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Main Author: Kappl, Michelle
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2502.19104
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author Kappl, Michelle
author_facet Kappl, Michelle
contents We present WinoMTDE, a new gender bias evaluation test set designed to assess occupational stereotyping and underrepresentation in German machine translation (MT) systems. Building on the automatic evaluation method introduced by arXiv:1906.00591v1, we extend the approach to German, a language with grammatical gender. The WinoMTDE dataset comprises 288 German sentences that are balanced in regard to gender, as well as stereotype, which was annotated using German labor statistics. We conduct a large-scale evaluation of five widely used MT systems and a large language model. Our results reveal persistent bias in most models, with the LLM outperforming traditional systems. The dataset and evaluation code are publicly available under https://github.com/michellekappl/mt_gender_german.
format Preprint
id arxiv_https___arxiv_org_abs_2502_19104
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Are All Spanish Doctors Male? Evaluating Gender Bias in German Machine Translation
Kappl, Michelle
Computation and Language
We present WinoMTDE, a new gender bias evaluation test set designed to assess occupational stereotyping and underrepresentation in German machine translation (MT) systems. Building on the automatic evaluation method introduced by arXiv:1906.00591v1, we extend the approach to German, a language with grammatical gender. The WinoMTDE dataset comprises 288 German sentences that are balanced in regard to gender, as well as stereotype, which was annotated using German labor statistics. We conduct a large-scale evaluation of five widely used MT systems and a large language model. Our results reveal persistent bias in most models, with the LLM outperforming traditional systems. The dataset and evaluation code are publicly available under https://github.com/michellekappl/mt_gender_german.
title Are All Spanish Doctors Male? Evaluating Gender Bias in German Machine Translation
topic Computation and Language
url https://arxiv.org/abs/2502.19104