From Traits to Empathy: Personality-Aware Multimodal Empathetic Response Generation

Abstract

Empathetic dialogue systems improve user experience across various domains. Existing approaches mainly focus on acquiring affective and cognitive knowledge from text, but neglect the unique personality traits of individuals and the inherently multimodal nature of human face-to-face conversation. To this end, we enhance the dialogue system with the ability to generate empathetic responses from a multimodal perspective, and consider the diverse personality traits of users. We incorporate multimodal data, such as images and texts, to understand the user’s emotional state and situation. Concretely, we first identify the user’s personality trait. Then, the dialogue system comprehends the user’s emotions and situation by the analysis of multimodal inputs. Finally, the response generator models the correlations among the personality, emotion, and multimodal data, to generate empathetic responses. Experiments on the MELD dataset and the MEDIC dataset validate the effectiveness of the proposed approach.