  Current approaches to empathetic response generation view the set of emotions
expressed in the input text as a flat structure, where all the emotions are
treated uniformly. We argue that empathetic responses often mimic the emotion
of the user to a varying degree, depending on its positivity or negativity and
content. We show that the consideration of this polarity-based emotion clusters
and emotional mimicry results in improved empathy and contextual relevance of
the response as compared to the state-of-the-art. Also, we introduce
stochasticity into the emotion mixture that yields emotionally more varied
empathetic responses than the previous work. We demonstrate the importance of
these factors to empathetic response generation using both automatic- and
human-based evaluations. The implementation of MIME is publicly available at
https://github.com/declare-lab/MIME.
