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

Exploitation of machine learning techniques in modelling phrase movements for machine translation
Exploitation of machine learning techniques in modelling phrase movements for machine translation
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.
1-30
Ni, Yizhao
f509bfa2-434d-4f5e-b7b6-8cc8c13c0975
Saunders, Craig
26634635-4d4d-4469-b9ec-1d68788aa47a
Szedmak, Sandor
c6a84aa3-2956-4acf-8293-a1b676f6d7d8
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Ni, Yizhao
f509bfa2-434d-4f5e-b7b6-8cc8c13c0975
Saunders, Craig
26634635-4d4d-4469-b9ec-1d68788aa47a
Szedmak, Sandor
c6a84aa3-2956-4acf-8293-a1b676f6d7d8
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

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.

Record type: Article

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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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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