A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-Oriented Dialogue Policy Learning

Abstract

Dialogue Policy Learning is a key component in a task-oriented dialogue system (TDS) that decides the next action of the system given the dialogue state at each turn. Reinforcement Learning (RL) is commonly chosen to learn the dialogue policy, regarding the user as the environment and the system as the agent. Many benchmark datasets and algorithms have been created to facilitate the development and evaluation of dialogue policy based on RL. In this paper, we survey recent advances and challenges in dialogue policy from the prescriptive of RL. More specifically, we identify the major problems and summarize corresponding solutions for RL-based dialogue policy learning. Besides, we provide a comprehensive survey of applying RL to dialogue policy learning by categorizing recent methods into basic elements in RL. We believe this survey can shed a light on future research in dialogue management.

References in corpus (6)

• Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System
• Why We Need New Evaluation Metrics for NLG
• An Application of Reinforcement Learning to Dialogue Strategy Selection in a Spoken Dialogue System for Email
• Speaker Role Contextual Modeling for Language Understanding and Dialogue Policy Learning
• AutoEG: Automated Experience Grafting for Off-Policy Deep Reinforcement Learning
• Integrating Pretrained Language Model for Dialogue Policy Learning

Cited by in corpus (2)

• Towards a Formal Characterization of User Simulation Objectives in Conversational Information Access
• Learning Top-k Subtask Planning Tree based on Discriminative Representation Pre-training for Decision Making