@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.}
}