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 9501–9525 of 10817 papers

TitleStatusHype
UofR at SemEval-2016 Task 8: Learning Synchronous Hyperedge Replacement Grammar for AMR Parsing—0
UoR at SemEval-2021 Task 4: Using Pre-trained BERT Token Embeddings for Question Answering of Abstract Meaning—0
UOUO: Uncontextualized Uncommon Objects for Measuring Knowledge Horizons of Vision Language Models—0
UoW: Multi-task Learning Gaussian Process for Semantic Textual Similarity—0
UParse: the Edinburgh system for the CoNLL 2017 UD shared task—0
UPB at SemEval-2021 Task 8: Extracting Semantic Information on Measurements as Multi-Turn Question Answering—0
UPC-USMBA at SemEval-2017 Task 3: Combining multiple approaches for CQA for Arabic—0
UPME: An Unsupervised Peer Review Framework for Multimodal Large Language Model Evaluation—0
UQuAD1.0: Development of an Urdu Question Answering Training Data for Machine Reading Comprehension—0
URAG: Implementing a Unified Hybrid RAG for Precise Answers in University Admission Chatbots -- A Case Study at HCMUT—0
User-Controlled, Robust Natural Language Generation from an Evolving Knowledge Base—0
User evaluation of a multiple answer extraction system on the Web (\'Evaluation d'un syst\`eme d'extraction de r\'eponses multiples sur le Web par comparaison \`a des humains) [in French]—0
User-Inspired Posterior Network for Recommendation Reason Generation—0
User Name Disambiguation in Community Question Answering—0
User Personalized Satisfaction Prediction via Multiple Instance Deep Learning—0
Using a Random Forest Classifier to recognise translations of biomedical terms across languages—0
Using Artificial Intelligence to aid Scientific Discovery of Climate Tipping Points—0
Using a Weighted Semantic Network for Lexical Semantic Relatedness—0
Using BERT Encoding and Sentence-Level Language Model for Sentence Ordering—0
Using Centroids of Word Embeddings and Word Mover's Distance for Biomedical Document Retrieval in Question Answering—0
Using Confusion Graphs to Understand Classifier Error—0
Using contradictions improves question answering systems—0
Using Explicit Discourse Connectives in Translation for Implicit Discourse Relation Classification—0
Using Holographically Compressed Embeddings in Question Answering—0
Using Integrated Gradients and Constituency Parse Trees to explain Linguistic Acceptability learnt by BERT—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