Title: Task-oriented Dialogue System for Automatic Disease Diagnosis via Hierarchical Reinforcement Learning. Abstract: In this paper, we focus on automatic disease diagnosis with reinforcement learning (RL) methods in task-oriented dialogues setting. Different from conventional RL tasks, the action space for disease diagnosis (i.e., symptoms) is inevitably large, especially when the number of diseases increases. However, existing approaches to this problem typically works well in simple tasks but has significant challenges in complex scenarios. Inspired by the offline consultation process, we propose to integrate a hierarchical policy of two levels into the dialogue policy learning. The high level policy consists of a master model that is responsible for triggering a low level model, the low level policy consists of several symptom checkers and a disease classifier. Experimental results on both self-constructed real-world and synthetic datasets demonstrate that our hierarchical framework achieves higher accuracy and symptom recall in disease diagnosis compared with existing systems.
