Towards a task-oriented dialogue system based on the integration of understanding and generation modules using reinforcement learning

Abstract

In order to accomplish tasks, it is important for task-oriented dialogue systems to adapt to users and dialogue situations. However, in many systems, each module is developed separately and connected, which makes it difficult for a system to respond flexibly to unexpected users and dialogue situations. In this research, we aim to realize a system that can adapt to users and dialogue situations by making each module share its own information with others and learn how to behave in order to maximize the system performance through reinforcement learning. With dialogue simulations in a tourist domain, we confirmed that the proposed method leads to an improvement in the task completion rate.