Fluency and Faithfulness in Human and Machine Literary Translation
Literary translation requires balancing target-language fluency with faithfulness to the source. Recent large language models (LLMs) often produce fluent translations, but it remains unclear whether fluency corresponds to semantic preservation in literary text. We examine this relationship using 130,486 translated paragraphs from 106 novels in 16 source languages, including human, Google Translate, and TranslateGemma translations. Fluency is measured as original-likeness with a translationese classifier trained on paragraph part-of-speech n-grams, and faithfulness with the automatic translation evaluation metric COMET-KIWI. We control for paragraph length and find a consistent negative co...
| Authors | Sarah Griebel, Ted Underwood |
|---|---|
| Primary category | cs.CL |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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