Open-Domain Conversational Question Answering with Historical Answers

Hung-Chieh Fang, Kuo-Han Hung, Chen-Wei Huang, Yun-Nung Chen


Abstract
Open-domain conversational question answering can be viewed as two tasks: passage retrieval and conversational question answering, where the former relies on selecting candidate passages from a large corpus and the latter requires better understanding of a question with contexts to predict the answers. This paper proposes ConvADR-QA that leverages historical answers to boost retrieval performance and further achieves better answering performance. Our experiments on the benchmark dataset, OR-QuAC, demonstrate that our model outperforms existing baselines in both extractive and generative reader settings, well justifying the effectiveness of historical answers for open-domain conversational question answering.
Anthology ID:
2022.findings-aacl.30
Volume:
Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022
Month:
November
Year:
2022
Address:
Online only
Editors:
Yulan He, Heng Ji, Sujian Li, Yang Liu, Chua-Hui Chang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
319–326
Language:
URL:
https://aclanthology.org/2022.findings-aacl.30
DOI:
Bibkey:
Cite (ACL):
Hung-Chieh Fang, Kuo-Han Hung, Chen-Wei Huang, and Yun-Nung Chen. 2022. Open-Domain Conversational Question Answering with Historical Answers. In Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022, pages 319–326, Online only. Association for Computational Linguistics.
Cite (Informal):
Open-Domain Conversational Question Answering with Historical Answers (Fang et al., Findings 2022)
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PDF:
https://aclanthology.org/2022.findings-aacl.30.pdf