Skip to content. | Skip to navigation

Personal tools
Log in
Sections
You are here: Home Papers Optimizing Generative Dialog State Tracker via Cascading Gradient Descent

Byung-Jun Lee, Woosang Lim, and Kee-Eung Kim (2014)

Optimizing Generative Dialog State Tracker via Cascading Gradient Descent

In: Proceedings of the SIGDIAL 2014 Conference, pp. 273–281.

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 attractive 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 component probability models, we present a gradient descent algorithm that simultaneously train all the component models. We show that the resulting tracker performs competitively with other top-performing trackers that participated in DSTC2.