@inproceedings{fishel-yankovskaya-2026-two,
title = "The Two Towers for {E}stonian-Centric and {F}inno-{U}gric Machine Translation",
author = "Fishel, Mark and
Yankovskaya, Lisa",
editor = "Shterionov, Dimitar and
Vanmassenhove, Eva and
De Sisto, Mirella and
Blain, Fred and
Pourmostafa Roshan Sharami, Javad and
Lepp, Lisa and
Manna, Chiara and
Rescigno, Argentina Anna and
Karakanta, Alina and
Rigouts Terryn, Ayla and
Lardelli, Manuel and
Resende, Natalia and
Murgolo, Elena and
Hackenbuchner, Jani{\c{c}}a and
Zaretskaya, Anna and
Espl{\`a}-Gomis, Miquel and
Etchegoyhen, Thierry and
Gromann, Dagmar and
Bawden, Rachel and
Haddow, Barry and
Szoc, Sara and
Forcada, Mikel and
Moniz, Helena",
booktitle = "Proceedings of the 26th Annual Conference of the {E}uropean Association for Machine Translation (Volume 1)",
month = jun,
year = "2026",
address = "Tilburg, The Netherlands",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2026.eamt-1.32/",
pages = "515--527",
ISBN = "9789403901411",
abstract = "We present two open-weight translation models for Estonian and its low-resource ``relatives'' in the Finno-Ugric language family. The training data includes 12 languages paired with Estonian as well as 23 more Finno-Ugric languages and varieties, ranging from mid-resource examples with tens of thousands of speakers to extremely low-resource critically endangered languages with less than a hundred speakers. The translation models use Unbabel Tower+ 2B and 9B as their starting point. We compare their performance on two benchmarks to DeepL and GPT-5.2 and show that in most cases we surpass the quality of DeepL and match or nearly match the quality of GPT-5.2{'}s output with just a fraction of the parameters. Among other contributions we also restore the paragraph structure of a massive synthetic multiparallel corpus for Estonian translation and use it in training the models. The resulting models, training scripts and training data are released openly (to be made public upon de-anonymization)."
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<abstract>We present two open-weight translation models for Estonian and its low-resource “relatives” in the Finno-Ugric language family. The training data includes 12 languages paired with Estonian as well as 23 more Finno-Ugric languages and varieties, ranging from mid-resource examples with tens of thousands of speakers to extremely low-resource critically endangered languages with less than a hundred speakers. The translation models use Unbabel Tower+ 2B and 9B as their starting point. We compare their performance on two benchmarks to DeepL and GPT-5.2 and show that in most cases we surpass the quality of DeepL and match or nearly match the quality of GPT-5.2’s output with just a fraction of the parameters. Among other contributions we also restore the paragraph structure of a massive synthetic multiparallel corpus for Estonian translation and use it in training the models. The resulting models, training scripts and training data are released openly (to be made public upon de-anonymization).</abstract>
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%0 Conference Proceedings
%T The Two Towers for Estonian-Centric and Finno-Ugric Machine Translation
%A Fishel, Mark
%A Yankovskaya, Lisa
%Y Shterionov, Dimitar
%Y Vanmassenhove, Eva
%Y De Sisto, Mirella
%Y Blain, Fred
%Y Pourmostafa Roshan Sharami, Javad
%Y Lepp, Lisa
%Y Manna, Chiara
%Y Rescigno, Argentina Anna
%Y Karakanta, Alina
%Y Rigouts Terryn, Ayla
%Y Lardelli, Manuel
%Y Resende, Natalia
%Y Murgolo, Elena
%Y Hackenbuchner, Janiça
%Y Zaretskaya, Anna
%Y Esplà-Gomis, Miquel
%Y Etchegoyhen, Thierry
%Y Gromann, Dagmar
%Y Bawden, Rachel
%Y Haddow, Barry
%Y Szoc, Sara
%Y Forcada, Mikel
%Y Moniz, Helena
%S Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1)
%D 2026
%8 June
%I European Association for Machine Translation
%C Tilburg, The Netherlands
%@ 9789403901411
%F fishel-yankovskaya-2026-two
%X We present two open-weight translation models for Estonian and its low-resource “relatives” in the Finno-Ugric language family. The training data includes 12 languages paired with Estonian as well as 23 more Finno-Ugric languages and varieties, ranging from mid-resource examples with tens of thousands of speakers to extremely low-resource critically endangered languages with less than a hundred speakers. The translation models use Unbabel Tower+ 2B and 9B as their starting point. We compare their performance on two benchmarks to DeepL and GPT-5.2 and show that in most cases we surpass the quality of DeepL and match or nearly match the quality of GPT-5.2’s output with just a fraction of the parameters. Among other contributions we also restore the paragraph structure of a massive synthetic multiparallel corpus for Estonian translation and use it in training the models. The resulting models, training scripts and training data are released openly (to be made public upon de-anonymization).
%U https://aclanthology.org/2026.eamt-1.32/
%P 515-527
Markdown (Informal)
[The Two Towers for Estonian-Centric and Finno-Ugric Machine Translation](https://aclanthology.org/2026.eamt-1.32/) (Fishel & Yankovskaya, EAMT 2026)
ACL