@InProceedings{Supelec23,
author = {Olivier Pietquin},
title = {Réseau Bayésien pour un Modèle d'Utilisateur et un Module de Compréhension pour l’Optimisation des Systèmes de Dialogues},
year = {2005},
booktitle = {Actes de la 12ème Conférence Francophone sur le Traitement du Langage Naturel (TALN 2005)},
volume = {I},
pages = {481-486},
month = {June},
address = {Dourdan (France)},
abstract = {In this paper we present a modular environment for simulating human-machine dialogues by computer means. This environment includes a consistent goal-directed user model and a natural language understanding system model. Both models rely on a special Bayesian network used with different parameters in such a way that it can generate a consistent user behaviour according to a goal and the history of the interaction, and been used as a concept classifier. This environment was tested in the framework of optimal strategy learning for the simple form-filling task. The results show that the environment allows pointing out problematic dialogues that may occur because of misunderstanding between the user and the system.}
}