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:
- 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)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2022.findings-aacl.30.pdf