Learning machines and teaching humans

; (). Learning machines and teaching humans. In: Points of View in Translation and Interpreting. Conference paper. (22-23 June 2017). Kraków: Jagiellonian University.

Developments in artificial intelligence (AI) and deep learning (DL) have led to increasing effective machine-translation systems, most recently evident in the measurable improvements achieved by neural machine translation (NMT) over existing statistical machine translation (SMT) systems. Although NMT is still at the R&D stage, initial test results on both in-domain (e.g. Junczys-Dowmunt et al. 2016) and out-of-domain (e.g. Wu et al. 2016) performance have been promising. New AI and DL technologies are set to change the face of translation as well the nature and the distribution of translators’ and other language mediators’ primary tasks. The Translation Automation User Society (TAUS), for instance, is predicting Fully Automatic Useful Translation (FAUT) by about 2030 (Massardo et al. 2016). Yet, although theoretical and practical courses on computer-aided and machine translation are widespread in language mediator education, less attention has been directed towards how recent advances in AI and DL might feasibly and coherently be accommodated in competence profiles, development models and curricula. This presentation addresses the challenge to language mediator education posed by AI in general and NMT in particular. From the perspective of competence-oriented curriculum development, it considers the ways in which students can gain the knowledge and toolkits to learn when and how new technologies should be embraced, and proposes a framework for when and how the added value of human intuition, creativity and ethics might be deployed in a future “re-humanized” approach to language mediator education.



Junczys-Dowmunt, Marcin, Tomasz Dwojak, &  Hieu Hoang (2016). Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation Directions. arXiv:1610.01108v3 [cs.CL]

Massardo, Isabella, Jaap van der Meer, & Maxim Khalilov (2016). Translation Technology Landscape Report. September 2016. De Rijp: TAUS.

Wu, Yonghui, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, et al. (2016). Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. arXiv:1609.08144v2 [cs.CL]