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arXiv · 1606.02560

Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning

Tiancheng Zhao·Maxine Eskenazi
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Abstract

This paper presents an end-to-end framework for task-oriented dialog systems using a variant of Deep Recurrent Q-Networks (DRQN). The model is able to interface with a relational database and jointly learn policies for both language understanding and dialog strategy. Moreover, we propose a hybrid algorithm that combines the strength of reinforcement learning and supervised learning to achieve faster learning speed. We evaluated the proposed model on a 20 Question Game conversational game simulator. Results show that the proposed method outperforms the modular-based baseline and learns a distributed representation of the latent dialog state.

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BibTeXRIS

Tiancheng Zhao, Maxine Eskenazi. 2016-06-08. Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning. https://arxiv.org/abs/1606.02560

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