# Continuously Learning Neural Dialogue Management

arXiv: https://arxiv.org/abs/1606.02689 · HTML: https://mlsanity.com/paper/1606.02689/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2016.

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

- [The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models](https://mlsanity.com/paper/2404.16019/) (2024)
- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [CLAM: Selective Clarification for Ambiguous Questions with Generative Language Models](https://mlsanity.com/paper/2212.07769/) (2022)
- [Neural Approaches to Conversational Information Retrieval](https://mlsanity.com/paper/2201.05176/) (2022)
- [CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning](https://mlsanity.com/paper/2204.08426/) (2022)
- [A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-Oriented Dialogue Policy Learning](https://mlsanity.com/paper/2202.13675/) (2022)
- [Context-Aware Language Modeling for Goal-Oriented Dialogue Systems](https://mlsanity.com/paper/2204.10198/) (2022)
- [Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning](https://mlsanity.com/paper/1801.06176/) (2018)
- [Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning](https://mlsanity.com/paper/1811.07550/) (2018)
- [Dynamic Dialogue Policy for Continual Reinforcement Learning](https://mlsanity.com/paper/2204.05928/) (2022)
- [Dialog Action-Aware Transformer for Dialog Policy Learning](https://mlsanity.com/paper/2309.02240/) (2023)
- [Many Hands Make Light Work: Task-Oriented Dialogue System with Module-Based Mixture-of-Experts](https://mlsanity.com/paper/2405.09744/) (2024)

## Abstract (arXiv)

We describe a two-step approach for dialogue management in task-oriented spoken dialogue systems. A unified neural network framework is proposed to enable the system to first learn by supervision from a set of dialogue data and then continuously improve its behaviour via reinforcement learning, all using gradient-based algorithms on one single model. The experiments demonstrate the supervised model's effectiveness in the corpus-based evaluation, with user simulation, and with paid human subjects. The use of reinforcement learning further improves the model's performance in both interactive settings, especially under higher-noise conditions.