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

Showing 751775 of 10817 papers

TitleStatusHype
DynaPipe: Optimizing Multi-task Training through Dynamic PipelinesCode1
ECBench: Can Multi-modal Foundation Models Understand the Egocentric World? A Holistic Embodied Cognition BenchmarkCode1
EgoTaskQA: Understanding Human Tasks in Egocentric VideosCode1
Engineering flexible machine learning systems by traversing functionally-invariant pathsCode1
Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question AnsweringCode1
DSPNet: Dual-vision Scene Perception for Robust 3D Question AnsweringCode1
DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related QueriesCode1
Dual-Key Multimodal Backdoors for Visual Question AnsweringCode1
DrBenchmark: A Large Language Understanding Evaluation Benchmark for French Biomedical DomainCode1
DREAM: Improving Situational QA by First Elaborating the SituationCode1
Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World EnvironmentsCode1
Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset BiasesCode1
DRESSing Up LLM: Efficient Stylized Question-Answering via Style Subspace EditingCode1
Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question AnsweringCode1
DocVXQA: Context-Aware Visual Explanations for Document Question AnsweringCode1
Does Time Have Its Place? Temporal Heads: Where Language Models Recall Time-specific InformationCode1
DocVQA: A Dataset for VQA on Document ImagesCode1
Does Vision-and-Language Pretraining Improve Lexical Grounding?Code1
Dynamic Multimodal Evaluation with Flexible Complexity by Vision-Language BootstrappingCode1
Dynamic Relevance Graph Network for Knowledge-Aware Question AnsweringCode1
EA^2E: Improving Consistency with Event Awareness for Document-Level Argument ExtractionCode1
EA^2E: Improving Consistency with Event Awareness for Document-Level Argument ExtractionCode1
Diversify Question Generation with Retrieval-Augmented Style TransferCode1
Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip PredictionCode1
Ditch the Gold Standard: Re-evaluating Conversational Question AnsweringCode1
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Benchmark Results

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