A discriminative sequence model for dialog state tracking using user goal change detection

Abstract

Due to the dominating influence of Partially Observable Markov Decision Process (POMDP) framework used in spoken dialog systems, most previously proposed dialog state tracking methods favor generative models. However, in this work we adopt a discriminative approach to model the evolution of the belief state within a spoken dialog system - more specifically, we use Conditional Random Fields (CRFs). Although we are not the first to apply CRFs to dialog state tracking, the proposed approach considers the dialog state tracking task as a sequence tagging problem, in the hope of capturing the evolving user goals during a dialog. Equipped with an incremental decoding strategy as well as user goal change detection, our results show that both sequence modeling and goal change information could bring advantage to the task.