# Distributed Structured Actor-Critic Reinforcement Learning for Universal Dialogue Management

arXiv: https://arxiv.org/abs/2009.10326 · HTML: https://mlsanity.com/paper/2009.10326/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2020.

## Built on by (most cited first, 12 of 12)

- [META-GUI: Towards Multi-modal Conversational Agents on Mobile GUI](https://mlsanity.com/paper/2205.11029/) (2022)
- [ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser](https://mlsanity.com/paper/2104.04689/) (2021)
- [Survey on reinforcement learning for language processing](https://mlsanity.com/paper/2104.05565/) (2021)
- [LET: Linguistic Knowledge Enhanced Graph Transformer for Chinese Short Text Matching](https://mlsanity.com/paper/2102.12671/) (2021)
- [Dynamic Dialogue Policy for Continual Reinforcement Learning](https://mlsanity.com/paper/2204.05928/) (2022)
- [UniDU: Towards A Unified Generative Dialogue Understanding Framework](https://mlsanity.com/paper/2204.04637/) (2022)
- [Uncertainty Measures in Neural Belief Tracking and the Effects on Dialogue Policy Performance](https://mlsanity.com/paper/2109.04349/) (2021)
- [TIE: Topological Information Enhanced Structural Reading Comprehension on Web Pages](https://mlsanity.com/paper/2205.06435/) (2022)
- [OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue](https://mlsanity.com/paper/2209.04595/) (2022)
- [What Does The User Want? Information Gain for Hierarchical Dialogue Policy Optimisation](https://mlsanity.com/paper/2109.07129/) (2021)
- [Graph Neural Network Policies and Imitation Learning for Multi-Domain Task-Oriented Dialogues](https://mlsanity.com/paper/2210.05252/) (2022)
- [Few-Shot Structured Policy Learning for Multi-Domain and Multi-Task Dialogues](https://mlsanity.com/paper/2302.11199/) (2023)

## Abstract (arXiv)

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.