Continually Improving Extractive QA via Human Feedback

Ge Gao, Hung Ting Chen, Yoav Artzi, Eunsol Choi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We study continually improving an extractive question answering (QA) system via human user feedback. We design and deploy an iterative approach, where information-seeking users ask questions, receive model-predicted answers, and provide feedback. We conduct experiments involving thousands of user interactions under diverse setups to broaden the understanding of learning from feedback over time. Our experiments show effective improvement from user feedback of extractive QA models over time across different data regimes, including significant potential for domain adaptation.

Original languageEnglish (US)
Title of host publicationEMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings
EditorsHouda Bouamor, Juan Pino, Kalika Bali
PublisherAssociation for Computational Linguistics (ACL)
Pages406-423
Number of pages18
ISBN (Electronic)9798891760608
DOIs
StatePublished - 2023
Event2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023 - Hybrid, Singapore, Singapore
Duration: Dec 6 2023Dec 10 2023

Publication series

NameEMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings

Conference

Conference2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023
Country/TerritorySingapore
CityHybrid, Singapore
Period12/6/2312/10/23

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems
  • Linguistics and Language

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