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dc.contributor.authorMassey, Gary-
dc.contributor.authorEhrensberger-Dow, Maureen-
dc.date.accessioned2018-01-31T16:21:51Z-
dc.date.available2018-01-31T16:21:51Z-
dc.date.issued2017-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/2408-
dc.description.abstractMachines are learning fast, and human translators must keep pace by learning about, with and from them. Deep learning (DL) and neural machine translation (NMT) are set to change the face of translation and the distributions of primary tasks, with TAUS predicting Fully Automatic Useful Translation (FAUT) by 2030. Although theoretical and practical courses on computer-aided and/or machine translation abound, less attention has been paid to DL and NMT. Although NMT is still at the R&D stage, it shows great promise for relieving human translators of the tedium of repetitive routine work. The challenge for translation education is to give students the knowledge and tool kits to learn when and how to embrace the new technologies, and to exploit how and when the added value of human intuition and creativity can and should be deployedde_CH
dc.language.isoende_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectNeural machine translationde_CH
dc.subjectTranslation pedagogyde_CH
dc.subjectTranslator educationde_CH
dc.subjectDeep learningde_CH
dc.subject.ddc410.285: Computerlinguistikde_CH
dc.subject.ddc418.02: Translationswissenschaftde_CH
dc.titleMachine learning : implications for translator educationde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementAngewandte Linguistikde_CH
zhaw.organisationalunitInstitut für Übersetzen und Dolmetschen (IUED)de_CH
zhaw.conference.detailsCIUTI Forum 2017, Geneva, Switzerland, 12-13 January 2017de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewNot specifiedde_CH
zhaw.webfeedÜbersetzungswissenschaftde_CH
Appears in collections:Publikationen Angewandte Linguistik

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