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:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.cai-1.5.pdf