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Findings of the WMT 2018 shared task on quality estimation

dc.contributor.authorSpecia, Lucia
dc.contributor.authorBlain, Frederic
dc.contributor.authorLogacheva, Varvara
dc.contributor.authorAstudillo, Ramón
dc.contributor.authorMartins, André
dc.date.accessioned2020-09-01T11:13:22Z
dc.date.available2020-09-01T11:13:22Z
dc.date.issued2018-11
dc.identifier.citationSpecia, L., Blain, F., Logacheva, V., Astudillo, R. et al. (2018) Findings of the WMT 2018 shared task on quality estimation. In Proceedings of the Third Conference on Machine Translation, Shared Task Papers, Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel et al. (Eds.), Belgium, Brussels: Association for Computational Linguistics, pp. 689-709.en
dc.identifier.isbn9781948087810en
dc.identifier.doi10.18653/v1/W18-6451en
dc.identifier.urihttp://hdl.handle.net/2436/623555
dc.description© 2018 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: http://dx.doi.org/10.18653/v1/W18-6451en
dc.description.abstractWe report the results of the WMT18 shared task on Quality Estimation, i.e. the task of predicting the quality of the output of machine translation systems at various granularity levels: word, phrase, sentence and document. This year we include four language pairs, three text domains, and translations produced by both statistical and neural machine translation systems. Participating teams from ten institutions submitted a variety of systems to different task variants and language pairs.en
dc.description.sponsorshipThe data and annotations collected for Tasks 1, 2 and 3 was supported by the EC H2020 QT21 project (grant agreement no. 645452). The shared task organisation was also supported by the QT21 project, national funds through Fundacao para a Ciencia e Tecnologia (FCT), with references UID/CEC/50021/2013 and UID/EEA/50008/2013, and by the European Research Council (ERC StG DeepSPIN 758969). We would also like to thank Julie Beliao and the Unbabel Quality Team for coordinating the annotation of the dataset used in Task 4.en
dc.formatapplication/pdfen
dc.language.isoenen
dc.publisherAssociation for Computational Linguisticsen
dc.relation.urlhttps://www.aclweb.org/anthology/W18-6451/en
dc.titleFindings of the WMT 2018 shared task on quality estimationen
dc.typeConference contributionen
dc.date.updated2020-08-25T14:22:22Z
pubs.place-of-publicationBrussels, Belgium
rioxxterms.funderHorizon 2020en
rioxxterms.identifier.projectQT21en
rioxxterms.versionVoRen
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2020-09-01en
refterms.dateFCD2020-09-01T11:11:37Z
refterms.versionFCDVoR
refterms.dateFOA2020-01-09T00:00:00Z


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