  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.
