@inproceedings{zhang-etal-2024-strength,
    title = "Strength Lies in Differences! Improving Strategy Planning for Non-collaborative Dialogues via Diversified User Simulation",
    author = "Zhang, Tong  and
      Huang, Chen  and
      Deng, Yang  and
      Liang, Hongru  and
      Liu, Jia  and
      Wen, Zujie  and
      Lei, Wenqiang  and
      Chua, Tat-Seng",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.emnlp-main.26/",
    doi = "10.18653/v1/2024.emnlp-main.26",
    pages = "424--444",
    abstract = "We investigate non-collaborative dialogue agents, which are expected to engage in strategic conversations with diverse users, for securing a mutual agreement that leans favorably towards the system`s objectives. This poses two main challenges for existing dialogue agents: 1) The inability to integrate user-specific characteristics into the strategic planning, and 2) The difficulty of training strategic planners that can be generalized to diverse users. To address these challenges, we propose TRIP to enhance the capability in tailored strategic planning, incorporating a user-aware strategic planning module and a population-based training paradigm. Through experiments on benchmark non-collaborative dialogue tasks, we demonstrate the effectiveness of TRIP in catering to diverse users."
}