Learn to resolve conversational dependency: A consistency training framework for conversational question answering

Gangwoo Kim, Hyunjae Kim, Jungsoo Park, Jaewoo Kang

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

Abstract

One of the main challenges in conversational question answering (CQA) is to resolve the conversational dependency, such as anaphora and ellipsis. However, existing approaches do not explicitly train QA models on how to resolve the dependency, and thus these models are limited in understanding human dialogues. In this paper, we propose a novel framework, EXCORD (Explicit guidance on how to resolve Conversational Dependency) to enhance the abilities of QA models in comprehending conversational context. EXCORD first generates self-contained questions that can be understood without the conversation history, then trains a QA model with the pairs of original and self-contained questions using a consistency-based regularizer. In our experiments, we demonstrate that EXCORD significantly improves the QA models' performance by up to 1.2 F1 on QuAC (Choi et al., 2018), and 5.2 F1 on CANARD (Elgohary et al., 2019), while addressing the limitations of the existing approaches.

Original languageEnglish
Title of host publicationACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages6130-6141
Number of pages12
ISBN (Electronic)9781954085527
Publication statusPublished - 2021
EventJoint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021 - Virtual, Online
Duration: 2021 Aug 12021 Aug 6

Publication series

NameACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference

Conference

ConferenceJoint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021
CityVirtual, Online
Period21/8/121/8/6

ASJC Scopus subject areas

  • Software
  • Computational Theory and Mathematics
  • Linguistics and Language
  • Language and Linguistics

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