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.
@InProceedings{RIKTERS18.75, author = {Matīss Rikters and Mārcis Pinnis and Rihards Krišlauks}, title = "{Training and Adapting Multilingual NMT for Less-resourced and Morphologically Rich Languages}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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