Syntactic Phrase Reordering for English-to-Arabic Statistical Machine Translation

By Ibrahim Badr, Rabih Zbib, James R. Glass. 2009

39 citations recorded by the source corpus.

Syntactic Reordering of the source language to better match the phrase structure of the target language has been shown to improve the performance of phrase-based Statistical Machine Translation. This paper applies syntactic reordering to English-to-Arabic translation. It introduces reordering rules, and motivates them linguistically. It also studies the effect of combining reordering with Arabic morphological segmentation, a preprocessing technique that has been shown to improve Arabic-English and English-Arabic translation. We report on results in the news text domain, the UN text domain and in the spoken travel domain.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Nizar Habash
2. Mohammed Attia

Citations in the corpus

• Book Reviews: Introduction to Arabic Natural Language Processing by Nizar Y. Habash (2010, background): “The majority of Arabic MT research is on Arabic-English; however, there are some published efforts in English-Arabic[99, 188, 13, 106] Arabic-French[190] and even Arabic-Chinese [191], Danish-Arabic [192] and Hebrew-Arabic [193].”