Dialogue state tracking (DST) is a crucial module in dialogue management. It is usually cast as a supervised training problem, which is not convenient for on-line optimization. In this paper, a novel companion teaching based deep reinforcement learning (DRL) framework for on-line DST optimization is proposed. To the best of our knowledge, this is the first effort to optimize the DST module within DRL framework for on-line task-oriented spoken dialogue systems. In addition, dialogue policy can be further jointly updated. Experiments show that on-line DST optimization can effectively improve the dialogue manager performance while keeping the flexibility of using predefined policy. Joint training of both DST and policy can further improve the performance.
@article{arxiv.2009.10321,
title = {Deep Reinforcement Learning for On-line Dialogue State Tracking},
author = {Zhi Chen and Lu Chen and Xiang Zhou and Kai Yu},
journal= {arXiv preprint arXiv:2009.10321},
year = {2020}
}