Seung-Hwan Cho


2022

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Insurance Question Answering via Single-turn Dialogue Modeling
Seon-Ok Na | Young-Min Kim | Seung-Hwan Cho
Proceedings of the Second Workshop on When Creative AI Meets Conversational AI

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.