Machine Translation and Post-Editing: Comparative Evaluation of Different MT Systems and Post-Editor Groups in Specialised Translation
2026-06-22 • Computation and Language
Computation and Language
AI summaryⓘ
The authors studied how well three different machine translation (MT) systems translate specialized English texts into French, and how two groups of people (language experts and AI experts) improve these translations by editing. They found clear differences in translation quality, especially with technical terms and sentence flow, depending on which MT system was used and who did the editing. Their work shows that knowing the subject well is important in specialized translation and that current MT systems can vary a lot in accuracy for specific fields.
Machine TranslationPost-EditingSpecialized TranslationTerminological AccuracyFluencyError AnnotationLanguage for Specific PurposesDeepLeTranslationSystran
Authors
Joachim Minder, Alexandra Mestivier, Natalie Kübler
Abstract
This article aims to evaluate the quality of machine translation (MT) and post-editing (PE) in the context of specialised translation from English into French. Three MT systems (DeepL, eTranslation and Systran) were compared, and two groups of post-editors -linguists/translators and NLP experts -were asked to perform post-editing. Translation assessment is based on error annotation using an error typology adapted to MT and PE evaluation. The results reveal significant differences between the three MT systems and the two groups of post-editors, particularly in terms of terminological accuracy and fluency. This study highlights the importance of domain knowledge in specialised translation, as well as the limitations and variable performance of MT systems in language for specific purposes (LSP).