Machine Translation and Post-Editing Practices
Summary
Machine translation has evolved from rule-based methods through statistical systems to modern neural approaches and large language models, fundamentally reshaping the translation industry. These systems generate draft translations at scale, but imperfections remain, demanding human post-editing to achieve publication-grade quality. Post-editing practices range from light editing to thorough revision, guided by considerations of fluency, adequacy and domain accuracy. Research has focused on error typologies, cognitive effort and productivity metrics to optimise human–machine collaboration. Advances in quality estimation enable project managers to forecast post-editing effort and assign resources accordingly. Globally, the integration of machine translation and post-editing accelerates multilingual communication in diplomacy, science, commerce and public services while preserving linguistic nuance and cultural context.
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Machine Translation and Post-Editing Practices publication trend
The graph below shows the total number of articles in machine translation and post-editing practices across all publications each year (not limited to Nature Index journals).
Technical terms
Machine translation: Automated conversion of text from one language to another without human intervention.
Neural machine translation: A translation approach that uses deep neural networks to model complex patterns across entire sentences.
Post-editing: Human revision of machine translation output to ensure accuracy, fluency and adherence to domain conventions.
Large language model: A deep learning model trained on extensive text corpora to generate or translate language with broad contextual understanding.
Quality estimation: Automated prediction of the expected effort required to post-edit a machine-translated segment.
References
- Large Language Models as Computational Linguistics Tools: A Comparative Analysis of ChatGPT and Google Machine Translations. Journal of Artificial Intelligence and Technology (2024).
- Identifying the Machine Translation Error Types with the Greatest Impact on Post-editing Effort. Frontiers in Psychology (2017).
- Translation Quality and Error Recognition in Professional Neural Machine Translation Post-Editing. Informatics (2019).
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