@inproceedings{fu-etal-2023-reasoning,
    title = "Reasoning before Responding: Integrating Commonsense-based Causality Explanation for Empathetic Response Generation",
    author = "Fu, Yahui  and
      Inoue, Koji  and
      Chu, Chenhui  and
      Kawahara, Tatsuya",
    editor = "Stoyanchev, Svetlana  and
      Joty, Shafiq  and
      Schlangen, David  and
      Dusek, Ondrej  and
      Kennington, Casey  and
      Alikhani, Malihe",
    booktitle = "Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2023",
    address = "Prague, Czechia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2023.sigdial-1.60/",
    doi = "10.18653/v1/2023.sigdial-1.60",
    pages = "645--656",
    abstract = "Recent approaches to empathetic response generation try to incorporate commonsense knowledge or reasoning about the causes of emotions to better understand the user`s experiences and feelings. However, these approaches mainly focus on understanding the causalities of context from the user`s perspective, ignoring the system`s perspective. In this paper, we propose a commonsense-based causality explanation approach for diverse empathetic response generation that considers both the user`s perspective (user`s desires and reactions) and the system`s perspective (system`s intentions and reactions). We enhance ChatGPT`s ability to reason for the system`s perspective by integrating in-context learning with commonsense knowledge. Then, we integrate the commonsense-based causality explanation with both ChatGPT and a T5-based model. Experimental evaluations demonstrate that our method outperforms other comparable methods on both automatic and human evaluations."
}