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.

翻译：共情对话在心理学上被认为是认知与情感有意识对齐和交互的结果。然而现有共情对话模型通常仅考虑情感层面，或将认知与情感孤立处理，这限制了共情响应生成的能力。本研究提出CASE模型用于共情对话生成，该模型首先构建常识认知图谱和情感概念图谱，进而在粗粒度和细粒度两个层面实现对用户认知与情感的对齐。通过自动评估与人工评估，我们证明CASE模型在共情对话任务上优于当前最先进的基线模型，能够生成更具共情性且信息更丰富的响应。