@inproceedings{cai-etal-2023-improving,
    title = "Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledge",
    author = "Cai, Hua  and
      Shen, Xuli  and
      Xu, Qing  and
      Shen, Weilin  and
      Wang, Xiaomei  and
      Ge, Weifeng  and
      Zheng, Xiaoqing  and
      Xue, Xiangyang",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.498",
    doi = "10.18653/v1/2023.findings-acl.498",
    pages = "7858--7873",
    abstract = "In empathetic conversations, individuals express their empathy towards others. Previous work has mainly focused on generating empathetic responses by utilizing the speaker{'}s emotion. Besides, external commonsense knowledge has been applied to enhance the system{'}s understandings of the speaker{'}s situation. However, given an event, commonsense knowledge base contains various relations, potentially leading to confusion for the dialogue system. Consequently, inconsistencies arise among the emotion, generated response and speaker{'}s contextual information. To this end, we propose a novel approach for empathetic response generation, which incorporates an adaptive module for commonsense knowledge selection to ensure consistency between the generated empathetic responses and the speaker{'}s situation. This selected knowledge is used to refine the commonsense cognition and empathy expression for generated responses. Experimental results show that our approach significantly outperforms baseline models in both automatic and human evaluations, exhibiting the generation of more coherent and empathetic responses. Moreover, case studies highlight the interpretability of knowledge selection in the responses and the effectiveness of adaptive module in our model. Code: \url{https://github.com/Hanscal/DCKS}.",
}

In empathetic conversations, individuals express their empathy towards others. Previous work has mainly focused on generating empathetic responses by utilizing the speaker{'}s emotion. Besides, external commonsense knowledge has been applied to enhance the system{'}s understandings of the speaker{'}s situation. However, given an event, commonsense knowledge base contains various relations, potentially leading to confusion for the dialogue system. Consequently, inconsistencies arise among the emotion, generated response and speaker{'}s contextual information. To this end, we propose a novel approach for empathetic response generation, which incorporates an adaptive module for commonsense knowledge selection to ensure consistency between the generated empathetic responses and the speaker{'}s situation. This selected knowledge is used to refine the commonsense cognition and empathy expression for generated responses. Experimental results show that our approach significantly outperforms baseline models in both automatic and human evaluations, exhibiting the generation of more coherent and empathetic responses. Moreover, case studies highlight the interpretability of knowledge selection in the responses and the effectiveness of adaptive module in our model. Code: \url{https://github.com/Hanscal/DCKS}.
Cai, Hua Shen, Xuli Xu, Qing Shen, Weilin Wang, Xiaomei Ge, Weifeng Zheng, Xiaoqing Xue, Xiangyang
Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledge