Exploitation of machine learning techniques in modelling phrase movements for machine translation

Y Ni, C Saunders, S Szedmak, M Niranjan

Research output: Contribution to journalArticle (Academic Journal)peer-review

5 Citations (Scopus)

Abstract

We propose a distance phrase reordering model (DPR) for statistical machine translation (SMT), where the aim is to learn the grammatical rules and context dependent changes using a phrase reordering classification framework. We consider a variety of machine learning techniques, including state-of-the-art structured prediction methods. Techniques are compared and evaluated on a Chinese-English corpus, a language pair known for the high reordering characteristics which cannot be adequately captured with current models. In the reordering classification task, the method significantly outperforms the baseline against which it was tested, and further, when integrated as a component of the state-of-the-art machine translation system, MOSES, it achieves improvement in translation results.
Translated title of the contributionExploitation of machine learning techniques in modelling phrase movements for machine translation
Original languageEnglish
Pages (from-to)1 - 30
Number of pages30
JournalJournal of Machine Learning Research
Volume12
Publication statusPublished - Jan 2011

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