Personality-aware Natural Language Generation for Task-oriented Dialogue using Reinforcement Learning

Ao Guo, Atsumoto Ohashi, Yuya Chiba, Yuiko Tsunomori, Ryu Hirai, Ryuichiro Higashinaka

Abstract: A task-oriented dialogue system capable of expressing personality can improve user engagement and satisfaction. To realize such a system, this paper presents a method of building a personality-aware natural language generation (NLG) module in task-oriented dialogue using reinforcement learning (RL). This method handles both the expression of personality and system intent. During the RL process, a positive reward is given when the generated utterance correctly expresses the assigned personality and conveys its system intent simultaneously. In our experiments on the MultiWOZ dataset, we fine-tuned a personality-aware NLG module for two personality traits (extraversion and neural perception sensitivity). We experimented with data from MultiWOZ and a user simulator to confirm its effectiveness in terms of the ability to express personality and task performance.