@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. }
}