@inproceedings{zhang-2026-lost,
title = "Lost in Translation: Structured-Span Corruption in Machine-Translated Multilingual Web Corpora",
author = "Zhang, Ruiyang",
editor = "Ortiz Suarez, Pedro and
Burchell, Laurie and
Ljube{\v{s}}i{\'c}, Nikola and
Scherrer, Yves and
Laippala, Veronika and
Dini{\'c}, Vuk and
Vaughan, Thom",
booktitle = "Proceedings of the 13th Web-as-Corpus Workshop",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wac-1.9/",
pages = "100--106",
ISBN = "979-8-89176-481-1",
abstract = "Machine translation of English web text is a default recipe for building multilingual and low-resource corpora. We show that this recipe systematically damages the structured spans embedded in web text{---}URLs, email addresses, numbers and identifiers, code, markup and quotation delimiters{---}and that fluency metrics do not reveal the damage. Translating 56,163 span-bearing segments from a CC-MAIN-2026-30 Common Crawl sample into nine typologically diverse languages with NLLB-200, we find that 12.6{\%} of 73,881 structured spans do not survive, ranging from 91.1{\%} for numbers to 50.6{\%} for quotation delimiters. Damage is strongly language-dependent (18.7 points), but in the opposite direction to the obvious prediction: the highest-resource targets are damaged most, an inversion that survives controlling for the systems' tendency to copy low-resource input through untranslated. Round-trip fluency barely tracks any of this ({\ensuremath{\rho}} = 0.48 across languages, p = 0.19; {\ensuremath{\rho}} = 0.25 per segment). We pre-registered the study, report the refuted hypothesis alongside the supported ones, and release the annotated parallel-span corpus with an independent re-derivation of every number."
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<abstract>Machine translation of English web text is a default recipe for building multilingual and low-resource corpora. We show that this recipe systematically damages the structured spans embedded in web text—URLs, email addresses, numbers and identifiers, code, markup and quotation delimiters—and that fluency metrics do not reveal the damage. Translating 56,163 span-bearing segments from a CC-MAIN-2026-30 Common Crawl sample into nine typologically diverse languages with NLLB-200, we find that 12.6% of 73,881 structured spans do not survive, ranging from 91.1% for numbers to 50.6% for quotation delimiters. Damage is strongly language-dependent (18.7 points), but in the opposite direction to the obvious prediction: the highest-resource targets are damaged most, an inversion that survives controlling for the systems’ tendency to copy low-resource input through untranslated. Round-trip fluency barely tracks any of this (\ensuremathρ = 0.48 across languages, p = 0.19; \ensuremathρ = 0.25 per segment). We pre-registered the study, report the refuted hypothesis alongside the supported ones, and release the annotated parallel-span corpus with an independent re-derivation of every number.</abstract>
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%0 Conference Proceedings
%T Lost in Translation: Structured-Span Corruption in Machine-Translated Multilingual Web Corpora
%A Zhang, Ruiyang
%Y Ortiz Suarez, Pedro
%Y Burchell, Laurie
%Y Ljubešić, Nikola
%Y Scherrer, Yves
%Y Laippala, Veronika
%Y Dinić, Vuk
%Y Vaughan, Thom
%S Proceedings of the 13th Web-as-Corpus Workshop
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-481-1
%F zhang-2026-lost
%X Machine translation of English web text is a default recipe for building multilingual and low-resource corpora. We show that this recipe systematically damages the structured spans embedded in web text—URLs, email addresses, numbers and identifiers, code, markup and quotation delimiters—and that fluency metrics do not reveal the damage. Translating 56,163 span-bearing segments from a CC-MAIN-2026-30 Common Crawl sample into nine typologically diverse languages with NLLB-200, we find that 12.6% of 73,881 structured spans do not survive, ranging from 91.1% for numbers to 50.6% for quotation delimiters. Damage is strongly language-dependent (18.7 points), but in the opposite direction to the obvious prediction: the highest-resource targets are damaged most, an inversion that survives controlling for the systems’ tendency to copy low-resource input through untranslated. Round-trip fluency barely tracks any of this (\ensuremathρ = 0.48 across languages, p = 0.19; \ensuremathρ = 0.25 per segment). We pre-registered the study, report the refuted hypothesis alongside the supported ones, and release the annotated parallel-span corpus with an independent re-derivation of every number.
%U https://aclanthology.org/2026.wac-1.9/
%P 100-106
Markdown (Informal)
[Lost in Translation: Structured-Span Corruption in Machine-Translated Multilingual Web Corpora](https://aclanthology.org/2026.wac-1.9/) (Zhang, WAC 2026)
ACL