@InProceedings{Supelec731,
author = {Stéphane Rossignol and Michel Ianotto and Olivier Pietquin},
title = {{Training a BN-based user model for dialogue simulation with missing data}},
year = {2011},
booktitle = {{Proceedings of the International Joint Conference on Natural Language Processing (IJCNLP 2011)}},
publisher = {ACLWeb},
pages = {598-604},
month = {November},
address = {Chiang Mai (Thailand)},
url = {http://www.aclweb.org/anthology/I/I11/I11-1067.pdf},
abstract = {The design of a Spoken Dialogue System (SDS) is a long,
iterative
and costly process. Especially, it requires test phases on
actual
users either for assessment of performance or optimization. The
number of test phases should be minimized, yet without
degrading the
final performance of the system. For these reasons, there has
been
an increasing interest for dialogue simulation during the last
decade. Dialogue simulation requires simulating the behavior of
users and therefore requires user modeling. User simulation is
often
done by statistical systems that have to be tuned or trained on
data. Yet data are generally incomplete with regard to the
necessary
information for simulating the user decision making process. For
example, the internal knowledge the user builds along the
conversation about the information exchanged while interacting
is
difficult to annotate.
In this contribution, we propose the use of a previously
developed
user simulation system based on Bayesian Networks (BN) and the
training of this model using algorithms dealing with missing
data.
Experiments show that this training method increases the
simulation
performance in terms of similarity with real dialogues.}
}