  Generating appropriate emotions for responses is essential for dialog systems
to provide human-like interaction in various application scenarios. Most
previous dialog systems tried to achieve this goal by learning empathetic
manners from anonymous conversational data. However, emotional responses
generated by those methods may be inconsistent, which will decrease user
engagement and service quality. Psychological findings suggest that the
emotional expressions of humans are rooted in personality traits. Therefore, we
propose a new task, Personality-affected Emotion Generation, to generate
emotion based on the personality given to the dialog system and further
investigate a solution through the personality-affected mood transition.
Specifically, we first construct a daily dialog dataset, Personality
EmotionLines Dataset (PELD), with emotion and personality annotations.
Subsequently, we analyze the challenges in this task, i.e., (1) heterogeneously
integrating personality and emotional factors and (2) extracting
multi-granularity emotional information in the dialog context. Finally, we
propose to model the personality as the transition weight by simulating the
mood transition process in the dialog system and solve the challenges above. We
conduct extensive experiments on PELD for evaluation. Results suggest that by
adopting our method, the emotion generation performance is improved by 13% in
macro-F1 and 5% in weighted-F1 from the BERT-base model.
