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Study finds literary translation trade-off between fluency and faithfulness

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv explores the trade-off between fluency and faithfulness in literary translation by both humans and machines. Researchers analyzed over 130,000 translated paragraphs from 106 novels, using a translationese classifier to measure fluency and the COMET-KIWI metric for faithfulness. The findings indicate a consistent negative correlation between fluency and faithfulness, suggesting that as translations become more fluent, they may deviate more from the source text's meaning. This pattern was observed in both human and Google Translate outputs, but was less pronounced in TranslateGemma. AI

IMPACT Highlights potential limitations in LLM-based literary translation, suggesting a need for models that better balance stylistic flair with semantic accuracy.

RANK_REASON Academic paper on translation quality metrics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Study finds literary translation trade-off between fluency and faithfulness

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Academic paper on translation quality metrics. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Ted Underwood ·

    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 re…

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