mlsanityData to 14 Oct 2026

A Dataset for Probing Translationese Preferences in English-to-Swedish Translation

arXiv 2603.08450 ↗First posted 9 Mar 2026cs.CLcited by 0

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Abstract (arXiv)

Translations often carry traces of the source language, a phenomenon known as translationese. We introduce the first freely available English-to-Swedish dataset contrasting translationese sentences with idiomatic alternatives, designed to probe intrinsic preferences of language models. It includes error tags and descriptions of the problems in the original translations. In experiments evaluating smaller Swedish and multilingual LLMs with our dataset, we find that they often favor the translationese phrasing. Human alternatives are chosen more often when the English source sentence is omitted, indicating that exposure to the source biases models toward literal translations, although even without context models often prefer the translationese variant. Our dataset and findings provide a resource and benchmark for developing models that produce more natural, idiomatic output in non-English languages.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 23 references that are in the map. First posted on arXiv 9 Mar 2026.

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References · 9

  1. 2023The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variantscited by 311
  2. 2023Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedbackcited by 190
  3. 2021Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translationcited by 70
  4. 2024A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelismcited by 35
  5. 2018Examining the Tip of the Iceberg: A Data Set for Idiom Translationcited by 34
  6. 2023Do GPTs Produce Less Literal Translations?cited by 30
  7. 2025Lost in Literalism: How Supervised Training Shapes Translationese in LLMscited by 11
  8. 2026Preferences for Idiomatic Language are Acquired Slowly -- and Forgotten Quickly: A Case Study on Swedishcited by 3
  9. 2025Decoding Machine Translationese in English-Chinese News: LLMs vs. NMTscited by 2

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  1. 2023Crossing the Threshold: Idiomatic Machine Translation through Retrieval Augmentation and Loss Weighting
  2. 2024Modeling User Preferences with Automatic Metrics: Creating a High-Quality Preference Dataset for Machine Translation
  3. 2025Estonian WinoGrande Dataset: Comparative Analysis of LLM Performance on Human and Machine Translation
  4. 2025The Art of Asking: Multilingual Prompt Optimization for Synthetic Data
  5. 2026An Investigation of Translationese in the Generations of Multilingual Large Language Models
  6. 2026The Interlingua Hypothesis: LLMs Translate via a Latent Task-agnostic Feature Space
  7. 2026A Rising Tide Lifts All Boats: MTQE Rewards for Idioms Improve General Translation Quality
  8. 2025M2PO: Multi-Perspective Multi-Pair Preference Optimization for Machine Translation