@inproceedings{lee-etal-2024-redefining,
    title = "Redefining Proactivity for Information Seeking Dialogue",
    author = "Lee, Jing Yang  and
      Kim, Seokhwan  and
      Mehta, Kartik  and
      Kao, Jiun-Yu  and
      Lin, Yu-Hsiang  and
      Gupta, Arpit",
    editor = "Hale, James  and
      Chawla, Kushal  and
      Garg, Muskan",
    booktitle = "Proceedings of the Second Workshop on Social Influence in Conversations (SICon 2024)",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.sicon-1.5/",
    doi = "10.18653/v1/2024.sicon-1.5",
    pages = "64--84",
    abstract = "Humans pay careful attention to the interlocutor{'}s internal state in dialogues. For example, in recommendation dialogues, we make recommendations while estimating the seeker{'}s internal state, such as his/her level of knowledge and interest. Since there are no existing annotated resources for the analysis and experiment, we constructed RecomMind, a movie recommendation dialogue dataset with annotations of the seeker{'}s internal state at the entity level. Each entity has a first-person label annotated by the seeker and a second-person label annotated by the recommender. Our analysis based on RecomMind reveals that the success of recommendations is enhanced when recommenders mention entities that seekers do not know but are interested in. We also propose a response generation framework that explicitly considers the seeker{'}s internal state, utilizing the chain-of-thought prompting. The human evaluation results show that our proposed method outperforms the baseline method in both consistency and the success of recommendations."
}