Fast and optimal decoding for machine translation

A good decoding algorithm is critical to the success of any statistical machine translation system. The decoder's job is to find the translation that is most likely according to a set of previously learned parameters (and a formula for combining them). Since the space of possible translations i...

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Bibliographic Details
Published in:Artificial intelligence Vol. 154; no. 1; pp. 127 - 143
Main Authors: Germann, Ulrich, Jahr, Michael, Knight, Kevin, Marcu, Daniel, Yamada, Kenji
Format: Journal Article
Language:English
Published: Elsevier B.V 01.04.2004
Subjects:
Artificial intelligence
Decoding
Machine translation
SMT
Statistical machine translation
Decoding
SMT
Statistical machine translation
Machine translation
MT
ISSN:0004-3702, 1872-7921
Online Access:Get full text
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Summary:A good decoding algorithm is critical to the success of any statistical machine translation system. The decoder's job is to find the translation that is most likely according to a set of previously learned parameters (and a formula for combining them). Since the space of possible translations is extremely large, typical decoding algorithms are only able to examine a portion of it, thus risking to miss good solutions. Unfortunately, examining more of the space leads to unacceptably slow decodings. In this paper, we compare the speed and output quality of a traditional stack-based decoding algorithm with two new decoders: a fast but non-optimal greedy decoder and a slow but optimal decoder that treats decoding as an integer-programming optimization problem.
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ISSN:0004-3702
1872-7921
DOI:10.1016/j.artint.2003.06.001