@InCollection{Supelec629,
author = {St├ęphane Rossignol and Olivier Pietquin and Michel Ianotto},
title = {Grounding Simulation in Spoken Dialog Systems with Bayesian Networks},
year = {2010},
booktitle = {Spoken Dialogue Systems for Ambient Environments},
publisher = {Springer-Verlag, Heidelberg-Berlin},
volume = {6392},
pages = {110-121},
month = {October},
note = {Proceedings of the 2nd International Workshop on Spoken Dialogue Systems (IWSDS 2010)},
editor = {Gary Geunbae Lee and Joseph Mariani and Wolfgang Minker and Satoshi Nakamura},
series = {Lecture Notes in Artificial Intelligence (LNAI)},
address = {Gotemba, Shizuoka (Japan)},
url = {http://www.metz.supelec.fr//metz/personnel/pietquin/pdf/IWSDS_2010_SRMIOP.pdf},
isbn = {978-3-642-16201-5},
abstract = {User simulation has become an important trend of research in the field of spoken dialog systems because collecting and annotating real man-machine interactions with users is often expensive and time consuming. Yet, such data are generally required for designing and assessing efficient dialog systems. The general problem of user simulation is thus to produce as many as necessary natural, various and consistent interactions from as few data as possible. In this paper, is proposed a user simulation method based on Bayesian Networks (BN) that is able to produce consistent interactions in terms of user goal and dialog history but also to simulate the grounding process that often appears in human-human interactions. The BN is trained on a database of 1234 human-machine dialogs in the TownInfo domain (a tourist information application). Experiments with a stateof- the-art dialog system (REALL-DUDE/DIPPER/OAA) have been realized and promising results are presented.}
}