detects hate speech publications base...	{ "caption": [ "Figure 1. Tweets from MMHS150K where the visual information adds relevant context for the hate speech detection task.", "Figure 2. Percentage of tweets per class in MMHS150K.", "Figure 3. Percentage of hate and not hate tweets for top keywords of MMHS150K.", "Figure 4. FCM architecture...	[ "What is the results of multimodal compared to unimodal models?" ]	[ [ "1910.03814-Results-0", "1910.03814-7-Table1-1.png" ] ]	[ "Unimodal LSTM vs Best Multimodal (FCM)\n- F score: 0.703 vs 0.704\n- AUC: 0.732 vs 0.734 \n- Mean Accuracy: 68.3 vs 68.4 " ]	84
1701.00185	Self-Taught Convolutional Neural Networks for Short Text Clustering	Short text clustering is a challenging problem due to its sparseness of text representation. Here we propose a flexible Self-Taught Convolutional neural network framework for Short Text Clustering (dubbed STC^2), which can flexibly and successfully incorporate more useful semantic features and learn non-biased deep tex...	{ "paragraphs": [ [ "Short text clustering is of great importance due to its various applications, such as user profiling BIBREF0 and recommendation BIBREF1 , for nowaday's social media dataset emerged day by day. However, short text clustering has the data sparsity problem and most words only occur once in...	{ "answers": [ { "annotation_id": [ "ce1b6507ec3bde25d3bf800bb829aae3b20f8e02" ], "answer": [ { "evidence": [ "The clustering performance is evaluated by comparing the clustering results of texts with the tags/labels provided by the text corpus. Two metrics,...	{ "caption": [ "Figure 1: The architecture of our proposed STC2 framework for short text clustering. Solid and hollow arrows represent forward and backward propagation directions of features and gradients respectively. The STC2 framework consist of deep convolutional neural network (CNN), unsupervised dimensional...	[ "What were their performance results?", "By how much did they outperform the other methods?" ]	[ [ "1701.00185-23-Table7-1.png", "1701.00185-22-Table6-1.png" ], [ "1701.00185-23-Table7-1.png", "1701.00185-22-Table6-1.png" ] ]	[ "On SearchSnippets dataset ACC 77.01%, NMI 62.94%, on StackOverflow dataset ACC 51.14%, NMI 49.08%, on Biomedical dataset ACC 43.00%, NMI 38.18%", "on SearchSnippets dataset by 6.72% in ACC, by 6.94% in NMI; on Biomedical dataset by 5.77% in ACC, 3.91% in NMI" ]	85
1911.03894	CamemBERT: a Tasty French Language Model	Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success, most available models have either been trained on English data or on the concatenation of data in multiple languages. This makes practical use of such models—in all languages except English—very limited. Aiming to addre...	{ "paragraphs": [ [ "Pretrained word representations have a long history in Natural Language Processing (NLP), from non-neural methods BIBREF0, BIBREF1, BIBREF2 to neural word embeddings BIBREF3, BIBREF4 and to contextualised representations BIBREF5, BIBREF6. Approaches shifted more recently from using thes...	{ "answers": [ { "annotation_id": [ "e9e1b87a031a0b9b9f2f47eede9097c58a6b500f" ], "answer": [ { "evidence": [ "We use the unshuffled version of the French OSCAR corpus, which amounts to 138GB of uncompressed text and 32.7B SentencePiece tokens." ],...	{ "caption": [ "Table 1: Sizes in Number of tokens, words and phrases of the 4 treebanks used in the evaluations of POS-tagging and dependency parsing.", "Table 2: Final POS and dependency parsing scores of CamemBERT and mBERT (fine-tuned in the exact same conditions as CamemBERT), UDify as reported in the or...	[ "What is the state of the art?" ]	[ [ "1911.03894-Evaluation ::: Named Entity Recognition ::: Baselines-2", "1911.03894-Evaluation ::: Part-of-speech tagging and dependency parsing ::: Baselines-2", "1911.03894-Evaluation ::: Natural Language Inference ::: Baselines-1" ] ]	[ "POS and DP task: CONLL 2018\nNER task: (no extensive work) Strong baselines CRF and BiLSTM-CRF\nNLI task: mBERT or XLM (not clear from text)" ]	89
1710.01492	Semantic Sentiment Analysis of Twitter Data	Internet and the proliferation of smart mobile devices have changed the way information is created, shared, and spreads, e.g., microblogs such as Twitter, weblogs such as LiveJournal, social networks such as Facebook, and instant messengers such as Skype and WhatsApp are now commonly used to share thoughts and opinions...	{ "paragraphs": [ [ "Microblog sentiment analysis; Twitter opinion mining" ], [ "Sentiment Analysis: This is text analysis aiming to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a piece of text." ], [ "Sentiment ...	{ "answers": [ { "annotation_id": [ "eaa2871ebfa0e132a84ca316dee33a4e45c9aba9" ], "answer": [ { "evidence": [ "Supervised learning. Traditionally, the above features were fed into classifiers such as Maximum Entropy (MaxEnt) and Support Vector Machines (SVM)...	{ "caption": [], "file": [] }	[ "What difficulties does sentiment analysis on Twitter have, compared to sentiment analysis in other domains?" ]	[ [ "1710.01492-Introduction-3", "1710.01492-Features and Learning-0" ] ]	[ "Tweets noisy nature, use of creative spelling and punctuation, misspellings, slang, new words, URLs, and genre-specific terminology and abbreviations, short (length limited) text" ]	91
1912.01673	COSTRA 1.0: A Dataset of Complex Sentence Transformations	COSTRA 1.0 is a dataset of Czech complex sentence transformations. The dataset is intended for the study of sentence-level embeddings beyond simple word alternations or standard paraphrasing. ::: The dataset consist of 4,262 unique sentences with average length of 10 words, illustrating 15 types of modifications such ...	{ "paragraphs": [ [ "Vector representations are becoming truly essential in majority of natural language processing tasks. Word embeddings became widely popular with the introduction of word2vec BIBREF0 and GloVe BIBREF1 and their properties have been analyzed in length from various aspects.", "Studie...	{ "answers": [ { "annotation_id": [ "0259888535c15dba7d2d5de40c53adb8dee11971" ], "answer": [ { "evidence": [ "In the second round, we collected 293 annotations from 12 annotators. After Korektor, there are 4262 unique sentences (including 150 seed sentences...	{ "caption": [ "Table 1: Examples of transformations given to annotators for the source sentence Several hunters slept on a clearing. The third column shows how many of all the transformation suggestions collected in the first round closely mimic the particular example. The number is approximate as annotators typ...	[ "How many sentence transformations on average are available per unique sentence in dataset?", "What annotations are available in the dataset?", "How are possible sentence transformations represented in dataset, as new sentences?", "What are all 15 types of modifications ilustrated in the dataset?" ]	[ [ "1912.01673-Dataset Description-0" ], [ "1912.01673-Annotation ::: Second Round: Collecting Data ::: Sentence Transformations-2", "1912.01673-Annotation ::: Second Round: Collecting Data ::: Sentence Transformations-1" ], [ "1912.01673-Dataset Description-0" ], [ "1912.01673-3-Tabl...	[ "27.41 transformation on average of single seed sentence is available in dataset.", "For each source sentence, transformation sentences that are transformed according to some criteria (paraphrase, minimal change etc.)", "Yes, as new sentences.", "- paraphrase 1\n- paraphrase 2\n- different meaning\n- opposite...	92
1706.08032	A Deep Neural Architecture for Sentence-level Sentiment Classification in Twitter Social Networking	This paper introduces a novel deep learning framework including a lexicon-based approach for sentence-level prediction of sentiment label distribution. We propose to first apply semantic rules and then use a Deep Convolutional Neural Network (DeepCNN) for character-level embeddings in order to increase information for ...	{ "paragraphs": [ [ "Twitter sentiment classification have intensively researched in recent years BIBREF0 BIBREF1 . Different approaches were developed for Twitter sentiment classification by using machine learning such as Support Vector Machine (SVM) with rule-based features BIBREF2 and the combination of ...	{ "answers": [ { "annotation_id": [ "0282506d82926af9792f42326478042758bdc913" ], "answer": [ { "evidence": [ "FLOAT SELECTED: Table IV ACCURACY OF DIFFERENT MODELS FOR BINARY CLASSIFICATION" ], "extractive_spans": [], "free_for...	{ "caption": [ "Figure 1. The overview of a deep learning system.", "Table II THE NUMBER OF TWEETS ARE PROCESSED BY USING SEMANTIC RULES", "Table I SEMANTIC RULES [12]", "Figure 2. Deep Convolutional Neural Network (DeepCNN) for the sequence of character embeddings of a word. For example with 1 region...	[ "What were their results on the three datasets?", "What semantic rules are proposed?" ]	[ [ "1706.08032-5-TableIV-1.png" ], [ "1706.08032-3-TableI-1.png", "1706.08032-Semantic Rules (SR)-0", "1706.08032-Semantic Rules (SR)-1", "1706.08032-Semantic Rules (SR)-2", "1706.08032-Semantic Rules (SR)-3" ] ]	[ "accuracy of 86.63 on STS, 85.14 on Sanders and 80.9 on HCR", "rules that compute polarity of words after POS tagging or parsing steps" ]	94
1909.00124	Learning with Noisy Labels for Sentence-level Sentiment Classification	Deep neural networks (DNNs) can fit (or even over-fit) the training data very well. If a DNN model is trained using data with noisy labels and tested on data with clean labels, the model may perform poorly. This paper studies the problem of learning with noisy labels for sentence-level sentiment classification. We prop...	{ "paragraphs": [ [ "It is well known that sentiment annotation or labeling is subjective BIBREF0. Annotators often have many disagreements. This is especially so for crowd-workers who are not well trained. That is why one always feels that there are many errors in an annotated dataset. In this paper, we st...	{ "answers": [ { "annotation_id": [ "36e4022e631bb303ba899a7b340d8024b3c5e19b" ], "answer": [ { "evidence": [], "extractive_spans": [], "free_form_answer": "", "highlighted_evidence": [], "unanswerable": true, "yes_no": ...	{ "caption": [ "Figure 1: The proposed NETAB model (left) and its training method (right). Components in light gray color denote that these components are deactivated during training in that stage. (Color online)", "Table 1: Summary statistics of the datasets. Number of positive (P) and negative (N) sentences...	[ "What is the performance of the model?" ]	[ [ "1909.00124-5-Figure2-1.png", "1909.00124-Experiments-5", "1909.00124-5-Table2-1.png", "1909.00124-Experiments-8" ] ]	[ "Experiment 1: ACC around 0.5 with 50% noise rate in worst case - clearly higher than baselines for all noise rates\nExperiment 2: ACC on real noisy datasets: 0.7 on Movie, 0.79 on Laptop, 0.86 on Restaurant (clearly higher than baselines in almost all cases)" ]	96
1911.01799	CN-CELEB: a challenging Chinese speaker recognition dataset	Recently, researchers set an ambitious goal of conducting speaker recognition in unconstrained conditions where the variations on ambient, channel and emotion could be arbitrary. However, most publicly available datasets are collected under constrained environments, i.e., with little noise and limited channel variation...	{ "paragraphs": [ [ "Speaker recognition including identification and verification, aims to recognize claimed identities of speakers. After decades of research, performance of speaker recognition systems has been vastly improved, and the technique has been deployed to a wide range of practical applications....	{ "answers": [ { "annotation_id": [ "45270b732239f93ee0e569f36984323d0dde8fd6" ], "answer": [ { "evidence": [ "FLOAT SELECTED: Table 4. EER(%) results of the i-vector and x-vector systems trained on VoxCeleb and evaluated on three evaluation sets." ...	{ "caption": [ "Table 2. The distribution over utterance length.", "Table 1. The distribution over genres.", "Table 3. Comparison between CN-Celeb and VoxCeleb.", "Table 4. EER(%) results of the i-vector and x-vector systems trained on VoxCeleb and evaluated on three evaluation sets.", "Table 5. E...	[ "What was the performance of both approaches on their dataset?", "What genres are covered?", "Which of the two speech recognition models works better overall on CN-Celeb?", "By how much is performance on CN-Celeb inferior to performance on VoxCeleb?" ]	[ [ "1911.01799-4-Table4-1.png" ], [ "1911.01799-2-Table1-1.png" ], [ "1911.01799-4-Table4-1.png" ], [ "1911.01799-4-Table4-1.png" ] ]	[ "ERR of 19.05 with i-vectors and 15.52 with x-vectors", "genre, entertainment, interview, singing, play, movie, vlog, live broadcast, speech, drama, recitation and advertisement", "x-vector", "For i-vector system, performances are 11.75% inferior to voxceleb. For x-vector system, performances are 10.74% infer...	98
1812.06705	Conditional BERT Contextual Augmentation	We propose a novel data augmentation method for labeled sentences called conditional BERT contextual augmentation. Data augmentation methods are often applied to prevent overfitting and improve generalization of deep neural network models. Recently proposed contextual augmentation augments labeled sentences by randomly...	{ "paragraphs": [ [ "Deep neural network-based models are easy to overfit and result in losing their generalization due to limited size of training data. In order to address the issue, data augmentation methods are often applied to generate more training samples. Recent years have witnessed great success in...	{ "answers": [ { "annotation_id": [ "da6a68609a4ef853fbdc85494dbb628978a9d63d" ], "answer": [ { "evidence": [ "SST BIBREF25 SST (Stanford Sentiment Treebank) is a dataset for sentiment classification on movie reviews, which are annotated with five labels (SS...	{ "caption": [ "Figure 1: Model architecture of conditional BERT. The label embeddings in conditional BERT corresponding to segmentation embeddings in BERT, but their functions are different.", "Table 1: Summary statistics for the datasets after tokenization. c: Number of target classes. l: Average sentence l...	[ "How do the authors measure performance?" ]	[ [ "1812.06705-7-Table2-1.png" ] ]	[ "Accuracy across six datasets" ]	99
1905.08949	Recent Advances in Neural Question Generation	Emerging research in Neural Question Generation (NQG) has started to integrate a larger variety of inputs, and generating questions requiring higher levels of cognition. These trends point to NQG as a bellwether for NLP, about how human intelligence embodies the skills of curiosity and integration. We present a compreh...	{ "paragraphs": [ [ "Question Generation (QG) concerns the task of “automatically generating questions from various inputs such as raw text, database, or semantic representation\" BIBREF0 . People have the ability to ask rich, creative, and revealing questions BIBREF1 ; e.g., asking Why did Gollum betray hi...	{ "answers": [ { "annotation_id": [ "f0dca97a210535659f8db4ad400dd5871135086f" ], "answer": [ { "evidence": [], "extractive_spans": [], "free_form_answer": "", "highlighted_evidence": [], "unanswerable": false, "yes_no":...	{ "caption": [ "Table 1: NQG datasets grouped by their cognitive level and answer type, where the number of documents, the number of questions, and the average number of questions per document (Q./Doc) for each corpus are listed.", "Table 2: Existing NQG models with their best-reported performance on SQuAD. L...	[ "What is the latest paper covered by this survey?", "What learning paradigms do they cover in this survey?", "What are all the input modalities considered in prior work in question generation?" ]	[ [ "1905.08949-7-Table2-1.png" ], [ "1905.08949-Learning Paradigm-2", "1905.08949-Learning Paradigm-1" ], [ "1905.08949-Input Modality-1", "1905.08949-Input Modality-0" ] ]	[ "Kim et al. (2019)", "Considering \"What\" and \"How\" separately versus jointly optimizing for both.", "Textual inputs, knowledge bases, and images." ]	100
1902.06843	Fusing Visual, Textual and Connectivity Clues for Studying Mental Health	With ubiquity of social media platforms, millions of people are sharing their online persona by expressing their thoughts, moods, emotions, feelings, and even their daily struggles with mental health issues voluntarily and publicly on social media. Unlike the most existing efforts which study depression by analyzing te...	{ "paragraphs": [ [ "0pt00", "0pt00", "0pt00 0.95", "1]Amir Hossein Yazdavar 1]Mohammad Saeid Mahdavinejad 2]Goonmeet Bajaj", " 3]William Romine 1]Amirhassan Monadjemi 1]Krishnaprasad Thirunarayan", " 1]Amit Sheth 4]Jyotishman Pathak [1]Department of Computer Science & En...	{ "answers": [ { "annotation_id": [ "9069ef5e523b402dc27ab4c3defb1b547af8c8f2" ], "answer": [ { "evidence": [], "extractive_spans": [], "free_form_answer": "", "highlighted_evidence": [], "unanswerable": true, "yes_no": ...	{ "caption": [ "Figure 1: Self-disclosure on Twitter from likely depressed users discovered by matching depressiveindicative terms", "Figure 2: The age distribution for depressed and control users in ground-truth dataset", "Figure 3: Gender and Depressive Behavior Association (Chi-square test: color-code:...	[ "How do this framework facilitate demographic inference from social media?", "How is the data annotated?", "Where does the information on individual-level demographics come from?", "What is the source of the user interaction data? ", "What is the source of the textual data? ", "What is the source of the v...	[ [ "1902.06843-Demographic Prediction-3" ], [ "1902.06843-Dataset-0" ], [ "1902.06843-Dataset-2" ], [ "1902.06843-Introduction-5" ], [ "1902.06843-Introduction-5" ], [ "1902.06843-Introduction-5" ] ]	[ "Demographic information is predicted using weighted lexicon of terms.", "The data are self-reported by Twitter users and then verified by two human experts.", "From Twitter profile descriptions of the users.", "Sociability from ego-network on Twitter", "Users' tweets", "Profile pictures from the Twitter ...	105
1910.02789	Natural Language State Representation for Reinforcement Learning	Recent advances in Reinforcement Learning have highlighted the difficulties in learning within complex high dimensional domains. We argue that one of the main reasons that current approaches do not perform well, is that the information is represented sub-optimally. A natural way to describe what we observe, is through ...	{ "paragraphs": [ [ "“The world of our experiences must be enormously simplified and generalized before it is possible to make a symbolic inventory of all our experiences of things and relations.\"", "(Edward Sapir, Language: An Introduction to the Study of Speech, 1921)", "Deep Learning based a...	{ "answers": [ { "annotation_id": [ "040faf49fbe5c02af982b966eec96f2efaef2243" ], "answer": [ { "evidence": [ "Results of the DQN-based agent are presented in fig: scenario comparison. Each plot depicts the average reward (across 5 seeds) of all representati...	{ "caption": [ "Figure 1: Example of Semantic Segmentation [Kundu et al., 2016].", "Figure 2: Left: Raw visual inputs and their corresponding semantic segmentation in the VizDoom enviornment. Right: Our suggested NLP-based semantic state representation framework.", "Figure 3: Frame division used for descr...	[ "What result from experiments suggest that natural language based agents are more robust?" ]	[ [ "1910.02789-Semantic State Representations in the Doom Environment ::: Experiments-4" ] ]	[ "Average reward across 5 seeds show that NLP representations are robust to changes in the environment as well task-nuisances" ]	108
2001.07209	Text-based inference of moral sentiment change	We present a text-based framework for investigating moral sentiment change of the public via longitudinal corpora. Our framework is based on the premise that language use can inform people's moral perception toward right or wrong, and we build our methodology by exploring moral biases learned from diachronic word embed...	{ "paragraphs": [ [ "People's moral sentiment—our feelings toward right or wrong—can change over time. For instance, the public's views toward slavery have shifted substantially over the past centuries BIBREF0. How society's moral views evolve has been a long-standing issue and a constant source of controve...	{ "answers": [ { "annotation_id": [ "047ca89bb05cf86c1747c79e310917a8225aebf3" ], "answer": [ { "evidence": [ "An emerging body of work in natural language processing and computational social science has investigated how NLP systems can detect moral sentimen...	{ "caption": [ "Figure 1: Illustration of moral sentiment change over the past two centuries. Moral sentiment trajectories of three probe concepts, slavery, democracy, and gay, are shown in moral sentiment embedding space through 2D projection from Fisher’s discriminant analysis with respect to seed words from th...	[ "Which datasets are used in the paper?", "How do they quantify moral relevance?" ]	[ [ "2001.07209-A three-tier modelling framework ::: Lexical data for moral sentiment-1", "2001.07209-Historical corpus data-4", "2001.07209-Historical corpus data-3", "2001.07209-Historical corpus data-2", "2001.07209-A three-tier modelling framework ::: Lexical data for moral sentiment-0" ], ...	[ "Google N-grams\nCOHA\nMoral Foundations Dictionary (MFD)\n", "By complementing morally relevant seed words with a set of morally irrelevant seed words based on the notion of valence" ]	110
1909.00279	Generating Classical Chinese Poems from Vernacular Chinese	Classical Chinese poetry is a jewel in the treasure house of Chinese culture. Previous poem generation models only allow users to employ keywords to interfere the meaning of generated poems, leaving the dominion of generation to the model. In this paper, we propose a novel task of generating classical Chinese poems fro...	{ "paragraphs": [ [ "During thousands of years, millions of classical Chinese poems have been written. They contain ancient poets' emotions such as their appreciation for nature, desiring for freedom and concerns for their countries. Among various types of classical poetry, quatrain poems stand out. On the ...	{ "answers": [ { "annotation_id": [ "04c432ed960ff69bb335b3eac687be8fe4ecf97a" ], "answer": [ { "evidence": [ "1) In classical Chinese poems, poetic images UTF8gbsn(意象) were widely used to express emotions and to build artistic conception. A certain poetic i...	{ "caption": [ "Figure 1: An example of the training procedures of our model. Here we depict two procedures, namely back translation and language modeling. Back translation has two paths, namely ES → DT → ET → DS and DT → ES → DS → ET . Language modeling also has two paths, namely ET → DT and ES → DS . Figure 1 s...	[ "How much is proposed model better in perplexity and BLEU score than typical UMT models?" ]	[ [ "1909.00279-7-Table3-1.png", "1909.00279-Experiment ::: Reborn Poems: Generating Poems from Vernacular Translations-0" ] ]	[ "Perplexity of the best model is 65.58 compared to best baseline 105.79.\nBleu of the best model is 6.57 compared to best baseline 5.50." ]	112
1812.07023	From FiLM to Video: Multi-turn Question Answering with Multi-modal Context	Understanding audio-visual content and the ability to have an informative conversation about it have both been challenging areas for intelligent systems. The Audio Visual Scene-aware Dialog (AVSD) challenge, organized as a track of the Dialog System Technology Challenge 7 (DSTC7), proposes a combined task, where a syst...	{ "paragraphs": [ [ "Deep neural networks have been successfully applied to several computer vision tasks such as image classification BIBREF0 , object detection BIBREF1 , video action classification BIBREF2 , etc. They have also been successfully applied to natural language processing tasks such as machine...	{ "answers": [ { "annotation_id": [ "ee2861105f2d63096676c4b63554fe0593a9c6a0" ], "answer": [ { "evidence": [], "extractive_spans": [], "free_form_answer": "", "highlighted_evidence": [], "unanswerable": true, "yes_no": ...	{ "caption": [ "Table 1: Tasks with audio, visual and text modalities", "Figure 1: FA-HRED uses the last question’s encoding to attend to video description, audio, and video features. These features along with the dialogue state enable the model to generate the answer to the current question. The ground truth...	[ "Do they train a different training method except from scheduled sampling?" ]	[ [ "1812.07023-Experiments-2" ] ]	[ "Answer with content missing: (list missing) \nScheduled sampling: In our experiments, we found that models trained with scheduled sampling performed better (about 0.004 BLEU-4 on validation set) than the ones trained using teacher-forcing for the AVSD dataset. Hence, we use scheduled sampling for all the results w...	114
1906.06448	Can neural networks understand monotonicity reasoning?	Monotonicity reasoning is one of the important reasoning skills for any intelligent natural language inference (NLI) model in that it requires the ability to capture the interaction between lexical and syntactic structures. Since no test set has been developed for monotonicity reasoning with wide coverage, it is still ...	{ "paragraphs": [ [ "Natural language inference (NLI), also known as recognizing textual entailment (RTE), has been proposed as a benchmark task for natural language understanding. Given a premise $P$ and a hypothesis $H$ , the task is to determine whether the premise semantically entails the hypothesis BIB...	{ "answers": [ { "annotation_id": [ "9ae76059d33b24d99445adb910a6ebc0ebc8a559" ], "answer": [ { "evidence": [ "To tackle this issue, we present a new evaluation dataset that covers a wide range of monotonicity reasoning that was created by crowdsourcing and ...	{ "caption": [ "Table 1: Determiners and their polarities.", "Table 2: Examples of downward operators.", "Figure 1: Overview of our human-oriented dataset creation. E: entailment, NE: non-entailment.", "Table 3: Numbers of cases where answers matched automatically determined gold labels.", "Table ...	[ "How do they define upward and downward reasoning?" ]	[ [ "1906.06448-Introduction-3" ] ]	[ "Upward reasoning is defined as going from one specific concept to a more general one. Downward reasoning is defined as the opposite, going from a general concept to one that is more specific." ]	116
1907.00758	Synchronising audio and ultrasound by learning cross-modal embeddings	Audiovisual synchronisation is the task of determining the time offset between speech audio and a video recording of the articulators. In child speech therapy, audio and ultrasound videos of the tongue are captured using instruments which rely on hardware to synchronise the two modalities at recording time. Hardware sy...	{ "paragraphs": [ [ "Ultrasound tongue imaging (UTI) is a non-invasive way of observing the vocal tract during speech production BIBREF0 . Instrumental speech therapy relies on capturing ultrasound videos of the patient's tongue simultaneously with their speech audio in order to provide a diagnosis, design ...	{ "answers": [ { "annotation_id": [ "89cd66698512e65e6d240af77f3fc829fe373b2a" ], "answer": [ { "evidence": [], "extractive_spans": [], "free_form_answer": "", "highlighted_evidence": [], "unanswerable": false, "yes_no":...	{ "caption": [ "Figure 1: UltraSync maps high dimensional inputs to low dimensional vectors using a contrastive loss function, such that the Euclidean distance is small between vectors from positive pairs and large otherwise. Inputs span '200ms: 5 consecutive raw ultrasound frames on one stream and 20 frames of t...	[ "Do they annotate their own dataset or use an existing one?", "What kind of neural network architecture do they use?" ]	[ [ "1907.00758-Data-0" ], [ "1907.00758-1-Figure1-1.png" ] ]	[ "Use an existing one", "CNN" ]	118
1701.02877	Generalisation in Named Entity Recognition: A Quantitative Analysis	Named Entity Recognition (NER) is a key NLP task, which is all the more challenging on Web and user-generated content with their diverse and continuously changing language. This paper aims to quantify how this diversity impacts state-of-the-art NER methods, by measuring named entity (NE) and context variability, featur...	{ "paragraphs": [ [ "Named entity recognition and classification (NERC, short NER), the task of recognising and assigning a class to mentions of proper names (named entities, NEs) in text, has attracted many years of research BIBREF0 , BIBREF1 , analyses BIBREF2 , starting from the first MUC challenge in 19...	{ "answers": [ { "annotation_id": [ "05dfe42d133923f3516fb680679bacc680589a03" ], "answer": [ { "evidence": [ "Since the goal of this study is to compare NER performance on corpora from diverse domains and genres, seven benchmark NER corpora are included, sp...	{ "caption": [ "Table 1 Corpora genres and number of NEs of different classes.", "Table 2 Sizes of corpora, measured in number of NEs, used for training and testing. Note that the for the ConLL corpus the dev set is called “Test A” and the test set “Test B”.", "Table 3 P, R and F1 of NERC with different m...	[ "What web and user-generated NER datasets are used for the analysis?" ]	[ [ "1701.02877-4-Table1-1.png", "1701.02877-Datasets-0" ] ]	[ "MUC, CoNLL, ACE, OntoNotes, MSM, Ritter, UMBC" ]	120
1904.05862	wav2vec: Unsupervised Pre-training for Speech Recognition	We explore unsupervised pre-training for speech recognition by learning representations of raw audio. wav2vec is trained on large amounts of unlabeled audio data and the resulting representations are then used to improve acoustic model training. We pre-train a simple multi-layer convolutional neural network optimized v...	{ "paragraphs": [ [ "Current state of the art models for speech recognition require large amounts of transcribed audio data to attain good performance BIBREF1 . Recently, pre-training of neural networks has emerged as an effective technique for settings where labeled data is scarce. The key idea is to learn...	{ "answers": [ { "annotation_id": [ "b5db6a885782bd0be2ae18fb5f4ee7b901f4899a" ], "answer": [ { "evidence": [ "We consider pre-training on the audio data (without labels) of WSJ, part of clean Librispeech (about 80h) and full Librispeech as well as a combina...	{ "caption": [ "Figure 1: Illustration of pre-training from audio data X which is encoded with two convolutional neural networks that are stacked on top of each other. The model is optimized to solve a next time step prediction task.", "Table 1: Replacing log-mel filterbanks (Baseline) by pre-trained embeddin...	[ "Which unlabeled data do they pretrain with?", "How many convolutional layers does their model have?" ]	[ [ "1904.05862-Pre-training for the WSJ benchmark-0", "1904.05862-Introduction-3" ], [ "1904.05862-Model-2", "1904.05862-Model-1" ] ]	[ "1000 hours of WSJ audio data", "wav2vec has 12 convolutional layers" ]	121
1911.00069	Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping	Relation extraction (RE) seeks to detect and classify semantic relationships between entities, which provides useful information for many NLP applications. Since the state-of-the-art RE models require large amounts of manually annotated data and language-specific resources to achieve high accuracy, it is very challengi...	{ "paragraphs": [ [ "Relation extraction (RE) is an important information extraction task that seeks to detect and classify semantic relationships between entities like persons, organizations, geo-political entities, locations, and events. It provides useful information for many NLP applications such as kno...	{ "answers": [ { "annotation_id": [ "0652ee6a3d11af5276f085ea7c4a098b4fd89508" ], "answer": [ { "evidence": [ "First we compare our neural network English RE models with the state-of-the-art RE models on the ACE05 English data. The ACE05 English data can be ...	{ "caption": [ "Figure 1: Neural cross-lingual relation extraction based on bilingual word embedding mapping - target language: Portuguese, source language: English.", "Table 1: Comparison with the state-of-the-art RE models on the ACE05 English data (S: Single Model; E: Ensemble Model).", "Table 2: Numbe...	[ "How big are the datasets?" ]	[ [ "1911.00069-Experiments ::: Datasets-0", "1911.00069-6-Table2-1.png", "1911.00069-Experiments ::: Datasets-1" ] ]	[ "In-house dataset consists of 3716 documents \nACE05 dataset consists of 1635 documents" ]	123
1810.00663	Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation	We propose an end-to-end deep learning model for translating free-form natural language instructions to a high-level plan for behavioral robot navigation. We use attention models to connect information from both the user instructions and a topological representation of the environment. We evaluate our model's performan...	{ "paragraphs": [ [ "Enabling robots to follow navigation instructions in natural language can facilitate human-robot interaction across a variety of applications. For instance, within the service robotics domain, robots can follow navigation instructions to help with mobile manipulation BIBREF0 and deliver...	{ "answers": [ { "annotation_id": [ "a38c1c344ccb96f3ff31ef6c371b2260c3d8db43" ], "answer": [ { "evidence": [ "This work also contributes a new dataset of INLINEFORM0 pairs of free-form natural language instructions and high-level navigation plans. This data...	{ "caption": [ "Figure 1: Map of an environment (a), its (partial) behavioral navigation graph (b), and the problem setting of interest (c). The red part of (b) corresponds to the representation of the route highlighted in blue in (a). The codes “oo-left”, “oo-right”, “cf”, “left-io”, and “right-io” correspond to...	[ "What baselines did they compare their model with?", "What was the performance of their model?", "What evaluation metrics are used?", "How were the navigation instructions collected?", "What language is the experiment done in?" ]	[ [ "1810.00663-Models Used in the Evaluation-2" ], [ "1810.00663-8-Table3-1.png" ], [ "1810.00663-Evaluation Metrics-3", "1810.00663-Evaluation Metrics-1", "1810.00663-Evaluation Metrics-5", "1810.00663-Evaluation Metrics-4", "1810.00663-Evaluation Metrics-2", "1810.00663-Eval...	[ "the baseline where path generation uses a standard sequence-to-sequence model augmented with attention mechanism and path verification uses depth-first search", "For test-repeated set, EM score of 61.17, F1 of 93.54, ED of 0.75 and GM of 61.36. For test-new set, EM score of 41.71, F1 of 91.02, ED of 1.22 and GM ...	125
1809.05752	Analysis of Risk Factor Domains in Psychosis Patient Health Records	Readmission after discharge from a hospital is disruptive and costly, regardless of the reason. However, it can be particularly problematic for psychiatric patients, so predicting which patients may be readmitted is critically important but also very difficult. Clinical narratives in psychiatric electronic health recor...	{ "paragraphs": [ [ "Psychotic disorders typically emerge in late adolescence or early adulthood BIBREF0 , BIBREF1 and affect approximately 2.5-4% of the population BIBREF2 , BIBREF3 , making them one of the leading causes of disability worldwide BIBREF4 . A substantial proportion of psychiatric inpatients ...	{ "answers": [ { "annotation_id": [ "096ace95350d743436952360918474c6160465ba" ], "answer": [ { "evidence": [ "Our current feature set for training a machine learning classifier is relatively small, consisting of paragraph domain scores, bag-of-words, length...	{ "caption": [ "Table 1: Demographic breakdown of the target cohort.", "Table 2: Annotation scheme for the domain classification task.", "Table 3: Inter-annotator agreement", "Table 4: Architectures of our highest-performing MLP and RBF networks.", "Figure 1: Data pipeline for training and evaluat...	[ "What additional features are proposed for future work?", "What are their initial results on this task?" ]	[ [ "1809.05752-Future Work and Conclusion-1" ], [ "1809.05752-6-Table5-1.png" ] ]	[ "distinguishing between clinically positive and negative phenomena within each risk factor domain and accounting for structured data collected on the target cohort", "Achieved the highest per-domain scores on Substance (F1 ≈ 0.8) and the lowest scores on Interpersonal and Mood (F1 ≈ 0.5), and show consistency in ...	126
2001.01589	Morphological Word Segmentation on Agglutinative Languages for Neural Machine Translation	Neural machine translation (NMT) has achieved impressive performance on machine translation task in recent years. However, in consideration of efficiency, a limited-size vocabulary that only contains the top-N highest frequency words are employed for model training, which leads to many rare and unknown words. It is rat...	{ "paragraphs": [ [ "Neural machine translation (NMT) has achieved impressive performance on machine translation task in recent years for many language pairs BIBREF0, BIBREF1, BIBREF2. However, in consideration of time cost and space capacity, the NMT model generally employs a limited-size vocabulary that o...	{ "answers": [ { "annotation_id": [ "8282253adbf7ac7e6158ff0b754a6b9d59034db0" ], "answer": [ { "evidence": [], "extractive_spans": [], "free_form_answer": "", "highlighted_evidence": [], "unanswerable": true, "yes_no": ...	{ "caption": [ "Table 1: The sentence examples with different segmentation strategies for Turkish-English.", "Table 2: The training corpus statistics of TurkishEnglish machine translation task.", "Table 3: The training corpus statistics of UyghurChinese machine translation task.", "Table 4: The traini...	[ "How is morphology knowledge implemented in the method?" ]	[ [ "2001.01589-Approach ::: Morphologically Motivated Segmentation-1", "2001.01589-Approach ::: Morphologically Motivated Segmentation-0" ] ]	[ "A BPE model is applied to the stem after morpheme segmentation." ]	127
1910.05456	Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge	How does knowledge of one language's morphology influence learning of inflection rules in a second one? In order to investigate this question in artificial neural network models, we perform experiments with a sequence-to-sequence architecture, which we train on different combinations of eight source and three target la...	{ "paragraphs": [ [ "A widely agreed-on fact in language acquisition research is that learning of a second language (L2) is influenced by a learner's native language (L1) BIBREF0, BIBREF1. A language's morphosyntax seems to be no exception to this rule BIBREF2, but the exact nature of this influence remains...	{ "answers": [ { "annotation_id": [ "a38dc2ad92ff5c2cda31f3be4f22daba2e001e98" ], "answer": [ { "evidence": [ "Spanish (SPA), in contrast, is morphologically rich, and disposes of much larger verbal paradigms than English. Like English, it is a suffixing lan...	{ "caption": [ "Table 1: Paradigms of the English lemmas dance and eat. dance has 4 distinct inflected forms; eat has 5.", "Table 2: WALS features from the Morphology category. 20A: 0=Exclusively concatenative, 1=N/A. 21A: 0=No case, 1=Monoexponential case, 2=Case+number, 3=N/A. 21B: 0=monoexponential TAM, 1=...	[ "How is the performance on the task evaluated?" ]	[ [ "1910.05456-Introduction-2", "1910.05456-Qualitative Results-0" ] ]	[ "Comparison of test accuracies of neural network models on an inflection task and qualitative analysis of the errors" ]	129
1806.00722	Dense Information Flow for Neural Machine Translation	Recently, neural machine translation has achieved remarkable progress by introducing well-designed deep neural networks into its encoder-decoder framework. From the optimization perspective, residual connections are adopted to improve learning performance for both encoder and decoder in most of these deep architectures...	{ "paragraphs": [ [ "Neural machine translation (NMT) is a challenging task that attracts lots of attention in recent years. Starting from the encoder-decoder framework BIBREF0 , NMT starts to show promising results in many language pairs. The evolving structures of NMT models in recent years have made them...	{ "answers": [ { "annotation_id": [ "99949e192d00f333149953b64edf7e6a9477fb4a" ], "answer": [ { "evidence": [ "As the baseline model (BASE-4L) for IWSLT14 German-English and Turkish-English, we use a 4-layer encoder, 4-layer decoder, residual-connected model...	{ "caption": [ "Figure 1: Comparison of dense-connected encoder and residual-connected encoder. Left: regular residual-connected encoder. Right: dense-connected encoder. Information is directly passed from blue blocks to the green block.", "Figure 2: Comparison of dense-connected decoder and residual-connecte...	[ "what datasets were used?" ]	[ [ "1806.00722-Datasets-0" ] ]	[ "IWSLT14 German-English, IWSLT14 Turkish-English, WMT14 English-German" ]	131
1904.08386	Casting Light on Invisible Cities: Computationally Engaging with Literary Criticism	Literary critics often attempt to uncover meaning in a single work of literature through careful reading and analysis. Applying natural language processing methods to aid in such literary analyses remains a challenge in digital humanities. While most previous work focuses on"distant reading"by algorithmically discoveri...	{ "paragraphs": [ [ "Literary critics form interpretations of meaning in works of literature. Building computational models that can help form and test these interpretations is a fundamental goal of digital humanities research BIBREF0 . Within natural language processing, most previous work that engages wit...	{ "answers": [ { "annotation_id": [ "e922a0f6eac0005885474470b7736de70242bb0e" ], "answer": [ { "evidence": [ "While each of the city descriptions is relatively short, Calvino's writing is filled with rare words, complex syntactic structures, and figurative ...	{ "caption": [ "Figure 1: Calvino labels the thematically-similar cities in the top row as cities & the dead. However, although the bottom two cities share a theme of desire, he assigns them to different groups.", "Figure 2: We first embed each city by averaging token representations derived from a pretrained...	[ "How do they obtain human judgements?" ]	[ [ "1904.08386-Introduction-2" ] ]	[ "Using crowdsourcing " ]	133
1909.00754	Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation	Existing approaches to dialogue state tracking rely on pre-defined ontologies consisting of a set of all possible slot types and values. Though such approaches exhibit promising performance on single-domain benchmarks, they suffer from computational complexity that increases proportionally to the number of pre-defined ...	{ "paragraphs": [ [ "A Dialogue State Tracker (DST) is a core component of a modular task-oriented dialogue system BIBREF7 . For each dialogue turn, a DST module takes a user utterance and the dialogue history as input, and outputs a belief estimate of the dialogue state. Then a machine action is decided ba...	{ "answers": [ { "annotation_id": [ "1719244c479765727dd6d5390c98e27c6542dcf3" ], "answer": [ { "evidence": [ "FLOAT SELECTED: Table 3: The joint goal accuracy of the DST models on the WoZ2.0 test set and the MultiWoZ test set. We also include the Inference ...	{ "caption": [ "Table 1: The Inference Time Complexity (ITC) of previous DST models. The ITC is calculated based on how many times inference must be performed to complete a prediction of the belief state in a dialogue turn, where m is the number of values in a pre-defined ontology list and n is the number of slot...	[ "Does this approach perform better in the multi-domain or single-domain setting?" ]	[ [ "1909.00754-7-Table3-1.png" ] ]	[ "single-domain setting" ]	134
1906.00180	Siamese recurrent networks learn first-order logic reasoning and exhibit zero-shot compositional generalization	Can neural nets learn logic? We approach this classic question with current methods, and demonstrate that recurrent neural networks can learn to recognize first order logical entailment relations between expressions. We define an artificial language in first-order predicate logic, generate a large dataset of sample 'se...	{ "paragraphs": [ [ "State-of-the-art models for almost all popular natural language processing tasks are based on deep neural networks, trained on massive amounts of data. A key question that has been raised in many different forms is to what extent these models have learned the compositional generalizatio...	{ "answers": [ { "annotation_id": [ "fbf076324c189bbfe7b495126bb96ec2d2615877" ], "answer": [ { "evidence": [], "extractive_spans": [], "free_form_answer": "", "highlighted_evidence": [], "unanswerable": true, "yes_no": ...	{ "caption": [ "Figure 1: Venn diagrams visualizing the taxonomy of (a) nouns NL and (b) verbs VL in L.", "Table 3: FOL axiom representations of lexical entailment relations. For definition of relations, see Table 2.", "Figure 3: Visualization of the general recurrent model. The region in the dashed box r...	[ "How many samples did they generate for the artificial language?" ]	[ [ "1906.00180-Unseen lengths-1" ] ]	[ "70,000" ]	135
1906.04571	Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology	Gender stereotypes are manifest in most of the world's languages and are consequently propagated or amplified by NLP systems. Although research has focused on mitigating gender stereotypes in English, the approaches that are commonly employed produce ungrammatical sentences in morphologically rich languages. We present...	{ "paragraphs": [ [ "One of the biggest challenges faced by modern natural language processing (NLP) systems is the inadvertent replication or amplification of societal biases. This is because NLP systems depend on language corpora, which are inherently “not objective; they are creations of human design” BI...	{ "answers": [ { "annotation_id": [ "075ffbc4f5f1ee3b32ee07258113e5fa1412fe04" ], "answer": [ { "evidence": [ "To date, the NLP community has focused primarily on approaches for detecting and mitigating gender stereotypes in English BIBREF5 , BIBREF6 , BIBRE...	{ "caption": [ "Figure 1: Transformation of Los ingenieros son expertos (i.e., The male engineers are skilled) to Las ingenieras son expertas (i.e., The female engineers are skilled). We extract the properties of each word in the sentence. We then fix a noun and its tags and infer the manner in which the remainin...	[ "Why does not the approach from English work on other languages?" ]	[ [ "1906.04571-Introduction-1" ] ]	[ "Because, unlike other languages, English does not mark grammatical genders" ]	137
2002.11402	Detecting Potential Topics In News Using BERT, CRF and Wikipedia	For a news content distribution platform like Dailyhunt, Named Entity Recognition is a pivotal task for building better user recommendation and notification algorithms. Apart from identifying names, locations, organisations from the news for 13+ Indian languages and use them in algorithms, we also need to identify n-gr...	{ "paragraphs": [ [ "Named-Entity-Recognition(NER) approaches can be categorised broadly in three types. Detecting NER with predefined dictionaries and rulesBIBREF2, with some statistical approachesBIBREF3 and with deep learning approachesBIBREF4.", "Stanford CoreNLP NER is a widely used baseline for ...	{ "answers": [ { "annotation_id": [ "79e09627dc6d58f94ae96f07ebbfa6e8bedb4338" ], "answer": [ { "evidence": [ "FLOAT SELECTED: Table 2. Comparison with Traditional NERs as reference", "FLOAT SELECTED: Table 3. Comparison with Wikipedia titles as ...	{ "caption": [ "Table 1. Parallel Corpus Preparation with BERT Tokenizer", "Table 2. Comparison with Traditional NERs as reference", "Table 3. Comparison with Wikipedia titles as reference", "Figure 1. BERT + Bi-GRU + CRF, Final Architecture Chosen For Topic Detection Task.", "Table 4. Recognised ...	[ "What is the difference in recall score between the systems?", "What is their f1 score and recall?", "How many layers does their system have?" ]	[ [ "2002.11402-3-Table2-1.png", "2002.11402-3-Table3-1.png" ], [ "2002.11402-3-Table2-1.png", "2002.11402-3-Table3-1.png" ], [ "2002.11402-3-Figure1-1.png" ] ]	[ "Between the model and Stanford, Spacy and Flair the differences are 42.91, 25.03, 69.8 with Traditional NERs as reference and 49.88, 43.36, 62.43 with Wikipedia titles as reference.", "F1 score and Recall are 68.66, 80.08 with Traditional NERs as reference and 59.56, 69.76 with Wikipedia titles as reference.", ...	141
2002.00652	How Far are We from Effective Context Modeling ? An Exploratory Study on Semantic Parsing in Context	Recently semantic parsing in context has received a considerable attention, which is challenging since there are complex contextual phenomena. Previous works verified their proposed methods in limited scenarios, which motivates us to conduct an exploratory study on context modeling methods under real-world semantic par...	{ "paragraphs": [ [ "Semantic parsing, which translates a natural language sentence into its corresponding executable logic form (e.g. Structured Query Language, SQL), relieves users from the burden of learning techniques behind the logic form. The majority of previous studies on semantic parsing assume tha...	{ "answers": [ { "annotation_id": [ "dd3f3fb7924027f3d1d27347939df4aa60f5b89e" ], "answer": [ { "evidence": [ "We consider three models as our baselines. SyntaxSQL-con and CD-Seq2Seq are two strong baselines introduced in the SParC dataset paper BIBREF2. Syn...	{ "caption": [ "Figure 1: An example dialogue (right) and its database schema (left).", "Figure 2: The grammar rule and the abstract syntax tree for the SQL", "Figure 3: Different methods to incorporate recent h questions [xi−h, ...,xi−1]. (a) CONCAT: concatenate recent questions with xi as input; (b) TUR...	[ "What context modelling methods are evaluated?" ]	[ [ "2002.00652-Experiment & Analysis ::: Model Comparison-1", "2002.00652-5-Figure5-1.png" ] ]	[ "Concat\nTurn\nGate\nAction Copy\nTree Copy\nSQL Attn\nConcat + Action Copy\nConcat + Tree Copy\nConcat + SQL Attn\nTurn + Action Copy\nTurn + Tree Copy\nTurn + SQL Attn\nTurn + SQL Attn + Action Copy" ]	143
1905.06566	HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization	Neural extractive summarization models usually employ a hierarchical encoder for document encoding and they are trained using sentence-level labels, which are created heuristically using rule-based methods. Training the hierarchical encoder with these \emph{inaccurate} labels is challenging. Inspired by the recent work...	{ "paragraphs": [ [ "Automatic document summarization is the task of rewriting a document into its shorter form while still retaining its important content. Over the years, many paradigms for document summarization have been explored (see Nenkova:McKeown:2011 for an overview). The most popular two among the...	{ "answers": [ { "annotation_id": [ "07f9afd79ec1426e67b10f5a598bbe3103f714cf" ], "answer": [ { "evidence": [ "FLOAT SELECTED: Table 1: Results of various models on the CNNDM test set using full-length F1 ROUGE-1 (R-1), ROUGE-2 (R2), and ROUGE-L (R-L).", ...	{ "caption": [ "Figure 1: The architecture of HIBERT during training. senti is a sentence in the document above, which has four sentences in total. sent3 is masked during encoding and the decoder predicts the original sent3.", "Figure 2: The architecture of our extractive summarization model. The sentence and...	[ "Is the baseline a non-heirarchical model like BERT?" ]	[ [ "1905.06566-Results-0", "1905.06566-7-Table1-1.png" ] ]	[ "There were hierarchical and non-hierarchical baselines; BERT was one of those baselines" ]	145
2004.03034	The Role of Pragmatic and Discourse Context in Determining Argument Impact	Research in the social sciences and psychology has shown that the persuasiveness of an argument depends not only the language employed, but also on attributes of the source/communicator, the audience, and the appropriateness and strength of the argument's claims given the pragmatic and discourse context of the argument...	{ "paragraphs": [ [ "Previous work in the social sciences and psychology has shown that the impact and persuasive power of an argument depends not only on the language employed, but also on the credibility and character of the communicator (i.e. ethos) BIBREF0, BIBREF1, BIBREF2; the traits and prior beliefs...	{ "answers": [ { "annotation_id": [ "08357ffcc372ab5b2dcdeef00478d3a45f7d1ddc" ], "answer": [ { "evidence": [ "We see that parent quality is a simple yet effective feature and SVM model with this feature can achieve significantly higher ($p<0.001$) F1 score ...	{ "caption": [ "Figure 1: Example partial argument tree with claims and corresponding impact votes for the thesis “PHYSICAL TORTURE OF PRISONERS IS AN ACCEPTABLE INTERROGATION TOOL.”.", "Table 1: Number of claims for the given range of number of votes. There are 19,512 claims in the dataset with 3 or more vot...	[ "How better are results compared to baseline models?" ]	[ [ "2004.03034-Results and Analysis-1", "2004.03034-Results and Analysis-3" ] ]	[ "F1 score of best authors' model is 55.98 compared to BiLSTM and FastText that have F1 score slighlty higher than 46.61." ]	147
1910.12618	Textual Data for Time Series Forecasting	While ubiquitous, textual sources of information such as company reports, social media posts, etc. are hardly included in prediction algorithms for time series, despite the relevant information they may contain. In this work, openly accessible daily weather reports from France and the United-Kingdom are leveraged to pr...	{ "paragraphs": [ [ "Whether it is in the field of energy, finance or meteorology, accurately predicting the behavior of time series is nowadays of paramount importance for optimal decision making or profit. While the field of time series forecasting is extremely prolific from a research point-of-view, up t...	{ "answers": [ { "annotation_id": [ "e6c530042231f1a95608b2495514fe8b5ad08d28" ], "answer": [ { "evidence": [ "Our work aims at predicting time series using exclusively text. Therefore for both countries the inputs of all our models consist only of written d...	{ "caption": [ "Figure 1: Net electricity consumption (Load) over time.", "Figure 2: Word counts for the two corpora after preprocessing.", "Table 3: Descriptive analysis of the two corpora (after preprocessing)", "Figure 3: Structure of our RNN. Dropout and batch normalization are not represented.", ...	[ "How big is dataset used for training/testing?", "What geometric properties do embeddings display?", "How accurate is model trained on text exclusively?" ]	[ [ "1910.12618-Presentation of the data ::: Text-0" ], [ "1910.12618-Experiments ::: Interpretability of the models ::: Vector embedding representation-2" ], [ "1910.12618-Introduction-2" ] ]	[ "4,261 days for France and 4,748 for the UK", "Winter and summer words formed two separate clusters. Week day and week-end day words also formed separate clusters.", "Relative error is less than 5%" ]	148
1911.12569	Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis	In this paper, we propose a two-layered multi-task attention based neural network that performs sentiment analysis through emotion analysis. The proposed approach is based on Bidirectional Long Short-Term Memory and uses Distributional Thesaurus as a source of external knowledge to improve the sentiment and emotion pre...	{ "paragraphs": [ [ "The emergence of social media sites with limited character constraint has ushered in a new style of communication. Twitter users within 280 characters per tweet share meaningful and informative messages. These short messages have a powerful impact on how we perceive and interact with ot...	{ "answers": [ { "annotation_id": [ "d06db6cb47479b16310c2b411473e15f7bf6a92d" ], "answer": [ { "evidence": [ "FLOAT SELECTED: TABLE II F-SCORE OF VARIOUS MODELS ON SENTIMENT AND EMOTION TEST DATASET.", "We compare the performance of our proposed...	{ "caption": [ "Fig. 1. Two-layered multi-task attention based network", "TABLE I DATASET STATISTICS OF SEMEVAL 2016 TASK 6 AND SSEC USED FOR SENTIMENT AND EMOTION ANALYSIS, RESPECTIVELY.", "TABLE II F-SCORE OF VARIOUS MODELS ON SENTIMENT AND EMOTION TEST DATASET.", "TABLE III COMPARISON WITH THE STAT...	[ "What was their result on Stance Sentiment Emotion Corpus?", "What performance did they obtain on the SemEval dataset?", "What are the state-of-the-art systems?" ]	[ [ "1911.12569-Datasets, Experiments and Analysis ::: Results and Analysis-0", "1911.12569-Datasets, Experiments and Analysis ::: Implementation Details-0", "1911.12569-5-TableII-1.png" ], [ "1911.12569-Datasets, Experiments and Analysis ::: Results and Analysis-0", "1911.12569-Datasets, Expe...	[ "F1 score of 66.66%", "F1 score of 82.10%", "For sentiment analysis UWB, INF-UFRGS-OPINION-MINING, LitisMind, pkudblab and SVM + n-grams + sentiment and for emotion analysis MaxEnt, SVM, LSTM, BiLSTM and CNN" ]	149
1901.04899	Conversational Intent Understanding for Passengers in Autonomous Vehicles	Understanding passenger intents and extracting relevant slots are important building blocks towards developing a contextual dialogue system responsible for handling certain vehicle-passenger interactions in autonomous vehicles (AV). When the passengers give instructions to AMIE (Automated-vehicle Multimodal In-cabin Ex...	{ "paragraphs": [ [ "Understanding passenger intents and extracting relevant slots are important building blocks towards developing a contextual dialogue system responsible for handling certain vehicle-passenger interactions in autonomous vehicles (AV). When the passengers give instructions to AMIE (Automat...	{ "answers": [ { "annotation_id": [ "ca8e0b7c0f1b3216656508fc0b7b097f3d0235b9" ], "answer": [ { "evidence": [ "Our AV in-cabin data-set includes 30 hours of multimodal data collected from 30 passengers (15 female, 15 male) in 20 rides/sessions. 10 types of p...	{ "caption": [ "Table 1: Slot Extraction Results (10-fold CV)", "Table 3: Utterance-level Intent Recognition Results (10-fold CV)", "Table 2: Intent Keyword Extraction Results (10-fold CV)", "Table 4: Intent-wise Performance Results of Utterance-level Intent Recognition Models: Hierarchical & Joint (1...	[ "What is the size of their collected dataset?" ]	[ [ "1901.04899-Methodology-0" ] ]	[ "3347 unique utterances " ]	151
1606.05320	Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models	As deep neural networks continue to revolutionize various application domains, there is increasing interest in making these powerful models more understandable and interpretable, and narrowing down the causes of good and bad predictions. We focus on recurrent neural networks (RNNs), state of the art models in speech re...	{ "paragraphs": [ [ "Following the recent progress in deep learning, researchers and practitioners of machine learning are recognizing the importance of understanding and interpreting what goes on inside these black box models. Recurrent neural networks have recently revolutionized speech recognition and tr...	{ "answers": [ { "annotation_id": [ "3be4a77ab3aaee94fae674de02f30c26a8ac92cc" ], "answer": [ { "evidence": [ "We compare a hybrid HMM-LSTM approach with a continuous emission HMM (trained on the hidden states of a 2-layer LSTM), and a discrete emission HMM ...	{ "caption": [ "Figure 1: Hybrid HMM-LSTM algorithms (the dashed blocks indicate the components trained using SGD in Torch).", "Table 1: Predictive loglikelihood (LL) comparison, sorted by validation set performance.", "Figure 2: Visualizing HMM and LSTM states on Linux data for the hybrid with 10 LSTM st...	[ "What kind of features are used by the HMM models, and how interpretable are those?", "What kind of information do the HMMs learn that the LSTMs don't?", "How large is the gap in performance between the HMMs and the LSTMs?" ]	[ [ "1606.05320-Experiments-2", "1606.05320-Methods-0", "1606.05320-4-Figure2-1.png" ], [ "1606.05320-Experiments-2", "1606.05320-4-Figure2-1.png" ], [ "1606.05320-3-Table1-1.png" ] ]	[ "A continuous emission HMM uses the hidden states of a 2-layer LSTM as features and a discrete emission HMM uses data as features. \nThe interpretability of the model is shown in Figure 2. ", "The HMM can identify punctuation or pick up on vowels.", "With similar number of parameters, the log likelihood is abou...	152
1809.10644	Predictive Embeddings for Hate Speech Detection on Twitter	We present a neural-network based approach to classifying online hate speech in general, as well as racist and sexist speech in particular. Using pre-trained word embeddings and max/mean pooling from simple, fully-connected transformations of these embeddings, we are able to predict the occurrence of hate speech on thr...	{ "paragraphs": [ [ "The increasing popularity of social media platforms like Twitter for both personal and political communication BIBREF0 has seen a well-acknowledged rise in the presence of toxic and abusive speech on these platforms BIBREF1 , BIBREF2 . Although the terms of services on these platforms t...	{ "answers": [ { "annotation_id": [ "7acdce6a3960c4cb8094d6e4544c30573fbd7f65" ], "answer": [ { "evidence": [ "In this paper, we use three data sets from the literature to train and evaluate our own classifier. Although all address the category of hateful sp...	{ "caption": [ "Table 1: Dataset Characteristics", "Table 2: F1 Results3", "Table 3: Projected Embedding Cluster Analysis from SR Dataset", "Table 5: SR Results", "Table 7: HAR Results", "Table 6: HATE Results", "Table 8: Projected Embedding Cluster Analysis from SR Dataset" ], "file":...	[ "what was their system's f1 performance?" ]	[ [ "1809.10644-3-Table2-1.png" ] ]	[ "Proposed model achieves 0.86, 0.924, 0.71 F1 score on SR, HATE, HAR datasets respectively." ]	153
1911.03243	Crowdsourcing a High-Quality Gold Standard for QA-SRL	Question-answer driven Semantic Role Labeling (QA-SRL) has been proposed as an attractive open and natural form of SRL, easily crowdsourceable for new corpora. Recently, a large-scale QA-SRL corpus and a trained parser were released, accompanied by a densely annotated dataset for evaluation. Trying to replicate the QA-...	{ "paragraphs": [ [ "Semantic Role Labeling (SRL) provides explicit annotation of predicate-argument relations, which have been found useful in various downstream tasks BIBREF0, BIBREF1, BIBREF2, BIBREF3. Question-Answer driven Semantic Role Labeling (QA-SRL) BIBREF4 is an SRL scheme in which roles are capt...	{ "answers": [ { "annotation_id": [ "12360275d5fa216c2ae92edd18d2b5a7e81fa3a9" ], "answer": [ { "evidence": [ "The measured precision with respect to PropBank is low for adjuncts due to the fact that our annotators were capturing many correct arguments not c...	{ "caption": [ "Table 1: Running examples of QA-SRL annotations; this set is a sample of the possible questions that can be asked. The bar (\|) separates multiple selected answers.", "Table 2: Automatic and manually-corrected evaluation of our gold standard and Dense (Fitzgerald et al., 2018) against the exper...	[ "How much more coverage is in the new dataset?", "How was quality measured?", "What is different in the improved annotation protocol?" ]	[ [ "1911.03243-Dataset Quality Analysis ::: Agreement with PropBank Data-1" ], [ "1911.03243-Dataset Quality Analysis ::: Agreement with PropBank Data-0", "1911.03243-Dataset Quality Analysis ::: Dataset Assessment and Comparison-0", "1911.03243-Dataset Quality Analysis ::: Inter-Annotator Agreem...	[ "278 more annotations", "Inter-annotator agreement, comparison against expert annotation, agreement with PropBank Data annotations.", "a trained worker consolidates existing annotations " ]	155
1809.04686	Zero-Shot Cross-lingual Classification Using Multilingual Neural Machine Translation	Transferring representations from large supervised tasks to downstream tasks has shown promising results in AI fields such as Computer Vision and Natural Language Processing (NLP). In parallel, the recent progress in Machine Translation (MT) has enabled one to train multilingual Neural MT (NMT) systems that can transla...	{ "paragraphs": [ [ "Transfer learning has been shown to work well in Computer Vision where pre-trained components from a model trained on ImageNet BIBREF0 are used to initialize models for other tasks BIBREF1 . In most cases, the other tasks are related to and share architectural components with the ImageN...	{ "answers": [ { "annotation_id": [ "09194b62d31ef50c74d81ba330cf0d816da83d95" ], "answer": [ { "evidence": [], "extractive_spans": [], "free_form_answer": "", "highlighted_evidence": [], "unanswerable": true, "yes_no": ...	{ "caption": [ "Table 1: Transfer learning results of the classification accuracy on all the datasets. Amazon (En) and Amazon (Fr) are the English and French versions of the task, training the models on the data for each language. The state-of-the-art results are cited from Fernndez, Esuli, and Sebastiani (2016) ...	[ "What data were they used to train the multilingual encoder?" ]	[ [ "1809.04686-Corpora-1" ] ]	[ "WMT 2014 En-Fr parallel corpus" ]	156
1703.09684	An Analysis of Visual Question Answering Algorithms	In visual question answering (VQA), an algorithm must answer text-based questions about images. While multiple datasets for VQA have been created since late 2014, they all have flaws in both their content and the way algorithms are evaluated on them. As a result, evaluation scores are inflated and predominantly determi...	{ "paragraphs": [ [ "In open-ended visual question answering (VQA) an algorithm must produce answers to arbitrary text-based questions about images BIBREF0 , BIBREF1 . VQA is an exciting computer vision problem that requires a system to be capable of many tasks. Truly solving VQA would be a milestone in art...	{ "answers": [ { "annotation_id": [ "0953d83d785f0b7533669425168108b142cdd82b" ], "answer": [ { "evidence": [ "VQA research began in earnest in late 2014 when the DAQUAR dataset was released BIBREF0 . Including DAQUAR, six major VQA datasets have been releas...	{ "caption": [ "Figure 1: A good VQA benchmark tests a wide range of computer vision tasks in an unbiased manner. In this paper, we propose a new dataset with 12 distinct tasks and evaluation metrics that compensate for bias, so that the strengths and limitations of algorithms can be better measured.", "Figur...	[ "From when are many VQA datasets collected?" ]	[ [ "1703.09684-Introduction-1" ] ]	[ "late 2014" ]	157
1911.11744	Imitation Learning of Robot Policies by Combining Language, Vision and Demonstration	In this work we propose a novel end-to-end imitation learning approach which combines natural language, vision, and motion information to produce an abstract representation of a task, which in turn is used to synthesize specific motion controllers at run-time. This multimodal approach enables generalization to a wide v...	{ "paragraphs": [ [ "A significant challenge when designing robots to operate in the real world lies in the generation of control policies that can adapt to changing environments. Programming such policies is a labor and time-consuming process which requires substantial technical expertise. Imitation learni...	{ "answers": [ { "annotation_id": [ "098e4ae256790d70e0f02709f0be0779e99b3770" ], "answer": [ { "evidence": [ "To test our model, we generated 500 new scenario testing each of the three features to identify the correct target among other bowls. A task is con...	{ "caption": [ "Figure 1: Network architecture overview. The network consists of two parts, a high-level semantic network and a low-level control network. Both networks are working seamlessly together and are utilized in an End-to-End fashion.", "Figure 2: Results for placing an object into bowls at different...	[ "What is task success rate achieved? ", "Does proposed end-to-end approach learn in reinforcement or supervised learning manner?" ]	[ [ "1911.11744-Results-3" ], [ "1911.11744-Results-3", "1911.11744-Results-1" ] ]	[ "96-97.6% using the objects color or shape and 79% using shape alone", "supervised learning" ]	158
1910.11949	Automatic Reminiscence Therapy for Dementia.	With people living longer than ever, the number of cases with dementia such as Alzheimer's disease increases steadily. It affects more than 46 million people worldwide, and it is estimated that in 2050 more than 100 million will be affected. While there are not effective treatments for these terminal diseases, therapie...	{ "paragraphs": [ [ "Increases in life expectancy in the last century have resulted in a large number of people living to old ages and will result in a double number of dementia cases by the middle of the century BIBREF0BIBREF1. The most common form of dementia is Alzheimer disease which contributes to 60–7...	{ "answers": [ { "annotation_id": [ "395868f357819b6de3a616992a33977f125f92d9" ], "answer": [ { "evidence": [ "We use the BLEU BIBREF30 metric on the validation set for the VQG model training. BLEU is a measure of similitude between generated and target sequ...	{ "caption": [ "Figure 1: Scheme of the interaction with Elisabot", "Figure 2: Samples from Bing 2a), Coco 2b) and Flickr 2c) datasets", "Table 1: Generated questions", "Figure 3: Elisabot running on Telegram application", "Figure 5: Sample of the session study with mild cognitive impairment patie...	[ "How is performance of this system measured?", "How big dataset is used for training this system?" ]	[ [ "1910.11949-Validation ::: Quantitative evaluation-1", "1910.11949-Validation ::: Quantitative evaluation-0" ], [ "1910.11949-Datasets ::: Persona-chat and Cornell-movie corpus-0", "1910.11949-Datasets ::: MS-COCO, Bing and Flickr datasets-0" ] ]	[ "using the BLEU score as a quantitative metric and human evaluation for quality", "For the question generation model 15,000 images with 75,000 questions. For the chatbot model, around 460k utterances over 230k dialogues." ]	163
1902.09087	Lattice CNNs for Matching Based Chinese Question Answering	Short text matching often faces the challenges that there are great word mismatch and expression diversity between the two texts, which would be further aggravated in languages like Chinese where there is no natural space to segment words explicitly. In this paper, we propose a novel lattice based CNN model (LCNs) to u...	{ "paragraphs": [ [ "Short text matching plays a critical role in many natural language processing tasks, such as question answering, information retrieval, and so on. However, matching text sequences for Chinese or similar languages often suffers from word segmentation, where there are often no perfect Chi...	{ "answers": [ { "annotation_id": [ "16a08b11f033b08e392175ed187aebd84970919c" ], "answer": [ { "evidence": [ "Word Lattice", "As shown in Figure FIGREF4 , a word lattice is a directed graph INLINEFORM0 , where INLINEFORM1 represents a node set a...	{ "caption": [ "Figure 1: A word lattice for the phrase “Chinese people have high quality of life.”", "Figure 2: An illustration of our LCN-gated, when “人民” (people) is being considered as the center of convolutional spans.", "Table 1: The performance of all models on the two datasets. The best results in...	[ "How do they obtain word lattices from words?" ]	[ [ "1902.09087-Word Lattice-0" ] ]	[ "By considering words as vertices and generating directed edges between neighboring words within a sentence" ]	164
1908.07816	A Multi-Turn Emotionally Engaging Dialog Model	Open-domain dialog systems (also known as chatbots) have increasingly drawn attention in natural language processing. Some of the recent work aims at incorporating affect information into sequence-to-sequence neural dialog modeling, making the response emotionally richer, while others use hand-crafted rules to determin...	{ "paragraphs": [ [ "Recent development in neural language modeling has generated significant excitement in the open-domain dialog generation community. The success of sequence-to-sequence learning BIBREF0, BIBREF1 in the field of neural machine translation has inspired researchers to apply the recurrent ne...	{ "answers": [ { "annotation_id": [ "3e9e850087de48e5d3228f9b691cf66ce2f76a7d" ], "answer": [ { "evidence": [ "Table TABREF34 gives the perplexity scores obtained by the three models on the two validation sets and the test set. As shown in the table, MEED ac...	{ "caption": [ "Figure 1: The overall architecture of our model.", "Table 1: Statistics of the two datasets.", "Table 2: Perplexity scores achieved by the models. Validation set 1 comes from the Cornell dataset, while validation set 2 comes from the DailyDialog dataset.", "Table 5: Human evaluation re...	[ "How better is proposed method than baselines perpexity wise?" ]	[ [ "1908.07816-7-Table2-1.png", "1908.07816-Evaluation ::: Results-0" ] ]	[ "Perplexity of proposed MEED model is 19.795 vs 19.913 of next best result on test set." ]	167
1808.09409	Semantic Role Labeling for Learner Chinese: the Importance of Syntactic Parsing and L2-L1 Parallel Data	This paper studies semantic parsing for interlanguage (L2), taking semantic role labeling (SRL) as a case task and learner Chinese as a case language. We first manually annotate the semantic roles for a set of learner texts to derive a gold standard for automatic SRL. Based on the new data, we then evaluate three off-t...	{ "paragraphs": [ [ "A learner language (interlanguage) is an idiolect developed by a learner of a second or foreign language which may preserve some features of his/her first language. Previously, encouraging results of automatically building the syntactic analysis of learner languages were reported BIBREF...	{ "answers": [ { "annotation_id": [ "0adb8e4cfb7d0907d69fb75e06419e00bdeee18b" ], "answer": [ { "evidence": [ "Our second concern is to mimic the human's robust semantic processing ability by computer programs. The feasibility of reusing the annotation speci...	{ "caption": [ "Table 1: Inter-annotator agreement.", "Table 2: Inter-annotator agreement (F-scores) relative to languages and role types.", "Table 3: Performances of the syntax-based and neural syntax-agnostic SRL systems on the L1 and L2 data. “ALL” denotes the overall performance.", "Table 4: Oracl...	[ "Who manually annotated the semantic roles for the set of learner texts?" ]	[ [ "1808.09409-An L2-L1 Parallel Corpus-3" ] ]	[ "Authors" ]	169
1808.00265	Interpretable Visual Question Answering by Visual Grounding from Attention Supervision Mining	A key aspect of VQA models that are interpretable is their ability to ground their answers to relevant regions in the image. Current approaches with this capability rely on supervised learning and human annotated groundings to train attention mechanisms inside the VQA architecture. Unfortunately, obtaining human annota...	{ "paragraphs": [ [ "We are interested in the problem of visual question answering (VQA), where an algorithm is presented with an image and a question that is formulated in natural language and relates to the contents of the image. The goal of this task is to get the algorithm to correctly answer the questi...	{ "answers": [ { "annotation_id": [ "0addc69c7a2f96afa92bfff2e2ec342bb635b4d8" ], "answer": [ { "evidence": [ "Table TABREF10 reports our main results. Our models are built on top of prior works with the additional Attention Supervision Module as described i...	{ "caption": [ "Figure 1. Interpretable VQA algorithms must ground their answer into image regions that are relevant to the question. In this paper, we aim at providing this ability by leveraging existing region descriptions and object annotations to construct grounding supervision automatically.", "Figure 2....	[ "How do they obtain region descriptions and object annotations?" ]	[ [ "1808.00265-Introduction-4" ] ]	[ "they are available in the Visual Genome dataset" ]	170
1810.09774	Testing the Generalization Power of Neural Network Models Across NLI Benchmarks	Neural network models have been very successful in natural language inference, with the best models reaching 90% accuracy in some benchmarks. However, the success of these models turns out to be largely benchmark specific. We show that models trained on a natural language inference dataset drawn from one benchmark fail...	{ "paragraphs": [ [ "Natural Language Inference (NLI) has attracted considerable interest in the NLP community and, recently, a large number of neural network-based systems have been proposed to deal with the task. One can attempt a rough categorization of these systems into: a) sentence encoding systems, a...	{ "answers": [ { "annotation_id": [ "0b0ee6e9614e9c96cd79c50344c5ebbe7727bc32" ], "answer": [ { "evidence": [ "FLOAT SELECTED: Table 4: Test accuracies (%). For the baseline results (highlighted in bold) the training data and test data have been drawn from t...	{ "caption": [ "Table 1: Dataset combinations used in the experiments. The rows in bold are baseline experiments, where the test data comes from the same benchmark as the training and development data.", "Table 2: Example sentence pairs from the three datasets.", "Table 3: Model architectures used in the ...	[ "Which training dataset allowed for the best generalization to benchmark sets?", "Which models were compared?" ]	[ [ "1810.09774-6-Table4-1.png" ], [ "1810.09774-Model and Training Details-1" ] ]	[ "MultiNLI", "BiLSTM-max, HBMP, ESIM, KIM, ESIM + ELMo, and BERT" ]	171
1910.05608	VAIS Hate Speech Detection System: A Deep Learning based Approach for System Combination	Nowadays, Social network sites (SNSs) such as Facebook, Twitter are common places where people show their opinions, sentiments and share information with others. However, some people use SNSs to post abuse and harassment threats in order to prevent other SNSs users from expressing themselves as well as seeking differen...	{ "paragraphs": [ [ "Currently, social networks are so popular. Some of the biggest ones include Facebook, Twitter, Youtube,... with extremely number of users. Thus, controlling content of those platforms is essential. For years, social media companies such as Twitter, Facebook, and YouTube have been invest...	{ "answers": [ { "annotation_id": [ "0b3cf44bc00d13112653dfd6e44be62454996080" ], "answer": [ { "evidence": [], "extractive_spans": [], "free_form_answer": "", "highlighted_evidence": [], "unanswerable": true, "yes_no": ...	{ "caption": [ "Figure 1. Hate Speech Detection System Overview", "Figure 2. TextCNN model architecture", "Figure 4. LSTM model architecture", "Figure 3. VDCNN model architecture", "Table I F1_MACRO SCORE OF DIFFERENT MODEL", "Figure 5. LSTMCNN model architecture", "Figure 6. SARNN model a...	[ "What is private dashboard?", "What is public dashboard?", "What dataset do they use?" ]	[ [ "1910.05608-Experiment-8" ], [ "1910.05608-Experiment-8" ], [ "1910.05608-Experiment-0", "1910.05608-System description ::: System overview-0" ] ]	[ "Private dashboard is leaderboard where competitors can see results after competition is finished - on hidden part of test set (private test set).", "Public dashboard where competitors can see their results during competition, on part of the test set (public test set).", "They used Wiki Vietnamese language and ...	172
2003.06279	Using word embeddings to improve the discriminability of co-occurrence text networks	Word co-occurrence networks have been employed to analyze texts both in the practical and theoretical scenarios. Despite the relative success in several applications, traditional co-occurrence networks fail in establishing links between similar words whenever they appear distant in the text. Here we investigate whether...	{ "paragraphs": [ [ "The ability to construct complex and diverse linguistic structures is one of the main features that set us apart from all other species. Despite its ubiquity, some language aspects remain unknown. Topics such as language origin and evolution have been studied by researchers from diverse...	{ "answers": [ { "annotation_id": [ "c98053f61caf0057e9b860a136f79840b47e83ab" ], "answer": [ { "evidence": [ "Our findings paves the way for research in several new directions. While we probed the effectiveness of virtual edges in a specific text classifica...	{ "caption": [ "FIG. 1. Example of a enriched word co-occurrence network created for a text. In this model, after the removal of stopwords, the remaining words are linked whenever they appear in the same context. In the proposed network representation, “virtual” edges are included whenever two nodes (words) are s...	[ "Do the use word embeddings alone or they replace some previous features of the model with word embeddings?" ]	[ [ "2003.06279-Introduction-2" ] ]	[ "They use it as addition to previous model - they add new edge between words if word embeddings are similar." ]	178
2004.03744	e-SNLI-VE-2.0: Corrected Visual-Textual Entailment with Natural Language Explanations	The recently proposed SNLI-VE corpus for recognising visual-textual entailment is a large, real-world dataset for fine-grained multimodal reasoning. However, the automatic way in which SNLI-VE has been assembled (via combining parts of two related datasets) gives rise to a large number of errors in the labels of this c...	{ "paragraphs": [ [ "Inspired by textual entailment BIBREF0, Xie BIBREF1 introduced the visual-textual entailment (VTE) task, which considers semantic entailment between a premise image and a textual hypothesis. Semantic entailment consists in determining if the hypothesis can be concluded from the premise,...	{ "answers": [ { "annotation_id": [ "94b90e9041b91232b87bfc13b5fa5ff8f7feb0b2" ], "answer": [ { "evidence": [ "Finally, we note that only about 62% of the originally neutral pairs remain neutral, while 21% become contradiction and 17% entailment pairs. There...	{ "caption": [ "Figure 1. Examples from SNLI-VE-2.0. (a) In red, the neutral label from SNLI-VE is wrong, since the picture clearly shows that the crowd is outdoors. We corrected it to entailment in SNLIVE-2.0. (b) In green, an ambiguous instance. There is indeed an American flag in the background but it is very ...	[ "How many natural language explanations are human-written?" ]	[ [ "2004.03744-8-Table3-1.png", "2004.03744-Appendix ::: Statistics of e-SNLI-VE-2.0-0" ] ]	[ "Totally 6980 validation and test image-sentence pairs have been corrected." ]	179
2001.09332	An Analysis of Word2Vec for the Italian Language	Word representation is fundamental in NLP tasks, because it is precisely from the coding of semantic closeness between words that it is possible to think of teaching a machine to understand text. Despite the spread of word embedding concepts, still few are the achievements in linguistic contexts other than English. In ...	{ "paragraphs": [ [ "In order to make human language comprehensible to a computer, it is obviously essential to provide some word encoding. The simplest approach is the one-hot encoding, where each word is represented by a sparse vector with dimension equal to the vocabulary size. In addition to the storage...	{ "answers": [ { "annotation_id": [ "707f16cbdcecaaf2438b2eea89bbbde0c2bf24a7" ], "answer": [ { "evidence": [ "The dataset needed to train the W2V was obtained using the information extracted from a dump of the Italian Wikipedia (dated 2019.04.01), from the ...	{ "caption": [ "Fig. 1. Representation of Word2Vec model.", "Table 1. Accuracy at the 20th epoch for the 6 Skip-gram models analysed when the W dimension of the window and the N value of negative sampling change.", "Fig. 2. Total accuracy using 3COSMUL at different epochs with negative sampling equal to 5...	[ "What is the dataset used as input to the Word2Vec algorithm?" ]	[ [ "2001.09332-Implementation details-0", "2001.09332-Implementation details-1" ] ]	[ "Italian Wikipedia and Google News extraction producing final vocabulary of 618224 words" ]	180
1904.07342	Learning Twitter User Sentiments on Climate Change with Limited Labeled Data	While it is well-documented that climate change accepters and deniers have become increasingly polarized in the United States over time, there has been no large-scale examination of whether these individuals are prone to changing their opinions as a result of natural external occurrences. On the sub-population of Twitt...	{ "paragraphs": [ [ "Much prior work has been done at the intersection of climate change and Twitter, such as tracking climate change sentiment over time BIBREF2 , finding correlations between Twitter climate change sentiment and seasonal effects BIBREF3 , and clustering Twitter users based on climate menta...	{ "answers": [ { "annotation_id": [ "344fc2c81c2b0173e51bafa2f8a8edbca4e1be14" ], "answer": [ { "evidence": [ "We henceforth refer to a tweet affirming climate change as a “positive\" sample (labeled as 1 in the data), and a tweet denying climate change as a...	{ "caption": [ "Table 1: Tweets collected for each U.S. 2018 natural disaster", "Figure 1: Four-clustering on sentiment, latitude, and longitude", "Table 2: Selected binary sentiment analysis accuracies", "Figure 2: Pre-event (left) and post-event (right) average climate sentiment aggregated over five...	[ "What methodology is used to compensate for limited labelled data?" ]	[ [ "1904.07342-Data-1" ] ]	[ "Influential tweeters ( who they define as individuals certain to have a classifiable sentiment regarding the topic at hand) is used to label tweets in bulk in the absence of manually-labeled tweets." ]	182
2001.06888	A multimodal deep learning approach for named entity recognition from social media	Named Entity Recognition (NER) from social media posts is a challenging task. User generated content which forms the nature of social media, is noisy and contains grammatical and linguistic errors. This noisy content makes it much harder for tasks such as named entity recognition. However some applications like automat...	{ "paragraphs": [ [ "A common social media delivery system such as Twitter supports various media types like video, image and text. This media allows users to share their short posts called Tweets. Users are able to share their tweets with other users that are usually following the source user. Hovewer ther...	{ "answers": [ { "annotation_id": [ "0c5be00c50cc9fa7c1921c32aca6b2cb254dd249" ], "answer": [ { "evidence": [ "In BIBREF8 a refined collection of tweets gathered from twitter is presented. Their dataset which is labeled for named entity recognition task cont...	{ "caption": [ "Figure 1: A Tweet containing Image and Text: Geoffrey Hinton and Demis Hassabis are referred in text and respective images are provided with Tweet", "Table 1: BIO Tags and their respective meaning", "Figure 2: Proposed CWI Model: Character (left), Word (middle) and Image (right) feature ex...	[ "What are the baseline state of the art models?" ]	[ [ "2001.06888-8-Table3-1.png" ] ]	[ "Stanford NER, BiLSTM+CRF, LSTM+CNN+CRF, T-NER and BiLSTM+CNN+Co-Attention" ]	183
1604.05781	What we write about when we write about causality: Features of causal statements across large-scale social discourse	Identifying and communicating relationships between causes and effects is important for understanding our world, but is affected by language structure, cognitive and emotional biases, and the properties of the communication medium. Despite the increasing importance of social media, much remains unknown about causal sta...	{ "paragraphs": [ [ "Social media and online social networks now provide vast amounts of data on human online discourse and other activities BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 . With so much communication taking place online and with social media being capable of hosting pow...	{ "answers": [ { "annotation_id": [ "f286d3a109fe0b38fcee6121e231001a4704e9c8" ], "answer": [ { "evidence": [ "Causal documents were chosen to contain one occurrence only of the exact unigrams: `caused', `causing', or `causes'. The word `cause' was not inclu...	{ "caption": [ "Fig. 1. Measuring the differences between causal and control documents. (A) Examples of processed documents tagged by Parts-of-Speech (POS) or Named Entities (NEs). Unigrams highlighted in red (yellow) are in the bottom 10% (top 10%) of the labMT sentiment scores. (B) Log Odds ratios with 95% Wald...	[ "How do they extract causality from text?", "What is the source of the \"control\" corpus?", "What are the selection criteria for \"causal statements\"?", "Do they use expert annotations, crowdsourcing, or only automatic methods to analyze the corpora?", "how do they collect the comparable corpus?", "How ...	[ [ "1604.05781-Dataset, filtering, and corpus selection-2" ], [ "1604.05781-Dataset, filtering, and corpus selection-0", "1604.05781-Dataset, filtering, and corpus selection-2" ], [ "1604.05781-Dataset, filtering, and corpus selection-2" ], [ "1604.05781-Introduction-4" ], [ "...	[ "They identify documents that contain the unigrams 'caused', 'causing', or 'causes'", "Randomly selected from a Twitter dump, temporally matched to causal documents", "Presence of only the exact unigrams 'caused', 'causing', or 'causes'", "Only automatic methods", "Randomly from a Twitter dump", "Randomly...	187

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