End-to-End Optimization of Task-Oriented Dialogue Model with Deep Reinforcement Learning

Understand

In this paper, we present a neural network based task-oriented dialogue system that can be optimized end-to-end with deep reinforcement learning (RL).

• The system is able to track dialogue state, interface with knowledge bases, and incorporate query results into agent's responses to successfully complete task-oriented dialogues.
• Dialogue policy learning is conducted with a hybrid supervised and deep RL methods.
• We first train the dialogue agent in a supervised manner by learning directly from task-oriented dialogue corpora, and further optimize it with deep RL during its interaction with users.