Findings of the WMT 2022 Shared Tasks in Unsupervised MT and Very Low Resource Supervised MT

Marion Weller-Di Marco, Alexander Fraser

Abstract
We present the findings of the WMT2022Shared Tasks in Unsupervised MT and VeryLow Resource Supervised MT with experiments on the language pairs German to/fromUpper Sorbian, German to/from Lower Sorbian and Lower Sorbian to/from Upper Sorbian. Upper and Lower Sorbian are minoritylanguages spoken in the Eastern parts of Germany. There are active language communitiesworking on the preservation of the languageswho also made the data used in this Shared Taskavailable.In total, four teams participated on this SharedTask, with submissions from three teams for theunsupervised sub task, and submissions fromall four teams for the supervised sub task. Inthis overview paper, we present and discuss theresults.
Anthology ID:
2022.wmt-1.73
Volume:
Proceedings of the Seventh Conference on Machine Translation (WMT)
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates (Hybrid)
Editors:
Philipp Koehn, Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Tom Kocmi, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri, Aurélie Névéol, Mariana Neves, Martin Popel, Marco Turchi, Marcos Zampieri
Venue:
WMT
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
801–805
Language:
URL:
https://aclanthology.org/2022.wmt-1.73
DOI:
Bibkey:
Cite (ACL):
Marion Weller-Di Marco and Alexander Fraser. 2022. Findings of the WMT 2022 Shared Tasks in Unsupervised MT and Very Low Resource Supervised MT. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 801–805, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
Cite (Informal):
Findings of the WMT 2022 Shared Tasks in Unsupervised MT and Very Low Resource Supervised MT (Weller-Di Marco & Fraser, WMT 2022)
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