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This study investigates the origins of "translationese," proposing that systematic differences between translated and native texts stem from cognitive load rather than just production tendencies or socio-cultural factors. By leveraging large language models to quantify task difficulty via surprisal, the research demonstrates that cross-lingual transfer complexity is a primary driver of these linguistic artifacts, particularly in written English-to-German translation. The findings offer a more unified, quantitative framework for understanding machine-generated text patterns and human translation behavior through the lens of computational linguistics.
Translationese as a Rational Response to Translation Task Difficulty
arXiv:2603.12050v3 Announce Type: replace Abstract: 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.
Abstract: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.
Computation and Language (cs.CL)
I.2.7; H.3.1; G.3; J.5
arXiv:2603.12050 [cs.CL]
(or arXiv:2603.12050v3 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2603.12050 Focus to learn more arXiv-issued DOI via DataCite
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