PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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 Volume 12, pp. 1-30, 2011.

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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
Information Retrieval & Textual Information Access
ID Code:7251
Deposited By:Ni Yizhao
Deposited On:14 March 2011