@InProceedings{Supelec718,
author = {Senthilkumar Chandramohan and Matthieu Geist and Fabrice Lefèvre and Olivier Pietquin},
title = {User Simulation in Dialogue Systems using Inverse Reinforcement Learning},
year = {2011},
booktitle = {Proceedings of the 12th Annual Conference of the International Speech Communication Association (Interspeech 2011)},
pages = {1025-1028},
month = {August},
address = {Florence (Italy)},
url = {http://www.metz.supelec.fr//metz/personnel/pietquin/pdf/IS_2011_SCMGFLOP.pdf},
abstract = {Spoken Dialogue Systems (SDS) are man-machine interfaces which use natural language as the medium of interaction. Dialogue corpora generation for the purpose of training and evaluating dialogue systems is an expensive process. User simulators focus on simulating human users in order to generate synthetic data. Existing methods for user simulation mainly focus on generating data with the same statistical consistency as in the dialogue corpus. This paper outlines a novel approach for user simulation based on Inverse Reinforcement Learning (IRL). The task of building the user simulator is perceived as a task of imitation learning.}
}