Causal Confusion Reduction for Robust Multi-Domain Dialogue Policy

In the multi-domain dialogue system, dialog policy plays an important role since it determines the suitable actions based on the user’s goals. However, in many recent works, most of the dialogue optimizations, especially that use reinforcement learning (RL) methods, do not perform well. The main problem is that the initial step of optimization that involves the behavior cloning (BC) methods suffer from the causal confusion problem, which means that the agent misidentifies true cause of an expert action in current state. This paper proposes a novel method to improve the performance of BC method in dialogue system. Instead of only predicting correct action given a state from dataset, we introduce the auxiliary tasks to predict both of current belief state and recent user utterance in order to reduce causal confusion of the expert action in the dataset since those features are important in every dialog turn. Experiments on ConvLab-2 shows that, by using this method, all of RL based optimizations are improved. Furthermore, the agent based on the proximal policy optimization shows very significant improvement with the help of the proposed BC agent weights both in policy evaluation as well as in end-to-end system evaluation.