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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9926–9950 of 10817 papers

TitleStatusHype
Selectively Answering Visual Questions—0
Self-alignment of Large Video Language Models with Refined Regularized Preference Optimization—0
Self-Attentive Constituency Parsing for UCCA-based Semantic Parsing—0
Self-Convinced Prompting: Few-Shot Question Answering with Repeated Introspection—0
Self-Crowdsourcing Training for Relation Extraction—0
Self-DC: When to Reason and When to Act? Self Divide-and-Conquer for Compositional Unknown Questions—0
Large Language Models are In-context Teachers for Knowledge Reasoning—0
SelfGraphVQA: A Self-Supervised Graph Neural Network for Scene-based Question Answering—0
Self-Improvement Programming for Temporal Knowledge Graph Question Answering—0
Self-Knowledge Guided Retrieval Augmentation for Large Language Models—0
Self-Refinement of Language Models from External Proxy Metrics Feedback—0
Self-Reflective Planning with Knowledge Graphs: Enhancing LLM Reasoning Reliability for Question Answering—0
Self-ReS: Self-Reflection in Large Vision-Language Models for Long Video Understanding—0
Self-Segregating and Coordinated-Segregating Transformer for Focused Deep Multi-Modular Network for Visual Question Answering—0
Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering—0
Self-supervised Dialogue Learning for Spoken Conversational Question Answering—0
Self-supervised Knowledge Triplet Learning for Zero-shot Question Answering—0
Self-Supervised learning with cross-modal transformers for emotion recognition—0
Self-supervised pre-training and contrastive representation learning for multiple-choice video QA—0
Self-Supervised Test-Time Learning for Reading Comprehension—0
WeaQA: Weak Supervision via Captions for Visual Question Answering—0
Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data—0
Self-Training for Jointly Learning to Ask and Answer Questions—0
Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction—0
Self-Training Large Language Models for Improved Visual Program Synthesis With Visual Reinforcement—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