Extremely low-resource machine translation for closely related languages

• URL: http://arxiv.org/abs/2105.13065v1
• Date: Thu, 27 May 2021 11:27:06 GMT
• Title: Extremely low-resource machine translation for closely related languages
• Authors: Maali Tars, Andre T\"attar, Mark Fi\v{s}el
• Abstract summary: This work focuses on closely related languages from the Uralic language family: from Estonian and Finnish. We find that multilingual learning and synthetic corpora increase the translation quality in every language pair. We show that transfer learning and fine-tuning are very effective for doing low-resource machine translation and achieve the best results.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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