  A key trait of daily conversations between individuals is the ability to
express empathy towards others, and exploring ways to implement empathy is a
crucial step towards human-like dialogue systems. Previous approaches on this
topic mainly focus on detecting and utilizing the user's emotion for generating
empathetic responses. However, since empathy includes both aspects of affection
and cognition, we argue that in addition to identifying the user's emotion,
cognitive understanding of the user's situation should also be considered. To
this end, we propose a novel approach for empathetic response generation, which
leverages commonsense to draw more information about the user's situation and
uses this additional information to further enhance the empathy expression in
generated responses. We evaluate our approach on EmpatheticDialogues, which is
a widely-used benchmark dataset for empathetic response generation. Empirical
results demonstrate that our approach outperforms the baseline models in both
automatic and human evaluations and can generate more informative and
empathetic responses.

Computation and Language;Artificial Intelligence