A Sparse Plus Low-Rank Exponential Language Model for Limited Resource Scenarios

This paper describes a new exponential language model that decomposes the model parameters into one or more low-rank matrices that learn regularities in the training data and one or more sparse matrices that learn exceptions (e.g., keywords). The low-rank matrices induce continuous-space representat...

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Bibliographic Details
Published in:	IEEE/ACM transactions on audio, speech, and language processing Vol. 23; no. 3; pp. 494 - 504
Main Authors:	Hutchinson, Brian, Ostendorf, Mari, Fazel, Maryam
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.03.2015 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Adaptation models Algorithms Data models exponential History Language model log bilinear low-hyphen Mathematical models Matrix decomposition Natural language processing Regularity Representations sparse Sparse matrices Speech Training
ISSN:	2329-9290, 2329-9304
Online Access:	Get full text
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