Exploitation of machine learning techniques in modelling phrase movements for machine translation
Yizhao Ni, Craig Saunders, Sandor Szedmak and Mahesan Niranjan
Journal of Machine Learning Research 2010.

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. For the reordering classification task the methods clearly outperform the baseline and furthermore, when placed as a component in the state-of-the-art machine translation system MOSES, we demonstrated improved translation results over the current system.

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