@inproceedings{na-etal-2022-insurance,
    title = "Insurance Question Answering via Single-turn Dialogue Modeling",
    author = "Na, Seon-Ok  and
      Kim, Young-Min  and
      Cho, Seung-Hwan",
    editor = "Wu, Xianchao  and
      Ruan, Peiying  and
      Li, Sheng  and
      Dong, Yi",
    booktitle = "Proceedings of the Second Workshop on When Creative AI Meets Conversational AI",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.cai-1.5/",
    pages = "35--41",
    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."
}