Semi-automatic Quasi-morphological Word Segmentation for Neural Machine Translation

  • Jānis ZutersEmail author
  • Gus Strazds
  • Kārlis Immers
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 838)


This paper proposes the Prefix-Root-Postfix-Encoding (PRPE) algorithm, which performs close-to-morphological segmentation of words as part of text pre-processing in machine translation. PRPE is a cross-language algorithm requiring only minor tweaking to adapt it for any particular language, a property which makes it potentially useful for morphologically rich languages with no morphological analysers available. As a key part of the proposed algorithm we introduce the ‘Root alignment’ principle to extract potential sub-words from a corpus, as well as a special technique for constructing words from potential sub-words. We conducted experiments with two different neural machine translation systems, training them on parallel corpora for English-Latvian and Latvian-English translation. Evaluation of translation quality showed improvements in BLEU scores when the data were pre-processed using the proposed algorithm, compared to a couple of baseline word segmentation algorithms. Although we were able to demonstrate improvements in both translation directions and for both NMT systems, they were relatively minor, and our experiments show that machine translation with inflected languages remains challenging, especially with translation direction towards a highly inflected language.


Neural machine translation Processing morphologically rich languages Word segmentation 



The research has been supported by the European Regional Development Fund within the research project “Neural Network Modelling for Inflected Natural Languages” No., and the Faculty of Computing, University of Latvia.


  1. 1.
    Pinnis, M., Krišlauks, R., Deksne, D., Miks, T.: Neural machine translation for morphologically rich languages with improved sub-word units and synthetic data. In: Ekštein, K., Matoušek, V. (eds.) TSD 2017. LNCS (LNAI), vol. 10415, pp. 237–245. Springer, Cham (2017). Scholar
  2. 2.
    Ruokolainen, T., Kohonen, O., Sirts, K., Grönroos, A., Kurimo, M., Virpioja, S.: A comparative study of minimally supervised morphological segmentation. Comput. Linguist. 42(1), 91–120 (2016)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016), Berlin, Germany (2016)Google Scholar
  4. 4.
    Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation. In: ACL 2002: 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002)Google Scholar
  5. 5.
    Hajič, J.: Morphological tagging: data vs. dictionaries. In: Proceedings of the 1st North American Chapter of the Association for Computational Linguistics conference (NAACL 2000), pp. 94–101 (2000)Google Scholar
  6. 6.
    Paikens, P., Rituma, L., Pretkalnina, L.: Morphological analysis with limited resources: Latvian example. In: Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA) (2013)Google Scholar
  7. 7.
    Pinnis, M., Goba, K.: Maximum entropy model for disambiguation of rich morphological tags. In: Mahlow, C., Piotrowski, M. (eds.) SFCM 2011. CCIS, vol. 100, pp. 14–22. Springer, Heidelberg (2011). Scholar
  8. 8.
    Virpioja, S., Smit P., Grönroos, S.-A., Kurimo, M.: Morfessor 2.0: Python implementation and extensions for Morfessor baseline. In: Aalto University publication series SCIENCE + TECHNOLOGY, 25/2013, Aalto University (2013)Google Scholar
  9. 9.
    Jurafsky, D., Martin, J.H.: Speech and Language Processing, 2nd edn, pp. 184–187. Prentice Hall, Englewood Cliffs (2009)Google Scholar
  10. 10.
    Clifton, A., Sarkar, A.: Combining morpheme-based machine translation with post-processing morpheme prediction. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 32–42 (2011)Google Scholar
  11. 11.
    Mermer, C., Akin, S.: Unsupervised search for the optimal segmentation for statistical machine translation. In: Proceedings of the ACL 2010 Student Research Workshop, Uppsala, Sweden, pp. 31–36 (2010)Google Scholar
  12. 12.
    Pinnis, M., Krišlauks, R., Miks, T., Deksne, D., Šics, V.: Tilde’s machine translation systems for WMT 2017. In: Proceedings of the Second Conference on Machine Translation (WMT 2017). Shared Task Papers, Copenhagen, Denmark, vol. 2, pp. 374–381. Association for Computational Linguistics (2017).
  13. 13.
    Grönroos, S.-A., Virpioja, S., Smit, P., Kurimo, M.: Morfessor FlatCat: an HMM-based method for unsupervised and semi-supervised learning of morphology. In: Proceedings of the 25th International Conference on Computational Linguistics, Dublin, Ireland, pp. 1177–1185. Association for Computational Linguistics (2014)Google Scholar
  14. 14.
    Sennrich, R., Firat, O., Cho, K., Birch, A., Haddow, B., Hitschler, J., Junczys-Dowmunt, M., Läubli, S., Barone, A.V.M., Mokry, J., Nadejde, M.: Nematus: a toolkit for neural machine translation. In: Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, Spain, pp. 65–68 (2017)Google Scholar
  15. 15.
    Gehring, J., Auli, M., Grangier, D., Yarats D., Dauphin, Y.: Convolutional sequence to sequence learning. In: Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, pp. 1243–1252 (2017)Google Scholar
  16. 16.
    Sennrich, R., Birch, A., Currey, A., Germann, U., Haddow, B., Heafield, K., Barone, A.V.M., Williams P.: The University of Edinburgh’s neural MT systems for WMT17. In: Proceedings of the Second Conference on Machine Translation. Shared Task Papers, vol. 2, Copenhagen, Denmark (2017)Google Scholar
  17. 17.
    Barone, A.V.M., Helcl, J., Sennrich, R., Haddow, B., Birch, A.: Deep Architectures for Neural Machine Translation (2017). arXiv Preprints: arXiv:1707.07631 [cs.CL]

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of LatviaRigaLatvia

Personalised recommendations