---
title: "Reinforcement Learning for Turn-Taking Management in Incremental Spoken Dialogue Systems"
authors:
  - "Hatim Khouzaimi"
  - "Romain Laroche"
  - "Fabrice Lefèvre"
doi: ""
url: "https://ijcai.org/proceedings/2016/402"
pdf: "https://ijcai.org/proceedings/2016/0402.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "2831-2836"
track: ""
keywords:
---

# Reinforcement Learning for Turn-Taking Management in Incremental Spoken Dialogue Systems

Hatim Khouzaimi, Romain Laroche, Fabrice Lefèvre

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 2831-2836.

## Abstract

In this article, reinforcement learning is used to learn an optimal turn-taking strategy for vocal human-machine dialogue. The Orange Labs' Majordomo dialogue system, which allows the users to have conversations within a smart home, has been upgraded to an incremental version. First, a user simulator is built in order to generate a dialogue corpus which thereafter is used to optimise the turn-taking strategy from delayed rewards with the Fitted-Q reinforcement learning algorithm. Real users test and evaluate the new learnt strategy, versus a non-incremental and a handcrafted incremental strategies. The data-driven strategy is shown to significantly improve the task completion ratio and to be preferred by the users according to subjective metrics.