testzin/QasperTitle
Dataset Card for Dataset Name This dataset card aims to be a base template for new datasets. It has been generated using this raw template. Dataset Details Dataset Description Curated by: [More Information Needed] Funded by [optional]: [More Information Needed] Shared by [optional]: [More Information Needed] Language(s) (NLP): [More Information Needed] License: [More Information Needed] Dataset Sources [optional]… See the full description on the dataset page: https://huggingface.co/datasets/testzin/QasperTitle.
016
1[2 {3 "_id": 0,4 "text": "Minimally Supervised Learning of Affective Events Using Discourse Relations"5 },6 {7 "_id": 1,8 "text": "PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry"9 },10 {11 "_id": 2,12 "text": "Community Identity and User Engagement in a Multi-Community Landscape"13 },14 {15 "_id": 3,16 "text": "Question Answering based Clinical Text Structuring Using Pre-trained Language Model"17 },18 {19 "_id": 4,20 "text": "Progress and Tradeoffs in Neural Language Models"21 },22 {23 "_id": 5,24 "text": "Stay On-Topic: Generating Context-specific Fake Restaurant Reviews"25 },26 {27 "_id": 6,28 "text": "Saliency Maps Generation for Automatic Text Summarization"29 },30 {31 "_id": 7,32 "text": "Probabilistic Bias Mitigation in Word Embeddings"33 },34 {35 "_id": 8,36 "text": "Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yor\\`ub\\'a and Twi"37 },38 {39 "_id": 9,40 "text": "Is there Gender bias and stereotype in Portuguese Word Embeddings?"41 },42 {43 "_id": 10,44 "text": "Citation Data of Czech Apex Courts"45 },46 {47 "_id": 11,48 "text": "LAXARY: A Trustworthy Explainable Twitter Analysis Model for Post-Traumatic Stress Disorder Assessment"49 },50 {51 "_id": 12,52 "text": "Comprehensive Named Entity Recognition on CORD-19 with Distant or Weak Supervision"53 },54 {55 "_id": 13,56 "text": "UniSent: Universal Adaptable Sentiment Lexica for 1000+ Languages"57 },58 {59 "_id": 14,60 "text": "Word Sense Disambiguation for 158 Languages using Word Embeddings Only"61 },62 {63 "_id": 15,64 "text": "Spoken Language Identification using ConvNets"65 },66 {67 "_id": 16,68 "text": "Unsupervised Bilingual Lexicon Induction from Mono-lingual Multimodal Data"69 },70 {71 "_id": 17,72 "text": "AraNet: A Deep Learning Toolkit for Arabic Social Media"73 },74 {75 "_id": 18,76 "text": "Generative Adversarial Nets for Multiple Text Corpora"77 },78 {79 "_id": 19,80 "text": "Stacked DeBERT: All Attention in Incomplete Data for Text Classification"81 },82 {83 "_id": 20,84 "text": "Gunrock: A Social Bot for Complex and Engaging Long Conversations"85 },86 {87 "_id": 21,88 "text": "Towards Detection of Subjective Bias using Contextualized Word Embeddings"89 },90 {91 "_id": 22,92 "text": "Sentence-Level Fluency Evaluation: References Help, But Can Be Spared!"93 },94 {95 "_id": 23,96 "text": "An empirical study on the effectiveness of images in Multimodal Neural Machine Translation"97 },98 {99 "_id": 24,100 "text": "Unsupervised Machine Commenting with Neural Variational Topic Model"101 },102 {103 "_id": 25,104 "text": "Enriching BERT with Knowledge Graph Embeddings for Document Classification"105 },106 {107 "_id": 26,108 "text": "Diachronic Topics in New High German Poetry"109 },110 {111 "_id": 27,112 "text": "Important Attribute Identification in Knowledge Graph"113 },114 {115 "_id": 28,116 "text": "Diversity, Density, and Homogeneity: Quantitative Characteristic Metrics for Text Collections"117 },118 {119 "_id": 29,120 "text": "What Drives the International Development Agenda? An NLP Analysis of the United Nations General Debate 1970-2016"121 },122 {123 "_id": 30,124 "text": "QnAMaker: Data to Bot in 2 Minutes"125 },126 {127 "_id": 31,128 "text": "A simple discriminative training method for machine translation with large-scale features"129 },130 {131 "_id": 32,132 "text": "Improving Spoken Language Understanding By Exploiting ASR N-best Hypotheses"133 },134 {135 "_id": 33,136 "text": "DisSim: A Discourse-Aware Syntactic Text Simplification Frameworkfor English and German"137 },138 {139 "_id": 34,140 "text": "Learning Word Embeddings from the Portuguese Twitter Stream: A Study of some Practical Aspects"141 },142 {143 "_id": 35,144 "text": "Procedural Reasoning Networks for Understanding Multimodal Procedures"145 },146 {147 "_id": 36,148 "text": "Active Learning for Chinese Word Segmentation in Medical Text"149 },150 {151 "_id": 37,152 "text": "InScript: Narrative texts annotated with script information"153 },154 {155 "_id": 38,156 "text": "Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications"157 },158 {159 "_id": 39,160 "text": "Learning Supervised Topic Models for Classification and Regression from Crowds"161 },162 {163 "_id": 40,164 "text": "CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset"165 },166 {167 "_id": 41,168 "text": "BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance"169 },170 {171 "_id": 42,172 "text": "Joint Entity Linking with Deep Reinforcement Learning"173 },174 {175 "_id": 43,176 "text": "Classification Betters Regression in Query-based Multi-document Summarisation Techniques for Question Answering: Macquarie University at BioASQ7b"177 },178 {179 "_id": 44,180 "text": "Marrying Universal Dependencies and Universal Morphology"181 },182 {183 "_id": 45,184 "text": "Towards Multimodal Emotion Recognition in German Speech Events in Cars using Transfer Learning"185 },186 {187 "_id": 46,188 "text": "Revisiting Low-Resource Neural Machine Translation: A Case Study"189 },190 {191 "_id": 47,192 "text": "Facilitating on-line opinion dynamics by mining expressions of causation. The case of climate change debates on The Guardian"193 },194 {195 "_id": 48,196 "text": "\"Hinglish\"Language -- Modeling a Messy Code-Mixed Language"197 },198 {199 "_id": 49,200 "text": "How Language-Neutral is Multilingual BERT?"201 },202 {203 "_id": 50,204 "text": "CAiRE: An End-to-End Empathetic Chatbot"205 },206 {207 "_id": 51,208 "text": "Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?"209 },210 {211 "_id": 52,212 "text": "Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference"213 },214 {215 "_id": 53,216 "text": "Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering"217 },218 {219 "_id": 54,220 "text": "SUM-QE: a BERT-based Summary Quality Estimation Model"221 },222 {223 "_id": 55,224 "text": "Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction"225 },226 {227 "_id": 56,228 "text": "Machine Translation from Natural Language to Code using Long-Short Term Memory"229 },230 {231 "_id": 57,232 "text": "A Survey and Taxonomy of Adversarial Neural Networks for Text-to-Image Synthesis"233 },234 {235 "_id": 58,236 "text": "Gating Mechanisms for Combining Character and Word-level Word Representations: An Empirical Study"237 },238 {239 "_id": 59,240 "text": "Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction"241 },242 {243 "_id": 60,244 "text": "Learning to Rank Scientific Documents from the Crowd"245 },246 {247 "_id": 61,248 "text": "Exploiting Deep Learning for Persian Sentiment Analysis"249 },250 {251 "_id": 62,252 "text": "Talk the Walk: Navigating New York City through Grounded Dialogue"253 },254 {255 "_id": 63,256 "text": "Real-time Claim Detection from News Articles and Retrieval of Semantically-Similar Factchecks"257 },258 {259 "_id": 64,260 "text": "RC-QED: Evaluating Natural Language Derivations in Multi-Hop Reading Comprehension"261 },262 {263 "_id": 65,264 "text": "Event Outcome Prediction using Sentiment Analysis and Crowd Wisdom in Microblog Feeds"265 },266 {267 "_id": 66,268 "text": "Learning High-order Structural and Attribute information by Knowledge Graph Attention Networks for Enhancing Knowledge Graph Embedding"269 },270 {271 "_id": 67,272 "text": "A Computational Approach to Automatic Prediction of Drunk Texting"273 },274 {275 "_id": 68,276 "text": "Answering Complex Questions Using Open Information Extraction"277 },278 {279 "_id": 69,280 "text": "An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages"281 },282 {283 "_id": 70,284 "text": "Quasar: Datasets for Question Answering by Search and Reading"285 },286 {287 "_id": 71,288 "text": "Error Analysis for Vietnamese Named Entity Recognition on Deep Neural Network Models"289 },290 {291 "_id": 72,292 "text": "Recurrent Neural Network Encoder with Attention for Community Question Answering"293 },294 {295 "_id": 73,296 "text": "Attentional Encoder Network for Targeted Sentiment Classification"297 },298 {299 "_id": 74,300 "text": "ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples"301 },302 {303 "_id": 75,304 "text": "DENS: A Dataset for Multi-class Emotion Analysis"305 },306 {307 "_id": 76,308 "text": "Multitask Learning with CTC and Segmental CRF for Speech Recognition"309 },310 {311 "_id": 77,312 "text": "Filling Gender&Number Gaps in Neural Machine Translation with Black-box Context Injection"313 },314 {315 "_id": 78,316 "text": "Exploring End-to-End Techniques for Low-Resource Speech Recognition"317 },318 {319 "_id": 79,320 "text": "Tag-based Multi-Span Extraction in Reading Comprehension"321 },322 {323 "_id": 80,324 "text": "Transfer Learning Between Related Tasks Using Expected Label Proportions"325 },326 {327 "_id": 81,328 "text": "The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection"329 },330 {331 "_id": 82,332 "text": "Hierarchical Multi-Task Natural Language Understanding for Cross-domain Conversational AI: HERMIT NLU"333 },334 {335 "_id": 83,336 "text": "Interactive Machine Comprehension with Information Seeking Agents"337 },338 {339 "_id": 84,340 "text": "Exploring Hate Speech Detection in Multimodal Publications"341 },342 {343 "_id": 85,344 "text": "Self-Taught Convolutional Neural Networks for Short Text Clustering"345 },346 {347 "_id": 86,348 "text": "Solving Arithmetic Word Problems Automatically Using Transformer and Unambiguous Representations"349 },350 {351 "_id": 87,352 "text": "What Do You Mean I'm Funny? Personalizing the Joke Skill of a Voice-Controlled Virtual Assistant"353 },354 {355 "_id": 88,356 "text": "A Measure of Similarity in Textual Data Using Spearman's Rank Correlation Coefficient"357 },358 {359 "_id": 89,360 "text": "CamemBERT: a Tasty French Language Model"361 },362 {363 "_id": 90,364 "text": "Vocabulary-based Method for Quantifying Controversy in Social Media"365 },366 {367 "_id": 91,368 "text": "Semantic Sentiment Analysis of Twitter Data"369 },370 {371 "_id": 92,372 "text": "COSTRA 1.0: A Dataset of Complex Sentence Transformations"373 },374 {375 "_id": 93,376 "text": "Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization"377 },378 {379 "_id": 94,380 "text": "A Deep Neural Architecture for Sentence-level Sentiment Classification in Twitter Social Networking"381 },382 {383 "_id": 95,384 "text": "Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding"385 },386 {387 "_id": 96,388 "text": "Learning with Noisy Labels for Sentence-level Sentiment Classification"389 },390 {391 "_id": 97,392 "text": "Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchange"393 },394 {395 "_id": 98,396 "text": "CN-CELEB: a challenging Chinese speaker recognition dataset"397 },398 {399 "_id": 99,400 "text": "Conditional BERT Contextual Augmentation"401 },402 {403 "_id": 100,404 "text": "Recent Advances in Neural Question Generation"405 },406 {407 "_id": 101,408 "text": "Open Named Entity Modeling from Embedding Distribution"409 },410 {411 "_id": 102,412 "text": "Efficient Twitter Sentiment Classification using Subjective Distant Supervision"413 },414 {415 "_id": 103,416 "text": "Dynamic Memory Networks for Visual and Textual Question Answering"417 },418 {419 "_id": 104,420 "text": "Low-Level Linguistic Controls for Style Transfer and Content Preservation"421 },422 {423 "_id": 105,424 "text": "Fusing Visual, Textual and Connectivity Clues for Studying Mental Health"425 },426 {427 "_id": 106,428 "text": "Incorporating Sememes into Chinese Definition Modeling"429 },430 {431 "_id": 107,432 "text": "RobBERT: a Dutch RoBERTa-based Language Model"433 },434 {435 "_id": 108,436 "text": "Natural Language State Representation for Reinforcement Learning"437 },438 {439 "_id": 109,440 "text": "Query-oriented text summarization based on hypergraph transversals"441 },442 {443 "_id": 110,444 "text": "Text-based inference of moral sentiment change"445 },446 {447 "_id": 111,448 "text": "Bringing Stories Alive: Generating Interactive Fiction Worlds"449 },450 {451 "_id": 112,452 "text": "Generating Classical Chinese Poems from Vernacular Chinese"453 },454 {455 "_id": 113,456 "text": "Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever"457 },458 {459 "_id": 114,460 "text": "From FiLM to Video: Multi-turn Question Answering with Multi-modal Context"461 },462 {463 "_id": 115,464 "text": "Civique: Using Social Media to Detect Urban Emergencies"465 },466 {467 "_id": 116,468 "text": "Can neural networks understand monotonicity reasoning?"469 },470 {471 "_id": 117,472 "text": "Enriching Existing Conversational Emotion Datasets with Dialogue Acts using Neural Annotators."473 },474 {475 "_id": 118,476 "text": "Synchronising audio and ultrasound by learning cross-modal embeddings"477 },478 {479 "_id": 119,480 "text": "Basic tasks of sentiment analysis"481 },482 {483 "_id": 120,484 "text": "Generalisation in Named Entity Recognition: A Quantitative Analysis"485 },486 {487 "_id": 121,488 "text": "wav2vec: Unsupervised Pre-training for Speech Recognition"489 },490 {491 "_id": 122,492 "text": "Cross-lingual, Character-Level Neural Morphological Tagging"493 },494 {495 "_id": 123,496 "text": "Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping"497 },498 {499 "_id": 124,500 "text": "Visual Natural Language Query Auto-Completion for Estimating Instance Probabilities"501 },502 {503 "_id": 125,504 "text": "Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation"505 },506 {507 "_id": 126,508 "text": "Analysis of Risk Factor Domains in Psychosis Patient Health Records"509 },510 {511 "_id": 127,512 "text": "Morphological Word Segmentation on Agglutinative Languages for Neural Machine Translation"513 },514 {515 "_id": 128,516 "text": "Deja-vu: Double Feature Presentation and Iterated Loss in Deep Transformer Networks"517 },518 {519 "_id": 129,520 "text": "Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge"521 },522 {523 "_id": 130,524 "text": "How Does Language Influence Documentation Workflow? Unsupervised Word Discovery Using Translations in Multiple Languages"525 },526 {527 "_id": 131,528 "text": "Dense Information Flow for Neural Machine Translation"529 },530 {531 "_id": 132,532 "text": "Frozen Binomials on the Web: Word Ordering and Language Conventions in Online Text"533 },534 {535 "_id": 133,536 "text": "Casting Light on Invisible Cities: Computationally Engaging with Literary Criticism"537 },538 {539 "_id": 134,540 "text": "Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation"541 },542 {543 "_id": 135,544 "text": "Siamese recurrent networks learn first-order logic reasoning and exhibit zero-shot compositional generalization"545 },546 {547 "_id": 136,548 "text": "A Simple Method for Commonsense Reasoning"549 },550 {551 "_id": 137,552 "text": "Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology"553 },554 {555 "_id": 138,556 "text": "Representation of Constituents in Neural Language Models: Coordination Phrase as a Case Study"557 },558 {559 "_id": 139,560 "text": "Investigating Linguistic Pattern Ordering in Hierarchical Natural Language Generation"561 },562 {563 "_id": 140,564 "text": "Deep Enhanced Representation for Implicit Discourse Relation Recognition"565 },566 {567 "_id": 141,568 "text": "Detecting Potential Topics In News Using BERT, CRF and Wikipedia"569 },570 {571 "_id": 142,572 "text": "Gender Bias in Coreference Resolution"573 },574 {575 "_id": 143,576 "text": "How Far are We from Effective Context Modeling ? An Exploratory Study on Semantic Parsing in Context"577 },578 {579 "_id": 144,580 "text": "A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis"581 },582 {583 "_id": 145,584 "text": "HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization"585 },586 {587 "_id": 146,588 "text": "Shallow Discourse Annotation for Chinese TED Talks"589 },590 {591 "_id": 147,592 "text": "The Role of Pragmatic and Discourse Context in Determining Argument Impact"593 },594 {595 "_id": 148,596 "text": "Textual Data for Time Series Forecasting"597 },598 {599 "_id": 149,600 "text": "Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis"601 },602 {603 "_id": 150,604 "text": "Mapping (Dis-)Information Flow about the MH17 Plane Crash"605 },606 {607 "_id": 151,608 "text": "Conversational Intent Understanding for Passengers in Autonomous Vehicles"609 },610 {611 "_id": 152,612 "text": "Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models"613 },614 {615 "_id": 153,616 "text": "Predictive Embeddings for Hate Speech Detection on Twitter"617 },618 {619 "_id": 154,620 "text": "Incorporating Discrete Translation Lexicons into Neural Machine Translation"621 },622 {623 "_id": 155,624 "text": "Crowdsourcing a High-Quality Gold Standard for QA-SRL"625 },626 {627 "_id": 156,628 "text": "Zero-Shot Cross-lingual Classification Using Multilingual Neural Machine Translation"629 },630 {631 "_id": 157,632 "text": "An Analysis of Visual Question Answering Algorithms"633 },634 {635 "_id": 158,636 "text": "Imitation Learning of Robot Policies by Combining Language, Vision and Demonstration"637 },638 {639 "_id": 159,640 "text": "Overcoming the Rare Word Problem for Low-Resource Language Pairs in Neural Machine Translation"641 },642 {643 "_id": 160,644 "text": "A framework for anomaly detection using language modeling, and its applications to finance"645 },646 {647 "_id": 161,648 "text": "What Gets Echoed? Understanding the\"Pointers\"in Explanations of Persuasive Arguments"649 },650 {651 "_id": 162,652 "text": "Automating Reading Comprehension by Generating Question and Answer Pairs"653 },654 {655 "_id": 163,656 "text": "Automatic Reminiscence Therapy for Dementia."657 },658 {659 "_id": 164,660 "text": "Lattice CNNs for Matching Based Chinese Question Answering"661 },662 {663 "_id": 165,664 "text": "On the coexistence of competing languages"665 },666 {667 "_id": 166,668 "text": "Speaker-independent classification of phonetic segments from raw ultrasound in child speech"669 },670 {671 "_id": 167,672 "text": "A Multi-Turn Emotionally Engaging Dialog Model"673 },674 {675 "_id": 168,676 "text": "Information Extraction in Illicit Domains"677 },678 {679 "_id": 169,680 "text": "Semantic Role Labeling for Learner Chinese: the Importance of Syntactic Parsing and L2-L1 Parallel Data"681 },682 {683 "_id": 170,684 "text": "Interpretable Visual Question Answering by Visual Grounding from Attention Supervision Mining"685 },686 {687 "_id": 171,688 "text": "Testing the Generalization Power of Neural Network Models Across NLI Benchmarks"689 },690 {691 "_id": 172,692 "text": "VAIS Hate Speech Detection System: A Deep Learning based Approach for System Combination"693 },694 {695 "_id": 173,696 "text": "Yoga-Veganism: Correlation Mining of Twitter Health Data"697 },698 {699 "_id": 174,700 "text": "Joint Learning of Sentence Embeddings for Relevance and Entailment"701 },702 {703 "_id": 175,704 "text": "MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning"705 },706 {707 "_id": 176,708 "text": "Aspect Term Extraction with History Attention and Selective Transformation"709 },710 {711 "_id": 177,712 "text": "Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset"713 },714 {715 "_id": 178,716 "text": "Using word embeddings to improve the discriminability of co-occurrence text networks"717 },718 {719 "_id": 179,720 "text": "e-SNLI-VE-2.0: Corrected Visual-Textual Entailment with Natural Language Explanations"721 },722 {723 "_id": 180,724 "text": "An Analysis of Word2Vec for the Italian Language"725 },726 {727 "_id": 181,728 "text": "Improving Character-based Decoding Using Target-Side Morphological Information for Neural Machine Translation"729 },730 {731 "_id": 182,732 "text": "Learning Twitter User Sentiments on Climate Change with Limited Labeled Data"733 },734 {735 "_id": 183,736 "text": "A multimodal deep learning approach for named entity recognition from social media"737 },738 {739 "_id": 184,740 "text": "Uncover Sexual Harassment Patterns from Personal Stories by Joint Key Element Extraction and Categorization"741 },742 {743 "_id": 185,744 "text": "Domain Adaptation of Recurrent Neural Networks for Natural Language Understanding"745 },746 {747 "_id": 186,748 "text": "Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models"749 },750 {751 "_id": 187,752 "text": "What we write about when we write about causality: Features of causal statements across large-scale social discourse"753 },754 {755 "_id": 188,756 "text": "Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering"757 },758 {759 "_id": 189,760 "text": "Unsupervised Ranking Model for Entity Coreference Resolution"761 },762 {763 "_id": 190,764 "text": "The First Evaluation of Chinese Human-Computer Dialogue Technology"765 },766 {767 "_id": 191,768 "text": "Multi-style Generative Reading Comprehension"769 },770 {771 "_id": 192,772 "text": "A Cascade Sequence-to-Sequence Model for Chinese Mandarin Lip Reading"773 },774 {775 "_id": 193,776 "text": "Dissecting Content and Context in Argumentative Relation Analysis"777 },778 {779 "_id": 194,780 "text": "Gibberish Semantics: How Good is Russian Twitter in Word Semantic Similarity Task?"781 },782 {783 "_id": 195,784 "text": "A New Corpus for Low-Resourced Sindhi Language with Word Embeddings"785 },786 {787 "_id": 196,788 "text": "The Wiki Music dataset: A tool for computational analysis of popular music"789 },790 {791 "_id": 197,792 "text": "An Annotated Corpus of Emerging Anglicisms in Spanish Newspaper Headlines"793 },794 {795 "_id": 198,796 "text": "Style Transfer for Texts: to Err is Human, but Error Margins Matter"797 },798 {799 "_id": 199,800 "text": "Efficient Attention using a Fixed-Size Memory Representation"801 },802 {803 "_id": 200,804 "text": "Duality Regularization for Unsupervised Bilingual Lexicon Induction"805 },806 {807 "_id": 201,808 "text": "Team Papelo: Transformer Networks at FEVER"809 },810 {811 "_id": 202,812 "text": "Automatic Differentiation in ROOT"813 },814 {815 "_id": 203,816 "text": "Controlling the Output Length of Neural Machine Translation"817 },818 {819 "_id": 204,820 "text": "Spectral decomposition method of dialog state tracking via collective matrix factorization"821 },822 {823 "_id": 205,824 "text": "Torch-Struct: Deep Structured Prediction Library"825 },826 {827 "_id": 206,828 "text": "Embedding Projection for Targeted Cross-Lingual Sentiment: Model Comparisons and a Real-World Study"829 },830 {831 "_id": 207,832 "text": "Improving Open Information Extraction via Iterative Rank-Aware Learning"833 },834 {835 "_id": 208,836 "text": "Character-Centric Storytelling"837 },838 {839 "_id": 209,840 "text": "Combining Adversarial Training and Disentangled Speech Representation for Robust Zero-Resource Subword Modeling"841 },842 {843 "_id": 210,844 "text": "Automatic Target Recovery for Hindi-English Code Mixed Puns"845 },846 {847 "_id": 211,848 "text": "CRWIZ: A Framework for Crowdsourcing Real-Time Wizard-of-Oz Dialogues"849 },850 {851 "_id": 212,852 "text": "Detecting Online Hate Speech Using Context Aware Models"853 },854 {855 "_id": 213,856 "text": "Plan, Write, and Revise: an Interactive System for Open-Domain Story Generation"857 },858 {859 "_id": 214,860 "text": "Collecting Indicators of Compromise from Unstructured Text of Cybersecurity Articles using Neural-Based Sequence Labelling"861 },862 {863 "_id": 215,864 "text": "Boosting Question Answering by Deep Entity Recognition"865 },866 {867 "_id": 216,868 "text": "Polysemy Detection in Distributed Representation of Word Sense"869 },870 {871 "_id": 217,872 "text": "Neural Domain Adaptation for Biomedical Question Answering"873 },874 {875 "_id": 218,876 "text": "Classifying topics in speech when all you have is crummy translations."877 },878 {879 "_id": 219,880 "text": "Word, Subword or Character? An Empirical Study of Granularity in Chinese-English NMT"881 },882 {883 "_id": 220,884 "text": "A Comparative Evaluation of Visual and Natural Language Question Answering Over Linked Data"885 },886 {887 "_id": 221,888 "text": "Binary and Multitask Classification Model for Dutch Anaphora Resolution: Die/Dat Prediction"889 },890 {891 "_id": 222,892 "text": "'Warriors of the Word' -- Deciphering Lyrical Topics in Music and Their Connection to Audio Feature Dimensions Based on a Corpus of Over 100,000 Metal Songs"893 },894 {895 "_id": 223,896 "text": "Abstractive Dialog Summarization with Semantic Scaffolds"897 },898 {899 "_id": 224,900 "text": "CommonGen: A Constrained Text Generation Dataset Towards Generative Commonsense Reasoning"901 },902 {903 "_id": 225,904 "text": "MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension"905 },906 {907 "_id": 226,908 "text": "Data Mining in Clinical Trial Text: Transformers for Classification and Question Answering Tasks"909 },910 {911 "_id": 227,912 "text": "RelNet: End-to-End Modeling of Entities & Relations"913 },914 {915 "_id": 228,916 "text": "Modeling Event Background for If-Then Commonsense Reasoning Using Context-aware Variational Autoencoder"917 },918 {919 "_id": 229,920 "text": "Comparing Human and Machine Errors in Conversational Speech Transcription"921 },922 {923 "_id": 230,924 "text": "An Empirical Comparison of Simple Domain Adaptation Methods for Neural Machine Translation"925 },926 {927 "_id": 231,928 "text": "Combining Search with Structured Data to Create a More Engaging User Experience in Open Domain Dialogue"929 },930 {931 "_id": 232,932 "text": "Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources"933 },934 {935 "_id": 233,936 "text": "Discriminative Acoustic Word Embeddings: Recurrent Neural Network-Based Approaches"937 },938 {939 "_id": 234,940 "text": "Semantic Holism and Word Representations in Artificial Neural Networks"941 },942 {943 "_id": 235,944 "text": "Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing"945 },946 {947 "_id": 236,948 "text": "Characterizing Diabetes, Diet, Exercise, and Obesity Comments on Twitter"949 },950 {951 "_id": 237,952 "text": "Rethinking travel behavior modeling representations through embeddings"953 },954 {955 "_id": 238,956 "text": "Sex Trafficking Detection with Ordinal Regression Neural Networks"957 },958 {959 "_id": 239,960 "text": "Modeling Trolling in Social Media Conversations"961 },962 {963 "_id": 240,964 "text": "Measuring Compositional Generalization: A Comprehensive Method on Realistic Data"965 },966 {967 "_id": 241,968 "text": "Prototypical Metric Transfer Learning for Continuous Speech Keyword Spotting With Limited Training Data"969 },970 {971 "_id": 242,972 "text": "FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow"973 },974 {975 "_id": 243,976 "text": "On Leveraging the Visual Modality for Neural Machine Translation"977 },978 {979 "_id": 244,980 "text": "Learning to Recover Reasoning Chains for Multi-Hop Question Answering via Cooperative Games"981 },982 {983 "_id": 245,984 "text": "A Set of Recommendations for Assessing Human-Machine Parity in Language Translation"985 },986 {987 "_id": 246,988 "text": "StructSum: Incorporating Latent and Explicit Sentence Dependencies for Single Document Summarization"989 },990 {991 "_id": 247,992 "text": "Effective Use of Transformer Networks for Entity Tracking"993 },994 {995 "_id": 248,996 "text": "Recognizing Musical Entities in User-generated Content"997 },998 {999 "_id": 249,1000 "text": "MIT-QCRI Arabic Dialect Identification System for the 2017 Multi-Genre Broadcast Challenge"1001 },1002 {1003 "_id": 250,1004 "text": "Bias in Semantic and Discourse Interpretation"1005 },1006 {1007 "_id": 251,1008 "text": "A Swiss German Dictionary: Variation in Speech and Writing"1009 },1010 {1011 "_id": 252,1012 "text": "QuaRel: A Dataset and Models for Answering Questions about Qualitative Relationships"1013 },1014 {1015 "_id": 253,1016 "text": "Natural Language Interactions in Autonomous Vehicles: Intent Detection and Slot Filling from Passenger Utterances"1017 },1018 {1019 "_id": 254,1020 "text": "Sentiment Analysis of Citations Using Word2vec"1021 },1022 {1023 "_id": 255,1024 "text": "Modeling Coherence for Neural Machine Translation with Dynamic and Topic Caches"1025 },1026 {1027 "_id": 256,1028 "text": "Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic Manuscripts"1029 },1030 {1031 "_id": 257,1032 "text": "Semantic Document Distance Measures and Unsupervised Document Revision Detection"1033 },1034 {1035 "_id": 258,1036 "text": "Multi-Task Bidirectional Transformer Representations for Irony Detection"1037 },1038 {1039 "_id": 259,1040 "text": "Evaluating Rewards for Question Generation Models"1041 },1042 {1043 "_id": 260,1044 "text": "Gated Convolutional Neural Networks for Domain Adaptation"1045 },1046 {1047 "_id": 261,1048 "text": "Deep contextualized word representations for detecting sarcasm and irony"1049 },1050 {1051 "_id": 262,1052 "text": "SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering"1053 },1054 {1055 "_id": 263,1056 "text": "Phonetic Feedback for Speech Enhancement With and Without Parallel Speech Data"1057 },1058 {1059 "_id": 264,1060 "text": "Assessing the Efficacy of Clinical Sentiment Analysis and Topic Extraction in Psychiatric Readmission Risk Prediction"1061 },1062 {1063 "_id": 265,1064 "text": "Attending to Characters in Neural Sequence Labeling Models"1065 },1066 {1067 "_id": 266,1068 "text": "Analysing Coreference in Transformer Outputs"1069 },1070 {1071 "_id": 267,1072 "text": "Exploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space"1073 },1074 {1075 "_id": 268,1076 "text": "NumNet: Machine Reading Comprehension with Numerical Reasoning"1077 },1078 {1079 "_id": 269,1080 "text": "Contextualized Word Embeddings Enhanced Event Temporal Relation Extraction for Story Understanding"1081 },1082 {1083 "_id": 270,1084 "text": "Learning Representations of Emotional Speech with Deep Convolutional Generative Adversarial Networks"1085 },1086 {1087 "_id": 271,1088 "text": "Subword-augmented Embedding for Cloze Reading Comprehension"1089 },1090 {1091 "_id": 272,1092 "text": "Kurdish (Sorani) Speech to Text: Presenting an Experimental Dataset"1093 },1094 {1095 "_id": 273,1096 "text": "Cohesion and Coalition Formation in the European Parliament: Roll-Call Votes and Twitter Activities"1097 },1098 {1099 "_id": 274,1100 "text": "Neural Language Modeling by Jointly Learning Syntax and Lexicon"1101 },1102 {1103 "_id": 275,1104 "text": "Extracting information from free text through unsupervised graph-based clustering: an application to patient incident records"1105 },1106 {1107 "_id": 276,1108 "text": "Question Answering from Unstructured Text by Retrieval and Comprehension"1109 },1110 {1111 "_id": 277,1112 "text": "UDS--DFKI Submission to the WMT2019 Similar Language Translation Shared Task"1113 },1114 {1115 "_id": 278,1116 "text": "Exploration on Generating Traditional Chinese Medicine Prescriptions from Symptoms with an End-to-End Approach"1117 },1118 {1119 "_id": 279,1120 "text": "Grounding the Semantics of Part-of-Day Nouns Worldwide using Twitter"1121 },1122 {1123 "_id": 280,1124 "text": "QA4IE: A Question Answering based Framework for Information Extraction"1125 },1126 {1127 "_id": 281,1128 "text": "A Resource for Studying Chatino Verbal Morphology"1129 },1130 {1131 "_id": 282,1132 "text": "N-GrAM: New Groningen Author-profiling Model"1133 },1134 {1135 "_id": 283,1136 "text": "Multi-modal Sentiment Analysis using Super Characters Method on Low-power CNN Accelerator Device"1137 },1138 {1139 "_id": 284,1140 "text": "Nefnir: A high accuracy lemmatizer for Icelandic"1141 },1142 {1143 "_id": 285,1144 "text": "Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation"1145 },1146 {1147 "_id": 286,1148 "text": "Efficient Vector Representation for Documents through Corruption"1149 },1150 {1151 "_id": 287,1152 "text": "Microsoft Research Asia's Systems for WMT19"1153 },1154 {1155 "_id": 288,1156 "text": "Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer"1157 },1158 {1159 "_id": 289,1160 "text": "Evaluation of basic modules for isolated spelling error correction in Polish texts"1161 },1162 {1163 "_id": 290,1164 "text": "Few-shot Natural Language Generation for Task-Oriented Dialog"1165 },1166 {1167 "_id": 291,1168 "text": "Using Whole Document Context in Neural Machine Translation"1169 },1170 {1171 "_id": 292,1172 "text": "Finding Street Gang Members on Twitter"1173 },1174 {1175 "_id": 293,1176 "text": "A Unified System for Aggression Identification in English Code-Mixed and Uni-Lingual Texts"1177 },1178 {1179 "_id": 294,1180 "text": "An Emotional Analysis of False Information in Social Media and News Articles"1181 },1182 {1183 "_id": 295,1184 "text": "STransE: a novel embedding model of entities and relationships in knowledge bases"1185 },1186 {1187 "_id": 296,1188 "text": "Doc2Vec on the PubMed corpus: study of a new approach to generate related articles"1189 },1190 {1191 "_id": 297,1192 "text": "Multi-Perspective Fusion Network for Commonsense Reading Comprehension"1193 },1194 {1195 "_id": 298,1196 "text": "Identifying Clickbait: A Multi-Strategy Approach Using Neural Networks"1197 },1198 {1199 "_id": 299,1200 "text": "Consistency of a Recurrent Language Model With Respect to Incomplete Decoding"