ABSTRACT
This chapter discusses the application of translation technologies in language learning and non-professional translation. An extensive literature review on the use of translation technology, mainly machine translation (MT) systems, in language learning shows that using MT as a ‘bad model’ in post-editing tasks can be an effective learning tool (e.g., Niño 2008). Though exposing learners to bad models was once considered methodologically unsound, recent research in second language acquisition (SLA), including Translation in Language Teaching (TILT), suggests that paying attention to bad models by having learners correct errors in MT output supports adult second language (L2) learning. Translation technologies also offer language learners opportunities to learn from controlled sample translations fed into the translation memory databases. This chapter then discusses the possible use of translation technology to empower L2 translators, non-professional translators and English as a Foreign Language (EFL) users by bolstering their translation skills. Finally, this chapter touches on an emerging issue related to the advent of neural machine translation (NMT), the output of which often reaches a CEFR (Common European Framework of Reference for Languages) level of B2 that exceeds the proficiency of the average college-level language learner and produces error types similar to those made by human translators for certain language combinations such as English and Japanese.
