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 5726–5750 of 10817 papers

TitleStatusHype
Leveraging Crowdsourcing for Paraphrase Recognition—0
ATM: Action Temporality Modeling for Video Question Answering—0
Convolutional Interaction Network for Natural Language Inference—0
Leveraging Extracted Model Adversaries for Improved Black Box Attacks—0
IIE-NLP-NUT at SemEval-2020 Task 4: Guiding PLM with Prompt Template Reconstruction Strategy for ComVE—0
Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering—0
Leveraging Graph Retrieval-Augmented Generation to Support Learners' Understanding of Knowledge Concepts in MOOCs—0
Look, Read and Ask: Learning to Ask Questions by Reading Text in Images—0
IIE-NLP-Eyas at SemEval-2021 Task 4: Enhancing PLM for ReCAM with Special Tokens, Re-Ranking, Siamese Encoders and Back Translation—0
IICONGRAPH: improved Iconographic and Iconological Statements in Knowledge Graphs—0
ConvKN at SemEval-2016 Task 3: Answer and Question Selection for Question Answering on Arabic and English Fora—0
Leveraging Linguistic Structure For Open Domain Information Extraction—0
DocPrompt: Large-scale continue pretrain for zero-shot and few-shot document question answering—0
IHS-RD-Belarus at SemEval-2016 Task 1: Multistage Approach for Measuring Semantic Similarity—0
I Have Seen Enough: A Teacher Student Network for Video Classification Using Fewer Frames—0
Leveraging Medical Visual Question Answering with Supporting Facts—0
Document AI: Benchmarks, Models and Applications—0
Look, Learn and Leverage (L^3): Mitigating Visual-Domain Shift and Discovering Intrinsic Relations via Symbolic Alignment—0
Look to the Right: Mitigating Relative Position Bias in Extractive Question Answering—0
Leveraging Pre-trained Models for Failure Analysis Triplets Generation—0
Leveraging Retrieval-Augmented Generation for Persian University Knowledge Retrieval—0
Lost in Overlap: Exploring Logit-based Watermark Collision in LLMs—0
If You Want to Go Far Go Together: Unsupervised Joint Candidate Evidence Retrieval for Multi-hop Question Answering—0
If You Can't Beat Them Join Them: Handcrafted Features Complement Neural Nets for Non-Factoid Answer Reranking—0
Converting Phrase Structures to Dependency Structures in Sanskrit—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