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Building the Tatar-Russian NMT System Based on Re-translation of Multilingual Data

  • Aidar Khusainov
  • Dzhavdet Suleymanov
  • Rinat Gilmullin
  • Ajrat Gatiatullin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11107)

Abstract

This paper assesses the possibility of combining the rule-based and the neural network approaches to the construction of the machine translation system for the Tatar-Russian language pair. We propose a rule-based system that allows using parallel data of a group of 6 Turkic languages (Tatar, Kazakh, Kyrgyz, Crimean-Tatar, Uzbek, Turkish) and the Russian language to overcome the problem of limited Tatar-Russian data. We incorporated modern approaches for data augmentation, neural networks training and linguistically motivated rule-based methods. The main results of the work are the creation of the first neural Tatar-Russian translation system and the improvement of the translation quality in this language pair in terms of BLEU scores from 12 to 39 and from 17 to 45 for both translation directions (comparing to the existing translation system). Also the translation between any of the Tatar, Kazakh, Kyrgyz, Crimean Tatar, Uzbek, Turkish languages becomes possible, which allows to translate from all of these Turkic languages into Russian using Tatar as an intermediate language.

Keywords

Neural machine translation Rule-based machine translation Turkic languages Low-resourced language Data augmentation 

References

  1. 1.
    ABBYY Aligner 2.0 (2017). https://www.abbyy.com/ru-ru/aligner/
  2. 2.
    ABBYY SmartCAT tool for professional translators (2017). https://smartcat.ai/workspace
  3. 3.
    Baisa, V.: Problems of machine translation evaluation. In: Sojka, P., s Horák, A. (eds.) Proceeding of Recent Advances in Slavonic Natural Language Processing, RASLAN 2009, Brno, pp. 17–22 (2009). https://nlp.fi.muni.cz/raslan/2009/papers/2.pdf
  4. 4.
    Bojar, O., et al.: Findings of the 2017 conference on machine translation (WMT17). In: Proceedings of the Second Conference on Machine Translation, Volume 2: Shared Task Papers, pp. 169–214. Association for Computational Linguistics, Copenhagen, September 2017. http://www.aclweb.org/anthology/W17-4717
  5. 5.
    Bojar, O., et al.: Findings of the 2016 conference on machine translation. In: Proceedings of the First Conference on Machine Translation, pp. 131–198. Association for Computational Linguistics, Berlin, August 2016. http://www.aclweb.org/anthology/W/W16/W16-2301
  6. 6.
    Fadaee, M., Bisazza, A., Monz, C.: Data augmentation for low-resource neural machine translation. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL 2017), pp. 567–573, January 2017Google Scholar
  7. 7.
    Moses, the machine translation system (2017). https://github.com/moses-smt/mosesdecoder/
  8. 8.
    Papineni, K., Roukos, S., Ward, T., Zhu, W.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 311–318 (2002)Google Scholar
  9. 9.
    Schiebinger, L., Klinge, I.: Gendered Innovations: How Gender Analysis Contributes to Research. Publications Office of the European Union, Luxembourg (2013)Google Scholar
  10. 10.
    Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. ArXiv e-prints, August 2015Google Scholar
  11. 11.
    Sennrich, R., Haddow, B., Burch, A.: Improving neural machine translation models with monolingual data. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, pp. 86–96 (2016)Google Scholar
  12. 12.
    Sennrich, R., et al.: The University of Edinburgh’s neural MT systems for WMT17. In: Proceedings of the Second Conference on Machine Translation, Volume 2: Shared Task Papers. Stroudsburg, PA, USA (2017)Google Scholar
  13. 13.
    Subword Neural Machine Translation (2017). https://github.com/rsennrich/subword-nmt/
  14. 14.
    Suleimanov, D., Gatiatullin, A., Almenova, A., Bashirov, A.: Multifunctional model of the Turkic morpheme: certain aspects. In: Proceedings of the International Conference on Computer and Cognitive Linguistics TEL-2016, Kazan , pp. 168–171 (2016)Google Scholar
  15. 15.
    Open-Source Neural Machine Translation in Theano (2017). https://github.com/rsennrich/nematus
  16. 16.
    Wu, Y., et al.: Google’s neural machine translation system: bridging the gap between human and machine translation. ArXiv e-prints, September 2016Google Scholar
  17. 17.
    Yandex translate (2017). https://translate.yandex.com/
  18. 18.
    One model is better than two. Yandex. Translate launches a hybrid machine translation system (2017). https://goo.gl/PddtYn
  19. 19.
    Zoph, B., Yuret, D., May, J., Knight, K.: Transfer learning for low-resource neural machine translation. ArXiv e-prints, April 2016Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Institute of Applied Semiotics of the Tatarstan Academy of SciencesKazan Federal UniversityKazanRussia

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