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Translationese as a Rational Response to Translation Task Difficulty

arXiv 2603.12050 ↗First posted 12 Mar 2026cs.CLcited by 0

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Abstract (arXiv)

Translated texts exhibit systematic differences from comparable texts originally written in the target language. Explaining this phenomenon, commonly known as translationese, remains an open challenge. Translationese has been attributed to production tendencies (e.g. interference, simplification), socio-cultural variables, and language-pair effects, yet a unified explanatory account is lacking. We investigate the hypothesis that translationese is a response to the cognitive load inherent in the translation task. We test whether observable translationese can be predicted from quantifiable measures of translation task difficulty. Translationese is measured as a segment-level probability of being a translation produced by an automatic classifier (translatedness score). Translation task difficulty includes source-text and cross-lingual transfer components. They are captured by information-theoretic metrics based on LLM surprisal and by established syntactic and semantic alternatives. We use a bidirectional English-German corpus comprising written and spoken subcorpora. Results indicate that translationese can only be explained in part by translation task difficulty, especially in the English-to-German direction. For most experiments, cross-lingual transfer difficulty contributes more than source-text complexity. Information-theoretic indicators match or outperform traditional features in written mode, but offer no advantage in spoken mode. Source-text syntactic complexity and translation-solution entropy emerged as the strongest predictors of translationese across language pairs and modes.

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References · 7

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2021Word Alignment by Fine-tuning Embeddings on Parallel Corporacited by 186
  3. 2022Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?cited by 111
  4. 2020Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modelingcited by 22
  5. 2021Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classificationcited by 13
  6. 2023Measuring Spurious Correlation in Classification: 'Clever Hans' in Translationesecited by 5
  7. 2026EPIC-EuroParl-UdS: Information-Theoretic Perspectives on Translation and Interpretingcited by 1

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