@inproceedings{fu-etal-2024-styemp,
    title = "{S}ty{E}mp: Stylizing Empathetic Response Generation via Multi-Grained Prefix Encoder and Personality Reinforcement",
    author = "Fu, Yahui  and
      Chu, Chenhui  and
      Kawahara, Tatsuya",
    editor = "Kawahara, Tatsuya  and
      Demberg, Vera  and
      Ultes, Stefan  and
      Inoue, Koji  and
      Mehri, Shikib  and
      Howcroft, David  and
      Komatani, Kazunori",
    booktitle = "Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2024",
    address = "Kyoto, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.sigdial-1.15/",
    doi = "10.18653/v1/2024.sigdial-1.15",
    pages = "172--185",
    abstract = "Recent approaches for empathetic response generation mainly focus on emotional resonance and user understanding, without considering the system`s personality. Consistent personality is evident in real human expression and is important for creating trustworthy systems. To address this problem, we propose StyEmp, which aims to stylize the empathetic response generation with a consistent personality. Specifically, it incorporates a multi-grained prefix mechanism designed to capture the intricate relationship between a system`s personality and its empathetic expressions. Furthermore, we introduce a personality reinforcement module that leverages contrastive learning to calibrate the generation model, ensuring that responses are both empathetic and reflective of a distinct personality. Automatic and human evaluations on the EMPATHETICDIALOGUES benchmark show that StyEmp outperforms competitive baselines in terms of both empathy and personality expressions. Our code is available at https://github.com/fuyahuii/StyEmp."
}