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Authors: Jesús González-Rubio 1 ; Daniel Ortiz-Martínez 1 and Francisco Casacuberta 2

Affiliations: 1 Instituto Tecnológico de Informática, Universidad Politécnica de Valencia, Spain ; 2 Universidad Politécnica de Valencia, Spain

Abstract: The state-of-the art in statistical machine translation is based on a log-linear combination of different models. In this approach, the coefficients of the combination are computed by using the MERT algorithm with a validation data set. This algorithm presents high computational costs. As an alternative, we propose a novel technique based on Support Vector Machines to calculate these coefficients using a loss function to be minimized. We report the experiments on a Italian-English translation task showing encouraging results.

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Paper citation in several formats:
González-Rubio, J.; Ortiz-Martínez, D. and Casacuberta, F. (2008). Optimization of Log-linear Machine Translation Model Parameters Using SVMs. In Proceedings of the 8th International Workshop on Pattern Recognition in Information Systems (ICEIS 2008) - PRIS; ISBN 978-989-8111-42-5, SciTePress, pages 48-56. DOI: 10.5220/0001739000480056

@conference{pris08,
author={Jesús González{-}Rubio. and Daniel Ortiz{-}Martínez. and Francisco Casacuberta.},
title={Optimization of Log-linear Machine Translation Model Parameters Using SVMs},
booktitle={Proceedings of the 8th International Workshop on Pattern Recognition in Information Systems (ICEIS 2008) - PRIS},
year={2008},
pages={48-56},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001739000480056},
isbn={978-989-8111-42-5},
}

TY - CONF

JO - Proceedings of the 8th International Workshop on Pattern Recognition in Information Systems (ICEIS 2008) - PRIS
TI - Optimization of Log-linear Machine Translation Model Parameters Using SVMs
SN - 978-989-8111-42-5
AU - González-Rubio, J.
AU - Ortiz-Martínez, D.
AU - Casacuberta, F.
PY - 2008
SP - 48
EP - 56
DO - 10.5220/0001739000480056
PB - SciTePress