2 research outputs found
Task-oriented Dialogue System for Automatic Disease Diagnosis via Hierarchical Reinforcement Learning
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 employ a flat RL policy, which typically
works well in simple tasks but has significant challenges in complex scenarios
like disease diagnosis. Towards this end, we propose to integrate a
hierarchical policy of two levels into the dialogue policy learning. The high
level policy consists of a model named master that is responsible for
triggering a model in low level, 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 in disease diagnosis compared
with existing systems. Besides, the datasets
(http://www.sdspeople.fudan.edu.cn/zywei/data/Fudan-Medical-Dialogue2.0) and
codes (https://github.com/nnbay/MeicalChatbot-HRL) are all available now