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

WS 2016 Tiancheng ZhaoMaxine Eskenazi

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... (read more)

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