Problem
Research questions and friction points this paper is trying to address.
Machine Translation
Post-Editing
Specialised Translation
Terminological Accuracy
Language for Specific Purposes
Machine Translation and Post-Editing: Comparative Evaluation of Different MT Systems and Post-Editor Groups in Specialised Translation
🤖 AI Summary
This study investigates the quality differences between machine translation (MT) and human post-editing (PE) in domain-specific English-to-French translation tasks, as well as the influence of post-editors’ professional backgrounds on editing outcomes. The experiment compares three leading MT systems—DeepL, eTranslation, and Systran—and involves two groups of post-editors: linguists/translators and NLP experts. A fine-grained error taxonomy tailored to MT and PE is employed for manual evaluation. For the first time in a specialized translation setting, the interaction between MT system performance and editor background is jointly examined. Findings reveal that terminological accuracy and linguistic fluency are significantly affected by domain expertise, highlighting current limitations of MT in handling specialized language and underscoring the value of interdisciplinary post-editing teams.
📝 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).
Innovation
Methods, ideas, or system contributions that make the work stand out.
machine translation
post-editing
specialised translation
error typology
domain knowledge
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