Volume 6, Issue 4 (12-2014)                   2014, 6(4): 57-67 | Back to browse issues page

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Rahimi Z, Khadivi S, Faili H. Using Dependency Tree Grammar to Enhance the Reordering Model of Statistical Machine Translation Systems . International Journal of Information and Communication Technology Research 2014; 6 (4) :57-67
URL: http://ijict.itrc.ac.ir/article-1-117-en.html
Using Dependency Tree Grammar to Enhance the Reordering Model of Statistical Machine Translation Systems
Zahra Rahimi , Shahram Khadivi , Heshaam Faili
Abstract:   (2666 Views)
We propose three novel reordering models for statistical machine translation. These reordering models use dependency tree to improve the translation quality. All reordering models are utilized as features in a log linear framework and therefore guide the decoder to make better decisions about reordering. These reordering models are tested on two English-Persian parallel corpora with different statistics and domains. The BLEU score is improved by 2.5 on the first corpus and by 1.2 on the other.
Keywords: statistical machine translation, reordering model, dependency tree, discriminative reordering model, discriminative decoder, long range reordering, maximum entropy, distortion model
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Type of Study: Research | Subject: Information Technology
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