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        <title>Insurance Question Answering via Single-turn Dialogue Modeling</title>
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        <namePart type="given">Seon-Ok</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Insurance Question Answering via Single-turn Dialogue Modeling
%A Na, Seon-Ok
%A Kim, Young-Min
%A Cho, Seung-Hwan
%Y Wu, Xianchao
%Y Ruan, Peiying
%Y Li, Sheng
%Y Dong, Yi
%S Proceedings of the Second Workshop on When Creative AI Meets Conversational AI
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F na-etal-2022-insurance
%X 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.
%U https://aclanthology.org/2022.cai-1.5
%P 35-41

Markdown (Informal)

[Insurance Question Answering via Single-turn Dialogue Modeling](https://aclanthology.org/2022.cai-1.5) (Na et al., CAI 2022)

• Insurance Question Answering via Single-turn Dialogue Modeling (Na et al., CAI 2022)

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.