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

Understand

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.