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id stringlengths 10 10	title stringlengths 12 156	abstract stringlengths 279 2.02k	full_text dict	qas dict	figures_and_tables dict
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...	{ "section_name": [ "Introduction", "Related Work", "Proposed Method", "Proposed Method ::: Polarity Function", "Proposed Method ::: Discourse Relation-Based Event Pairs", "Proposed Method ::: Discourse Relation-Based Event Pairs ::: AL (Automatically Labeled Pairs)", "Proposed Method ::: ...	{ "question": [ "What is the seed lexicon?", "What are the results?", "How are relations used to propagate polarity?", "How big is the Japanese data?", "What are labels available in dataset for supervision?", "How big are improvements of supervszed learning results trained on smalled labeled d...	{ "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...
2003.07723	PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry	Most approaches to emotion analysis regarding social media, literature, news, and other domains focus exclusively on basic emotion categories as defined by Ekman or Plutchik. However, art (such as literature) enables engagement in a broader range of more complex and subtle emotions that have been shown to also include ...	{ "section_name": [ "", " ::: ", " ::: ::: ", "Introduction", "Related Work ::: Poetry in Natural Language Processing", "Related Work ::: Emotion Annotation", "Related Work ::: Emotion Classification", "Data Collection", "Data Collection ::: German", "Data Collection ::: Engli...	{ "question": [ "Does the paper report macro F1?", "How is the annotation experiment evaluated?", "What are the aesthetic emotions formalized?" ], "question_id": [ "3a9d391d25cde8af3334ac62d478b36b30079d74", "8d8300d88283c73424c8f301ad9fdd733845eb47", "48b12eb53e2d507343f19b8a667696a39b719...	{ "caption": [ "Figure 1: Temporal distribution of poetry corpora (Kernel Density Plots with bandwidth = 0.2).", "Table 1: Statistics on our poetry corpora PO-EMO.", "Table 2: Aesthetic Emotion Factors (Schindler et al., 2017).", "Table 3: Cohen’s kappa agreement levels and normalized line-level emoti...
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...	{ "section_name": [ "Introduction", "A typology of community identity", "Overview and intuition", "Language-based formalization", "Community-level measures", "Applying the typology to Reddit", "Community identity and user retention", "Community-type and monthly retention", "Communi...	{ "question": [ "Do they report results only on English data?", "How do the various social phenomena examined manifest in different types of communities?", "What patterns do they observe about how user engagement varies with the characteristics of a community?", "How did the select the 300 Reddit comm...	{ "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...
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...	{ "section_name": [ "Introduction", "Related Work ::: Clinical Text Structuring", "Related Work ::: Pre-trained Language Model", "Question Answering based Clinical Text Structuring", "The Proposed Model for QA-CTS Task", "The Proposed Model for QA-CTS Task ::: Contextualized Representation of ...	{ "question": [ "What data is the language model pretrained on?", "What baselines is the proposed model compared against?", "How is the clinical text structuring task defined?", "What are the specific tasks being unified?", "Is all text in this dataset a question, or are there unrelated sentences ...	{ "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...
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...	{ "section_name": [ "Introduction", "Background and Related Work", "Experimental Setup", "Hyperparameters and Training", "Infrastructure", "Results and Discussion", "Conclusion" ], "paragraphs": [ [ "Deep learning has unquestionably advanced the state of the art in many natur...	{ "question": [ "What aspects have been compared between various language models?", "what classic language models are mentioned in the paper?", "What is a commonly used evaluation metric for language models?" ], "question_id": [ "dd155f01f6f4a14f9d25afc97504aefdc6d29c13", "a9d530d68fb45b52d9ba...	{ "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...
1805.02400	Stay On-Topic: Generating Context-specific Fake Restaurant Reviews	Automatically generated fake restaurant reviews are a threat to online review systems. Recent research has shown that users have difficulties in detecting machine-generated fake reviews hiding among real restaurant reviews. The method used in this work (char-LSTM ) has one drawback: it has difficulties staying in conte...	{ "section_name": [ "Introduction", "Background", "System Model", "Attack Model", "Generative Model" ], "paragraphs": [ [ "Automatically generated fake reviews have only recently become natural enough to fool human readers. Yao et al. BIBREF0 use a deep neural network (a so-called 2-...	{ "question": [ "Which dataset do they use a starting point in generating fake reviews?", "Do they use a pretrained NMT model to help generating reviews?", "How does using NMT ensure generated reviews stay on topic?", "What kind of model do they use for detection?", "Does their detection tool work...	{ "caption": [ "Fig. 1: Näıve text generation with NMT vs. generation using our NTM model. Repetitive patterns are underlined. Contextual words are italicized. Both examples here are generated based on the context given in Example 1.", "Table 1: Six different parametrizations of our NMT reviews and one examp...
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...	{ "section_name": [ "Introduction", "The Task and the Model", "Dataset and Training Task", "The Model", "Obtained Summaries", "Layer-Wise Relevance Propagation", "Mathematical Description", "Generation of the Saliency Maps", "Experimental results", "First Observations", "Va...	{ "question": [ "Which baselines did they compare?", "How many attention layers are there in their model?", "Is the explanation from saliency map correct?" ], "question_id": [ "6e2ad9ad88cceabb6977222f5e090ece36aa84ea", "aacb0b97aed6fc6a8b471b8c2e5c4ddb60988bf5", "710c1f8d4c137c8dad9972f5c...	{ "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...
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...	{ "section_name": [ "Introduction", "Background ::: Geometric Bias Mitigation", "Background ::: Geometric Bias Mitigation ::: WEAT", "Background ::: Geometric Bias Mitigation ::: RIPA", "Background ::: Geometric Bias Mitigation ::: Neighborhood Metric", "A Probabilistic Framework for Bias Miti...	{ "question": [ "How is embedding quality assessed?", "What are the three measures of bias which are reduced in experiments?", "What are the probabilistic observations which contribute to the more robust algorithm?" ], "question_id": [ "47726be8641e1b864f17f85db9644ce676861576", "8958465d1eaf8...	{ "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...
1912.02481	Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yor\`ub\'a and Twi	The success of several architectures to learn semantic representations from unannotated text and the availability of these kind of texts in online multilingual resources such as Wikipedia has facilitated the massive and automatic creation of resources for multiple languages. The evaluation of such resources is usually ...	{ "section_name": [ "Introduction", "Related Work", "Languages under Study ::: Yorùbá", "Languages under Study ::: Twi", "Data", "Data ::: Training Corpora", "Data ::: Evaluation Test Sets ::: Yorùbá.", "Data ::: Evaluation Test Sets ::: Twi", "Semantic Representations", "Seman...	{ "question": [ "What turn out to be more important high volume or high quality data?", "How much is model improved by massive data and how much by quality?", "What two architectures are used?" ], "question_id": [ "347e86893e8002024c2d10f618ca98e14689675f", "10091275f777e0c2890c3ac0fd0a7d8e266...	{ "caption": [ "Table 1: Summary of the corpora used in the analysis. The last 3 columns indicate in which dataset (C1, C2 or C3) are the different sources included (see text, Section 5.2.).", "Table 2: Number of tokens per named entity type in the Global Voices Yorùbá corpus.", "Table 3: FastText embed...
1810.04528	Is there Gender bias and stereotype in Portuguese Word Embeddings?	In this work, we propose an analysis of the presence of gender bias associated with professions in Portuguese word embeddings. The objective of this work is to study gender implications related to stereotyped professions for women and men in the context of the Portuguese language.	{ "section_name": [ "Introduction", "Related Work", "Portuguese Embedding", "Proposed Approach", "Experiments", "Final Remarks" ], "paragraphs": [ [ "Recently, the transformative potential of machine learning (ML) has propelled ML into the forefront of mainstream media. In Brazil...	{ "question": [ "Does this paper target European or Brazilian Portuguese?", "What were the word embeddings trained on?", "Which word embeddings are analysed?" ], "question_id": [ "519db0922376ce1e87fcdedaa626d665d9f3e8ce", "99a10823623f78dbff9ccecb210f187105a196e9", "09f0dce416a1e40cc6a24a...	{ "caption": [ "Fig. 1. Proposal", "Fig. 2. Extreme Analogies" ], "file": [ "3-Figure1-1.png", "5-Figure2-1.png" ] }
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...	{ "section_name": [ "Introduction", "Related work ::: Legal Citation Analysis", "Related work ::: Reference Recognition", "Related work ::: Data Availability", "Related work ::: Document Segmentation", "Methodology", "Methodology ::: Dataset and models ::: CzCDC 1.0 dataset", "Methodol...	{ "question": [ "Did they experiment on this dataset?", "How is quality of the citation measured?", "How big is the dataset?" ], "question_id": [ "ac706631f2b3fa39bf173cd62480072601e44f66", "8b71ede8170162883f785040e8628a97fc6b5bcb", "fa2a384a23f5d0fe114ef6a39dced139bddac20e" ], "nlp_b...	{ "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": [ "...
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...	{ "section_name": [ "Introduction", "Overview", "Related Works", "Demographics of Clinically Validated PTSD Assessment Tools", "Twitter-based PTSD Detection", "Twitter-based PTSD Detection ::: Data Collection", "Twitter-based PTSD Detection ::: Pre-processing", "Twitter-based PTSD Dete...	{ "question": [ "Do they evaluate only on English datasets?", "Do the authors mention any possible confounds in this study?", "How is the intensity of the PTSD established?", "How is LIWC incorporated into this system?", "How many twitter users are surveyed using the clinically validated survey?",...	{ "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"...
2003.12218	Comprehensive Named Entity Recognition on CORD-19 with Distant or Weak Supervision	We created this CORD-19-NER dataset with comprehensive named entity recognition (NER) on the COVID-19 Open Research Dataset Challenge (CORD-19) corpus (2020- 03-13). This CORD-19-NER dataset covers 74 fine-grained named entity types. It is automatically generated by combining the annotation results from four sources: (...	{ "section_name": [ "Introduction", "CORD-19-NER Dataset ::: Corpus", "CORD-19-NER Dataset ::: NER Methods", "Results ::: NER Annotation Results", "Results ::: Top-Frequent Entity Summarization", "Conclusion", "Acknowledgment" ], "paragraphs": [ [ "Coronavirus disease 2019 (C...	{ "question": [ "Did they experiment with the dataset?", "What is the size of this dataset?", "Do they list all the named entity types present?" ], "question_id": [ "ce6201435cc1196ad72b742db92abd709e0f9e8d", "928828544e38fe26c53d81d1b9c70a9fb1cc3feb", "4f243056e63a74d1349488983dc1238228ca...	{ "caption": [ "Table 1: Performance comparison on three major biomedical entity types in COVID-19 corpus.", "Figure 1: Examples of the annotation results with CORD-NER system.", "Figure 2: Annotation result comparison with other NER methods.", "Table 2: Examples of the most frequent entities annotate...
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 ...	{ "section_name": [ "Introduction", "Method", "Experimental Setup", "Results", "Conclusion" ], "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 langu...	{ "question": [ "how is quality measured?", "how many languages exactly is the sentiment lexica for?", "what sentiment sources do they compare with?" ], "question_id": [ "8f87215f4709ee1eb9ddcc7900c6c054c970160b", "b04098f7507efdffcbabd600391ef32318da28b3", "8fc14714eb83817341ada708b9a0b6b...	{ "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...
2003.06651	Word Sense Disambiguation for 158 Languages using Word Embeddings Only	Disambiguation of word senses in context is easy for humans, but is a major challenge for automatic approaches. Sophisticated supervised and knowledge-based models were developed to solve this task. However, (i) the inherent Zipfian distribution of supervised training instances for a given word and/or (ii) the quality ...	{ "section_name": [ "", " ::: ", " ::: ::: ", "Introduction", "Related Work", "Algorithm for Word Sense Induction", "Algorithm for Word Sense Induction ::: SenseGram: A Baseline Graph-based Word Sense Induction Algorithm", "Algorithm for Word Sense Induction ::: egvi (Ego-Graph Vector...	{ "question": [ "Is the method described in this work a clustering-based method?", "How are the different senses annotated/labeled? ", "Was any extrinsic evaluation carried out?" ], "question_id": [ "d94ac550dfdb9e4bbe04392156065c072b9d75e1", "eeb6e0caa4cf5fdd887e1930e22c816b99306473", "3c...	{ "caption": [ "Table 1: Top nearest neighbours of the fastText vector of the word Ruby are clustered according to various senses of this word: programming language, gem, first name, color, but also its spelling variations (typeset in black color).", "Figure 1: The graph of nearest neighbours of the word Ruby...
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...	{ "section_name": [ "Introduction", "Related Work", "Proposed Method ::: Motivations", "Proposed Method ::: Description of Features", "Proposed Method ::: Model Description", "Proposed Method ::: Model Details: 1D ConvNet", "Proposed Method ::: Model Details: 1D ConvNet ::: Hyperparameter ...	{ "question": [ "Does the model use both spectrogram images and raw waveforms as features?", "Is the performance compared against a baseline model?", "What is the accuracy reported by state-of-the-art methods?" ], "question_id": [ "dc1fe3359faa2d7daa891c1df33df85558bc461b", "922f1b740f8b13fdc8...	{ "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...
1906.00378	Unsupervised Bilingual Lexicon Induction from Mono-lingual Multimodal Data	Bilingual lexicon induction, translating words from the source language to the target language, is a long-standing natural language processing task. Recent endeavors prove that it is promising to employ images as pivot to learn the lexicon induction without reliance on parallel corpora. However, these vision-based appr...	{ "section_name": [ "Introduction", "Related Work", "Unsupervised Bilingual Lexicon Induction", "Multi-lingual Image Caption Model", "Visual-guided Word Representation", "Word Translation Prediction", "Datasets", "Experimental Setup", "Evaluation of Multi-lingual Image Caption", ...	{ "question": [ "Which vision-based approaches does this approach outperform?", "What baseline is used for the experimental setup?", "Which languages are used in the multi-lingual caption model?" ], "question_id": [ "591231d75ff492160958f8aa1e6bfcbbcd85a776", "9e805020132d950b54531b1a2620f6155...	{ "caption": [ "Figure 1: Comparison of previous vision-based approaches and our proposed approach for bilingual lexicon induction. Best viewed in color.", "Figure 2: Multi-lingual image caption model. The source and target language caption models share the same image encoder and language decoder, which enfor...
1912.13072	AraNet: A Deep Learning Toolkit for Arabic Social Media	We describe AraNet, a collection of deep learning Arabic social media processing tools. Namely, we exploit an extensive host of publicly available and novel social media datasets to train bidirectional encoders from transformer models (BERT) to predict age, dialect, gender, emotion, irony, and sentiment. AraNet deliver...	{ "section_name": [ "Introduction", "Introduction ::: ", "Methods", "Data and Models ::: Age and Gender", "Data and Models ::: Age and Gender ::: ", "Data and Models ::: Dialect", "Data and Models ::: Emotion", "Data and Models ::: Irony", "Data and Models ::: Sentiment", "AraN...	{ "question": [ "Did they experiment on all the tasks?", "What models did they compare to?", "What datasets are used in training?" ], "question_id": [ "2419b38624201d678c530eba877c0c016cccd49f", "b99d100d17e2a121c3c8ff789971ce66d1d40a4d", "578d0b23cb983b445b1a256a34f969b34d332075" ], "...	{ "caption": [ "Figure 1: A map of Arab countries. Our different datasets cover varying regions of the Arab world as we describe in each section.", "Table 1: Distribution of age and gender classes in our Arab-Tweet data splits", "Table 2: Model performance in accuracy of Arab-Tweet age and gender classifi...
1712.09127	Generative Adversarial Nets for Multiple Text Corpora	Generative adversarial nets (GANs) have been successfully applied to the artificial generation of image data. In terms of text data, much has been done on the artificial generation of natural language from a single corpus. We consider multiple text corpora as the input data, for which there can be two applications of G...	{ "section_name": [ "Introduction", "Literature Review", "Models and Algorithms", "weGAN: Training cross-corpus word embeddings", "deGAN: Generating document embeddings for multi-corpus text data", "Experiments", "The CNN data set", "The TIME data set", "The 20 Newsgroups data set"...	{ "question": [ "Which GAN do they use?", "Do they evaluate grammaticality of generated text?", "Which corpora do they use?" ], "question_id": [ "6548db45fc28e8a8b51f114635bad14a13eaec5b", "4c4f76837d1329835df88b0921f4fe8bda26606f", "819d2e97f54afcc7cdb3d894a072bcadfba9b747" ], "nlp_ba...	{ "caption": [ "Figure 1: Model structure of weGAN.", "Figure 2: Model structure of deGAN.", "Table 1: A comparison between word2vec and weGAN in terms of the Rand index and the classification accuracy for the CNN data set.", "Table 3: A comparison between word2vec and deGAN in terms of the accuracy f...
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...	{ "section_name": [ "Introduction", "Proposed model", "Dataset ::: Twitter Sentiment Classification", "Dataset ::: Intent Classification from Text with STT Error", "Experiments ::: Baseline models", "Experiments ::: Baseline models ::: NLU service platforms", "Experiments ::: Baseline mode...	{ "question": [ "Do they report results only on English datasets?", "How do the authors define or exemplify 'incorrect words'?", "How many vanilla transformers do they use after applying an embedding layer?", "Do they test their approach on a dataset without incomplete data?", "Should their approa...	{ "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.",...
1910.03042	Gunrock: A Social Bot for Complex and Engaging Long Conversations	Gunrock is the winner of the 2018 Amazon Alexa Prize, as evaluated by coherence and engagement from both real users and Amazon-selected expert conversationalists. We focus on understanding complex sentences and having in-depth conversations in open domains. In this paper, we introduce some innovative system designs and...	{ "section_name": [ "Introduction", "System Architecture", "System Architecture ::: Automatic Speech Recognition", "System Architecture ::: Natural Language Understanding", "System Architecture ::: Dialog Manager", "System Architecture ::: Knowledge Databases", "System Architecture ::: Nat...	{ "question": [ "What is the sample size of people used to measure user satisfaction?", "What are all the metrics to measure user engagement?", "What the system designs introduced?", "Do they specify the model they use for Gunrock?", "Do they gather explicit user satisfaction data on Gunrock?", ...	{ "caption": [ "Figure 1: Gunrock system architecture", "Figure 2: Mean user rating by mean number of words. Error bars show standard error.", "Figure 3: Mean user rating based on number of queries to Gunrock’s backstory. Error bars show standard error.", "Figure 4: Mean user rating based ’Has Pet’. E...
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...	{ "section_name": [ "Introduction", "Baselines and Approach", "Baselines and Approach ::: Baselines", "Baselines and Approach ::: Proposed Approaches", "Experiments ::: Dataset and Experimental Settings", "Experiments ::: Experimental Results", "Conclusion" ], "paragraphs": [ [ ...	{ "question": [ "Do the authors report only on English?", "What is the baseline for the experiments?", "Which experiments are perfomed?" ], "question_id": [ "830de0bd007c4135302138ffa8f4843e4915e440", "680dc3e56d1dc4af46512284b9996a1056f89ded", "bd5379047c2cf090bea838c67b6ed44773bcd56f" ...	{ "caption": [ "Table 1: Experimental Results for the Subjectivity Detection Task" ], "file": [ "2-Table1-1.png" ] }
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...	{ "section_name": [ "Introduction", "On Acceptability", "Method", "SLOR", "WordPieces", "WPSLOR", "Experiment", "Dataset", "LM Hyperparameters and Training", "Baseline Metrics", "Correlation and Evaluation Scores", "Results and Discussion", "Analysis I: Fluency Eval...	{ "question": [ "Is ROUGE their only baseline?", "what language models do they use?", "what questions do they ask human judges?" ], "question_id": [ "7aa8375cdf4690fc3b9b1799b0f5a9ec1c1736ed", "3ac30bd7476d759ea5d9a5abf696d4dfc480175b", "0e57a0983b4731eba9470ba964d131045c8c7ea7" ], "nl...	{ "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...
1707.00995	An empirical study on the effectiveness of images in Multimodal Neural Machine Translation	In state-of-the-art Neural Machine Translation (NMT), an attention mechanism is used during decoding to enhance the translation. At every step, the decoder uses this mechanism to focus on different parts of the source sentence to gather the most useful information before outputting its target word. Recently, the effect...	{ "section_name": [ "Introduction", "Neural Machine Translation", "Text-based NMT", "Conditional GRU", "Multimodal NMT", "Attention-based Models", "Soft attention", "Hard Stochastic attention", "Local Attention", "Image attention optimization", "Experiments", "Training ...	{ "question": [ "What misbehavior is identified?", "What is the baseline used?", "Which attention mechanisms do they compare?" ], "question_id": [ "f0317e48dafe117829e88e54ed2edab24b86edb1", "ec91b87c3f45df050e4e16018d2bf5b62e4ca298", "f129c97a81d81d32633c94111018880a7ffe16d1" ], "nlp_...	{ "caption": [ "Figure 1: Die beiden Kinder spielen auf dem Spielplatz .", "Figure 2: Ein Junge sitzt auf und blickt aus einem Mikroskop .", "Figure 3: Ein Mann sitzt neben einem Computerbildschirm .", "Figure 4: Ein Mann in einem orangefarbenen Hemd und mit Helm .", "Figure 5: Ein Mädchen mit ei...
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...	{ "section_name": [ "Introduction", "Machine Commenting", "Challenges", "Solutions", "Proposed Approach", "Retrieval-based Commenting", "Neural Variational Topic Model", "Training", "Datasets", "Implementation Details", "Baselines", "Retrieval Evaluation", "Generati...	{ "question": [ "Which paired corpora did they use in the other experiment?", "By how much does their system outperform the lexicon-based models?", "Which lexicon-based models did they compare with?", "How many comments were used?", "How many articles did they have?", "What news comment datase...	{ "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...
1909.08402	Enriching BERT with Knowledge Graph Embeddings for Document Classification	In this paper, we focus on the classification of books using short descriptive texts (cover blurbs) and additional metadata. Building upon BERT, a deep neural language model, we demonstrate how to combine text representations with metadata and knowledge graph embeddings, which encode author information. Compared to the...	{ "section_name": [ "Introduction", "Related Work", "Dataset and Task", "Experiments", "Experiments ::: Metadata Features", "Experiments ::: Author Embeddings", "Experiments ::: Pre-trained German Language Model", "Experiments ::: Model Architecture", "Experiments ::: Implementatio...	{ "question": [ "By how much do they outperform standard BERT?", "What dataset do they use?", "How do they combine text representations with the knowledge graph embeddings?" ], "question_id": [ "f5cf8738e8d211095bb89350ed05ee7f9997eb19", "bed527bcb0dd5424e69563fba4ae7e6ea1fca26a", "aeab579...	{ "caption": [ "Table 1: Availability of additional data with respect to the dataset (relative numbers in parenthesis).", "Figure 1: Visualization of Wikidata embeddings for Franz Kafka (3D-projection with PCA)5. Nearest neighbours in original 200D space: Arthur Schnitzler, E.T.A Hoffmann and Hans Christian A...
1909.11189	Diachronic Topics in New High German Poetry	Statistical topic models are increasingly and popularly used by Digital Humanities scholars to perform distant reading tasks on literary data. It allows us to estimate what people talk about. Especially Latent Dirichlet Allocation (LDA) has shown its usefulness, as it is unsupervised, robust, easy to use, scalable, and...	{ "section_name": [ "Corpus", "Experiments", "Experiments ::: Topic Trends", "Experiments ::: Classification of Time Periods and Authorship", "Experiments ::: Conclusion & Future Work" ], "paragraphs": [ [ "The Digital Library in the TextGrid Repository represents an extensive collec...	{ "question": [ "What is the algorithm used for the classification tasks?", "Is the outcome of the LDA analysis evaluated in any way?", "What is the corpus used in the study?" ], "question_id": [ "bfa3776c30cb30e0088e185a5908e5172df79236", "a2a66726a5dca53af58aafd8494c4de833a06f14", "ee876...	{ "caption": [ "Fig. 1: 25 year Time Slices of Textgrid Poetry (1575–1925)", "Fig. 2: left: Topic 27 ’Virtue, Arts’ (Period: Enlightenment), right: Topic 55 ’Flowers, Spring, Garden’ (Period: Early Romanticism)", "Fig. 3: left: Topic 63 ’Song’ (Period: Romanticism), right: Topic 33 ’German Nation’ (Period...
1810.05320	Important Attribute Identification in Knowledge Graph	The knowledge graph(KG) composed of entities with their descriptions and attributes, and relationship between entities, is finding more and more application scenarios in various natural language processing tasks. In a typical knowledge graph like Wikidata, entities usually have a large number of attributes, but it is d...	{ "section_name": [ "The problem we solve in this paper", "Related Research", "What we propose and what we have done", "Our proposed Method", "Application Scenario", "FastText Introduction", "Matching", "Data introduction", "Data preprocessing", "Proposed method vs previous met...	{ "question": [ "What are the traditional methods to identifying important attributes?", "What do you use to calculate word/sub-word embeddings", "What user generated text data do you use?" ], "question_id": [ "cda4612b4bda3538d19f4b43dde7bc30c1eda4e5", "e12674f0466f8c0da109b6076d9939b30952c7d...	{ "caption": [ "Fig. 1. A typical product enquiry example on Alibaba.com", "Fig. 2. Each sentence obtained from the enquiry is scored against possible attributes under that category.", "Table 1. Proposed method vs other methods metrics: precision, recall and F1-score." ], "file": [ "5-Figure1-1.pn...
2003.08529	Diversity, Density, and Homogeneity: Quantitative Characteristic Metrics for Text Collections	Summarizing data samples by quantitative measures has a long history, with descriptive statistics being a case in point. However, as natural language processing methods flourish, there are still insufficient characteristic metrics to describe a collection of texts in terms of the words, sentences, or paragraphs they co...	{ "section_name": [ "Introduction", "Related Work", "Proposed Characteristic Metrics", "Proposed Characteristic Metrics ::: Diversity", "Proposed Characteristic Metrics ::: Density", "Proposed Characteristic Metrics ::: Homogeneity", "Simulations", "Simulations ::: Simulation Setup", ...	{ "question": [ "Did they propose other metrics?", "Which real-world datasets did they use?", "How did they obtain human intuitions?" ], "question_id": [ "b5c3787ab3784214fc35f230ac4926fe184d86ba", "9174aded45bc36915f2e2adb6f352f3c7d9ada8b", "a8f1029f6766bffee38a627477f61457b2d6ed5c" ], ...	{ "caption": [ "Figure 1: Visualization of the simulations including base setting, down-sampling, varying spreads, adding outliers, and multiple sub-clusters in 2-dimensional and 768-dimensional spaces.", "Figure 2: Diversity, density, and homogeneity metric values in each simulation scenario.", "Table 1:...
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 ...	{ "section_name": [ "Introduction", "The UN General Debate and international development", "Estimation of topic models", "Topics in the UN General Debate", "Explaining the rhetoric", "Conclusion" ], "paragraphs": [ [ "Decisions made in international organisations are fundamental ...	{ "question": [ "What are the country-specific drivers of international development rhetoric?", "Is the dataset multilingual?", "How are the main international development topics that states raise identified?" ], "question_id": [ "a2103e7fe613549a9db5e65008f33cf2ee0403bd", "13b36644357870008d7...	{ "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...
2003.08553	QnAMaker: Data to Bot in 2 Minutes	Having a bot for seamless conversations is a much-desired feature that products and services today seek for their websites and mobile apps. These bots help reduce traffic received by human support significantly by handling frequent and directly answerable known questions. Many such services have huge reference document...	{ "section_name": [ "Introduction", "System description ::: Architecture", "System description ::: Bot Development Process", "System description ::: Extraction", "System description ::: Retrieval And Ranking", "System description ::: Retrieval And Ranking ::: Pre-Processing", "System descr...	{ "question": [ "What experiments do the authors present to validate their system?", "How does the conversation layer work?", "What components is the QnAMaker composed of?" ], "question_id": [ "fd0ef5a7b6f62d07776bf672579a99c67e61a568", "071bcb4b054215054f17db64bfd21f17fd9e1a80", "f399d5a8...	{ "caption": [ "Figure 1: Interactions between various components of QnaMaker, along with their scopes: server-side and client-side", "Table 1: Retrieval And Ranking Measurements", "Figure 2: QnAMaker Runtime Pipeline", "Figure 3: Active Learning Suggestions", "Figure 4: Multi-Turn Knowledge Base"...
1909.09491	A simple discriminative training method for machine translation with large-scale features	Margin infused relaxed algorithms (MIRAs) dominate model tuning in statistical machine translation in the case of large scale features, but also they are famous for the complexity in implementation. We introduce a new method, which regards an N-best list as a permutation and minimizes the Plackett-Luce loss of ground-t...	{ "section_name": [ "Introduction", "Plackett-Luce Model", "Plackett-Luce Model in Statistical Machine Translation", "Plackett-Luce Model in Statistical Machine Translation ::: N-best Hypotheses Resample", "Evaluation", "Evaluation ::: Plackett-Luce Model for SMT Tuning", "Evaluation ::: P...	{ "question": [ "How they measure robustness in experiments?", "Is new method inferior in terms of robustness to MIRAs in experiments?", "What experiments with large-scale features are performed?" ], "question_id": [ "d28260b5565d9246831e8dbe594d4f6211b60237", "8670989ca39214eda6c1d1d272457a3f...	{ "caption": [ "Table 2: PL(k): Plackett-Luce model optimizing the ground-truth permutation with length k. The significant symbols (+ at 0.05 level) are compared with MERT. The bold font numbers signifies better results compared to M(1) system.", "Figure 1: PL(k) with 500 L-BFGS iterations, k=1,3,5,7,9,12,15 ...
2001.05284	Improving Spoken Language Understanding By Exploiting ASR N-best Hypotheses	In a modern spoken language understanding (SLU) system, the natural language understanding (NLU) module takes interpretations of a speech from the automatic speech recognition (ASR) module as the input. The NLU module usually uses the first best interpretation of a given speech in downstream tasks such as domain and in...	{ "section_name": [ "Introduction", "Baseline, Oracle and Direct Models ::: Baseline and Oracle", "Baseline, Oracle and Direct Models ::: Direct Models", "Integration of N-BEST Hypotheses", "Integration of N-BEST Hypotheses ::: Hypothesized Text Concatenation", "Integration of N-BEST Hypothese...	{ "question": [ "Which ASR system(s) is used in this work?", "What are the series of simple models?", "Over which datasets/corpora is this work evaluated?" ], "question_id": [ "67131c15aceeb51ae1d3b2b8241c8750a19cca8e", "579a0603ec56fc2b4aa8566810041dbb0cd7b5e7", "c9c85eee41556c6993f40e428...	{ "caption": [ "Fig. 3: Integration of n-best hypotheses with two possible ways: 1) concatenate hypothesized text and 2) concatenate hypothesis embedding.", "Table 3: Micro and Macro F1 score for multi-class domain classification.", "Table 4: Performance comparison for the subset (∼ 19%) where ASR first b...
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...	{ "section_name": [ "Introduction", "System Description", "System Description ::: Split into Minimal Propositions", "System Description ::: Establish a Semantic Hierarchy", "System Description ::: Establish a Semantic Hierarchy ::: Constituency Type Classification.", "System Description ::: Es...	{ "question": [ "Is the semantic hierarchy representation used for any task?", "What are the corpora used for the task?", "Is the model evaluated?" ], "question_id": [ "f8281eb49be3e8ea0af735ad3bec955a5dedf5b3", "a5ee9b40a90a6deb154803bef0c71c2628acb571", "e286860c41a4f704a3a08e45183cb8b14...	{ "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 ...
1709.00947	Learning Word Embeddings from the Portuguese Twitter Stream: A Study of some Practical Aspects	This paper describes a preliminary study for producing and distributing a large-scale database of embeddings from the Portuguese Twitter stream. We start by experimenting with a relatively small sample and focusing on three challenges: volume of training data, vocabulary size and intrinsic evaluation metrics. Using a s...	{ "section_name": [ "Introduction", "Related Work", "Our Neural Word Embedding Model", "Experimental Setup", "Training Data", "Metrics related with the Learning Process", "Tests and Gold-Standard Data for Intrinsic Evaluation", "Results and Analysis", "Intrinsic Evaluation", "F...	{ "question": [ "What new metrics are suggested to track progress?", "What intrinsic evaluation metrics are used?", "What experimental results suggest that using less than 50% of the available training examples might result in overfitting?" ], "question_id": [ "982979cb3c71770d8d7d2d1be8f92b66223d...	{ "caption": [ "Table 1. Number of 5-grams available for training for different sizes of target vocabulary \|V \|", "Table 2. Overall statistics for 12 combinations of models learned varying \|V \| and volume of training data. Results observed after 40 training epochs.", "Fig. 1. Continuous line represents lo...
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...	{ "section_name": [ "Introduction", "Visual Reasoning in RecipeQA", "Procedural Reasoning Networks", "Procedural Reasoning Networks ::: Input Module", "Procedural Reasoning Networks ::: Reasoning Module", "Procedural Reasoning Networks ::: Attention Module", "Procedural Reasoning Networks ...	{ "question": [ "What multimodality is available in the dataset?", "What are previously reported models?", "How better is accuracy of new model compared to previously reported models?" ], "question_id": [ "a883bb41449794e0a63b716d9766faea034eb359", "5d83b073635f5fd8cd1bdb1895d3f13406583fbd", ...	{ "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...
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...	{ "section_name": [ "Introduction", "Chinese Word Segmentation", "Active Learning", "Active Learning for Chinese Word Segmentation", "CRF-based Word Segmenter", "Information Entropy Based Scoring Model", "Datasets", "Parameter Settings", "Experimental Results", "Conclusion and ...	{ "question": [ "How does the scoring model work?", "How does the active learning model work?", "Which neural network architectures are employed?" ], "question_id": [ "3c3cb51093b5fd163e87a773a857496a4ae71f03", "53a0763eff99a8148585ac642705637874be69d4", "0bfed6f9cfe93617c5195c848583e3945f...	{ "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...
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...	{ "section_name": [ "Motivation", "Collection via Amazon M-Turk", "Data Statistics", "Annotation", "Annotation Schema", "Development of the Schema", "First Annotation Phase", "Modification of the Schema", "Special Cases", "Inter-Annotator Agreement", "Annotated Corpus Stati...	{ "question": [ "What are the key points in the role of script knowledge that can be studied?", "Did the annotators agreed and how much?", "How many subjects have been used to create the annotations?" ], "question_id": [ "352c081c93800df9654315e13a880d6387b91919", "18fbf9c08075e3b696237d22473c...	{ "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...
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...	{ "section_name": [ "Introduction", "Background and Notation", "Completion Robustness and Interpretability via Adversarial Graph Edits ()", "Removing a fact ()", "Adding a new fact ()", "Challenges", "Efficiently Identifying the Modification", "First-order Approximation of Influence", ...	{ "question": [ "What datasets are used to evaluate this approach?", "How is this approach used to detect incorrect facts?", "Can this adversarial approach be used to directly improve model accuracy?" ], "question_id": [ "bc9c31b3ce8126d1d148b1025c66f270581fde10", "185841e979373808d99dccdade52...	{ "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...
1808.05902	Learning Supervised Topic Models for Classification and Regression from Crowds	The growing need to analyze large collections of documents has led to great developments in topic modeling. Since documents are frequently associated with other related variables, such as labels or ratings, much interest has been placed on supervised topic models. However, the nature of most annotation tasks, prone to ...	{ "section_name": [ "Introduction", "Supervised topic models", "Learning from multiple annotators", "Classification model", "Proposed model", "Approximate inference", "Parameter estimation", "Stochastic variational inference", "Document classification", "Regression model", ...	{ "question": [ "what are the advantages of the proposed model?", "what are the state of the art approaches?", "what datasets were used?" ], "question_id": [ "330f2cdeab689670b68583fc4125f5c0b26615a8", "c87b2dd5c439d5e68841a705dd81323ec0d64c97", "f7789313a804e41fcbca906a4e5cf69039eeef9f" ...	{ "caption": [ "Fig. 1. Graphical representation of the proposed model for classification.", "TABLE 1 Correspondence Between Variational Parameters and the Original Parameters", "Fig. 3. Graphical representation of the proposed model for regression.", "Fig. 2. Example of four different annotators (rep...
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...	{ "section_name": [ "Introduction", "Related Work", "Data Collection", "Data Collection ::: Database Construction", "Data Collection ::: Goal Generation", "Data Collection ::: Dialogue Collection", "Data Collection ::: Dialogue Collection ::: User Side", "Data Collection ::: Dialogue C...	{ "question": [ "How was the dataset collected?", "What are the benchmark models?", "How was the corpus annotated?" ], "question_id": [ "2376c170c343e2305dac08ba5f5bda47c370357f", "0137ecebd84a03b224eb5ca51d189283abb5f6d9", "5f6fbd57cce47f20a0fda27d954543c00c4344c2" ], "nlp_background"...	{ "caption": [], "file": [] }
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...	{ "section_name": [ "Introduction", "Related Work", "Model ::: Form-Context Model", "Model ::: Bertram", "Model ::: Training", "Generation of Rare Word Datasets", "Generation of Rare Word Datasets ::: Dataset Splitting", "Generation of Rare Word Datasets ::: Baseline Training", "Ge...	{ "question": [ "What models other than standalone BERT is new model compared to?", "How much is representaton improved for rare/medum frequency words compared to standalone BERT and previous work?", "What are three downstream task datasets?", "What is dataset for word probing task?" ], "question_...	{ "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...
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 ...	{ "section_name": [ "Introduction", "Methodology", "Preliminaries", "Local Encoder", "Global Encoder", "Entity Selector", "Experiment", "Experiment Setup", "Comparing with Previous Work", "Discussion on different RLEL variants", "Case Study", "Related Work", "Entity...	{ "question": [ "How fast is the model compared to baselines?", "How big is the performance difference between this method and the baseline?", "What datasets used for evaluation?", "what are the mentioned cues?" ], "question_id": [ "9aca4b89e18ce659c905eccc78eda76af9f0072a", "b0376a7f67f15...	{ "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...
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...	{ "section_name": [ "Introduction", "Related Work", "Classification vs. Regression Experiments", "Deep Learning Models", "Reinforcement Learning", "Evaluation Correlation Analysis", "Submitted Runs", "Conclusions" ], "paragraphs": [ [ "The BioASQ Challenge includes a ques...	{ "question": [ "How did the author's work rank among other submissions on the challenge?", "What approaches without reinforcement learning have been tried?", "What classification approaches were experimented for this task?", "Did classification models perform better than previous regression one?" ]...	{ "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...
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...	{ "section_name": [ "Introduction", "Background: Morphological Inflection", "Two Schemata, Two Philosophies", "Universal Dependencies", "UniMorph", "Similarities in the annotation", "UD treebanks and UniMorph tables", "A Deterministic Conversion", "Experiments", "Intrinsic eval...	{ "question": [ "What are the main sources of recall errors in the mapping?", "Do they look for inconsistencies between different languages' annotations in UniMorph?", "Do they look for inconsistencies between different UD treebanks?", "Which languages do they validate on?" ], "question_id": [ ...	{ "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 ...
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...	{ "section_name": [ "Introduction", "Related Work ::: Facial Expressions", "Related Work ::: Acoustic", "Related Work ::: Text", "Data set Collection", "Data set Collection ::: Study Setup and Design", "Data set Collection ::: Procedure", "Data set Collection ::: Data Analysis", "M...	{ "question": [ "Does the paper evaluate any adjustment to improve the predicion accuracy of face and audio features?", "How is face and audio data analysis evaluated?", "What is the baseline method for the task?", "What are the emotion detection tools used for audio and face input?" ], "question_...	{ "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...
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...	{ "section_name": [ "Introduction", "Low-Resource Translation Quality Compared Across Systems", "Improving Low-Resource Neural Machine Translation", "Mainstream Improvements", "Language Representation", "Hyperparameter Tuning", "Lexical Model", "Data and Preprocessing", "PBSMT Base...	{ "question": [ "what amounts of size were used on german-english?", "what were their experimental results in the low-resource dataset?", "what are the methods they compare with in the korean-english dataset?", "what pitfalls are mentioned in the paper?" ], "question_id": [ "4547818a3bbb727c4b...	{ "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...
1912.01252	Facilitating on-line opinion dynamics by mining expressions of causation. The case of climate change debates on The Guardian	News website comment sections are spaces where potentially conflicting opinions and beliefs are voiced. Addressing questions of how to study such cultural and societal conflicts through technological means, the present article critically examines possibilities and limitations of machine-guided exploration and potential...	{ "section_name": [ "Introduction ::: Background", "Introduction ::: Objective", "Introduction ::: Data: the communicative setting of TheGuardian.com", "Mining opinions and beliefs from texts", "Mining opinions and beliefs from texts ::: Causal mapping methods and the climate change debate", "...	{ "question": [ "Does the paper report the results of previous models applied to the same tasks?", "How is the quality of the discussion evaluated?", "What is the technique used for text analysis and mining?", "What are the causal mapping methods employed?" ], "question_id": [ "5679fabeadf680e...	{ "caption": [ "Figure 1. Communicative setting of many online newspaper sites. The newspaper publishes articles on different topics and users can comment on these articles and previous comments.", "Figure 2. This is a global representation of the data produced by considering a 10 percent subsample of all the...
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...	{ "section_name": [ "Introduction", "Introduction ::: Modeling challenges", "Related Work ::: Transfer learning based approaches", "Related Work ::: Hybrid models", "Dataset and Features", "Dataset and Features ::: Challenges", "Model Architecture", "Model Architecture ::: Loss functio...	{ "question": [ "What is the previous work's model?", "What dataset is used?", "How big is the dataset?", "How is the dataset collected?", "Was each text augmentation technique experimented individually?", "What models do previous work use?", "Does the dataset contain content from various ...	{ "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", ...
1911.03310	How Language-Neutral is Multilingual BERT?	Multilingual BERT (mBERT) provides sentence representations for 104 languages, which are useful for many multi-lingual tasks. Previous work probed the cross-linguality of mBERT using zero-shot transfer learning on morphological and syntactic tasks. We instead focus on the semantic properties of mBERT. We show that mBER...	{ "section_name": [ "Introduction", "Related Work", "Centering mBERT Representations", "Probing Tasks", "Probing Tasks ::: Language Identification.", "Probing Tasks ::: Language Similarity.", "Probing Tasks ::: Parallel Sentence Retrieval.", "Probing Tasks ::: Word Alignment.", "Pr...	{ "question": [ "How they demonstrate that language-neutral component is sufficiently general in terms of modeling semantics to allow high-accuracy word-alignment?", "Are language-specific and language-neutral components disjunctive?", "How they show that mBERT representations can be split into a language...	{ "caption": [ "Table 1: Accuracy of language identification, values from the best-scoring layers.", "Figure 1: Language centroids of the mean-pooled representations from the 8th layer of cased mBERT on a tSNE plot with highlighted language families.", "Table 2: V-Measure for hierarchical clustering of la...
1907.12108	CAiRE: An End-to-End Empathetic Chatbot	In this paper, we present an end-to-end empathetic conversation agent CAiRE. Our system adapts TransferTransfo (Wolf et al., 2019) learning approach that fine-tunes a large-scale pre-trained language model with multi-task objectives: response language modeling, response prediction and dialogue emotion detection. We eva...	{ "section_name": [ "Introduction", "User Interface", "Scalable to Multiple Users", "Generative Conversational Model", "Active Learning of Ethical Values and Persona", "Conclusion" ], "paragraphs": [ [ "Empathetic chatbots are conversational agents that can understand user emotio...	{ "question": [ "What is the performance of their system?", "What evaluation metrics are used?", "What is the source of the dialogues?", "What pretrained LM is used?" ], "question_id": [ "b1ced2d6dcd1d7549be2594396cbda34da6c3bca", "f3be1a27df2e6ad12eed886a8cd2dfe09b9e2b30", "a45a86b6a0...	{ "caption": [ "Table 1: An example of the empathetic dialogue dataset. Two people are discussing a situation that happened to one of them, and that led to the experience of a given feeling.", "Figure 1: Fine-tuning schema for empathetic dialogues.", "Table 2: Comparison of different automatic metrics bet...
2004.03685	Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?	With the growing popularity of deep-learning based NLP models, comes a need for interpretable systems. But what is interpretability, and what constitutes a high-quality interpretation? In this opinion piece we reflect on the current state of interpretability evaluation research. We call for more clearly differentiating...	{ "section_name": [ "Introduction", "Faithfulness vs. Plausibility", "Inherently Interpretable?", "Evaluation via Utility", "Guidelines for Evaluating Faithfulness", "Guidelines for Evaluating Faithfulness ::: Be explicit in what you evaluate.", "Guidelines for Evaluating Faithfulness ::: ...	{ "question": [ "What approaches they propose?", "What faithfulness criteria does they propose?", "Which are three assumptions in current approaches for defining faithfulness?", "Which are key points in guidelines for faithfulness evaluation?" ], "question_id": [ "eeaceee98ef1f6c971dac7b0b8930...	{ "caption": [], "file": [] }
1808.03894	Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference	Deep learning models have achieved remarkable success in natural language inference (NLI) tasks. While these models are widely explored, they are hard to interpret and it is often unclear how and why they actually work. In this paper, we take a step toward explaining such deep learning based models through a case study...	{ "section_name": [ "Introduction", "Task and Model", "Visualization of Attention and Gating", "Attention", "LSTM Gating Signals", "Conclusion" ], "paragraphs": [ [ "Deep learning has achieved tremendous success for many NLP tasks. However, unlike traditional methods that provide...	{ "question": [ "Did they use the state-of-the-art model to analyze the attention?", "What is the performance of their model?", "How many layers are there in their model?", "Did they compare with gradient-based methods?" ], "question_id": [ "aceac4ad16ffe1af0f01b465919b1d4422941a6b", "f707...	{ "caption": [ "Figure 1: Normalized attention and attention saliency visualization. Each column shows visualization of one sample. Top plots depict attention visualization and bottom ones represent attention saliency visualization. Predicted (the same as Gold) label of each sample is shown on top of each column....
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...	{ "section_name": [ "Introduction", "Related Work", "The Baseline Model", "Question Understanding and Adaptation", "Set-Up", "Results", "Conclusions" ], "paragraphs": [ [ "Enabling computers to understand given documents and answer questions about their content has recently a...	{ "question": [ "What MC abbreviate for?", "how much of improvement the adaptation model can get?", "what is the architecture of the baseline model?", "What is the exact performance on SQUAD?" ], "question_id": [ "a891039441e008f1fd0a227dbed003f76c140737", "73738e42d488b32c9db89ac8adefc754...	{ "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...
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...	{ "section_name": [ "Introduction", "Related Work", "Datasets", "Methods ::: The Sum-QE Model", "Methods ::: The Sum-QE Model ::: Single-task (BERT-FT-S-1):", "Methods ::: The Sum-QE Model ::: Multi-task with one regressor (BERT-FT-M-1):", "Methods ::: The Sum-QE Model ::: Multi-task with ...	{ "question": [ "What are their correlation results?", "What dataset do they use?", "What simpler models do they look at?", "What linguistic quality aspects are addressed?" ], "question_id": [ "ff28d34d1aaa57e7ad553dba09fc924dc21dd728", "ae8354e67978b7c333094c36bf9d561ca0c2d286", "0234...	{ "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...
1911.09419	Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction	Knowledge graph embedding, which aims to represent entities and relations as low dimensional vectors (or matrices, tensors, etc.), has been shown to be a powerful technique for predicting missing links in knowledge graphs. Existing knowledge graph embedding models mainly focus on modeling relation patterns such as symm...	{ "section_name": [ "Introduction", "Related Work", "Related Work ::: Model Category", "Related Work ::: The Ways to Model Hierarchy Structures", "The Proposed HAKE", "The Proposed HAKE ::: Two Categories of Entities", "The Proposed HAKE ::: Hierarchy-Aware Knowledge Graph Embedding", ...	{ "question": [ "What benchmark datasets are used for the link prediction task?", "What are state-of-the art models for this task?", "How better does HAKE model peform than state-of-the-art methods?", "How are entities mapped onto polar coordinate system?" ], "question_id": [ "6852217163ea678f...	{ "caption": [ "Table 1: Details of several knowledge graph embedding models, where ◦ denotes the Hadamard product, f denotes a activation function, ∗ denotes 2D convolution, and ω denotes a filter in convolutional layers. ·̄ denotes conjugate for complex vectors in ComplEx model and 2D reshaping for real vectors...
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...	{ "section_name": [ "Introduction", "Problem Description", "Problem Description ::: Programming Language Diversity", "Problem Description ::: Human Language Factor", "Problem Description ::: NLP of statements", "Proposed Methodology", "Proposed Methodology ::: Statistical Machine Translati...	{ "question": [ "What additional techniques are incorporated?", "What dataset do they use?", "Do they compare to other models?", "What is the architecture of the system?", "How long are expressions in layman's language?", "What additional techniques could be incorporated to further improve acc...	{ "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" ] }
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...	{ "section_name": [ "Introduction", "Introduction ::: blackTraditional Learning Based Text-to-image Synthesis", "Introduction ::: GAN Based Text-to-image Synthesis", "Related Work", "Preliminaries and Frameworks", "Preliminaries and Frameworks ::: Generative Adversarial Neural Network", "P...	{ "question": [ "Is text-to-image synthesis trained is suppervized or unsuppervized manner?", "What challenges remain unresolved?", "What is the conclusion of comparison of proposed solution?", "What is typical GAN architecture for each text-to-image synhesis group?" ], "question_id": [ "e96ad...	{ "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...
1904.05584	Gating Mechanisms for Combining Character and Word-level Word Representations: An Empirical Study	In this paper we study how different ways of combining character and word-level representations affect the quality of both final word and sentence representations. We provide strong empirical evidence that modeling characters improves the learned representations at the word and sentence levels, and that doing so is par...	{ "section_name": [ "Introduction", "Background", "Mapping Characters to Character-level Word Representations", "Combining Character and Word-level Representations", "Obtaining Sentence Representations", "Experimental Setup", "Datasets", "Word Similarity", "Word Frequencies and Gat...	{ "question": [ "Where do they employ feature-wise sigmoid gating?", "Which model architecture do they use to obtain representations?", "Which downstream sentence-level tasks do they evaluate on?", "Which similarity datasets do they use?" ], "question_id": [ "7fe48939ce341212c1d801095517dc552b...	{ "caption": [ "Figure 1: Character and Word-level combination methods.", "Table 1: Word-level evaluation results. Each value corresponds to average Pearson correlation of 7 identical models initialized with different random seeds. Correlations were scaled to the [−100; 100] range for easier reading. Bold val...
1911.09886	Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction	A relation tuple consists of two entities and the relation between them, and often such tuples are found in unstructured text. There may be multiple relation tuples present in a text and they may share one or both entities among them. Extracting such relation tuples from a sentence is a difficult task and sharing of en...	{ "section_name": [ "Introduction", "Task Description", "Encoder-Decoder Architecture", "Encoder-Decoder Architecture ::: Embedding Layer & Encoder", "Encoder-Decoder Architecture ::: Word-level Decoder & Copy Mechanism", "Encoder-Decoder Architecture ::: Pointer Network-Based Decoder", "E...	{ "question": [ "Are there datasets with relation tuples annotated, how big are datasets available?", "Which one of two proposed approaches performed better in experiments?", "What is previous work authors reffer to?", "How higher are F1 scores compared to previous work?" ], "question_id": [ "...	{ "caption": [ "Table 1: Relation tuple representation for encoder-decoder models.", "Figure 1: The architecture of an encoder-decoder model (left) and a pointer network-based decoder block (right).", "Table 2: Statistics of train/test split of the two datasets.", "Table 4: Ablation of attention mecha...
1611.01400	Learning to Rank Scientific Documents from the Crowd	Finding related published articles is an important task in any science, but with the explosion of new work in the biomedical domain it has become especially challenging. Most existing methodologies use text similarity metrics to identify whether two articles are related or not. However biomedical knowledge discovery is...	{ "section_name": [ null, "Introduction", "Benchmark Datasets", "Learning to Rank", "Features", "Baseline Systems", "Evaluation Measures", "Forward Feature Selection", "Results", "Discussion", "Acknowledgments" ], "paragraphs": [ [ "[block]I.1em", "[bloc...	{ "question": [ "what were the baselines?", "what is the supervised model they developed?", "what is the size of this built corpus?", "what crowdsourcing platform is used?" ], "question_id": [ "d32b6ac003cfe6277f8c2eebc7540605a60a3904", "c10f38ee97ed80484c1a70b8ebba9b1fb149bc91", "3405...	{ "caption": [ "Figure 1: The basic pipeline of a learning-to-rank system. An initial set of results for a query is retrieved from a search engine, and then that subset is reranked. During the reranking phase new features may be extracted.", "Table 1: Results for the citation baselines. The number of times a ...
1808.05077	Exploiting Deep Learning for Persian Sentiment Analysis	The rise of social media is enabling people to freely express their opinions about products and services. The aim of sentiment analysis is to automatically determine subject's sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as topic, product, movie, news etc. Deep learning has recently...	{ "section_name": [ "Introduction", "Related Works", "Methodology and Experimental Results", "Conclusion", "Acknowledgment" ], "paragraphs": [ [ "In recent years, social media, forums, blogs and other forms of online communication tools have radically affected everyday life, especial...	{ "question": [ "Which deep learning model performed better?", "By how much did the results improve?", "What was their performance on the dataset?", "How large is the dataset?" ], "question_id": [ "1951cde612751410355610074c3c69cec94824c2", "4140d8b5a78aea985546aa1e323de12f63d24add", "...	{ "caption": [ "Fig. 1. Multilayer Perceptron", "Fig. 2. Autoencoder", "Fig. 3. Deep Convolutional Neural Network", "Table 1. Results: MLP vs. Autoencoder vs. Convolutional Neural Network" ], "file": [ "5-Figure1-1.png", "5-Figure2-1.png", "6-Figure3-1.png", "7-Table1-1.png" ] }
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...	{ "section_name": [ null, "Introduction", "Talk The Walk", "Task", "Data Collection", "Dataset Statistics", "Experiments", "Tourist Localization", "Model", "The Tourist", "The Guide", "Comparisons", "Results and Discussion", "Analysis of Localization Task", ...	{ "question": [ "Did the authors use crowdsourcing platforms?", "How was the dataset collected?", "What language do the agents talk in?", "What evaluation metrics did the authors look at?", "What data did they use?" ], "question_id": [ "0cd0755ac458c3bafbc70e4268c1e37b87b9721b", "c1ce6...	{ "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 ...
1907.02030	Real-time Claim Detection from News Articles and Retrieval of Semantically-Similar Factchecks	Factchecking has always been a part of the journalistic process. However with newsroom budgets shrinking it is coming under increasing pressure just as the amount of false information circulating is on the rise. We therefore propose a method to increase the efficiency of the factchecking process, using the latest devel...	{ "section_name": [ "Introduction", "Related Work", "Method", "Choosing an embedding", "Clustering Method", "Next Steps" ], "paragraphs": [ [ "In recent years, the spread of misinformation has become a growing concern for researchers and the public at large BIBREF1 . Researchers ...	{ "question": [ "Do the authors report results only on English data?", "How is the accuracy of the system measured?", "How is an incoming claim used to retrieve similar factchecked claims?", "What existing corpus is used for comparison in these experiments?", "What are the components in the factch...	{ "caption": [ "Table 1: Examples of claims taken from real articles.", "Table 2: Claim Detection Results.", "Figure 1: Analysis of Different Embeddings on the Quora Question Answering Dataset", "Table 3: Comparing Sentence Embeddings for Clustering News Claims." ], "file": [ "2-Table1-1.png",...
1910.04601	RC-QED: Evaluating Natural Language Derivations in Multi-Hop Reading Comprehension	Recent studies revealed that reading comprehension (RC) systems learn to exploit annotation artifacts and other biases in current datasets. This allows systems to "cheat" by employing simple heuristics to answer questions, e.g. by relying on semantic type consistency. This means that current datasets are not well-suite...	{ "section_name": [ "Introduction", "Task formulation: RC-QED ::: Input, output, and evaluation metrics", "Task formulation: RC-QED ::: RC-QED@!START@$^{\\rm E}$@!END@", "Data collection for RC-QED@!START@$^{\\rm E}$@!END@ ::: Crowdsourcing interface", "Data collection for RC-QED@!START@$^{\\rm E}...	{ "question": [ "What is the baseline?", "What dataset was used in the experiment?", "Did they use any crowdsourcing platform?", "How was the dataset annotated?", "What is the source of the proposed dataset?" ], "question_id": [ "b11ee27f3de7dd4a76a1f158dc13c2331af37d9f", "7aba5e448329...	{ "caption": [ "Figure 1: Overview of the proposed RC-QED task. Given a question and supporting documents, a system is required to give an answer and its derivation steps.", "Figure 2: Crowdsourcing interface: judgement task.", "Figure 3: Crowdsourcing interface: derivation task.", "Table 1: Distribut...
1912.05066	Event Outcome Prediction using Sentiment Analysis and Crowd Wisdom in Microblog Feeds	Sentiment Analysis of microblog feeds has attracted considerable interest in recent times. Most of the current work focuses on tweet sentiment classification. But not much work has been done to explore how reliable the opinions of the mass (crowd wisdom) in social network microblogs such as twitter are in predicting ou...	{ "section_name": [ "Introduction", "Related Work", "Data Set and Preprocessing ::: Data Collection", "Data Set and Preprocessing ::: Preprocessing", "Methodology ::: Procedure", "Methodology ::: Machine Learning Models", "Methodology ::: Machine Learning Models ::: Single-label Classifica...	{ "question": [ "How many label options are there in the multi-label task?", "What is the interannotator agreement of the crowd sourced users?", "Who are the experts?", "Who is the crowd in these experiments?", "How do you establish the ground truth of who won a debate?" ], "question_id": [ ...	{ "caption": [ "TABLE I: Debates chosen, listed in chronological order. A total of 10 debates were considered out of which 7 are Republican and 3 are Democratic.", "TABLE II: Statistics of the Data Collected: Debates", "Fig. 1: Histograms of Tweet Frequency vs. Debates and TV Viewers vs. Debates shown sid...
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...	{ "section_name": [ "Introduction", "Related Work", "Problem Formulation", "Proposed Model", "Proposed Model ::: Overall Architecture", "Proposed Model ::: Attribute Embedding Layer", "Proposed Model ::: Embedding Propagation Layer", "Proposed Model ::: Output Layer and Training Detail...	{ "question": [ "How much better is performance of proposed method than state-of-the-art methods in experiments?", "What further analysis is done?", "What seven state-of-the-art methods are used for comparison?", "What three datasets are used to measure performance?", "How does KANE capture both h...	{ "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 ...
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...	{ "section_name": [ "Introduction", "Motivation", "Definition and Challenges", "Dataset Creation", "Feature Design", "Evaluation", "Performance for Datasets 1 and 2", "Performance for Held-out Dataset H", "Error Analysis", "Conclusion & Future Work" ], "paragraphs": [ [...	{ "question": [ "Do they report results only on English data?", "Do the authors mention any confounds to their study?", "What baseline model is used?", "What stylistic features are used to detect drunk texts?", "Is the data acquired under distant supervision verified by humans at any stage?", ...	{ "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...
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 ...	{ "section_name": [ "Introduction", "Related Work", "Tuple Inference Solver", "Tuple KB", "Tuple Selection", "Support Graph Search", "Experiments", "Results", "Error Analysis", "Conclusion", "Appendix: ILP Model Details", "Experiment Details", "Using curated tables ...	{ "question": [ "What corpus was the source of the OpenIE extractions?", "What is the accuracy of the proposed technique?", "Is an entity linking process used?", "Are the OpenIE extractions all triples?", "What method was used to generate the OpenIE extractions?", "Can the method answer multi-...	{ "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...
1804.10686	An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages	In this paper, we present Watasense, an unsupervised system for word sense disambiguation. Given a sentence, the system chooses the most relevant sense of each input word with respect to the semantic similarity between the given sentence and the synset constituting the sense of the target word. Watasense has two modes ...	{ "section_name": [ "Introduction", "Related Work", "Watasense, an Unsupervised System for Word Sense Disambiguation", "System Architecture", "User Interface", "Word Sense Disambiguation", "Evaluation", "Quality Measure", "Dataset", "Results", "Conclusion", "Acknowledge...	{ "question": [ "Do the authors offer any hypothesis about why the dense mode outperformed the sparse one?", "What evaluation is conducted?", "Which corpus of synsets are used?", "What measure of semantic similarity is used?" ], "question_id": [ "7d5ba230522df1890619dedcfb310160958223c1", ...	{ "caption": [ "Figure 1: A snapshot of the online demo, which is available at http://watasense.nlpub.org/ (in Russian).", "Figure 2: The UML class diagram of Watasense.", "Figure 3: The word sense disambiguation results with the word “experiments” selected. The tooltip shows its lemma “experiment”, the s...
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...	{ "section_name": [ "Introduction", "Dataset Construction", "Question sets", "Context Retrieval", "Candidate solutions", "Postprocessing", "Metrics", "Human Evaluation", "Baseline Systems", "Results", "Conclusion", "Acknowledgments", "Quasar-S Relation Definitions",...	{ "question": [ "Which retrieval system was used for baselines?" ], "question_id": [ "dcb18516369c3cf9838e83168357aed6643ae1b8" ], "nlp_background": [ "five" ], "topic_background": [ "familiar" ], "paper_read": [ "somewhat" ], "search_query": [ "question" ], "question_w...	{ "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...
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...	{ "section_name": [ "Introduction", "Related work", "Error-analysis method", "Data and model ::: Data sets", "Data and model ::: Pre-trained word Embeddings", "Data and model ::: Model", "Experiment and Results", "Experiment and Results ::: Error analysis on gold data", "Experiment...	{ "question": [ "What word embeddings were used?", "What type of errors were produced by the BLSTM-CNN-CRF system?", "How much better was the BLSTM-CNN-CRF than the BLSTM-CRF?" ], "question_id": [ "f46a907360d75ad566620e7f6bf7746497b6e4a9", "79d999bdf8a343ce5b2739db3833661a1deab742", "71d5...	{ "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...
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...	{ "section_name": [ "Introduction", "Related Work", "Method", "LSTM Models", "Neural Attention", "Predicting Relationships of Object Pairs with an Attention Model", "Modeling Question-External Comments", "Experiments", "Preliminary Results", "Robust Parameter Initialization", ...	{ "question": [ "What supplemental tasks are used for multitask learning?", "Is the improvement actually coming from using an RNN?", "How much performance gap between their approach and the strong handcrafted method?", "What is a strong feature-based method?", "Did they experimnet in other languag...	{ "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...
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...	{ "section_name": [ "Introduction", "Related Work", "Proposed Methodology", "Embedding Layer", "Attentional Encoder Layer", "Target-specific Attention Layer", "Output Layer", "Regularization and Model Training", "Datasets and Experimental Settings", "Model Comparisons", "Ma...	{ "question": [ "Do they use multi-attention heads?", "How big is their model?", "How is their model different from BERT?" ], "question_id": [ "9bffc9a9c527e938b2a95ba60c483a916dbd1f6b", "8434974090491a3c00eed4f22a878f0b70970713", "b67420da975689e47d3ea1c12b601851018c4071" ], "nlp_back...	{ "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...
1904.03339	ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples	This paper describes our system, Joint Encoders for Stable Suggestion Inference (JESSI), for the SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums. JESSI is a combination of two sentence encoders: (a) one using multiple pre-trained word embeddings learned from log-bilinear regression (GloVe) and tra...	{ "section_name": [ "Introduction", "Joint Encoders for Stable Suggestion Inference", "Experiments", "Conclusion", "Acknowledgement" ], "paragraphs": [ [ "Opinion mining BIBREF0 is a huge field that covers many NLP tasks ranging from sentiment analysis BIBREF1 , aspect extraction BIB...	{ "question": [ "What datasets were used?", "How did they do compared to other teams?" ], "question_id": [ "01d91d356568fca79e47873bd0541bd22ba66ec0", "37e45a3439b048a80c762418099a183b05772e6a" ], "nlp_background": [ "", "" ], "topic_background": [ "", "" ], "paper_read...	{ "caption": [ "Figure 1: The overall architecture of JESSI for Subtask B. The thinner arrows correspond to the forward propagations, while the thicker arrows correspond to the backward propagations, where gradient calculations are indicated. For Subtask A, a CNN encoder is used instead of the BiSRU encoder, and ...
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 ...	{ "section_name": [ "Introduction", "Background", "Dataset", "Dataset ::: Plutchik’s Wheel of Emotions", "Dataset ::: Passage Selection", "Dataset ::: Mechanical Turk (MTurk)", "Dataset ::: Dataset Statistics", "Benchmarks", "Benchmarks ::: Bag-of-Words-based Benchmarks", "Benc...	{ "question": [ "Which tested technique was the worst performer?", "How many emotions do they look at?", "What are the baseline benchmarks?", "What is the size of this dataset?", "How many annotators were there?" ], "question_id": [ "a4e66e842be1438e5cd8d7cb2a2c589f494aee27", "cb78e280...	{ "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...
1702.06378	Multitask Learning with CTC and Segmental CRF for Speech Recognition	Segmental conditional random fields (SCRFs) and connectionist temporal classification (CTC) are two sequence labeling methods used for end-to-end training of speech recognition models. Both models define a transcription probability by marginalizing decisions about latent segmentation alternatives to derive a sequence p...	{ "section_name": [ "Introduction", "Segmental Conditional Random Fields", "Feature Function and Acoustic Embedding", "Loss Function", "Connectionist Temporal Classification ", "Joint Training Loss", "Experiments", "Baseline Results", "Multitask Learning Results", "Conclusion",...	{ "question": [ "Can SCRF be used to pretrain the model?" ], "question_id": [ "aecb485ea7d501094e50ad022ade4f0c93088d80" ], "nlp_background": [ "" ], "topic_background": [ "familiar" ], "paper_read": [ "no" ], "search_query": [ "pretrain" ], "question_writer": [ "50...	{ "caption": [ "Figure 1: A Segmental RNN with the context aware embedding. The acoustic segmental embedding vector is composed by the hidden states from the RNN encoder corresponding to the beginning and end time tags.", "Table 1: Phone error rates of baseline CTC and SRNN models.", "Figure 2: Convergenc...
1903.03467	Filling Gender&Number Gaps in Neural Machine Translation with Black-box Context Injection	When translating from a language that does not morphologically mark information such as gender and number into a language that does, translation systems must"guess"this missing information, often leading to incorrect translations in the given context. We propose a black-box approach for injecting the missing informatio...	{ "section_name": [ "Introduction", "Morphological Ambiguity in Translation", "Black-Box Knowledge Injection", "Experiments & Results", "Quantitative Results", "Qualitative Results", "Comparison to vanmassenhove-hardmeier-way:2018:EMNLP", "Other Languages", "Related Work", "Con...	{ "question": [ "What conclusions are drawn from the syntactic analysis?", "What type of syntactic analysis is performed?", "How is it demonstrated that the correct gender and number information is injected using this system?", "Which neural machine translation system is used?", "What are the comp...	{ "caption": [ "Table 1: BLEU results on the Silverman dataset", "Figure 1: Gender inflection statistics for verbs governed by first-person pronouns.", "Table 2: Comparison of our approach (using Google Translate) to Vanmassenhove et al. (2018) on their English-French gender corpus.", "Table 3: Exampl...
1807.00868	Exploring End-to-End Techniques for Low-Resource Speech Recognition	In this work we present simple grapheme-based system for low-resource speech recognition using Babel data for Turkish spontaneous speech (80 hours). We have investigated different neural network architectures performance, including fully-convolutional, recurrent and ResNet with GRU. Different features and normalization...	{ "section_name": [ "Introduction", "Related work", "Basic setup", "Experiments with architecture", "Loss modification: segmenting during training", "Using different features", "Varying model size and number of layers", "Training the best model", "Conclusions and future work", ...	{ "question": [ "What normalization techniques are mentioned?", "What features do they experiment with?", "Which architecture is their best model?", "What kind of spontaneous speech is used?" ], "question_id": [ "d20d6c8ecd7cb0126479305d27deb0c8b642b09f", "11e6b79f1f48ddc6c580c4d0a3cb9bcb4...	{ "caption": [ "Fig. 1: Architectures", "Table 1: Baseline models trained with CTC-loss", "Table 2: Models trained with CTC and proposed CTC modification", "Table 3: 6-layers bLSTM trained using different features and normalization", "Table 4: Comparison of bLSTM models with different number of hi...
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...	{ "section_name": [ "Introduction", "Related Work", "Model", "Model ::: NABERT+", "Model ::: NABERT+ ::: Heads Shared with NABERT+", "Model ::: Multi-Span Head", "Model ::: Objective and Training", "Model ::: Objective and Training ::: Multi-Span Head Training Objective", "Model ::...	{ "question": [ "What approach did previous models use for multi-span questions?", "How they use sequence tagging to answer 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 ent...	{ "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...
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...	{ "section_name": [ "Introduction", "Lightly Supervised Learning", "Expectation Regularization (XR)", "Aspect-based Sentiment Classification", "Transfer-training between related tasks with XR", "Stochastic Batched Training for Deep XR", "Application to Aspect-based Sentiment", "Relatin...	{ "question": [ "How much more data does the model trained using XR loss have access to, compared to the fully supervised model?", "Does the system trained only using XR loss outperform the fully supervised neural system?", "How accurate is the aspect based sentiment classifier trained only using the XR l...	{ "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...
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...	{ "section_name": [ "Introduction", "Tasks and Evaluation ::: Task 1: Cross-lingual transfer for morphological inflection", "Tasks and Evaluation ::: Task 1: Cross-lingual transfer for morphological inflection ::: Example", "Tasks and Evaluation ::: Task 1: Cross-lingual transfer for morphological inf...	{ "question": [ "What were the non-neural baselines used for the task?" ], "question_id": [ "b65b1c366c8bcf544f1be5710ae1efc6d2b1e2f1" ], "nlp_background": [ "two" ], "topic_background": [ "unfamiliar" ], "paper_read": [ "no" ], "search_query": [ "morphology" ], "questi...	{ "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: ...
1910.00912	Hierarchical Multi-Task Natural Language Understanding for Cross-domain Conversational AI: HERMIT NLU	We present a new neural architecture for wide-coverage Natural Language Understanding in Spoken Dialogue Systems. We develop a hierarchical multi-task architecture, which delivers a multi-layer representation of sentence meaning (i.e., Dialogue Acts and Frame-like structures). The architecture is a hierarchy of self-at...	{ "section_name": [ "Introduction", "Introduction ::: Cross-domain NLU", "Introduction ::: Multi-task NLU", "Introduction ::: Multi-dialogue act and -intent NLU", "Related Work", "Jointly parsing dialogue acts and frame-like structures", "Jointly parsing dialogue acts and frame-like struct...	{ "question": [ "Which publicly available NLU dataset is used?", "What metrics other than entity tagging are compared?" ], "question_id": [ "bd3ccb63fd8ce5575338d7332e96def7a3fabad6", "7c794fa0b2818d354ca666969107818a2ffdda0c" ], "nlp_background": [ "zero", "zero" ], "topic_backgro...	{ "caption": [ "Figure 1: Dialogue Acts (DAs), Frames (FRs – here semantic frames) and Arguments (ARs – here frame elements) IOB2 tagging for the sentence Where can I find Starbucks?", "Figure 2: HERMIT Network topology", "Table 2: Statistics of the ROMULUS dataset.", "Table 1: Statistics of the NLU-B...
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...	{ "section_name": [ "Introduction", "Related Works", "iMRC: Making MRC Interactive", "iMRC: Making MRC Interactive ::: Interactive MRC as a POMDP", "iMRC: Making MRC Interactive ::: Action Space", "iMRC: Making MRC Interactive ::: Query Types", "iMRC: Making MRC Interactive ::: Evaluation ...	{ "question": [ "Do they provide decision sequences as supervision while training models?", "What are the models evaluated on?", "How do they train models in this setup?", "What commands does their setup provide to models seeking information?" ], "question_id": [ "1ef5fc4473105f1c72b4d35cf93d3...	{ "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.", ...
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...	{ "section_name": [ "Introduction", "Related Work ::: Hate Speech Detection", "Related Work ::: Visual and Textual Data Fusion", "The MMHS150K dataset", "The MMHS150K dataset ::: Tweets Gathering", "The MMHS150K dataset ::: Textual Image Filtering", "The MMHS150K dataset ::: Annotation", ...	{ "question": [ "What models do they propose?", "Are all tweets in English?", "How large is the dataset?", "What is the results of multimodal compared to unimodal models?", "What is author's opinion on why current multimodal models cannot outperform models analyzing only text?", "What metrics ...	{ "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...
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...	{ "section_name": [ "Introduction", "Related Work", "Short Text Clustering", "Deep Neural Networks", "Methodology", "Deep Convolutional Neural Networks", "Unsupervised Dimensionality Reduction", "Learning", "K-means for Clustering", "Datasets", "Pre-trained Word Vectors", ...	{ "question": [ "What were the evaluation metrics used?", "What were their performance results?", "By how much did they outperform the other methods?", "Which popular clustering methods did they experiment with?", "What datasets did they use?" ], "question_id": [ "62a6382157d5f9c1dce6e6c24...	{ "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...
1912.00871	Solving Arithmetic Word Problems Automatically Using Transformer and Unambiguous Representations	Constructing accurate and automatic solvers of math word problems has proven to be quite challenging. Prior attempts using machine learning have been trained on corpora specific to math word problems to produce arithmetic expressions in infix notation before answer computation. We find that custom-built neural networks...	{ "section_name": [ "Introduction", "Related Work", "Approach", "Approach ::: Data", "Approach ::: Representation Conversion", "Approach ::: Pre-training", "Approach ::: Method: Training and Testing", "Approach ::: Method: Training and Testing ::: Objective Function", "Approach :::...	{ "question": [ "Does pre-training on general text corpus improve performance?", "What neural configurations are explored?", "Are the Transformers masked?", "How is this problem evaluated?", "What datasets do they use?" ], "question_id": [ "3f6610d1d68c62eddc2150c460bf1b48a064e5e6", "4...	{ "caption": [ "TABLE I BLEU-2 COMPARISON FOR EXPERIMENT 1.", "TABLE II SUMMARY OF BLEU SCORES FROM TABLE I.", "TABLE III TEST RESULTS FOR EXPERIMENT 2 (* DENOTES AVERAGES ON PRESENT VALUES ONLY).", "TABLE IV SUMMARY OF ACCURACIES FROM TABLE III." ], "file": [ "4-TableI-1.png", "4-TableII-...
1912.03234	What Do You Mean I'm Funny? Personalizing the Joke Skill of a Voice-Controlled Virtual Assistant	A considerable part of the success experienced by Voice-controlled virtual assistants (VVA) is due to the emotional and personalized experience they deliver, with humor being a key component in providing an engaging interaction. In this paper we describe methods used to improve the joke skill of a VVA through personali...	{ "section_name": [ "Introduction", "Method ::: Labelling Strategies", "Method ::: Features", "Method ::: NLP-based: LR-Model", "Method ::: Deep-Learning-based: DL-Models", "Validation", "Validation ::: Online Results: A/B Testing", "Validation ::: Offline Results", "Conclusions an...	{ "question": [ "What evaluation metrics were used?", "Where did the real production data come from?", "What feedback labels are used?" ], "question_id": [ "57e783f00f594e08e43a31939aedb235c9d5a102", "9646fa1abbe3102a0364f84e0a55d107d45c97f0", "29983f4bc8a5513a198755e474361deee93d4ab6" ]...	{ "caption": [ "Table 1: Example of labelling strategies: five-minute reuse (label 1) and 1-day return (label 2)", "Table 2: Examples of features within each category", "Figure 1: Architecture of the transformer-based model", "Table 3: Hyperparameter values tuned over, LR (top) and DL models (bottom)"...
1911.11750	A Measure of Similarity in Textual Data Using Spearman's Rank Correlation Coefficient	In the last decade, many diverse advances have occurred in the field of information extraction from data. Information extraction in its simplest form takes place in computing environments, where structured data can be extracted through a series of queries. The continuous expansion of quantities of data have therefore p...	{ "section_name": [ "Introduction", "Background", "Background ::: Document Representation", "Background ::: Measures of Similarity", "Related Work", "The Spearman's Rank Correlation Coefficient Similarity Measure", "The Spearman's Rank Correlation Coefficient Similarity Measure ::: Spearma...	{ "question": [ "What representations for textual documents do they use?", "Which dataset(s) do they use?", "How do they evaluate knowledge extraction performance?" ], "question_id": [ "6c0f97807cd83a94a4d26040286c6f89c4a0f8e0", "13ca4bf76565564c8ec3238c0cbfacb0b41e14d2", "70797f66d96aa163...	{ "caption": [ "TABLE II. A COMPARISON BETWEEN THE MEASURES CS, SRCC, PCC", "Fig. 1. A visual comparison of similarities produced by CS, SRCC and PCC", "Fig. 2. The association between documents" ], "file": [ "3-TableII-1.png", "4-Figure1-1.png", "4-Figure2-1.png" ] }
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...	{ "section_name": [ "Introduction", "Related Work ::: From non-contextual to contextual word embeddings", "Related Work ::: Non-contextual word embeddings for languages other than English", "Related Work ::: Contextualised models for languages other than English", "CamemBERT", "CamemBERT ::: A...	{ "question": [ "What is CamemBERT trained on?", "Which tasks does CamemBERT not improve on?", "What is the state of the art?", "How much better was results of CamemBERT than previous results on these tasks?", "Was CamemBERT compared against multilingual BERT on these tasks?", "How long was Ca...	{ "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...
2001.09899	Vocabulary-based Method for Quantifying Controversy in Social Media	Identifying controversial topics is not only interesting from a social point of view, it also enables the application of methods to avoid the information segregation, creating better discussion contexts and reaching agreements in the best cases. In this paper we develop a systematic method for controversy detection bas...	{ "section_name": [ "Introduction", "Related work", "Method", "Experiments", "Experiments ::: Topic definition", "Experiments ::: Datasets", "Experiments ::: Results", "Discussions", "Discussions ::: Limitations", "Discussions ::: Conclusions", "Details on the discussions" ...	{ "question": [ "What are the state of the art measures?", "What controversial topics are experimented with?", "What datasets did they use?", "What social media platform is observed?", "How many languages do they experiment with?" ], "question_id": [ "bf25a202ac713a34e09bf599b3601058d9cace...	{ "caption": [ "Fig. 1", "Fig. 2", "Table 1: Datasets statistics, the top group represent controversial topics, while the bottom one represent non-controversial ones" ], "file": [ "8-Figure1-1.png", "11-Figure2-1.png", "15-Table1-1.png" ] }
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...	{ "section_name": [ "Synonyms", "Glossary", "Definition", "Introduction", "Key Points", "Historical Background", "Variants of the Task at SemEval", "Features and Learning", "Sentiment Polarity Lexicons", "Key Applications", "Future Directions", "Cross-References", "...	{ "question": [ "What is the current SOTA for sentiment analysis on Twitter at the time of writing?", "What difficulties does sentiment analysis on Twitter have, compared to sentiment analysis in other domains?", "What are the metrics to evaluate sentiment analysis on Twitter?" ], "question_id": [ ...	{ "caption": [], "file": [] }
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 ...	{ "section_name": [ "Introduction", "Background", "Annotation", "Annotation ::: First Round: Collecting Ideas", "Annotation ::: Second Round: Collecting Data ::: Sentence Transformations", "Annotation ::: Second Round: Collecting Data ::: Seed Data", "Annotation ::: Second Round: Collectin...	{ "question": [ "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 t...	{ "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...
1909.12231	Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization	Linking facts across documents is a challenging task, as the language used to express the same information in a sentence can vary significantly, which complicates the task of multi-document summarization. Consequently, existing approaches heavily rely on hand-crafted features, which are domain-dependent and hard to cra...	{ "section_name": [ "Introduction", "Method", "Method ::: Sentence Semantic Relation Graph", "Method ::: Sentence Encoder", "Method ::: Graph Convolutional Network", "Method ::: Saliency Estimation", "Method ::: Training", "Method ::: Summary Generation Process", "Experiments ::: D...	{ "question": [ "How big is dataset domain-specific embedding are trained on?", "How big is unrelated corpus universal embedding is traned on?", "How better are state-of-the-art results than this model? " ], "question_id": [ "1a7d28c25bb7e7202230e1b70a885a46dac8a384", "6bc45d4f9086729451923906...	{ "caption": [ "Figure 1: Overview of SemSentSum. This illustration includes two documents in the collection, where the first one has three sentences and the second two. A sentence semantic relation graph is firstly built and each sentence node is processed by an encoder network at the same time. Thereafter, a si...
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 ...	{ "section_name": [ "Introduction", "Basic idea", "Data Preparation", "Preprocessing", "Semantic Rules (SR)", "Representation Levels", "Deep Learning Module", "Regularization", " Experimental setups", "Experimental results", "Analysis", "Conclusions" ], "paragraphs"...	{ "question": [ "What were their results on the three datasets?", "What was the baseline?", "Which datasets did they use?", "Are results reported only on English datasets?", "Which three Twitter sentiment classification datasets are used for experiments?", "What semantic rules are proposed?" ...	{ "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...
1811.01399	Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding	Knowledge graph embedding aims at modeling entities and relations with low-dimensional vectors. Most previous methods require that all entities should be seen during training, which is unpractical for real-world knowledge graphs with new entities emerging on a daily basis. Recent efforts on this issue suggest training ...	{ "section_name": [ "Introduction", "Transductive Embedding Models", "Inductive Embedding Models", "Notations", "Framework", "Logic Attention Network", "Incorporating Neighborhood Attention", "Training Objective", "Experimental Configurations", "Data Construction", "Experim...	{ "question": [ "Which knowledge graph completion tasks do they experiment with?", "Apart from using desired properties, do they evaluate their LAN approach in some other way?", "Do they evaluate existing methods in terms of desired properties?" ], "question_id": [ "69a7a6675c59a4c5fb70006523b9fe0...	{ "caption": [ "Figure 1: A motivating example of emerging KG entities. Dotted circles and arrows represent the existing KG while solid ones are brought by the emerging entity.", "Figure 2: The encoder-decoder framework.", "Table 1: Statistics of the processed FB15K dataset.", "Table 2: Evaluation acc...
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...	{ "section_name": [ "Introduction", "Related Work", "Proposed Model", "Experiments", "Conclusions", "Acknowledgments" ], "paragraphs": [ [ "It is well known that sentiment annotation or labeling is subjective BIBREF0. Annotators often have many disagreements. This is especially s...	{ "question": [ "How does the model differ from Generative Adversarial Networks?", "What is the dataset used to train the model?", "What is the performance of the model?", "Is the model evaluated against a CNN baseline?" ], "question_id": [ "045dbdbda5d96a672e5c69442e30dbf21917a1ee", "c20b...	{ "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...
1909.00088	Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchange	In this paper, we present a novel method for measurably adjusting the semantics of text while preserving its sentiment and fluency, a task we call semantic text exchange. This is useful for text data augmentation and the semantic correction of text generated by chatbots and virtual assistants. We introduce a pipeline c...	{ "section_name": [ "Introduction", "Related Work ::: Word and Sentence-level Embeddings", "Related Work ::: Text Infilling", "Related Work ::: Style and Sentiment Transfer", "Related Work ::: Review Generation", "SMERTI ::: Overview", "SMERTI ::: Entity Replacement Module (ERM)", "SME...	{ "question": [ "Does the model proposed beat the baseline models for all the values of the masking parameter tested?", "Has STES been previously used in the literature to evaluate similar tasks?", "What are the baseline models mentioned in the paper?" ], "question_id": [ "dccc3b182861fd19ccce5bd0...	{ "caption": [ "Table 1: Example masked outputs. S is the original input text; RE is the replacement entity; S′′1 corresponds to MRT = 0.2, base ST = 0.4; S ′′ 2 corresponds to MRT = 0.4, base ST = 0.3; S′′3 corresponds to MRT = 0.6, base ST = 0.2; S ′′ 4 corresponds to MRT = 0.8, base ST = 0.1", "Table 2: Tr...
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...	{ "section_name": [ "Introduction", "The CN-Celeb dataset ::: Data description", "The CN-Celeb dataset ::: Challenges with CN-Celeb", "The CN-Celeb dataset ::: Collection pipeline", "Experiments on speaker recognition", "Experiments on speaker recognition ::: Data", "Experiments on speaker...	{ "question": [ "What was the performance of both approaches on their dataset?", "What kind of settings do the utterances come from?", "What genres are covered?", "Do they experiment with cross-genre setups?", "Which of the two speech recognition models works better overall on CN-Celeb?", "By ...	{ "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...
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...	{ "section_name": [ "Introduction", "Fine-tuning on Pre-trained Language Model", "Text Data Augmentation", "Preliminary: Masked Language Model Task", "Conditional BERT", "Conditional BERT Contextual Augmentation", "Experiment", "Datasets", "Text classification", "Connection to ...	{ "question": [ "On what datasets is the new model evaluated on?", "How do the authors measure performance?", "Does the new objective perform better than the original objective bert is trained on?", "Are other pretrained language models also evaluated for contextual augmentation? ", "Do the author...	{ "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...

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Retrieves validation examples with long-form answers, helping identify complex questions that require detailed responses for analysis.