An interpretable machine learning framework for classifying human and machine translations across genres
Neural modeling and large language models (LLMs) has led to a significant improvement in the quality of machine translation (MT) output. While MT increasingly rivals human output, “translationese”—systematic linguistic fingerprints left by the translation process—has long been studied qualitatively,...
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| Автори: | , , |
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| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
Frontiers Media S.A.
2026-06-01
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| Серія: | Frontiers in Artificial Intelligence |
| Предмети: | |
| Онлайн доступ: | https://www.frontiersin.org/articles/10.3389/frai.2026.1813227/full |
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