Massey, Gary; Ehrensberger-Dow, Maureen (2017). Machine learning: Implications for translator education. In: CIUTI Forum 2017: Short- and longterm impact of artificial intelligence on language professions. Conference paper. (12-13 January 2017). Geneva: United Nations.
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 toolkits 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