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dc.contributor.authorFomicheva, Marina
dc.contributor.authorSun, Shuo
dc.contributor.authorYankovskaya, Lisa
dc.contributor.authorBlain, Frédéric
dc.contributor.authorChaudhary, Vishrav
dc.contributor.authorFishel, Mark
dc.contributor.authorGuzmán, Francisco
dc.contributor.authorSpecia, Lucia
dc.date.accessioned2021-01-06T09:52:05Z
dc.date.available2021-01-06T09:52:05Z
dc.date.issued2020-11-30
dc.identifier.citationFomicheva, M., Sun, S., Yankovskaya, L. et al. (2020) BERGAMOT-LATTE submissions for the WMT20 quality estimation shared task, Proceedings of the Fifth Conference on Machine Translation, November 2020, pp. 1010–1017.en
dc.identifier.isbn9781948087810en
dc.identifier.urihttp://hdl.handle.net/2436/623856
dc.description© 2020 The Authors. Published by Association for Computational Linguistics. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://www.aclweb.org/anthology/2020.wmt-1.116/en
dc.description.abstractThis paper presents our submission to the WMT2020 Shared Task on Quality Estimation (QE). We participate in Task and Task 2 focusing on sentence-level prediction. We explore (a) a black-box approach to QE based on pre-trained representations; and (b) glass-box approaches that leverage various indicators that can be extracted from the neural MT systems. In addition to training a feature-based regression model using glass-box quality indicators, we also test whether they can be used to predict MT quality directly with no supervision. We assess our systems in a multi-lingual setting and show that both types of approaches generalise well across languages. Our black-box QE models tied for the winning submission in four out of seven language pairs inTask 1, thus demonstrating very strong performance. The glass-box approaches also performed competitively, representing a light-weight alternative to the neural-based models.en
dc.formatapplication/pdfen
dc.language.isoenen
dc.publisherAssociation for Computational Linguisticsen
dc.relation.urlhttps://www.aclweb.org/anthology/2020.wmt-1.116en
dc.titleBERGAMOT-LATTE submissions for the WMT20 quality estimation shared tasken
dc.typeConference contributionen
dc.date.updated2021-01-04T13:27:27Z
dc.conference.nameEMNLP 2020, Fifth Conference on Machine Translation
pubs.finish-date2020-11-20
pubs.place-of-publicationOnline
pubs.start-date2020-11-19
rioxxterms.funderUniversity of Wolverhamptonen
rioxxterms.identifier.projectUOW06012021FBen
rioxxterms.versionVoRen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2021-01-06en
dc.source.beginpage1010
dc.source.endpage1017
refterms.dateFCD2021-01-06T09:51:42Z
refterms.versionFCDVoR
refterms.dateFOA2021-01-06T09:52:08Z


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