@InProceedings{Supelec246,
author = {Olivier Pietquin},
title = {{Learning to Ground in Spoken Dialogue Systems}},
year = {2007},
booktitle = {{Proceedings of the 32nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)}},
volume = {IV},
pages = {165-168},
month = {April},
address = {Honolulu (Hawaii, USA)},
url = {http://hal-supelec.archives-ouvertes.fr/hal-00213410/fr/},
abstract = {Machine learning methods such as reinforcement learning applied
to dialogue strategy optimization has become a leading subject
of researches since the mid 90’s. Indeed, the great variability
of factors to take into account makes the design of a spoken
dialogue system a tailoring task and reusability of previous
work is very difficult. 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 present a
user model for realistic spoken dialogue simulation and a
method for using this model so as to simulate the grounding
process. This allows including grounding subdialogues as
actions in the reinforcement learning process and learning
adapted strategy.}
}