LAUSR

It is important for chatbots to emotionally communicate with users. However, most emotional response generation models generate responses simply based on a specified emotion, neglecting the impacts of speaker’s personality on emotional expression. In this work, we propose a novel model named GERP to generate emotional responses based on the pre-defined personality. GERP simulates the emotion conversion process of humans during the conversation to make the chatbot more anthropomorphic. GERP adopts the OCEAN model to precisely define the chatbot’s personality. It can generate the response containing the emotion predicted based on the personality. Specifically, to select the most-appropriate response, a proposed beam evaluator was integrated into GERP. A Chinese sentiment vocabulary and a Chinese emotional response dataset were constructed to facilitate the emotional response generation task. The effectiveness and superiority of the proposed model over five baseline models was verified by the experiments.