@inproceedings{fraser-2020-findings,
title = "Findings of the {WMT} 2020 Shared Tasks in Unsupervised {MT} and Very Low Resource Supervised {MT}",
author = "Fraser, Alexander",
editor = {Barrault, Lo{\"\i}c and
Bojar, Ond{\v{r}}ej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss{\`a}, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Graham, Yvette and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr{\'e} and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.80",
pages = "765--771",
abstract = "We describe the WMT 2020 Shared Tasks in Unsupervised MT and Very Low Resource Supervised MT. In both tasks, the community studied German to Upper Sorbian and Upper Sorbian to German MT, which is a very realistic machine translation scenario (unlike the simulated scenarios used in particular in much of the unsupervised MT work in the past). We were able to obtain most of the digital data available for Upper Sorbian, a minority language of Germany, which was the original motivation for the Unsupervised MT shared task. As we were defining the task, we also obtained a small amount of parallel data (about 60000 parallel sentences), allowing us to offer a Very Low Resource Supervised MT task as well. Six primary systems participated in the unsupervised shared task, two of these systems used additional data beyond the data released by the organizers. Ten primary systems participated in the very low resource supervised task. The paper discusses the background, presents the tasks and results, and discusses best practices for the future.",
}
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%0 Conference Proceedings
%T Findings of the WMT 2020 Shared Tasks in Unsupervised MT and Very Low Resource Supervised MT
%A Fraser, Alexander
%Y Barrault, Loïc
%Y Bojar, Ondřej
%Y Bougares, Fethi
%Y Chatterjee, Rajen
%Y Costa-jussà, Marta R.
%Y Federmann, Christian
%Y Fishel, Mark
%Y Fraser, Alexander
%Y Graham, Yvette
%Y Guzman, Paco
%Y Haddow, Barry
%Y Huck, Matthias
%Y Yepes, Antonio Jimeno
%Y Koehn, Philipp
%Y Martins, André
%Y Morishita, Makoto
%Y Monz, Christof
%Y Nagata, Masaaki
%Y Nakazawa, Toshiaki
%Y Negri, Matteo
%S Proceedings of the Fifth Conference on Machine Translation
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F fraser-2020-findings
%X We describe the WMT 2020 Shared Tasks in Unsupervised MT and Very Low Resource Supervised MT. In both tasks, the community studied German to Upper Sorbian and Upper Sorbian to German MT, which is a very realistic machine translation scenario (unlike the simulated scenarios used in particular in much of the unsupervised MT work in the past). We were able to obtain most of the digital data available for Upper Sorbian, a minority language of Germany, which was the original motivation for the Unsupervised MT shared task. As we were defining the task, we also obtained a small amount of parallel data (about 60000 parallel sentences), allowing us to offer a Very Low Resource Supervised MT task as well. Six primary systems participated in the unsupervised shared task, two of these systems used additional data beyond the data released by the organizers. Ten primary systems participated in the very low resource supervised task. The paper discusses the background, presents the tasks and results, and discusses best practices for the future.
%U https://aclanthology.org/2020.wmt-1.80
%P 765-771
Markdown (Informal)
[Findings of the WMT 2020 Shared Tasks in Unsupervised MT and Very Low Resource Supervised MT](https://aclanthology.org/2020.wmt-1.80) (Fraser, WMT 2020)
ACL