Training and Adapting Multilingual NMT for Less-resourced and Morphologically Rich Languages

Abstract

In this paper, we present results of employing multilingual and multi-way neural machine translation approaches for morphologically rich languages, such as Estonian and Russian. We experiment with different NMT architectures that allow achieving state-of-the-art translation quality and compare the multi-way model performance to one-way model performance. We report improvements of up to +3.27 BLEU points over our baseline results, when using a multi-way model trained using the transformer network architecture. We also provide open-source scripts used for shuffling and combining multiple parallel datasets for training of the multilingual systems.

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