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    <abstract>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 correlation between fluency and faithfulness. The pattern appears for both human and Google Translate, but is weaker and often non-significant for TranslateGemma. These results show that segment length matters for automatic evaluation and suggest a tradeoff between fluency and faithfulness in literary translation.</abstract>
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%0 Conference Proceedings
%T Fluency and Faithfulness in Human and Machine Literary Translation
%A Griebel, Sarah
%A Underwood, Ted
%Y Hamilton, Sil
%Y Öhman, Emily
%Y Hicke, Rebecca M. M.
%Y Bizzoni, Yuri
%Y Bax, Axel
%Y Matthews, Jacob A.
%Y Hämäläinen, Mika
%S Proceedings of the 6th International Conference on Natural Language Processing for the Digital Humanities
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, USA
%@ 979-8-89176-427-9
%F griebel-underwood-2026-fluency
%X 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 correlation between fluency and faithfulness. The pattern appears for both human and Google Translate, but is weaker and often non-significant for TranslateGemma. These results show that segment length matters for automatic evaluation and suggest a tradeoff between fluency and faithfulness in literary translation.
%U https://aclanthology.org/2026.nlp4dh-1.17/
%P 178-189

Markdown (Informal)

[Fluency and Faithfulness in Human and Machine Literary Translation](https://aclanthology.org/2026.nlp4dh-1.17/) (Griebel & Underwood, NLP4DH 2026)

• Fluency and Faithfulness in Human and Machine Literary Translation (Griebel & Underwood, NLP4DH 2026)

ACL

• Sarah Griebel and Ted Underwood. 2026. Fluency and Faithfulness in Human and Machine Literary Translation. In Proceedings of the 6th International Conference on Natural Language Processing for the Digital Humanities, pages 178–189, San Diego, USA. Association for Computational Linguistics.