@inproceedings{shafiabadi-yvon-2026-biases,
title = "Biases in Translation: Assessing Opinion Distortion in Machine Translated Texts",
author = "Shafiabadi, Nazanin and
Yvon, Fran{\c{c}}ois",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.679/",
doi = "10.63317/2pjio9ho8rxg",
pages = "8596--8614",
abstract = "Current machine translation (MT) evaluation practices largely assume that high lexical and semantic fidelity implies preservation of meaning. We question this assumption by introducing a framework for detecting and quantifying translation-induced distortion{---}the systematic alteration of a text{'}s subjective properties during translation. Focusing on stance as a socially consequential property, we formalize stance preservation as an invariance problem and adapt two classical statistical tests, McNemar{'}s test and the two-proportion Z-test, to diagnose systematic opinion shifts between source texts and their translations. Unlike standard MT metrics such as BLEU or COMET, which prioritize surface similarity and adequacy, our approach explicitly targets preservation of subjective meaning. In controlled experiments with synthetically distorted translations, we demonstrate that the proposed tests are sensitive to graded levels of stance manipulation. We apply our framework to evaluate twelve multilingual models and find that none reliably preserve stance across all tested language directions. Our findings reveal a critical gap in current MT evaluation practices and highlight the need for explicit evaluation of subjective meaning preservation in socially and politically sensitive contexts."
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<abstract>Current machine translation (MT) evaluation practices largely assume that high lexical and semantic fidelity implies preservation of meaning. We question this assumption by introducing a framework for detecting and quantifying translation-induced distortion—the systematic alteration of a text’s subjective properties during translation. Focusing on stance as a socially consequential property, we formalize stance preservation as an invariance problem and adapt two classical statistical tests, McNemar’s test and the two-proportion Z-test, to diagnose systematic opinion shifts between source texts and their translations. Unlike standard MT metrics such as BLEU or COMET, which prioritize surface similarity and adequacy, our approach explicitly targets preservation of subjective meaning. In controlled experiments with synthetically distorted translations, we demonstrate that the proposed tests are sensitive to graded levels of stance manipulation. We apply our framework to evaluate twelve multilingual models and find that none reliably preserve stance across all tested language directions. Our findings reveal a critical gap in current MT evaluation practices and highlight the need for explicit evaluation of subjective meaning preservation in socially and politically sensitive contexts.</abstract>
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%0 Conference Proceedings
%T Biases in Translation: Assessing Opinion Distortion in Machine Translated Texts
%A Shafiabadi, Nazanin
%A Yvon, François
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F shafiabadi-yvon-2026-biases
%X Current machine translation (MT) evaluation practices largely assume that high lexical and semantic fidelity implies preservation of meaning. We question this assumption by introducing a framework for detecting and quantifying translation-induced distortion—the systematic alteration of a text’s subjective properties during translation. Focusing on stance as a socially consequential property, we formalize stance preservation as an invariance problem and adapt two classical statistical tests, McNemar’s test and the two-proportion Z-test, to diagnose systematic opinion shifts between source texts and their translations. Unlike standard MT metrics such as BLEU or COMET, which prioritize surface similarity and adequacy, our approach explicitly targets preservation of subjective meaning. In controlled experiments with synthetically distorted translations, we demonstrate that the proposed tests are sensitive to graded levels of stance manipulation. We apply our framework to evaluate twelve multilingual models and find that none reliably preserve stance across all tested language directions. Our findings reveal a critical gap in current MT evaluation practices and highlight the need for explicit evaluation of subjective meaning preservation in socially and politically sensitive contexts.
%R 10.63317/2pjio9ho8rxg
%U https://aclanthology.org/2026.lrec-1.679/
%U https://doi.org/10.63317/2pjio9ho8rxg
%P 8596-8614
Markdown (Informal)
[Biases in Translation: Assessing Opinion Distortion in Machine Translated Texts](https://aclanthology.org/2026.lrec-1.679/) (Shafiabadi & Yvon, LREC 2026)
ACL