  In empathetic conversations, humans express their empathy to others with
empathetic intents. However, most existing empathetic conversational methods
suffer from a lack of empathetic intents, which leads to monotonous empathy. To
address the bias of the empathetic intents distribution between empathetic
dialogue models and humans, we propose a novel model to generate empathetic
responses with human-consistent empathetic intents, EmpHi for short. Precisely,
EmpHi learns the distribution of potential empathetic intents with a discrete
latent variable, then combines both implicit and explicit intent representation
to generate responses with various empathetic intents. Experiments show that
EmpHi outperforms state-of-the-art models in terms of empathy, relevance, and
diversity on both automatic and human evaluation. Moreover, the case studies
demonstrate the high interpretability and outstanding performance of our model.
