Google Translate and DeepL: Breaking taboos in translator training. Observational study and analysis.

Abstract

The literature published in the last decades on the use of machine translation (MT) generally conveys the idea that it is not suitable for professional translation. Furthermore, translation students are usually warned that they should not use this tool in their assignments or exams and that doing so would be penalized. The reason behind such reluctance may be that MT produced poor results unless it was used on controlled language in pre-edited texts within a specific specialization field and the target texts were subsequently post-edited with appropriate tools. However, three main considerations lead us to propose a re-evaluation of the usefulness of MT in university translation courses: first, students use MT despite warnings not to do so; second, current tools like Google Translate or DeepL have considerably improved their outcomes; and third, MT is already being used as an assistant tool in computer-assisted translation (CAT) – although the results are usually monitored by a human translator. As translation teachers, we propose that post-editing MT-translated texts could be used as a didactic tool, where the diagnosis of MT-errors could help improve students’ translation skills and linguistic proficiency both in their mother tongue and a second language. In this article, we present the results of using MT plus post-editing in a university course of Spanish-German Specialized Translation. This type of translation poses considerable challenge on students and often requires reviewing and deepening their knowledge of the target language grammar in class. Our aim was to contribute to the didactics of translation by training students in error prevention through the analysis of MT-errors, and to teach them deal with post-editing with a critical mind.