In this article, we investigate translation specialists’ capacity to identify raw machine translation (MT) output in comparison with so-called “human” translations produced without any use of MT. Specifically, we measure this capacity via an online activity, based on different criteria: (i) degree of expertise (translation students vs. professionals with at least 5 years’ experience), (ii) MT engine (DeepL, Google Translate, Reverso, ChatGPT), and (iii) length of input (1-3 sentences). A complementary, qualitative analysis, based on participants’ feedback, provides interesting insight on how they discriminate between raw MT output and human translations.

In this paper, we discuss a use of machine translation (MT) that has been quite overlooked up to now, namely by students not enrolled in a professional translation program. A number of studies have reported massive use of free online translators (OTs), and it seems important to uncover such users’ abilities and difficulties when using MT output, whether to improve their understanding, writing, or translation skills. We report here a study on students enrolled in a French ‘applied languages program’ (where students study two languages, as well as law, economics, and management). The aim was to uncover how they use OTs, as well as their (in)ability to identify and correct MT errors. Obtained through two online surveys and several tests conducted with students from 2020 to 2022, our results show an unsurprising widespread use of OTs for many different tasks, but also some specific difficulties in identifying MT errors, in particular in relation to target language fluency.