|Publication type:||Conference paper|
|Type of review:||Not specified|
|Title:||Machine learning : implications for translator education|
|Conference details:||CIUTI Forum 2017, Geneva, Switzerland, 12-13 January 2017|
|Subjects:||Neural machine translation; Translation pedagogy; Translator education; Deep learning|
|Subject (DDC):||410.285: Computational linguistics |
418.02: Translating and interpreting
|Abstract:||Machines 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 deployed|
|Fulltext version:||Published version|
|License (according to publishing contract):||Licence according to publishing contract|
|Organisational Unit:||Institute of Translation and Interpreting (IUED)|
|Appears in collections:||Publikationen Angewandte Linguistik|
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