Reinforcement Learning for Turn-Taking Management in Incremental Spoken Dialogue Systems
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.
@inproceedings{ijcai2016p402,
title = {Reinforcement Learning for Turn-Taking Management in Incremental Spoken Dialogue Systems},
author = {Khouzaimi, Hatim and Laroche, Romain and Lefèvre, Fabrice},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {2831--2836},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/402},
}
TY - CPAPER TI - Reinforcement Learning for Turn-Taking Management in Incremental Spoken Dialogue Systems AU - Khouzaimi, Hatim AU - Laroche, Romain AU - Lefèvre, Fabrice T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 2831 EP - 2836 UR - https://ijcai.org/proceedings/2016/402 ER -Copied
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