Insurance Question Answering via Single-turn Dialogue Modeling

Seon-Ok Na, Young-Min Kim, Seung-Hwan Cho


Abstract
With great success in single-turn question answering (QA), conversational QA is currently receiving considerable attention. Several studies have been conducted on this topic from different perspectives. However, building a real-world conversational system remains a challenge. This study introduces our ongoing project, which uses Korean QA data to develop a dialogue system in the insurance domain. The goal is to construct a system that provides informative responses to general insurance questions. We present the current results of single-turn QA. A unique aspect of our approach is that we borrow the concepts of intent detection and slot filling from task-oriented dialogue systems. We present details of the data construction process and the experimental results on both learning tasks.
Anthology ID:
2022.cai-1.5
Volume:
Proceedings of the Second Workshop on When Creative AI Meets Conversational AI
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Editors:
Xianchao Wu, Peiying Ruan, Sheng Li, Yi Dong
Venue:
CAI
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
35–41
Language:
URL:
https://aclanthology.org/2022.cai-1.5
DOI:
Bibkey:
Cite (ACL):
Seon-Ok Na, Young-Min Kim, and Seung-Hwan Cho. 2022. Insurance Question Answering via Single-turn Dialogue Modeling. In Proceedings of the Second Workshop on When Creative AI Meets Conversational AI, pages 35–41, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Cite (Informal):
Insurance Question Answering via Single-turn Dialogue Modeling (Na et al., CAI 2022)
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PDF:
https://aclanthology.org/2022.cai-1.5.pdf