@inproceedings{lai-etal-2021-lmu,
title = "The {LMU} {M}unich System for the {WMT} 2021 Large-Scale Multilingual Machine Translation Shared Task",
author = "Lai, Wen and
Libovick{\'y}, Jind{\v{r}}ich and
Fraser, Alexander",
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-1.49",
pages = "412--417",
abstract = "This paper describes the submission of LMU Munich to the WMT 2021 multilingual machine translation task for small track {\#}1, which studies translation between 6 languages (Croatian, Hungarian, Estonian, Serbian, Macedonian, English) in 30 directions. We investigate the extent to which bilingual translation systems can influence multilingual translation systems. More specifically, we trained 30 bilingual translation systems, covering all language pairs, and used data augmentation technologies such as back-translation and knowledge distillation to improve the multilingual translation systems. Our best translation system scores 5 to 6 BLEU higher than a strong baseline system provided by the organizers. As seen in the dynalab leaderboard, our submission is the only fully constrained submission that uses only the corpus provided by the organizers and does not use any pre-trained models.",
}
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%0 Conference Proceedings
%T The LMU Munich System for the WMT 2021 Large-Scale Multilingual Machine Translation Shared Task
%A Lai, Wen
%A Libovický, Jindřich
%A Fraser, Alexander
%S Proceedings of the Sixth Conference on Machine Translation
%D 2021
%8 nov
%I Association for Computational Linguistics
%C Online
%F lai-etal-2021-lmu
%X This paper describes the submission of LMU Munich to the WMT 2021 multilingual machine translation task for small track #1, which studies translation between 6 languages (Croatian, Hungarian, Estonian, Serbian, Macedonian, English) in 30 directions. We investigate the extent to which bilingual translation systems can influence multilingual translation systems. More specifically, we trained 30 bilingual translation systems, covering all language pairs, and used data augmentation technologies such as back-translation and knowledge distillation to improve the multilingual translation systems. Our best translation system scores 5 to 6 BLEU higher than a strong baseline system provided by the organizers. As seen in the dynalab leaderboard, our submission is the only fully constrained submission that uses only the corpus provided by the organizers and does not use any pre-trained models.
%U https://aclanthology.org/2021.wmt-1.49
%P 412-417
Markdown (Informal)
[The LMU Munich System for the WMT 2021 Large-Scale Multilingual Machine Translation Shared Task](https://aclanthology.org/2021.wmt-1.49) (Lai et al., WMT 2021)
ACL