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 8676–8700 of 10817 papers

TitleStatusHype
Learning Answer Embeddings for Visual Question Answering—0
Learning Answer-Entailing Structures for Machine Comprehension—0
Learning Answer Generation using Supervision from Automatic Question Answering Evaluators—0
Learning Based Approaches for Vietnamese Question Classification Using Keywords Extraction from the Web—0
Learning Better Sentence Representation with Syntax Information—0
Learning Biological Processes with Global Constraints—0
ELBA: Learning by Asking for Embodied Visual Navigation and Task Completion—0
Learning by Asking Questions—0
Learning by Hallucinating: Vision-Language Pre-training with Weak Supervision—0
Learning Compact Lexicons for CCG Semantic Parsing—0
Learning Compositional Representation for Few-shot Visual Question Answering—0
Learning Constraints for Consistent Timeline Extraction—0
Learning Contextual Embeddings for Structural Semantic Similarity using Categorical Information—0
Learning Contextualized Knowledge Graph Structures for Commonsense Reasoning—0
Learning Contextually Informed Representations for Linear-Time Discourse Parsing—0
Learning Continuous Word Embedding with Metadata for Question Retrieval in Community Question Answering—0
Learning Corresponded Rationales for Text Matching—0
Learning Cross-lingual Representations with Matrix Factorization—0
Learning Embeddings for Transitive Verb Disambiguation by Implicit Tensor Factorization—0
Learning Entailment Relations by Global Graph Structure Optimization—0
Learning Executable Semantic Parsers for Natural Language Understanding—0
Learning Faithful Representations of Causal Graphs—0
Learning Fine-Grained Knowledge about Contingent Relations between Everyday Events—0
Learning from a Neighbor: Adapting a Japanese Parser for Korean Through Feature Transfer Learning—0
Learning from Inside: Self-driven Siamese Sampling and Reasoning for Video Question Answering—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