Optimizing Generative Dialog State Tracker via Cascading Gradient Descent
Byung-Jun Lee, Woosang Lim, Daejoong Kim, Kee-Eung Kim · 2014
For robust spoken dialog management, various dialog state tracking methods have been proposed. Although discriminative models are gaining popularity due to their superior performance, generative models based on the Partially Observable Markov Decision Process model still remain at-tractive since they provide an integrated framework for dialog state tracking and dialog policy optimization. Although a straightforward way to fit a generative model is to independently train the com-ponent probability models, we present a gradient descent algorithm that simultane-ously train all the component models. We show that the resulting tracker performs competitively with other top-performing trackers that participated in DSTC2. 1