Dynamic Bayesian Networks for NLU Simulation with Applications to Dialog Optimal Strategy Learning

In this paper, we propose to add a model for NLU-related error generation in a modular environment for computer-based simulation of man-machine spoken dialogs. This model is jointly designed with a user model. Both of them are based on the same underlying Bayesian network used with different parameters in such a way that it can generate a consistent user behavior, according to a goal and the interaction history, and been used as a concept classifier. The proposed simulation environment was used to train a reinforcement-learning algorithm on a simple form-filling task and the results of this experiment show that the addition of the NLU model helps pointing out problematic situations that may occur because of misunderstandings and modifying the dialog strategy accordingly