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 8401–8425 of 10817 papers

TitleStatusHype
Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control—0
Improving Data Augmentation in Low-resource Question Answering with Active Learning in Multiple Stages—0
Improving Data Augmentation for Robust Visual Question Answering with Effective Curriculum Learning—0
Improving Cross-Modal Understanding in Visual Dialog via Contrastive Learning—0
Improving Cross-lingual Representation for Semantic Retrieval with Code-switching—0
Correction of Faulty Background Knowledge based on Condition Aware and Revise Transformer for Question Answering—0
Improving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced Optimization—0
Corpus-Driven Thematic Hierarchy Induction—0
Improving Conditioning in Context-Aware Sequence to Sequence Models—0
Corpus-Based Paraphrase Detection Experiments and Review—0
Attention-based Aspect Reasoning for Knowledge Base Question Answering on Clinical Notes—0
Improving Commonsense Question Answering by Graph-based Iterative Retrieval over Multiple Knowledge Sources—0
Corpus annotation with a linguistic analysis of the associations between event mentions and spatial expressions—0
Improving Clinical Question Answering with Multi-Task Learning: A Joint Approach for Answer Extraction and Medical Categorization—0
Improving Clinical Diagnosis Inference through Integration of Structured and Unstructured Knowledge—0
Corpus Annotation through Crowdsourcing: Towards Best Practice Guidelines—0
Attention Alignment and Flexible Positional Embeddings Improve Transformer Length Extrapolation—0
Analyzing and Characterizing User Intent in Information-seeking Conversations—0
Improving Chinese Semantic Role Labeling using High-quality Surface and Deep Case Frames—0
Improving Biomedical Information Retrieval with Neural Retrievers—0
CORN: Co-Reasoning Network for Commonsense Question Answering—0
Improving Automatic VQA Evaluation Using Large Language Models—0
CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction—0
Improving and Diagnosing Knowledge-Based Visual Question Answering via Entity Enhanced Knowledge Injection—0
Improve Neural Entity Recognition via Multi-Task Data Selection and Constrained Decoding—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