@InProceedings{Supelec13,
author = {Olivier Pietquin},
title = {{Consistent Goal-Directed User Model for Realistic Man-Machine Task-Oriented Spoken Dialogue Simulation}},
year = {2006},
booktitle = {{Proceedings of the 7th IEEE International Conference on Multimedia and Expo}},
pages = {425-428},
month = {July},
address = {Toronto (Canada)},
url = {http://hal-supelec.archives-ouvertes.fr/hal-00215968/fr/},
abstract = {Because of the great variability of factors to take into
account, designing a spoken dialogue system is still a
tailoring task. Rapid design and reusability of previous work
is made very difficult. For these reasons, the application of
machine learning methods to dialogue strategy optimization has
become a leading subject of researches this last decade. Yet,
techniques such as reinforcement learning are very demanding in
training data while obtaining a substantial amount of data in
the particular case of spoken dialogues is time-consuming and
therefore expansive. In order to expand existing data sets,
dialogue simulation techniques are becoming a standard
solution. In this paper we describe a user modeling technique
for realistic simulation of man-machine goal-directed spoken
dialogues. This model, based on a stochastic description of man-
machine communication, unlike previously proposed models, is
consistent along the interaction according to its history and a
predefined user goal.
}
}