Affective feature knowledge interaction for empathetic conversation generation.

A popular chatbot can generate natural and human-like responses, and the crucial technology is the ability to understand and appreciate the emotions and demands expressed from the perspective of the user. However, some empathetic dialogue generation models only specialise in commonsense and neglect emotion, which can only get a one-sided understanding of the user's situation and makes the model unable to express emotion better. In this paper, we propose a novel affective feature knowledge interactive model named AFKI, to enhance response generation performance, which enriches conversation history to obtain emotional interactive context by leveraging fine-grained emotional features and commonsense knowledge. Furthermore, we utilise an emotional interactive context encoder to learn higher-level affective interaction information and distill the emotional state feature to guide the empathetic response generation. The emotional features are to well capture the subtle differences of the user's emotional expression, and the commonsense knowledge improves the representation of affective information on generated responses. Extensive experiments on the empathetic conversation task demonstrate that our model generates multiple responses with higher emotion accuracy and stronger empathetic ability compared with baseline model approaches for empathetic response generation. [ABSTRACT FROM AUTHOR]