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 5876–5900 of 10817 papers

TitleStatusHype
Bend but Don't Break? Multi-Challenge Stress Test for QA Models—0
Learning to Solve Geometry Problems from Natural Language Demonstrations in Textbooks—0
Learning to Select the Relevant History Turns in Conversational Question Answering—0
Learning to Select Question-Relevant Relations for Visual Question Answering—0
Diverse Multi-Answer Retrieval with Determinantal Point Processes—0
Benchmarks for Pirá 2.0, a Reading Comprehension Dataset about the Ocean, the Brazilian Coast, and Climate Change—0
LLMs to Support a Domain Specific Knowledge Assistant—0
An Intelligent Question Answering System based on Power Knowledge Graph—0
Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching—0
Diverse and Non-redundant Answer Set Extraction on Community QA based on DPPs—0
Benchmarking Vision Language Models for Cultural Understanding—0
Ditch the Gold Standard: Re-evaluating Conversational Question Answering—0
Learning to Rehearse in Long Sequence Memorization—0
LMSim : Computing Domain-specific Semantic Word Similarities Using a Language Modeling Approach—0
Learning to Recover Reasoning Chains for Multi-Hop Question Answering via Cooperative Games—0
Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection—0
An Initial Investigation of Non-Native Spoken Question-Answering—0
Aesthetic Visual Question Answering of Photographs—0
Localize, Group, and Select: Boosting Text-VQA by Scene Text Modeling—0
A Comprehensive Survey of Knowledge-Based Vision Question Answering Systems: The Lifecycle of Knowledge in Visual Reasoning Task—0
Localizing AI: Evaluating Open-Weight Language Models for Languages of Baltic States—0
Learning to Recognize the Unseen Visual Predicates—0
Neural Reasoning, Fast and Slow, for Video Question Answering—0
Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models—0
Learning to Reason Iteratively and Parallelly for Complex Visual Reasoning Scenarios—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