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A dependency-based word reordering approach for Statistical Machine Translation

A dependency-based word reordering approach for Statistical Machine Translation,10.1109/RIVF.2008.4586343,Vu Hoang,Mai Ngo,Dien Dinh

A dependency-based word reordering approach for Statistical Machine Translation  
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Reordering is of crucial importance for machine translation. Solving the reordering problem can lead to remarkable improvements in translation performance. In this paper, we propose a novel approach to solve the word reordering problem in statistical machine translation. We rely on the dependency relations retrieved from a statistical parser incorporating with linguistic hand-crafted rules to create the transformations. These dependency-based transformations can produce the problem of word movement on both phrase and word reordering which is a difficult problem on parse tree based approaches. Such transformations are then applied as a preprocessor to English language both in training and decoding process to obtain an underlying word order closer to the Vietnamese language. About the hand-crafted rules, we extract from the syntactic differences of word order between English and Vietnamese language. This approach is simple and easy to implement with a small rule set, not lead to the rule explosion. We describe the experiments using our model on VCLEVC corpus [18] and consider the translation from English to Vietnamese, showing significant improvements about 2-4% BLEU score in comparison with the MOSES phrase-based baseline system [19].
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