  A humanized dialogue system is expected to generate empathetic replies, which
should be sensitive to the users' expressed emotion. The task of empathetic
dialogue generation is proposed to address this problem. The essential
challenges lie in accurately capturing the nuances of human emotion and
considering the potential of user feedback, which are overlooked by the
majority of existing work. In response to this problem, we propose a
multi-resolution adversarial model -- EmpDG, to generate more empathetic
responses. EmpDG exploits both the coarse-grained dialogue-level and
fine-grained token-level emotions, the latter of which helps to better capture
the nuances of user emotion. In addition, we introduce an interactive
adversarial learning framework which exploits the user feedback, to identify
whether the generated responses evoke emotion perceptivity in dialogues.
Experimental results show that the proposed approach significantly outperforms
the state-of-the-art baselines in both content quality and emotion
perceptivity.
