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 8501–8525 of 10817 papers

TitleStatusHype
RAVEN: A Dataset for Relational and Analogical Visual rEasoNing—0
KBQA: Learning Question Answering over QA Corpora and Knowledge Bases—0
Multi-Instance Learning for End-to-End Knowledge Base Question Answering—0
Bidirectional Attentive Memory Networks for Question Answering over Knowledge BasesCode1
Complex Program Induction for Querying Knowledge Bases in the Absence of Gold Programs—0
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over ParagraphsCode0
Answer Them All! Toward Universal Visual Question Answering ModelsCode0
Data-driven Approach for Quality Evaluation on Knowledge Sharing PlatformCode0
From Visual to Acoustic Question Answering—0
Multiresolution Graph Attention Networks for Relevance Matching—0
Learning to Generate Questions by Learning What not to Generate—0
Learning When Not to Answer: A Ternary Reward Structure for Reinforcement Learning based Question Answering—0
Dual Attention Networks for Visual Reference Resolution in Visual DialogCode0
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question AnsweringCode1
Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds—0
Lattice CNNs for Matching Based Chinese Question AnsweringCode1
Learning to Perform Role-Filler Binding with Schematic KnowledgeCode0
Evidence Sentence Extraction for Machine Reading ComprehensionCode0
Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering—0
Cycle-Consistency for Robust Visual Question Answering—0
Generating Natural Language Explanations for Visual Question Answering using Scene Graphs and Visual Attention—0
Contextual Word Representations: A Contextual IntroductionCode1
Language Models are Unsupervised Multitask LearnersCode1
Table2answer: Read the database and answer without SQLCode0
Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded—0
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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