Optimising Information Presentation for Spoken Dialogue Systems
Verena Rieser, Oliver Lemon, Xingkun Liu · 2010
We present a novel approach to Information Presentation (IP) in Spoken Dialogue Systems (SDS) using a data-driven statistical optimisation framework for content planning and attribute selection. First we collect data in a Wizard-of-Oz (WoZ) experiment 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 policies 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 perform especially well in more complex scenarios. We are also the first to show that adding predictive “lower level ” features (e.g. from the NLG realiser) is important for optimising IP strategies according to user preferences. This provides new insights into the nature of the IP problem for SDS. 1