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Learning Machine Translation$
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Cyril Goutte, Nicola Cancedda, Marc Dymetman, and George Foster

Print publication date: 2008

Print ISBN-13: 9780262072977

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262072977.001.0001

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Reranking for Large-Scale Statistical Machine Translation

Reranking for Large-Scale Statistical Machine Translation

Chapter:
(p.151) 8 Reranking for Large-Scale Statistical Machine Translation
Source:
Learning Machine Translation
Author(s):

Kenji Yamada

Ion Muslea

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262072977.003.0008

Statistical machine translation (SMT) systems, which are trained on parallel corpora of bilingual text (e.g., French and English), typically work as follows: for each sentence to be translated, they generate a plethora of possible translations, from which they keep a smaller n-best list of the most likely translations. Even though the typical n-best list contains mostly high-quality candidates, the actual ranking is far from accurate. This chapter presents a novel approach to reranking the n-best list produced by an SMT system. It uses an ensemble of perceptrons that are trained in parallel, each of them on just a fraction of the available data. Experiments were performed on two large-scale commercial systems: a Chinese-to-English system trained on 80 million words and a French-to-English system trained on 1.1 billion words. The reranker obtained statistically significant improvements of about 0.5 and 0.2 BLEU points on the Chinese-to-English and the French-to-English system, respectively.

Keywords:   reranking, n-best list, statistical machine translation, perceptrons, Chinese-to-English system, French-to-English system

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