  Recent approaches to empathetic response generation incorporate emotion
causalities to enhance comprehension of both the user's feelings and
experiences. However, these approaches suffer from two critical issues. First,
they only consider causalities between the user's emotion and the user's
experiences, and ignore those between the user's experiences. Second, they
neglect interdependence among causalities and reason them independently. To
solve the above problems, we expect to reason all plausible causalities
interdependently and simultaneously, given the user's emotion, dialogue
history, and future dialogue content. Then, we infuse these causalities into
response generation for empathetic responses. Specifically, we design a new
model, i.e., the Conditional Variational Graph Auto-Encoder (CVGAE), for the
causality reasoning, and adopt a multi-source attention mechanism in the
decoder for the causality infusion. We name the whole framework as CARE,
abbreviated for CAusality Reasoning for Empathetic conversation. Experimental
results indicate that our method achieves state-of-the-art performance.
