Reinforcement Learning for Turn-Taking Management in Incremental Spoken Dialogue Systems
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. PDF
Cite
Text
Khouzaimi et al. "Reinforcement Learning for Turn-Taking Management in Incremental Spoken Dialogue Systems." International Joint Conference on Artificial Intelligence, 2016.Markdown
[Khouzaimi et al. "Reinforcement Learning for Turn-Taking Management in Incremental Spoken Dialogue Systems." International Joint Conference on Artificial Intelligence, 2016.](https://mlanthology.org/ijcai/2016/khouzaimi2016ijcai-reinforcement/)BibTeX
@inproceedings{khouzaimi2016ijcai-reinforcement,
title = {{Reinforcement Learning for Turn-Taking Management in Incremental Spoken Dialogue Systems}},
author = {Khouzaimi, Hatim and Laroche, Romain and Lefèvre, Fabrice},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2016},
pages = {2831-2837},
url = {https://mlanthology.org/ijcai/2016/khouzaimi2016ijcai-reinforcement/}
}