Deep Reinforcement Learning for On-line Dialogue State Tracking

Zhi Chen; Lu Chen; Xiang Zhou; Kai Yu

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arXiv · 2009.10321

Deep Reinforcement Learning for On-line Dialogue State Tracking

Zhi Chen·Lu Chen·Xiang Zhou·Kai Yu

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Abstract

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.

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BibTeX RIS

Zhi Chen, Lu Chen, Xiang Zhou, Kai Yu. 2020-09-22. Deep Reinforcement Learning for On-line Dialogue State Tracking. https://arxiv.org/abs/2009.10321

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