APTNESS: Incorporating Appraisal Theory and Emotion Support Strategies for Empathetic Response Generation
<|reference_start|>APTNESS: Incorporating Appraisal Theory and Emotion Support Strategies for Empathetic Response Generation: Empathetic response generation is designed to comprehend the emotions of others and select the most appropriate strategies to assist them in resolving emotional challenges. Empathy can be categorized into cognitive empathy and affective empathy. The former pertains to the ability to understand and discern the emotional issues and situations of others, while the latter involves the capacity to provide comfort. To enhance one's empathetic abilities, it is essential to develop both these aspects. Therefore, we develop an innovative framework that combines retrieval augmentation and emotional support strategy integration. Our framework starts with the introduction of a comprehensive emotional palette for empathy. We then apply appraisal theory to decompose this palette and create a database of empathetic responses. This database serves as an external resource and enhances the LLM's empathy by integrating semantic retrieval mechanisms. Moreover, our framework places a strong emphasis on the proper articulation of response strategies. By incorporating emotional support strategies, we aim to enrich the model's capabilities in both cognitive and affective empathy, leading to a more nuanced and comprehensive empathetic response. Finally, we extract datasets ED and ET from the empathetic dialogue dataset \textsc{EmpatheticDialogues} and ExTES based on dialogue length. Experiments demonstrate that our framework can enhance the empathy ability of LLMs from both cognitive and affective empathy perspectives. Our code is released at https://github.com/CAS-SIAT-XinHai/APTNESS.<|reference_end|>
@article{hu2024aptness:,
    title={APTNESS: Incorporating Appraisal Theory and Emotion Support Strategies
  for Empathetic Response Generation},
    author={Yuxuan Hu, Minghuan Tan, Chenwei Zhang, Zixuan Li, Xiaodan Liang, Min
  Yang, Chengming Li, Xiping Hu},
    journal={arXiv preprint arXiv:2407.21048},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.21048},
    primaryClass={cs.CL cs.AI}
}