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dc.contributor.author Specia, Lucia
dc.contributor.author Logacheva, Varvara
dc.date.accessioned 2017-04-13T08:16:50Z
dc.date.available 2017-04-13T08:16:50Z
dc.date.issued 2017-04-13
dc.identifier.uri http://hdl.handle.net/11372/LRT-2135
dc.description Test data for the WMT17 QE task. Train data can be downloaded from http://hdl.handle.net/11372/LRT-1974 This shared task will build on its previous five editions to further examine automatic methods for estimating the quality of machine translation output at run-time, without relying on reference translations. We include word-level, phrase-level and sentence-level estimation. All tasks will make use of a large dataset produced from post-editions by professional translators. The data will be domain-specific (IT and Pharmaceutical domains) and substantially larger than in previous years. In addition to advancing the state of the art at all prediction levels, our goals include: - To test the effectiveness of larger (domain-specific and professionally annotated) datasets. We will do so by increasing the size of one of last year's training sets. - To study the effect of language direction and domain. We will do so by providing two datasets created in similar ways, but for different domains and language directions. - To investigate the utility of detailed information logged during post-editing. We will do so by providing post-editing time, keystrokes, and actual edits. This year's shared task provides new training and test datasets for all tasks, and allows participants to explore any additional data and resources deemed relevant. A in-house MT system was used to produce translations for all tasks. MT system-dependent information can be made available under request. The data is publicly available but since it has been provided by our industry partners it is subject to specific terms and conditions. However, these have no practical implications on the use of this data for research purposes.
dc.language.iso eng
dc.language.iso deu
dc.publisher University of Sheffield
dc.relation info:eu-repo/grantAgreement/EC/H2020/645452
dc.relation.isreplacedby http://hdl.handle.net/11372/LRT-2805
dc.rights AGREEMENT ON THE USE OF DATA IN QT21
dc.rights.uri https://lindat.mff.cuni.cz/repository/xmlui/page/licence-TAUS_QT21
dc.source.uri http://www.statmt.org/wmt17/quality-estimation-task.html
dc.subject machine translation
dc.subject quality estimation
dc.subject machine learning
dc.title WMT17 Quality Estimation Shared Test Data
dc.type corpus
metashare.ResourceInfo#ContentInfo.mediaType text
dc.rights.label PUB
hidden false
hasMetadata false
has.files yes
branding LRT + Open Submissions
contact.person Lucia Specia l.specia@sheffield.ac.uk University of Sheffield
sponsor European Union H2020-ICT-2014-1-645452 QT21: Quality Translation 21 euFunds info:eu-repo/grantAgreement/EC/H2020/645452
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