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        <title>Dialects of Translationese Shape Language Model Learning</title>
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    <abstract>Machine-translated data is widely used in multilingual NLP, particularly where native text is scarce. However, translated text differs systematically from native text. This phenomenon is known as translationese, and it reflects both traces of the source language and characteristic properties of translation itself. In this paper, we study how training on machine-translated data affects small English language models, focusing on how translationese from different source languages shapes linguistic acceptability judgments and language modeling for different domains. We train models on English text translated from 24 typologically and resource-diverse source languages, enabling a systematic analysis of how source language and corpus properties influence what models learn. Our results show that the source language has a clear impact on model behavior: general perplexity is more driven by the lexical diversity of the translated corpus, but grammatical performance is strongly correlated to typological similarity to English if trained on enough data. Even translation quality is a strong predictor of language modeling performance.</abstract>
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%0 Conference Proceedings
%T Dialects of Translationese Shape Language Model Learning
%A Kunz, Jenny
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F kunz-2026-dialects
%X Machine-translated data is widely used in multilingual NLP, particularly where native text is scarce. However, translated text differs systematically from native text. This phenomenon is known as translationese, and it reflects both traces of the source language and characteristic properties of translation itself. In this paper, we study how training on machine-translated data affects small English language models, focusing on how translationese from different source languages shapes linguistic acceptability judgments and language modeling for different domains. We train models on English text translated from 24 typologically and resource-diverse source languages, enabling a systematic analysis of how source language and corpus properties influence what models learn. Our results show that the source language has a clear impact on model behavior: general perplexity is more driven by the lexical diversity of the translated corpus, but grammatical performance is strongly correlated to typological similarity to English if trained on enough data. Even translation quality is a strong predictor of language modeling performance.
%U https://aclanthology.org/2026.findings-emnlp.2272/
%P 48640-48659

Markdown (Informal)

[Dialects of Translationese Shape Language Model Learning](https://aclanthology.org/2026.findings-emnlp.2272/) (Kunz, Findings 2026)

• Dialects of Translationese Shape Language Model Learning (Kunz, Findings 2026)

ACL

• Jenny Kunz. 2026. Dialects of Translationese Shape Language Model Learning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 48640–48659, Budapest, Hungary. Association for Computational Linguistics.