Training and Adapting Multilingual NMT for Less resourced and Morphologically Rich Languages
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.
LREC