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Findings of the 2022 Conference on Machine Translation (WMT22)

Kocmi, Tom orcid logoORCID: 0000-0002-7993-9859, Bawden, Rachel orcid logoORCID: 0000-0001-9553-1768, Bojar, Ondřej orcid logoORCID: 0000-0002-0606-0050, Dvorkovich, Anton orcid logoORCID: 0000-0003-1190-3582, Federmann, Christian, Fishel, Mark orcid logoORCID: 0000-0003-1932-2600, Gowda, Thamme orcid logoORCID: 0000-0001-5422-8674, Graham, Yvette, Grundkiewicz, Roman, Haddow, Barry, Knowles, Rebecca orcid logoORCID: 0000-0001-5686-8966, Koehn, Philipp orcid logoORCID: 0000-0003-1565-064X, Monz, Christof orcid logoORCID: 0000-0003-1872-1888, Morishita, Makoto, Nagata, Masaaki, Nakazawa, Toshiaki, Novák, Michal orcid logoORCID: 0000-0002-0420-7127, Popel, Martin orcid logoORCID: 0000-0002-3628-8419, Popović, Maja orcid logoORCID: 0000-0001-8234-8745 and Shmatova, Mariya (2022) Findings of the 2022 Conference on Machine Translation (WMT22). In: Seventh Conference on Machine Translation (WMT), 7-8 Dec 2022, Abu Dhabi, United Arab Emirates.

Abstract
This paper presents the results of the General Machine Translation Task organised as part of the Conference on Machine Translation (WMT) 2022. In the general MT task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting of four different domains. We evaluate system outputs with human annotators using two different techniques: reference-based direct assessment and (DA) and a combination of DA and scalar quality metric (DA+SQM).
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Machine learning
Computer Science > Machine translating
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Proceedings of the Seventh Conference on Machine Translation (WMT). . Association for Computational Linguistics (ACL).
Publisher:Association for Computational Linguistics (ACL)
Official URL:https://aclanthology.org/2022.wmt-1.1
Copyright Information:© 2022 Association for Computational Linguistics
Funders:Microsoft, Charles University, Toloka, NTT Resonant, Lingua Custodia, Webinterpret, Google, Cyber Agent, Phrase, European Commission via its H2020 Program (project WELCOME, contract no. 870930) and by 20-16819X (LUSyD), Science Foundation Ireland through the SFI Research Centres Programme and co-funded under the European Regional Development Fund (ERDF) through Grant 13/RC/2106., LM2018101 (LINDAT/CLARIAHCZ) of the Ministry of Education, Youth, and Sports of the Czech Republic
ID Code:28361
Deposited On:24 May 2023 11:33 by Maja Popovic . Last Modified 24 May 2023 11:34
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