# Modeling Multi-Action Policy for Task-Oriented Dialogues

arXiv: https://arxiv.org/abs/1908.11546 · HTML: https://mlsanity.com/paper/1908.11546/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2019.

## Built on by (most cited first, 4 of 4)

- [A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-Oriented Dialogue Policy Learning](https://mlsanity.com/paper/2202.13675/) (2022)
- [Dynamic Dialogue Policy for Continual Reinforcement Learning](https://mlsanity.com/paper/2204.05928/) (2022)
- ["Think Before You Speak": Improving Multi-Action Dialog Policy by Planning Single-Action Dialogs](https://mlsanity.com/paper/2204.11481/) (2022)
- [Multi-Action Dialog Policy Learning from Logged User Feedback](https://mlsanity.com/paper/2302.13505/) (2023)

## Abstract (arXiv)

Dialogue management (DM) plays a key role in the quality of the interaction with the user in a task-oriented dialogue system. In most existing approaches, the agent predicts only one DM policy action per turn. This significantly limits the expressive power of the conversational agent and introduces unwanted turns of interactions that may challenge users' patience. Longer conversations also lead to more errors and the system needs to be more robust to handle them. In this paper, we compare the performance of several models on the task of predicting multiple acts for each turn. A novel policy model is proposed based on a recurrent cell called gated Continue-Act-Slots (gCAS) that overcomes the limitations of the existing models. Experimental results show that gCAS outperforms other approaches. The code is available at https://leishu02.github.io/