@InCollection{Supelec629,
author = {Stéphane 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.}
}