Fluency and Faithfulness in Human and Machine Literary Translation

• URL: http://arxiv.org/abs/2605.15282v1
• Date: Thu, 14 May 2026 18:00:34 GMT
• Title: Fluency and Faithfulness in Human and Machine Literary Translation
• Abstract summary: Literary translation requires balancing target-language fluency with faithfulness to the source.<n>Recent large language models (LLMs) often produce fluent translations, but it remains unclear whether fluency corresponds to semantic preservation in literary text.<n>We examine this relationship using 130,486 translated paragraphs from 106 novels in 16 source languages, including human, Google Translate, and TranslateGemma translations.
• Score: 0.059497893465854525
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• The Role of Disfluencies in Speech Translation [74.9653116322058]
  Current speech translation systems tend to strip disfluencies like filled pauses and false starts rather than translate them.<n>We show this comes at a cost: disfluencies carry meaning that gets lost when speech is cleaned up.<n>We show inference-time decoding can mitigate this without retraining, and release the benchmark and code.
  arXiv  Detail & Related papers  (2026-08-03T12:26:39Z)
• Translationese as a Rational Response to Translation Task Difficulty [0.0]
  We test whether observable translationese can be predicted from quantifiable measures of translation task difficulty.<n>We use a bidirectional English-German corpus comprising written and spoken subcorpora.<n>Results indicate that translationese can be partly explained by translation task difficulty, especially in English-to-German.
  arXiv  Detail & Related papers  (2026-03-12T15:24:00Z)
• Liaozhai through the Looking-Glass: On Paratextual Explicitation of Culture-Bound Terms in Machine Translation [70.43884512651668]
  We formalize Genette's (1987) theory of paratexts from literary and translation studies to introduce the task of paratextual explicitation for machine translation.<n>We construct a dataset of 560 expert-aligned paratexts from four English translations of the classical Chinese short story collection Liaozhai.<n>Our findings demonstrate the potential of paratextual explicitation in advancing machine translation beyond linguistic equivalence.
  arXiv  Detail & Related papers  (2025-09-27T16:27:36Z)
• Do LLMs Understand Your Translations? Evaluating Paragraph-level MT with Question Answering [68.3400058037817]
  We introduce TREQA (Translation Evaluation via Question-Answering), a framework that extrinsically evaluates translation quality.<n>We show that TREQA is competitive with and, in some cases, outperforms state-of-the-art neural and LLM-based metrics in ranking alternative paragraph-level translations.
  arXiv  Detail & Related papers  (2025-04-10T09:24:54Z)
• Lost in Literalism: How Supervised Training Shapes Translationese in LLMs [51.04435855143767]
  Large language models (LLMs) have achieved remarkable success in machine translation.<n>However, translationese, characterized by overly literal and unnatural translations, remains a persistent challenge.<n>We introduce methods to mitigate these biases, including polishing golden references and filtering unnatural training instances.
  arXiv  Detail & Related papers  (2025-03-06T12:14:45Z)
• Characterizing the Effects of Translation on Intertextuality using Multilingual Embedding Spaces [0.0]
  Rhetorical devices are difficult to translate, but they are crucial to the translation of literary documents.<n>We investigate the use of multilingual embedding spaces to characterize the preservation of intertextuality across human and machine translation.
  arXiv  Detail & Related papers  (2025-01-18T11:36:17Z)
• Evaluation of Google Translate for Mandarin Chinese translation using sentiment and semantic analysis [1.3999481573773074]
  Machine translation using large language models (LLMs) is having a significant global impact. Mandarin Chinese is the official language used for communication by the government and media in China. In this study, we provide an automated assessment of translation quality of Google Translate with human experts using sentiment and semantic analysis.
  arXiv  Detail & Related papers  (2024-09-08T04:03:55Z)
• Crossing the Threshold: Idiomatic Machine Translation through Retrieval Augmentation and Loss Weighting [66.02718577386426]
  We provide a simple characterization of idiomatic translation and related issues. We conduct a synthetic experiment revealing a tipping point at which transformer-based machine translation models correctly default to idiomatic translations. To improve translation of natural idioms, we introduce two straightforward yet effective techniques.
  arXiv  Detail & Related papers  (2023-10-10T23:47:25Z)
• Mitigating Hallucinations and Off-target Machine Translation with Source-Contrastive and Language-Contrastive Decoding [53.84948040596055]
  We introduce two related methods to mitigate failure cases with a modified decoding objective. Experiments on the massively multilingual models M2M-100 (418M) and SMaLL-100 show that these methods suppress hallucinations and off-target translations.
  arXiv  Detail & Related papers  (2023-09-13T17:15:27Z)
• Large language models effectively leverage document-level context for literary translation, but critical errors persist [32.54546652197316]
  Large language models (LLMs) are competitive with the state of the art on a wide range of sentence-level translation datasets. We show through a rigorous human evaluation that asking the Gpt-3.5 (text-davinci-003) LLM to translate an entire literary paragraph results in higher-quality translations.
  arXiv  Detail & Related papers  (2023-04-06T17:27:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.