@inproceedings{nozue-etal-2025-enhancing,
    title = "Enhancing Persuasive Dialogue Agents by Synthesizing {C}ross{-}{D}isciplinary Communication Strategies",
    author = "Nozue, Shinnosuke  and
      Nakano, Yuto  and
      Watanabe, Yotaro  and
      Takasaki, Meguru  and
      Moriya, Shoji  and
      Akama, Reina  and
      Suzuki, Jun",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.emnlp-industry.158/",
    doi = "10.18653/v1/2025.emnlp-industry.158",
    pages = "2287--2312",
    ISBN = "979-8-89176-333-3",
    abstract = "Current approaches to developing persuasive dialogue agents often rely on a limited set of predefined persuasive strategies that fail to capture the complexity of real-world interactions. We applied a cross-disciplinary approach to develop a framework for designing persuasive dialogue agents that draws on proven strategies from social psychology, behavioral economics, and communication theory. We validated our proposed framework through experiments on two distinct datasets: the Persuasion for Good dataset, which represents a specific in-domain scenario, and the DailyPersuasion dataset, which encompasses a wide range of scenarios. The proposed framework achieved strong results for both datasets and demonstrated notable improvement in the persuasion success rate as well as promising generalizability. Notably, the proposed framework also excelled at persuading individuals with initially low intent, which addresses a critical challenge for persuasive dialogue agents."
}