Dialogue Environments are Different from Games: Investigating Variants of Deep Q-Networks for Dialogue Policy

摘要

The dialogue manager is an important component in a task-oriented dialogue system, which focuses on deciding dialogue policy given the dialogue state in order to fulfill the user goal. Learning dialogue policy is usually framed as a reinforcement learning (RL) problem, where the objective is to maximize the reward indicating whether the conversation is successful and how efficient it is. However, even there are many variants of deep Q-networks (DQN) achieving better performance on game playing scenarios, no prior work analyzed the performance of dialogue policy learning using these improved versions. Considering that dialogue interactions differ a lot from game playing, this paper investigates variants of DQN models together with different exploration strategies in a benchmark experimental setup, and then we examine which RL methods are more suitable for task-completion dialogue policy learning^{11The code is available at https://github.com/MiuLab/Dialogue-DQN-Variants.}