Translationese as a Rational Response to Translation Task Difficulty

TL;DR

This paper investigates how translationese, the divergence in translated texts, can be explained as a response to the cognitive load of translation tasks, using information-theoretic measures and linguistic features.

Contribution

It introduces a novel explanation that translationese results from translation task difficulty and demonstrates this with quantifiable measures and a bilingual corpus analysis.

Findings

Translationese can be partly predicted by task difficulty measures.

Cross-lingual transfer difficulty impacts translationese more than source-text complexity.

Information-theoretic metrics outperform traditional features in written translation modes.

Abstract

Translations systematically diverge from texts originally produced in the target language, a phenomenon widely referred to as translationese. Translationese has been attributed to production tendencies (e.g. interference, simplification), socio-cultural variables, and language-pair effects, yet a unified explanatory account is still lacking. We propose that translationese reflects cognitive load inherent in the translation task itself. We test whether observable translationese can be predicted from quantifiable measures of translation task difficulty. Translationese is operationalised as a segment-level translatedness score produced by an automatic classifier. Translation task difficulty is conceptualised as comprising source-text and cross-lingual transfer components, operationalised mainly through information-theoretic metrics based on LLM surprisal, complemented by established syntactic and semantic alternatives. We use a bidirectional English-German corpus comprising written and spoken subcorpora. Results indicate that translationese can be partly explained by translation task difficulty, especially in English-to-German. 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.