Adaptive Information Presentation for Spoken Dialogue Systems: Evaluation with real users

We present evaluation results with human subjects for a novel data-driven approach to Natural Language Generation in spoken dialogue systems. We evaluate a trained Information Presentation (IP) strategy in a deployed tourist-information spoken dialogue system. The IP problem is formulated as statistical decision making under uncertainty using Reinforcement Learning, where both content planning and attribute selection are jointly optimised based on data collected in a Wizard-ofOz study. After earlier work testing and training this model in simulation, we now present results from an extensive online user study, involving 131 users and more than 800 test dialogues, which explores its contribution to overall ‘global’ task success. We find that the trained Information Presentation strategy significantly improves dialogue task completion, with up to a 9.7% increase (30% relative) compared to the deployed dialogue system which uses conventional, hand-coded presentation prompts. We also present subjective evaluation results and discuss the implications of these results for future work in dialogue management and NLG.