DORA: Towards policy optimization for task-oriented dialogue system with efficient context. (March 2022) - Jeon, Hyunmin; Lee, Gary Geunbae
Abstract: Recently, reinforcement learning (RL) has been applied to task-oriented dialogue systems by using latent actions to solve shortcomings of supervised learning (SL). In this paper, we propose a multi-domain task-oriented dialogue system, called D ialogue System with O ptimizing a R ecurrent A ction Policy using Efficient Context (DORA), that uses SL, with subsequently applied RL to optimize dialogue systems using a recurrent dialogue policy. This dialogue policy recurrently generates explicit system actions as a both word-level and high-level policy. As a result, DORA is clearly optimized during both SL and RL steps by using an explicit system action policy that considers an efficient context instead of the entire dialogue history. The system actions are both interpretable and controllable, whereas the latent actions are not. DORA improved the success rate by 6.6 points on MultiWOZ 2.0 and by 10.9 points on MultiWOZ 2.1. Highlights: Recurrent action policy optimizes well the dialogue system with both supervised learning and reinforcement learning. Explicit system actions enable effective reward shaping for dialogue policy optimization. Optimized dialogue policy can be controlled by post-processing of system actions. Dialogue system operates as a hybrid system by system action control. Efficient input context can replace the entire dialogue history.