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Combining Machine Translation Systems with Quality Estimation

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Computational Linguistics and Intelligent Text Processing (CICLing 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10762))

Abstract

Improving the quality of Machine Translation (MT) systems is an important task not only for researchers but it is a substantial need for translating companies to create translations in a quicker and cheaper way. Combining the outputs of more than one machine translation systems is a common technique to get better translation quality because the strengths of the different systems could be utilized. The main question is to find the best method for the combination. In this paper, we used the Quality Estimation (QE) technique to combine a phrase-based and a hierarchical-based machine translation systems. The composite system was tested on several language combinations. The QE module was used to compare the outputs of the different MT systems and gave the best one as the result translation of the composite system. The composite system gained better translation quality than the separated systems.

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Notes

  1. 1.

    https://ec.europa.eu/jrc/en/language-technologies/jrc-acquis.

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Acknowledgement

We would like to thank MorphoLogic Lokalizáció Kft. for the data sets which were used in our research.

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Authors and Affiliations

  1. MTA-PPKE Hungarian Language Technology Research Group, Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, Práter str. 50/A, Budapest, 1083, Hungary

    László János Laki & Zijian Győző Yang

  2. MorphoLogic Lokalizáció Kft., Logodi str. 54, Budapest, 1012, Hungary

    László János Laki

Authors
  1. László János Laki
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  2. Zijian Győző Yang
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Correspondence to Zijian Győző Yang .

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Editors and Affiliations

  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Laki, L.J., Yang, Z.G. (2018). Combining Machine Translation Systems with Quality Estimation. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10762. Springer, Cham. https://doi.org/10.1007/978-3-319-77116-8_32

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  • DOI: https://doi.org/10.1007/978-3-319-77116-8_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77115-1

  • Online ISBN: 978-3-319-77116-8

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