Emotional Intensity Response Generation Model

Abstract

Emotional dialogue generation models do not consider the emotional intensity factor in response generation, which leads to the inappropriateness of the emotional expression in generated response, and reduces the user interaction experience. Inspired by the work of emotional intensity in emotional psychology, this paper proposes an emotional intensity response generation model (EIRGM), which includes an emotional intensity prediction unit, a context encoding module and an emotional intensity response generation unit. An emotional intensity prediction unit provides emotion categories and emotional intensity for reply sentences; a context encoding module provides content basis for response; an emotional intensity response generation unit is used to express the emotion and intensity in response. Based on the NLPCC2018 open-domain dialogue dataset, experiments are carried out in terms of emotional appropriateness, emotional intensity appropriateness, content relevance, and dialogue persistence. Experimental results show that EIRGM is not much different from the optimal model in terms of emotional appropriateness, and EIRGM is improved by 4.1 percentage points and 0.8 percentage points compared with the optimal model in terms of emotional intensity appropriateness and dialogue persistence, respectively. It shows that the model improves the emotional intensity appropriateness of emotional expression, and improves the user’s willingness to interact.

Keywords

• emotional intensity; emotion categories; emotional dialogue; response generation; emotional expression