Training a BN-based user model for dialogue simulation with missing data
Résumé
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.