Revisiting Machine Translation Post-Editing Workflows with AI: Insights from a Case Study

Öz

Recent advances in neural machine translation and generative AI have reshaped translation and technical communication workflows, raising new questions about the role and scope of post-editing. This article reports on a case study, examining how machine translation and large language models perform in translation and post-editing scenarios. A Turkish user-manual text prepared for the purposes of the study was translated into English using Google Translate and ChatGPT under zero-shot conditions with no pre-editing to reflect common industry scenarios. Both tools produced fluent and largely accurate translations; however, the resulting outputs retained many of the instructional and usability issues present in the original text, such as long sentence structures, repetition, weak imperative style, and non-linear task flow. These findings underscore the distinction between linguistic accuracy and functional usability in technical communication. The study then explores how instruction-based prompting can support AI-assisted revision aligned with user-manual standards and plain-language principles, followed by expert human refinement. This two-stage process allows for a closer examination of how responsibility for text quality is distributed between the source text, the AI system, and the human professional. The article offers empirical insight into how post-editing functions in contemporary hybrid environments and discusses implications for professional training, emphasizing the need for competencies in source-text evaluation, prompt design, standards awareness, and human–AI collaboration. The study situates generative AI within existing post-editing practices and highlights its potential as a supportive, task-oriented assistant when guided by professional judgment.

Anahtar Kelimeler

• post-editing
• machine translation
• generative artificial intelligence
• prompting
• text optimization

Kaynakça

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