  Previous research on empathetic dialogue systems has mostly focused on
generating responses given certain emotions. However, being empathetic not only
requires the ability of generating emotional responses, but more importantly,
requires the understanding of user emotions and replying appropriately. In this
paper, we propose a novel end-to-end approach for modeling empathy in dialogue
systems: Mixture of Empathetic Listeners (MoEL). Our model first captures the
user emotions and outputs an emotion distribution. Based on this, MoEL will
softly combine the output states of the appropriate Listener(s), which are each
optimized to react to certain emotions, and generate an empathetic response.
Human evaluations on empathetic-dialogues (Rashkin et al., 2018) dataset
confirm that MoEL outperforms multitask training baseline in terms of empathy,
relevance, and fluency. Furthermore, the case study on generated responses of
different Listeners shows high interpretability of our model.

Computation and Language