Survey on Neural Machine Translation into Polish

  • Krzysztof WolkEmail author
  • Krzysztof Marasek
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 833)


In this article we try to survey most modern approaches to machine translation. To be more precise we apply state of the art statistical machine translation and neural machine translation using recurrent and convolutional neural networks on Polish data set. We survey current toolkits that can be used for such purpose like Tensorflow, ModernMT, OpenNMT, MarianMT and FairSeq by doing experiments on Polish to English and English to Polish translation task. We do proper hyperparameter search for Polish language as well as we facilitate in our experiments sub-word units like syllables and stemming. We also augment our data with POS tags and polish grammatical groups. The results are being compared to SMT as well as to Google Translate engine. In both cases we success in reaching higher BLEU score.


NMT CNN in translation RNN in translation Machine translation into Polish 


  1. 1.
    Koehn, P., Och, F.J., Marcu, D.: Statistical phrase-based translation. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, vol. 1, pp. 48–54. Association for Computational Linguistics, May 2003Google Scholar
  2. 2.
    Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)Google Scholar
  3. 3.
    Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
  4. 4.
    Gehring, J., Auli, M., Grangier, D., Yarats, D., Dauphin, Y.N.: Convolutional sequence to sequence learning. arXiv preprint arXiv:1705.03122 (2017)
  5. 5.
    Luong, M.T., Manning, C.D.: Stanford neural machine translation systems for spoken language domains. In: Proceedings of the International Workshop on Spoken Language Translation, pp. 76–79 (2015)Google Scholar
  6. 6.
    Koehn, P., Knowles, R.: Six challenges for neural machine translation. arXiv preprint arXiv:1706.03872 (2017)
  7. 7.
    Koehn, P., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., Bertoldi, N., Cowan, B., Shen, W., Moran, C., Zens, R., Dyer, C.: Moses: open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions, pp. 177–180. Association for Computational Linguistics, June 2007Google Scholar
  8. 8.
    Vasiļjevs, A., Skadiņš, R., Tiedemann, J.: LetsMT!: a cloud-based platform for do-it-yourself machine translation. In: Proceedings of the ACL 2012 System Demonstrations, pp. 43–48. Association for Computational Linguistics, July 2012Google Scholar
  9. 9.
    Stolcke, A.: SRILM-an extensible language modeling toolkit. In: Seventh International Conference on Spoken Language Processing (2002)Google Scholar
  10. 10.
    Junczys-Dowmunt, M., Szał, A.: Symgiza++: symmetrized word alignment models for statistical machine translation. In: Security and Intelligent Information Systems, pp. 379–390. Springer, Heidelberg (2012)Google Scholar
  11. 11.
    Heafield, K.: KenLM: faster and smaller language model queries. In: Proceedings of the Sixth Workshop on Statistical Machine Translation, pp. 187–197. Association for Computational Linguistics, July 2011Google Scholar
  12. 12.
    Jelinek, R.: Modern MT systems and the myth of human translation: Real World Status Quo. In: Proceedings of the International Conference Translating and the Computer, November 2004Google Scholar
  13. 13.
    PyTorch Core Team: Pytorch: Tensors and dynamic neural networks in python with strong GPU acceleration (2017)Google Scholar
  14. 14.
    Klein, G., Kim, Y., Deng, Y., Senellart, J., Rush, A.M.: Opennmt: open-source toolkit for neural machine translation. arXiv preprint arXiv:1701.02810 (2017)
  15. 15.
    Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. arXiv preprint arXiv:1508.07909 (2015)
  16. 16.
    Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
  17. 17.
    Junczys-Dowmunt, M., Grundkiewicz, R., Grundkiewicz, T., Hoang, H., Heafield, K., Neckermann, T., Seide, F., Germann, U., Aji, A.F., Bogoychev, N., Martins, A.: Marian: Fast Neural Machine Translation in C++. arXiv preprint arXiv:1804.00344 (2018)
  18. 18.
    Sennrich, R., Firat, O., Cho, K., Birch, A., Haddow, B., Hitschler, J., Junczys-Dowmunt, M., Läubli, S., Barone, A.V.M., Mokry, J., Nădejde, M.: Nematus: a toolkit for neural machine translation. arXiv preprint arXiv:1703.04357 (2017)
  19. 19.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  20. 20.
    Wołk, A., Wołk, K., Marasek, K.: Analysis of complexity between spoken and written language for statistical machine translation in West-Slavic group. In: Multimedia and Network Information Systems, pp. 251–260. Springer, Cham (2017)Google Scholar
  21. 21.
    Wołk, K., Marasek, K.: Polish-English speech statistical machine translation systems for the IWSLT 2013. arXiv preprint arXiv:1509.09097 (2013)
  22. 22.
    Koehn, P.: Europarl: a parallel corpus for statistical machine translation. In: MT Summit, vol. 5, pp. 79–86, September 2005Google Scholar
  23. 23.
    Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., Klingner, J.: Google’s neural machine translation system: bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016)
  24. 24.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  25. 25.
    Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 311–318. Association for Computational Linguistics, July 2002Google Scholar
  26. 26.
    Groves, M., Mundt, K.: Friend or foe? Google Translate in language for academic purposes. Engl. Specif. Purp. 37, 112–121 (2015)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Polish-Japanese Academy of Information TechnologyWarsawPoland

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