Learning with Weighted Transducers

Authors:
Corinna Cortes;Mehryar Mohri
Affiliations:
Google Research, 76 Ninth Avenue, New York, NY 10011;Courant Institute of Mathematical Sciences and Google Research, 251 Mercer Street, New York, NY 10012
Venue:
Proceedings of the 2009 conference on Finite-State Methods and Natural Language Processing: Post-proceedings of the 7th International Workshop FSMNLP 2008
Year:
2009

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Abstract

Weighted finite-state transducers have been used successfully in a variety of natural language processing applications, including speech recognition, speech synthesis, and machine translation. This paper shows how weighted transducers can be combined with existing learning algorithms to form powerful techniques for sequence learning problems.