  Lack of external knowledge makes empathetic dialogue systems difficult to
perceive implicit emotions and learn emotional interactions from limited
dialogue history. To address the above problems, we propose to leverage
external knowledge, including commonsense knowledge and emotional lexical
knowledge, to explicitly understand and express emotions in empathetic dialogue
generation. We first enrich the dialogue history by jointly interacting with
external knowledge and construct an emotional context graph. Then we learn
emotional context representations from the knowledge-enriched emotional context
graph and distill emotional signals, which are the prerequisites to predicate
emotions expressed in responses. Finally, to generate the empathetic response,
we propose an emotional cross-attention mechanism to learn the emotional
dependencies from the emotional context graph. Extensive experiments conducted
on a benchmark dataset verify the effectiveness of the proposed method. In
addition, we find the performance of our method can be further improved by
integrating with a pre-trained model that works orthogonally.
