Distributed Structured Actor-Critic Reinforcement Learning for Universal Dialogue Management
This work addresses dialogue management for task-oriented spoken dialogue systems, but it appears incremental as it builds on existing deep reinforcement learning frameworks without claiming major breakthroughs.
The paper tackles the problem of dialogue decision-making in task-oriented spoken dialogue systems by proposing a distributed structured actor-critic reinforcement learning method, but no concrete results or numbers are provided in the abstract.
The task-oriented spoken dialogue system (SDS) aims to assist a human user in accomplishing a specific task (e.g., hotel booking). The dialogue management is a core part of SDS. There are two main missions in dialogue management: dialogue belief state tracking (summarising conversation history) and dialogue decision-making (deciding how to reply to the user). In this work, we only focus on devising a policy that chooses which dialogue action to respond to the user. The sequential system decision-making process can be abstracted into a partially observable Markov decision process (POMDP). Under this framework, reinforcement learning approaches can be used for automated policy optimization. In the past few years, there are many deep reinforcement learning (DRL) algorithms, which use neural networks (NN) as function approximators, investigated for dialogue policy.