Jinxian Qu

Dream to Chat: Model-based Reinforcement Learning on Dialogues with User Belief Modeling

World models have been widely utilized in robotics, gaming, and autonomous driving. However, their applications to natural language tasks are relatively limited. In this paper, we construct the dialogue world model, which could predict future utterances and user beliefs, including emotion, sentiment