← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-long.781 · 记录 #1
Lost in Translation, and Found: Detecting and Interpreting Translation Effects
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 22:00
摘要
Translationese refers to the statistical patterns that distinguish translated texts from original texts, which are often subtle and imperceptible to human readers. When translated texts appear in either training or testing data, these patterns can negatively affect model performance or warp model evaluation. We approach the task of discerning whether a text was originally written in English or translated into English by fine-tuning contemporary foundation models at distinct item lengths and achieve state-of-the-art performance (94% Macro F1). Given that these linguistic cues are subtle and often imperceptible to humans, we analyze the features which enable our model’s high performance. Employing a suite of interpretability-based techniques, we find that: (1) our high accuracy is enabled by a collection of linguistic features, a number of which correspond with linguistic theories of translationese, and (2) pretrained neural models are adept at picking up these features without any fine-tuning.
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记录 #1 · 2026-07 · 最新观测版本
Lost in Translation, and Found: Detecting and Interpreting Translation Effects
Translationese refers to the statistical patterns that distinguish translated texts from original texts, which are often subtle and imperceptible to human readers. When translated texts appear in either training or testing data, these patterns can negatively affect model performance or warp model evaluation. We approach the task of discerning whether a text was originally written in English or translated into English by fine-tuning contemporary foundation models at distinct item lengths and achieve state-of-the-art performance (94% Macro F1). Given that these linguistic cues are subtle and often imperceptible to humans, we analyze the features which enable our model’s high performance. Employing a suite of interpretability-based techniques, we find that: (1) our high accuracy is enabled by a collection of linguistic features, a number of which correspond with linguistic theories of translationese, and (2) pretrained neural models are adept at picking up these features without any fine-tuning.
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