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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

(p.151) 8 Reranking for Large-Scale Statistical Machine Translation
Learning Machine Translation

Kenji Yamada

Ion Muslea

The MIT Press

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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