Use of a Taxonomy of Empathetic Response Intents to Control and
  Interpret Empathy in Neural Chatbots
Anuradha Welivita Pearl Pu
A recent trend in the domain of open-domain conversational agents is enabling
them to converse empathetically to emotional prompts. Current approaches either
follow an end-to-end approach or condition the responses on similar emotion
labels to generate empathetic responses. But empathy is a broad concept that
refers to the cognitive and emotional reactions of an individual to the
observed experiences of another and it is more complex than mere mimicry of
emotion. Hence, it requires identifying complex human conversational strategies
and dynamics in addition to generic emotions to control and interpret
empathetic responding capabilities of chatbots. In this work, we make use of a
taxonomy of eight empathetic response intents in addition to generic emotion
categories in building a dialogue response generation model capable of
generating empathetic responses in a controllable and interpretable manner. It
consists of two modules: 1) a response emotion/intent prediction module; and 2)
a response generation module. We propose several rule-based and neural
approaches to predict the next response's emotion/intent and generate responses
conditioned on these predicted emotions/intents. Automatic and human evaluation
results emphasize the importance of the use of the taxonomy of empathetic
response intents in producing more diverse and empathetically more appropriate
responses than end-to-end models.