@InProceedings{Supelec792,
author = {Senthilkumar Chandramohan and Matthieu Geist and Fabrice Lefèvre and Olivier Pietquin},
title = {{Behavior Specific User Simulation in Spoken Dialogue Systems}},
year = {2012},
booktitle = {{Proceedings of the 10th ITG Conference on Speech Communication}},
pages = {1 - 4},
month = {September},
note = {http://www.metz.supelec.fr/~geist_mat/pdfs/Supelec792.pdf},
address = {Braunschweig (Germany)},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp\'ereload=true\&arnumber=6309603},
abstract = {Spoken dialogue systems provide an opportunity for man machine
interaction using spoken language as the medium of interaction.
In recent years reinforcement learning-based dialogue policy
optimization has evolved to be state of the art. In order to
cope with the data requirement for policy optimization and also
to evaluate dialogue policies user simulators are introduced.
Almost all existing data driven methods for user modelling aims
at simulating some generic user behavior from some reference
dialogue corpus. However, this corpus consists of dialogues
from multiple users and thus exhibit different user behaviors.
In this paper we explore the possibility of identifying and
simulating different user behaviors observed in the corpus. For
this purpose inverse reinforcement learning-based user
simulation method is employed. Using experimental results, we
validate the effectiveness of the proposed method for building
multiple behavior specific user simulators. }
}