@inproceedings{edman-etal-2021-unsupervised,
title = "Unsupervised Translation of {G}erman{--}{L}ower {S}orbian: Exploring Training and Novel Transfer Methods on a Low-Resource Language",
author = {Edman, Lukas and
{\"U}st{\"u}n, Ahmet and
Toral, Antonio and
van Noord, Gertjan},
editor = "Barrault, Loic and
Bojar, Ondrej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-jussa, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Kocmi, Tom and
Martins, Andre and
Morishita, Makoto and
Monz, Christof",
booktitle = "Proceedings of the Sixth Conference on Machine Translation",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wmt-1.104",
pages = "982--988",
abstract = "This paper describes the methods behind the systems submitted by the University of Groningen for the WMT 2021 Unsupervised Machine Translation task for German{--}Lower Sorbian (DE{--}DSB): a high-resource language to a low-resource one. Our system uses a transformer encoder-decoder architecture in which we make three changes to the standard training procedure. First, our training focuses on two languages at a time, contrasting with a wealth of research on multilingual systems. Second, we introduce a novel method for initializing the vocabulary of an unseen language, achieving improvements of 3.2 BLEU for DE-{\textgreater}DSB and 4.0 BLEU for DSB-{\textgreater}DE.Lastly, we experiment with the order in which offline and online back-translation are used to train an unsupervised system, finding that using online back-translation first works better for DE-{\textgreater}DSB by 2.76 BLEU. Our submissions ranked first (tied with another team) for DSB-{\textgreater}DE and third for DE-{\textgreater}DSB.",
}
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<abstract>This paper describes the methods behind the systems submitted by the University of Groningen for the WMT 2021 Unsupervised Machine Translation task for German–Lower Sorbian (DE–DSB): a high-resource language to a low-resource one. Our system uses a transformer encoder-decoder architecture in which we make three changes to the standard training procedure. First, our training focuses on two languages at a time, contrasting with a wealth of research on multilingual systems. Second, we introduce a novel method for initializing the vocabulary of an unseen language, achieving improvements of 3.2 BLEU for DE-\textgreaterDSB and 4.0 BLEU for DSB-\textgreaterDE.Lastly, we experiment with the order in which offline and online back-translation are used to train an unsupervised system, finding that using online back-translation first works better for DE-\textgreaterDSB by 2.76 BLEU. Our submissions ranked first (tied with another team) for DSB-\textgreaterDE and third for DE-\textgreaterDSB.</abstract>
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%0 Conference Proceedings
%T Unsupervised Translation of German–Lower Sorbian: Exploring Training and Novel Transfer Methods on a Low-Resource Language
%A Edman, Lukas
%A Üstün, Ahmet
%A Toral, Antonio
%A van Noord, Gertjan
%Y Barrault, Loic
%Y Bojar, Ondrej
%Y Bougares, Fethi
%Y Chatterjee, Rajen
%Y Costa-jussa, Marta R.
%Y Federmann, Christian
%Y Fishel, Mark
%Y Fraser, Alexander
%Y Freitag, Markus
%Y Graham, Yvette
%Y Grundkiewicz, Roman
%Y Guzman, Paco
%Y Haddow, Barry
%Y Huck, Matthias
%Y Yepes, Antonio Jimeno
%Y Koehn, Philipp
%Y Kocmi, Tom
%Y Martins, Andre
%Y Morishita, Makoto
%Y Monz, Christof
%S Proceedings of the Sixth Conference on Machine Translation
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online
%F edman-etal-2021-unsupervised
%X This paper describes the methods behind the systems submitted by the University of Groningen for the WMT 2021 Unsupervised Machine Translation task for German–Lower Sorbian (DE–DSB): a high-resource language to a low-resource one. Our system uses a transformer encoder-decoder architecture in which we make three changes to the standard training procedure. First, our training focuses on two languages at a time, contrasting with a wealth of research on multilingual systems. Second, we introduce a novel method for initializing the vocabulary of an unseen language, achieving improvements of 3.2 BLEU for DE-\textgreaterDSB and 4.0 BLEU for DSB-\textgreaterDE.Lastly, we experiment with the order in which offline and online back-translation are used to train an unsupervised system, finding that using online back-translation first works better for DE-\textgreaterDSB by 2.76 BLEU. Our submissions ranked first (tied with another team) for DSB-\textgreaterDE and third for DE-\textgreaterDSB.
%U https://aclanthology.org/2021.wmt-1.104
%P 982-988
Markdown (Informal)
[Unsupervised Translation of German–Lower Sorbian: Exploring Training and Novel Transfer Methods on a Low-Resource Language](https://aclanthology.org/2021.wmt-1.104) (Edman et al., WMT 2021)
ACL