Maali Tars


2022

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MTee: Open Machine Translation Platform for Estonian Government
Toms Bergmanis | Marcis Pinnis | Roberts Rozis | Jānis Šlapiņš | Valters Šics | Berta Bernāne | Guntars Pužulis | Endijs Titomers | Andre Tättar | Taido Purason | Hele-Andra Kuulmets | Agnes Luhtaru | Liisa Rätsep | Maali Tars | Annika Laumets-Tättar | Mark Fishel
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

We present the MTee project - a research initiative funded via an Estonian public procurement to develop machine translation technology that is open-source and free of charge. The MTee project delivered an open-source platform serving state-of-the-art machine translation systems supporting four domains for six language pairs translating from Estonian into English, German, and Russian and vice-versa. The platform also features grammatical error correction and speech translation for Estonian and allows for formatted document translation and automatic domain detection. The software, data and training workflows for machine translation engines are all made publicly available for further use and research.

2021

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Extremely low-resource machine translation for closely related languages
Maali Tars | Andre Tättar | Mark Fišel
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)

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 Võro, North and South Saami and present first results of neural machine translation for these languages.