MTee: Open Machine Translation Platform for Estonian Government

no code implementations • EAMT 2022 • 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

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.

Document Translation Grammatical Error Correction +2

Extremely low-resource machine translation for closely related languages

no code implementations • NoDaLiDa 2021 • Maali Tars, Andre Tättar, Mark Fišel

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.

Low-Resource Neural Machine Translation Transfer Learning +1