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

• URL: http://arxiv.org/abs/2603.08450v1
• Date: Mon, 09 Mar 2026 14:46:35 GMT
• Title: A Dataset for Probing Translationese Preferences in English-to-Swedish Translation
• Abstract summary: We introduce the first freely available English-to-Swedish dataset contrasting translationese sentences with idiomatic alternatives.<n>It includes error tags and descriptions of the problems in the original translations.
• Score: 0.9558392439655014
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

Related papers

• 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)
• Aligning Large Language Models to Low-Resource Languages through LLM-Based Selective Translation: A Systematic Study [3.9680588541743718]
  selective translation is a technique that translates only the translatable parts of a text while preserving non-translatable content and sentence structure.<n>Our experiments focus on the low-resource Indic language Hindi and compare translations generated by Google Cloud Translation (GCP) and Llama-3.1-405B.
  arXiv  Detail & Related papers  (2025-07-18T18:21:52Z)
• 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)
• A Data Selection Approach for Enhancing Low Resource Machine Translation Using Cross-Lingual Sentence Representations [0.4499833362998489]
  This study focuses on the case of English-Marathi language pairs, where existing datasets are notably noisy. To mitigate the impact of data quality issues, we propose a data filtering approach based on cross-lingual sentence representations. Results demonstrate a significant improvement in translation quality over the baseline post-filtering with IndicSBERT.
  arXiv  Detail & Related papers  (2024-09-04T13:49:45Z)
• Mitigating Translationese in Low-resource Languages: The Storyboard Approach [9.676710061071809]
  We propose a novel approach for data collection by leveraging storyboards to elicit more fluent and natural sentences. Our method involves presenting native speakers with visual stimuli in the form of storyboards and collecting their descriptions without direct exposure to the source text. We conducted a comprehensive evaluation comparing our storyboard-based approach with traditional text translation-based methods in terms of accuracy and fluency.
  arXiv  Detail & Related papers  (2024-07-14T10:47:03Z)
• Iterative Translation Refinement with Large Language Models [25.90607157524168]
  We propose iteratively prompting a large language model to self-correct a translation. We also discuss the challenges in evaluation and relation to human performance and translationese.
  arXiv  Detail & Related papers  (2023-06-06T16:51:03Z)
• The Best of Both Worlds: Combining Human and Machine Translations for Multilingual Semantic Parsing with Active Learning [50.320178219081484]
  We propose an active learning approach that exploits the strengths of both human and machine translations. An ideal utterance selection can significantly reduce the error and bias in the translated data.
  arXiv  Detail & Related papers  (2023-05-22T05:57:47Z)
• Towards Fine-Grained Information: Identifying the Type and Location of Translation Errors [80.22825549235556]
  Existing approaches can not synchronously consider error position and type. We build an FG-TED model to predict the textbf addition and textbfomission errors. Experiments show that our model can identify both error type and position concurrently, and gives state-of-the-art results.
  arXiv  Detail & Related papers  (2023-02-17T16:20:33Z)
• Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval [109.62363167257664]
  We propose a generative model for learning multilingual text embeddings. Our model operates on parallel data in $N$ languages. We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval.
  arXiv  Detail & Related papers  (2022-12-21T02:41:40Z)
• Towards Debiasing Translation Artifacts [15.991970288297443]
  We propose a novel approach to reducing translationese by extending an established bias-removal technique. We use the Iterative Null-space Projection (INLP) algorithm, and show by measuring classification accuracy before and after debiasing, that translationese is reduced at both sentence and word level. To the best of our knowledge, this is the first study to debias translationese as represented in latent embedding space.
  arXiv  Detail & Related papers  (2022-05-16T21:46:51Z)
• On the Language Coverage Bias for Neural Machine Translation [81.81456880770762]
  Language coverage bias is important for neural machine translation (NMT) because the target-original training data is not well exploited in current practice. By carefully designing experiments, we provide comprehensive analyses of the language coverage bias in the training data. We propose two simple and effective approaches to alleviate the language coverage bias problem.
  arXiv  Detail & Related papers  (2021-06-07T01:55:34Z)
• Comparison of Interactive Knowledge Base Spelling Correction Models for Low-Resource Languages [81.90356787324481]
  Spelling normalization for low resource languages is a challenging task because the patterns are hard to predict. This work shows a comparison of a neural model and character language models with varying amounts on target language data. Our usage scenario is interactive correction with nearly zero amounts of training examples, improving models as more data is collected.
  arXiv  Detail & Related papers  (2020-10-20T17:31:07Z)

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.