  A good empathetic dialogue system should first track and understand a user's
emotion and then reply with an appropriate emotion. However, current approaches
to this task either focus on improving the understanding of users' emotion or
on proposing better responding strategies, and very few works consider both at
the same time. Our work attempts to fill this vacancy. Inspired by
task-oriented dialogue systems, we propose a novel empathetic response
generation model with emotion-aware dialogue management. The emotion-aware
dialogue management contains two parts: (1) Emotion state tracking maintains
the current emotion state of the user and (2) Empathetic dialogue policy
selection predicts a target emotion and a user's intent based on the results of
the emotion state tracking. The predicted information is then used to guide the
generation of responses. Experimental results show that dynamically managing
different information can help the model generate more empathetic responses
compared with several baselines under both automatic and human evaluations.
