@inproceedings{lee-etal-2022-focus,
    title = "Focus on {F}o{C}us: Is {F}o{C}us focused on Context, Knowledge and Persona?",
    author = "Lee, SeungYoon  and
      Lee, Jungseob  and
      Park, Chanjun  and
      Eo, Sugyeong  and
      Moon, Hyeonseok  and
      Seo, Jaehyung  and
      Park, Jeongbae  and
      Lim, Heuiseok",
    editor = "Lim, Heuiseok  and
      Kim, Seungryong  and
      Lee, Yeonsoo  and
      Lin, Steve  and
      Seo, Paul Hongsuck  and
      Suh, Yumin  and
      Jang, Yoonna  and
      Lim, Jungwoo  and
      Hur, Yuna  and
      Son, Suhyune",
    booktitle = "Proceedings of the 1st Workshop on Customized Chat Grounding Persona and Knowledge",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.ccgpk-1.1",
    pages = "1--8",
    abstract = "Rather than continuing the conversation based on personalized or implicit information, the existing conversation system generates dialogue by focusing only on the superficial content. To solve this problem, FoCus was recently released. FoCus is a persona-knowledge grounded dialogue generation dataset that leverages Wikipedia{'}s knowledge and personal persona, focusing on the landmarks provided by Google, enabling user-centered conversation. However, a closer empirical study is needed since research in the field is still in its early stages. Therefore, we fling two research questions about FoCus. {``}Is the FoCus whether for conversation or question answering?{''} to identify the structural problems of the dataset. {``}Does the FoCus model do real knowledge blending?{''} to closely demonstrate that the model acquires actual knowledge. As a result of the experiment, we present that the FoCus model could not correctly blend the knowledge according to the input dialogue and that the dataset design is unsuitable for the multi-turn conversation.",
}