Abstract
This article presents the results of experiments on the use of various methods and algorithms in creating the Russian-Tatar machine translation system. As a basic algorithm, we used a neural network approach based on the Transformer architecture as well as various algorithms to increase the amount of parallel data using monolingual corpora (back-translation). For the first time experiments were conducted for the Russian-Tatar language pair on the use of transfer learning (based on Kazakh-Russian parallel corpus). As the main training data, we created and used the parallel corpus with a total volume of about 1 million Russian-Tatar sentence pairs. Experiments show that the created system is superior in quality to the currently existing Russian-Tatar translators. The best quality for the Russian-Tatar translation direction was achieved by our basic model (BLEU 35.4), and for the Tatar-Russian direction – by the model for which the back-translation algorithm was used (BLEU 39.2).
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The reported study was funded by RFBR, project number 20-07-00823.
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Khusainov, A., Suleymanov, D., Gilmullin, R. (2020). The Influence of Different Methods on the Quality of the Russian-Tatar Neural Machine Translation. In: Kuznetsov, S.O., Panov, A.I., Yakovlev, K.S. (eds) Artificial Intelligence. RCAI 2020. Lecture Notes in Computer Science(), vol 12412. Springer, Cham. https://doi.org/10.1007/978-3-030-59535-7_18
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