Discriminative Models for Spoken Language Understanding.

This paper studies several discriminative models for spoken language understanding (SLU). While all of them fall into the conditional model framework, different optimization criteria lead to conditional random fields, perceptron, minimum classification error and large margin models. The paper discusses the relationship amongst these models and compares them in terms of accuracy, training speed and robustness.

2006-wang-acero-icslp.pdf
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In  the International Conference on Spoken Language Processing

Publisher  International Speech Communication Association
© 2007 ISCA. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the ISCA and/or the author.

Details

TypeInproceedings
Pages1766-1769
AddressPittsburgh, PA, USA
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