Multitask Generative Adversarial Imitation Learning for Multi-Domain Dialogue System

In the task-oriented dialogue system, dialog policy plays an important role since it determines the suitable actions based on the user's goals. However, in real situations, user's goals are varying so that the system needs to deal with the complex optimization problem for dialog policy. This paper presents a novel approach to build the multi-domain dialog system based on the multitask generative adversarial imitation learning (MGAIL). MGAIL combines hierarchical reinforcement learning and generative adversarial imitation learning where a mixture of generators are represented for multitask learning. Unlike the traditional imitation learning, this method decomposes each of complex tasks into several subtasks and builds the policy in a hierarchical way to relax the agent in handling multiple complex tasks. Experiments on a multi-domain dialogue system using MultiWOZ 2.1 under ConvLab-2 frame-work show that the proposed method outperforms the other reinforcement learning methods in system-wise evaluation in terms of complete rate, success rate and book rate.