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 9526–9550 of 10817 papers

TitleStatusHype
Using Interactive Feedback to Improve the Accuracy and Explainability of Question Answering Systems Post-Deployment—0
Using Large Language Models for Cybersecurity Capture-The-Flag Challenges and Certification Questions—0
Using Large Language Models to Understand Telecom Standards—0
Using Large Pretrained Language Models for Answering User Queries from Product Specifications—0
Using lexical and Dependency Features to Disambiguate Discourse Connectives in Hindi—0
Using Lexical Expansion to Learn Inference Rules from Sparse Data—0
Using NLU in Context for Question Answering: Improving on Facebook's bAbI Tasks—0
Using OpenWordnet-PT for Question Answering on Legal Domain—0
Using Paraphrases and Lexical Semantics to Improve the Accuracy and the Robustness of Supervised Models in Situated Dialogue Systems—0
Using paraphrases for improving first story detection in news and Twitter—0
Using Pretrained Large Language Model with Prompt Engineering to Answer Biomedical Questions—0
Using Question-Answering Techniques to Implement a Knowledge-Driven Argument Mining Approach—0
Using Recurrent Neural Network for Learning Expressive Ontologies—0
Using Shallow Semantic Parsing and Relation Extraction for Finding Contradiction in Text—0
Using Similarity to Evaluate Factual Consistency in Summaries—0
Learning When Not to Answer: A Ternary Reward Structure for Reinforcement Learning based Question Answering—0
Using the Hammer Only on Nails: A Hybrid Method for Evidence Retrieval for Question Answering—0
Using the Web as an Implicit Training Set: Application to Noun Compound Syntax and Semantics—0
Using Visual Cropping to Enhance Fine-Detail Question Answering of BLIP-Family Models—0
Using Weak Supervision and Data Augmentation in Question Answering—0
Using Wikipedia and Semantic Resources to Find Answer Types and Appropriate Answer Candidate Sets in Question Answering—0
UTA DLNLP at SemEval-2016 Task 12: Deep Learning Based Natural Language Processing System for Clinical Information Identification from Clinical Notes and Pathology Reports—0
Utilizing Graph Measure to Deduce Omitted Entities in Paragraphs—0
Utilizing Large Language Models for Automating Technical Customer Support—0
Utilizing Large Language Models for Named Entity Recognition in Traditional Chinese Medicine against COVID-19 Literature: Comparative Study—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