  An effective method to improve extremely low-resource neural machine
translation is multilingual training, which can be improved by leveraging
monolingual data to create synthetic bilingual corpora using the
back-translation method. This work focuses on closely related languages from
the Uralic language family: from Estonian and Finnish geographical regions. We
find that multilingual learning and synthetic corpora increase the translation
quality in every language pair for which we have data. We show that transfer
learning and fine-tuning are very effective for doing low-resource machine
translation and achieve the best results. We collected new parallel data for
V\~oro, North and South Saami and present first results of neural machine
translation for these languages.
