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 2976–3000 of 10817 papers

TitleStatusHype
Answering Complex Questions by Combining Information from Curated and Extracted Knowledge Bases—0
Agent-Based Detection and Resolution of Incompleteness and Ambiguity in Interactions with Large Language Models—0
Estimating semantic structure for the VQA answer space—0
ESG Accountability Made Easy: DocQA at Your Service—0
A Corpus for Visual Question Answering Annotated with Frame Semantic Information—0
70B-parameter large language models in Japanese medical question-answering—0
ESQA: Event Sequences Question Answering—0
BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filtering—0
BLCU\_NLP at SemEval-2019 Task 8: A Contextual Knowledge-enhanced GPT Model for Fact Checking—0
Answering Ambiguous Questions with a Database of Questions, Answers, and Revisions—0
BizBench: A Quantitative Reasoning Benchmark for Business and Finance—0
BIT at SemEval-2017 Task 1: Using Semantic Information Space to Evaluate Semantic Textual Similarity—0
BIT at SemEval-2016 Task 1: Sentence Similarity Based on Alignments and Vector with the Weight of Information Content—0
Bisecting K-Means in RAG for Enhancing Question-Answering Tasks Performance in Telecommunications—0
Answer Generation for Retrieval-based Question Answering Systems—0
A Generative Model for Relation Extraction and Classification—0
BiRdQA: A Bilingual Dataset for Question Answering on Tricky Riddles—0
Answer Generation for Questions With Multiple Information Sources in E-Commerce—0
BiQuAD: Towards QA based on deeper text understanding—0
BioTABQA: Instruction Learning for Biomedical Table Question Answering—0
Answer-focused and Position-aware Neural Question Generation—0
A Generative Approach to Question Answering—0
A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories—0
A Generate-Validate Approach to Answering Questions about Qualitative Relationships—0
AnswerFact: Fact Checking in Product 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