  Expressing empathy is important in everyday conversations, and exploring how
empathy arises is crucial in automatic response generation. Most previous
approaches consider only a single factor that affects empathy. However, in
practice, empathy generation and expression is a very complex and dynamic
psychological process. A listener needs to find out events which cause a
speaker's emotions (emotion cause extraction), project the events into some
experience (knowledge extension), and express empathy in the most appropriate
way (communication mechanism). To this end, we propose a novel approach, which
integrates the three components - emotion cause, knowledge graph, and
communication mechanism for empathetic response generation. Experimental
results on the benchmark dataset demonstrate the effectiveness of our method
and show that incorporating the key components generates more informative and
empathetic responses.
