# Graph Neural Network Policies and Imitation Learning for Multi-Domain Task-Oriented Dialogues

arXiv: https://arxiv.org/abs/2210.05252 · HTML: https://mlsanity.com/paper/2210.05252/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 11 Oct 2022.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [Sample Efficient Actor-Critic with Experience Replay](https://mlsanity.com/paper/1611.01224/) (2016)
- [Neural Approaches to Conversational AI](https://mlsanity.com/paper/1809.08267/) (2018)
- [ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems](https://mlsanity.com/paper/2002.04793/) (2020)
- [Distributed Structured Actor-Critic Reinforcement Learning for Universal Dialogue Management](https://mlsanity.com/paper/2009.10326/) (2020)
- [Feudal Reinforcement Learning for Dialogue Management in Large Domains](https://mlsanity.com/paper/1803.03232/) (2018)
- [Learning Dialog Policies from Weak Demonstrations](https://mlsanity.com/paper/2004.11054/) (2020)
- [Sub-domain Modelling for Dialogue Management with Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1706.06210/) (2017)
- [A Benchmarking Environment for Reinforcement Learning Based Task Oriented Dialogue Management](https://mlsanity.com/paper/1711.11023/) (2017)
- [Diluted Near-Optimal Expert Demonstrations for Guiding Dialogue Stochastic Policy Optimisation](https://mlsanity.com/paper/2012.04687/) (2020)
- [Structured Hierarchical Dialogue Policy with Graph Neural Networks](https://mlsanity.com/paper/2009.10355/) (2020)

## Built on by (most cited first, 2 of 2)

- [Exploring ReAct Prompting for Task-Oriented Dialogue: Insights and Shortcomings](https://mlsanity.com/paper/2412.01262/) (2024)
- [Bridging Reasoning and Action: Hybrid LLM-RL Framework for Efficient Cross-Domain Task-Oriented Dialogue](https://mlsanity.com/paper/2604.23345/) (2026)

## Abstract (arXiv)

Task-oriented dialogue systems are designed to achieve specific goals while conversing with humans. In practice, they may have to handle simultaneously several domains and tasks. The dialogue manager must therefore be able to take into account domain changes and plan over different domains/tasks in order to deal with multidomain dialogues. However, learning with reinforcement in such context becomes difficult because the state-action dimension is larger while the reward signal remains scarce. Our experimental results suggest that structured policies based on graph neural networks combined with different degrees of imitation learning can effectively handle multi-domain dialogues. The reported experiments underline the benefit of structured policies over standard policies.