Paper: Optimising Information Presentation for Spoken Dialogue Systems

ACL ID P10-1103
Title Optimising Information Presentation for Spoken Dialogue Systems
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010

We present a novel approach to Informa- tion Presentation (IP) in Spoken Dialogue Systems (SDS) using a data-driven statis- tical optimisation framework for content planning and attribute selection. First we collect data in a Wizard-of-Oz (WoZ) ex- periment and use it to build a supervised model of human behaviour. This forms a baseline for measuring the performance of optimised policies, developed from this data using Reinforcement Learning (RL) methods. We show that the optimised poli- cies significantly outperform the baselines in a variety of generation scenarios: while the supervised model is able to attain up to 87.6% of the possible reward on this task, the RL policies are significantly better in 5 out of 6 scenarios, gaining up to 91.5% of the total possible reward. The RL policies...