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Refining Kazakh Word Alignment Using Simulation Modeling Methods for Statistical Machine Translation

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Natural Language Processing and Chinese Computing (NLPCC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9362))

Abstract

Word alignment play an important role in the training of statistical machine translation systems. We present a technique to refine word alignments at phrase level after the collection of sentences from the Kazakh-English parallel corpora. The estimation technique extracts the phrase pairs from the word alignment and then incorporates them into the translation system for further steps. Although it is a pretty important step in training procedure, an word alignment process often has practical concerns with agglutinative languages. We consider an approach, which is a step towards an improved statistical translation model that incorporates morphological information and has better translation performance. Our goal is to present a statistical model of the morphology dependent procedure, which was evaluated over the Kazakh-English language pair and has obtained an improved BLEU score over state-of-the-art models.

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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. Tsinghua University, Beijing, China

    Juanzi Li

  2. Rensselaer Polytechnic Institute, Troy, NY, USA

    Heng Ji

  3. Peking University, Beijing, China

    Dongyan Zhao

  4. Peking University, Beijing, China

    Yansong Feng

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Kartbayev, A. (2015). Refining Kazakh Word Alignment Using Simulation Modeling Methods for Statistical Machine Translation. In: Li, J., Ji, H., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2015. Lecture Notes in Computer Science(), vol 9362. Springer, Cham. https://doi.org/10.1007/978-3-319-25207-0_38

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  • DOI: https://doi.org/10.1007/978-3-319-25207-0_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25206-3

  • Online ISBN: 978-3-319-25207-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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