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2012

A Ranking-based Approach to Word Reordering for Statistical Machine Translation

7 years 5 months ago
A Ranking-based Approach to Word Reordering for Statistical Machine Translation
Long distance word reordering is a major challenge in statistical machine translation research. Previous work has shown using source syntactic trees is an effective way to tackle this problem between two languages with substantial word order difference. In this work, we further extend this line of exploration and propose a novel but simple approach, which utilizes a ranking model based on word order precedence in the target language to reposition nodes in the syntactic parse tree of a source sentence. The ranking model is automatically derived from word aligned parallel data with a syntactic parser for source language based on both lexical and syntactical features. We evaluated our approach on largescale Japanese-English and English-Japanese machine translation tasks, and show that it can significantly outperform the baseline phrasebased SMT system.
Nan Yang, Mu Li, Dongdong Zhang, Nenghai Yu
Added 29 Sep 2012
Updated 29 Sep 2012
Type Journal
Year 2012
Where ACL
Authors Nan Yang, Mu Li, Dongdong Zhang, Nenghai Yu
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