Learning Decision Models in Spoken Dialogue Systems via User Simulation

Contents

This paper describes a set of experiments designed to explore the utility of simulated dialogues and automatic rule induction in spoken dialogue systems. The experiments were conducted within a flight domain task, where the user supplies source, destination, and date to the system. The system was configured to support explicitly about 500 large cities; any other cities could only be recovered through a spell-mode subdialogue. Two specific problems were identified: the conflict problem, and the compliance problem. A RIPPER-based rule induction algorithm was applied to data from user simulation runs, and the resulting system was compared against a manually developed baseline system. The learned rules performed significantly better than the manual ones for a number of different measures of success, for both simulations and real user dialogues.