@inproceedings{f3cf04f80c8c49439754cb65e9f6d31e,
title = "Optimizing generative dialog state tracker via cascading gradient descent",
abstract = "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.",
author = "Lee, {Byung Jun} and Woosang Lim and Daejoong Kim and Kim, {Kee Eung}",
note = "Publisher Copyright: {\textcopyright} 2014 Association for Computational Linguistics.; 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue, SIGDIAL 2014 ; Conference date: 18-06-2014 Through 20-06-2014",
year = "2014",
doi = "10.3115/v1/w14-4338",
language = "English",
series = "SIGDIAL 2014 - 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "273--281",
booktitle = "SIGDIAL 2014 - 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Proceedings of the Conference",
}