  Empathetic dialog generation aims at generating coherent responses following
previous dialog turns and, more importantly, showing a sense of caring and a
desire to help. Existing models either rely on pre-defined emotion labels to
guide the response generation, or use deterministic rules to decide the emotion
of the response. With the advent of advanced language models, it is possible to
learn subtle interactions directly from the dataset, providing that the emotion
categories offer sufficient nuances and other non-emotional but emotional
regulating intents are included. In this paper, we describe how to incorporate
a taxonomy of 32 emotion categories and 8 additional emotion regulating intents
to succeed the task of empathetic response generation. To facilitate the
training, we also curated a large-scale emotional dialog dataset from movie
subtitles. Through a carefully designed crowdsourcing experiment, we evaluated
and demonstrated how our model produces more empathetic dialogs compared with
its baselines.
