CASE：对齐粗到细的认知与情感以生成共情回应
CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation
AI总结：
提出CASE模型，通过构建常识认知图和情感概念图，在粗粒度和细粒度上对齐用户认知与情感，从而生成更共情且信息丰富的对话回应，优于现有基线。
摘要：
共情对话在心理学上被认为是共情的认知与情感之间有意识的对齐和相互作用的结果。然而，现有的共情对话模型通常只考虑情感方面，或将认知与情感分开处理，这限制了共情回应生成的能力。在本工作中，我们提出了CASE模型用于共情对话生成。该模型首先构建于一个常识认知图和一个情感概念图之上，然后在粗粒度和细粒度两个层面上对齐用户的认知与情感。通过自动评估和人工评估，我们证明CASE在共情对话方面优于最先进的基线模型，并且能够生成更具共情性和信息量的回应。
英文摘要：
Empathetic conversation is psychologically supposed to be the result of conscious alignment and interaction between the cognition and affection of empathy. However, existing empathetic dialogue models usually consider only the affective aspect or treat cognition and affection in isolation, which limits the capability of empathetic response generation. In this work, we propose the CASE model for empathetic dialogue generation. It first builds upon a commonsense cognition graph and an emotional concept graph and then aligns the user's cognition and affection at both the coarse-grained and fine-grained levels. Through automatic and manual evaluation, we demonstrate that CASE outperforms state-of-the-art baselines of empathetic dialogues and can generate more empathetic and informative responses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
