SOTAVerified

Question Answering

Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include SQuAD, HotPotQA, bAbI, TriviaQA, WikiQA, and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.

( Image credit: SQuAD )

Papers

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Showing 7301–7350 of 10817 papers

TitleStatusHype
A Graph Representation of Semi-structured Data for Web Question Answering—0
Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with SearchCode1
A Wrong Answer or a Wrong Question? An Intricate Relationship between Question Reformulation and Answer Selection in Conversational Question AnsweringCode0
CoRel: Seed-Guided Topical Taxonomy Construction by Concept Learning and Relation TransferringCode1
F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question AnsweringCode0
Does my multimodal model learn cross-modal interactions? It's harder to tell than you might think!—0
Contrast and Classify: Training Robust VQA ModelsCode1
Cross-Modal BERT for Text-Audio Sentiment AnalysisCode1
End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering SystemsCode0
BioMegatron: Larger Biomedical Domain Language Model—0
Neural, Symbolic and Neural-Symbolic Reasoning on Knowledge Graphs—0
Counterfactual Variable Control for Robust and Interpretable Question AnsweringCode1
Towards Accurate and Reliable Energy Measurement of NLP ModelsCode0
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise TasksCode0
Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine TranslationCode0
Beyond Language: Learning Commonsense from Images for ReasoningCode0
Interpretable Neural Computation for Real-World Compositional Visual Question Answering—0
Open-Domain Question Answering Goes Conversational via Question RewritingCode1
Artificial Intelligence (AI) in Action: Addressing the COVID-19 Pandemic with Natural Language Processing (NLP)—0
AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training DataCode1
Relation Classification as Two-way Span-Prediction—0
Characterizing Datasets for Social Visual Question Answering, and the New TinySocial Dataset—0
Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name RecognitionCode1
On the importance of pre-training data volume for compact language models—0
Exposing Shallow Heuristics of Relation Extraction Models with Challenge DataCode1
SRLGRN: Semantic Role Labeling Graph Reasoning NetworkCode1
MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension MetricsCode1
Cross-Thought for Sentence Encoder Pre-trainingCode1
Unsupervised Evaluation for Question Answering with Transformers—0
Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-AnsweringCode1
DiPair: Fast and Accurate Distillation for Trillion-Scale Text Matching and Pair Modeling—0
Learning a Cost-Effective Annotation Policy for Question AnsweringCode0
Improving QA Generalization by Concurrent Modeling of Multiple BiasesCode0
Pathological Visual Question Answering—0
Neural Mask Generator: Learning to Generate Adaptive Word Maskings for Language Model AdaptationCode1
Multi-Fact Correction in Abstractive Text Summarization—0
Efficient One-Pass End-to-End Entity Linking for QuestionsCode2
Efficient Meta Lifelong-Learning with Limited Memory—0
PolicyQA: A Reading Comprehension Dataset for Privacy PoliciesCode1
BERT Knows Punta Cana is not just beautiful, it's gorgeous: Ranking Scalar Adjectives with Contextualised RepresentationsCode0
Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a StartCode1
Joint Semantics and Data-Driven Path Representation for Knowledge Graph Inference—0
UnQovering Stereotyping Biases via Underspecified QuestionsCode1
DaNetQA: a yes/no Question Answering Dataset for the Russian Language—0
Context Modeling with Evidence Filter for Multiple Choice Question Answering—0
Finding the Evidence: Localization-aware Answer Prediction for Text Visual Question Answering—0
InfoBERT: Improving Robustness of Language Models from An Information Theoretic PerspectiveCode1
Attention Guided Semantic Relationship Parsing for Visual Question Answering—0
When in Doubt, Ask: Generating Answerable and Unanswerable Questions, UnsupervisedCode0
Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous SpaceCode0
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All datasetsSQuAD2.0SQuAD1.1HotpotQAPIQABoolQCOPATriviaQASQuAD1.1 devNatural QuestionsOpenBookQATruthfulQAMultiRC

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1IE-Net (ensemble)EM90.94—Unverified
2FPNet (ensemble)EM90.87—Unverified
3IE-NetV2 (ensemble)EM90.86—Unverified
4SA-Net on Albert (ensemble)EM90.72—Unverified
5SA-Net-V2 (ensemble)EM90.68—Unverified
6FPNet (ensemble)EM90.6—Unverified
7Retro-Reader (ensemble)EM90.58—Unverified
8EntitySpanFocusV2 (ensemble)EM90.52—Unverified
9TransNets + SFVerifier + SFEnsembler (ensemble)EM90.49—Unverified
10EntitySpanFocus+AT (ensemble)EM90.45—Unverified