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

TL;DR

This paper introduces an end-to-end neural network system for task-oriented dialogues optimized with deep reinforcement learning, improving success rates and efficiency over traditional methods.

Contribution

It presents a novel hybrid supervised and deep RL training approach for dialogue systems, enabling end-to-end optimization and better performance.

Findings

Deep RL improves task success rate significantly.

End-to-end training outperforms component-wise optimization.

Model demonstrates robust dialogue state tracking and response generation.

Abstract

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. In the experiments on two different dialogue task domains, our model demonstrates robust performance in tracking dialogue state and producing reasonable system responses. We show that deep RL based optimization leads to significant improvement on task success rate and reduction in dialogue length comparing to supervised training model. We further show benefits of training task-oriented dialogue model end-to-end comparing to component-wise optimization with experiment results on dialogue simulations and human evaluations.