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International Baltic Conference on Databases and Information Systems

DB&IS 2018: Databases and Information Systems pp 302–313Cite as

Effective Online Learning Implementation for Statistical Machine Translation

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 838))

Abstract

Online learning has been an active research area in statistical machine translation. However, as we have identified in our research, the implementation of successful online learning capabilities in the Moses SMT system can be challenging. In this work, we show how to use open source and freely available tools and methods in order to successfully implement online learning for SMT systems that allow improving translation quality. In our experiments, we compare the baseline implementation in Moses to an improved implementation utilising a two-step tuning strategy. We show that the baseline implementation achieves unstable performance (from −6 to \(+\)6 BLEU points in online learning scenarios and over −6 BLEU points in translation scenarios, i.e., when post-edits were not returned to the SMT system). However, our devised two-step tuning strategy is able to successfully utilise online learning capabilities and is able to improve MT quality in the online learning scenario by up to \(+\)12 BLEU points.

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Notes

  1. 1.

    More information about the QT21 project can be found online at http://www.qt21.eu/.

  2. 2.

    www.memsource.com.

  3. 3.

    www.tilde.com/mt.

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Acknowledgements

We would like to thank Tilde’s Localization Department for the hard work they did to prepare the post-edited data analyses in this work. The research has been supported by the ICT Competence Centre (www.itkc.lv) within the project “2.2. Prototype of a Software and Hardware Platform for Integration of Machine Translation in Corporate Infrastructure” of EU Structural funds, ID \(\hbox {n}^{\circ }\) 1.2.1.1/16/A/007.

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Authors and Affiliations

  1. Tilde, Vienības gatve 75A, Riga, 1004, Latvia

    Toms Miks, Mārcis Pinnis, Matīss Rikters & Rihards Krišlauks

Authors
  1. Toms Miks
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  2. Mārcis Pinnis
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  3. Matīss Rikters
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  4. Rihards Krišlauks
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Corresponding author

Correspondence to Mārcis Pinnis .

Editor information

Editors and Affiliations

  1. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Audrone Lupeikiene

  2. Information Systems Department, Vilnius Gediminas Technical University, Vilnius, Lithuania

    Olegas Vasilecas

  3. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Gintautas Dzemyda

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Miks, T., Pinnis, M., Rikters, M., Krišlauks, R. (2018). Effective Online Learning Implementation for Statistical Machine Translation. In: Lupeikiene, A., Vasilecas, O., Dzemyda, G. (eds) Databases and Information Systems. DB&IS 2018. Communications in Computer and Information Science, vol 838. Springer, Cham. https://doi.org/10.1007/978-3-319-97571-9_24

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  • DOI: https://doi.org/10.1007/978-3-319-97571-9_24

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