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

Record type: Article

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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Citation

Ni, Yizhao, Saunders, Craig, Szedmak, Sandor and Niranjan, Mahesan (2011) Exploitation of machine learning techniques in modelling phrase movements for machine translation Journal of Machine Learning Research, 12, pp. 1-30.

More information

Published date: 1 January 2011
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 272421
URI: http://eprints.soton.ac.uk/id/eprint/272421
PURE UUID: 6fc17051-a391-4199-937a-9d2a58edae89

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Date deposited: 06 Jun 2011 19:13
Last modified: 18 Jul 2017 06:24

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Contributors

Author: Yizhao Ni
Author: Craig Saunders
Author: Sandor Szedmak

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