CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation
This work addresses the challenge of creating more human-like empathetic dialogue systems, which is incremental as it builds on existing models by integrating cognition and affection.
The authors tackled the problem of generating empathetic responses in dialogue by aligning cognition and affection at coarse and fine levels, resulting in a model that outperforms state-of-the-art baselines in generating more empathetic and informative responses.
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.