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.
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References
ABBYY Aligner 2.0 (2017). https://www.abbyy.com/ru-ru/aligner/
ABBYY SmartCAT tool for professional translators (2017). https://smartcat.ai/workspace
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
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
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
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 2017
Moses, the machine translation system (2017). https://github.com/moses-smt/mosesdecoder/
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)
Schiebinger, L., Klinge, I.: Gendered Innovations: How Gender Analysis Contributes to Research. Publications Office of the European Union, Luxembourg (2013)
Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. ArXiv e-prints, August 2015
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)
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)
Subword Neural Machine Translation (2017). https://github.com/rsennrich/subword-nmt/
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)
Open-Source Neural Machine Translation in Theano (2017). https://github.com/rsennrich/nematus
Wu, Y., et al.: Google’s neural machine translation system: bridging the gap between human and machine translation. ArXiv e-prints, September 2016
Yandex translate (2017). https://translate.yandex.com/
One model is better than two. Yandex. Translate launches a hybrid machine translation system (2017). https://goo.gl/PddtYn
Zoph, B., Yuret, D., May, J., Knight, K.: Transfer learning for low-resource neural machine translation. ArXiv e-prints, April 2016
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Khusainov, A., Suleymanov, D., Gilmullin, R., Gatiatullin, A. (2018). Building the Tatar-Russian NMT System Based on Re-translation of Multilingual Data. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2018. Lecture Notes in Computer Science(), vol 11107. Springer, Cham. https://doi.org/10.1007/978-3-030-00794-2_17
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DOI: https://doi.org/10.1007/978-3-030-00794-2_17
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