Machine learning: Implications for translator education

Gary Massey 1  and Maureen Ehrensberger-Dow 2
  • 1 ZHAW Zurich University of Applied Sciences, Theaterstraße 15c, 8401 Winterthur, Switzerland
  • 2 ZHAW Zurich University of Applied Sciences, Theaterstraße 15c, 8401 Winterthur, Switzerland
Gary Massey
  • Corresponding author
  • ZHAW Zurich University of Applied Sciences, IUED Institute of Translation and Interpreting, Theaterstraße 15c, 8401 Winterthur, Switzerland
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and Maureen Ehrensberger-Dow
  • ZHAW Zurich University of Applied Sciences, UED Institute of Translation and Interpreting, Theaterstraße 15c, 8401 Winterthur, Switzerland
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Abstract

Machines are learning fast, and human translators must keep pace by learning with, from and about them. Deep learning (DL) and neural machine translation (NMT) are set to change the reality of translation and the distributions of tasks. Although theoretical and practical courses on computer-aided and/or machine translation abound, less attention has been paid to DL and NMT in most translation programmes. The challenge for translation education is to give students the knowledge and toolkits to learn when and how to embrace the new technologies, and to exploit how and when the added value of human intuition, creativity and ethics can and should be deployed.

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Since its founding in 1956, Lebende Sprachen [Living Languages] has been the leading German journal for foreign languages in research and practice. It contains articles and reviews on language in general and also covers topics on specific languages and cultures, living languages and the life of language.

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