No more rage against the machine: how the corpus-based identification of machine-translationese can lead to student empowerment
Résumé
The aim of this article is to show how a linguistic analysis of a corpus of machine-translated texts, both quantitative and qualitative, can empower translation trainees by helping them define their added value over machine translation (MT) systems. In particular the aim is to show that MT, even when providing grammatically correct output, does not comply with linguistic usage, thus failing to provide natural-sounding translations as expected in today’s market for specialised translation. Following two avenues left open for future research in Loock (2018), this article provides the results of a corpus analysis of EN-FR machine-translated texts using 3 MT systems: DeepL (NMT) and the European Commission’s eTranslation in both its SMT and NMT versions. The quantitative results show that the linguistic characteristics of machine-translated texts differ from French original texts, with an almost systematic over-representation of a series of linguistic features, possibly but partially due to source language interference, while the qualitative analysis of a sample reveals finer-grained results (e.g. variability of results depending on (N)MT tool, frequency of adverb deletion). It is then explained how such results, leading to the identification of ‘machine-translationese’, are meant to be used in an educational setting to improve translator education, by (i) making students aware of the gap between machine-translated texts and original texts, and (ii) providing them with information on what to focus on during the post-editing process.