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Learning Word Alignment Models for Kazakh-English Machine Translation

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Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9376))

Abstract

In this paper, we address to the most essential challenges in the word alignment quality. Word alignment is a widely used phenomenon in the field of machine translation. However, a small research has been dedicated to the revealing of its discrete properties. This paper presents word segmentation, the probability distributions, and the statistical properties of word alignment in the transparent and a real life dataset. The result suggests that there is no single best method for alignment evaluation. For Kazakh-English pair we attempted to improve the phrase tables with the choice of alignment method, which need to be adapted to the requirements in the specific project. Experimental results show that the processed parallel data reduced word alignment error rate and achieved the highest BLEU improvement on the random parallel corpora.

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Author information

Authors and Affiliations

  1. Laboratory of Intelligent Information Systems, Al-Farabi Kazakh National University, Almaty, Kazakhstan

    Amandyk Kartbayev

Authors
  1. Amandyk Kartbayev
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Correspondence to Amandyk Kartbayev .

Editor information

Editors and Affiliations

  1. Science and Technology, Japan Advanced Institute of, Nomi, Japan

    Van-Nam Huynh

  2. Science, Dept of Systems Innovation, Osaka University,Graduate School of, Osaka, Japan

    Masahiro Inuiguchi

  3. Université de Technologie de Compiègne, Compiègne, France

    Thierry Demoeux

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Kartbayev, A. (2015). Learning Word Alignment Models for Kazakh-English Machine Translation. In: Huynh, VN., Inuiguchi, M., Demoeux, T. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2015. Lecture Notes in Computer Science(), vol 9376. Springer, Cham. https://doi.org/10.1007/978-3-319-25135-6_31

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  • DOI: https://doi.org/10.1007/978-3-319-25135-6_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25134-9

  • Online ISBN: 978-3-319-25135-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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