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 5976–6000 of 10817 papers

TitleStatusHype
Compressing And Debiasing Vision-Language Pre-Trained Models for Visual Question AnsweringCode0
Generalization Differences between End-to-End and Neuro-Symbolic Vision-Language Reasoning Systems—0
Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence—0
Bridging the Training-Inference Gap for Dense Phrase Retrieval—0
Event-Centric Question Answering via Contrastive Learning and Invertible Event TransformationCode0
TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge BasesCode0
Learning by Hallucinating: Vision-Language Pre-training with Weak Supervision—0
Multi-Type Conversational Question-Answer Generation with Closed-ended and Unanswerable Questions—0
Speeding Up Question Answering Task of Language Models via Inverted Index—0
TAPE: Assessing Few-shot Russian Language UnderstandingCode0
RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data—0
Knowledge Transfer from Answer Ranking to Answer Generation—0
Varifocal Question Generation for Fact-checkingCode0
Exploring The Landscape of Distributional Robustness for Question Answering Models—0
P^3LM: Probabilistically Permuted Prophet Language Modeling for Generative Pre-Training—0
Multi-view Semantic Matching of Question retrieval using Fine-grained Semantic Representations—0
Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning—0
LittleBird: Efficient Faster & Longer Transformer for Question Answering—0
LiteVL: Efficient Video-Language Learning with Enhanced Spatial-Temporal Modeling—0
WikiWhy: Answering and Explaining Cause-and-Effect Questions—0
Pre-training Language Models with Deterministic Factual Knowledge—0
Large Language Models Can Self-Improve—0
Transcending Scaling Laws with 0.1% Extra Compute—0
Composing Ensembles of Pre-trained Models via Iterative Consensus—0
Two-Turn Debate Doesn't Help Humans Answer Hard Reading Comprehension Questions—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