@inproceedings{khouzaimi2016reinforcement,
author = {Khouzaimi, Hatim and Laroche, Romain and Lefevre, Fabrice},
title = {Reinforcement Learning for Turn-Taking Management in Incremental Spoken Dialogue Systems},
booktitle = {Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI)},
year = {2016},
month = {July},
abstract = {In this article, reinforcement learning is used to learn an optimal turn-taking strategy for vocal human-machine dialogue. The Orange Labs' Ma-jordomo 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 incremen-tal 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.},
url = {http://approjects.co.za/?big=en-us/research/publication/reinforcement-learning-turn-taking-management-incremental-spoken-dialogue-systems/},
edition = {Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI)},
}