[Submitted on 6 May 2026]
Title:Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution
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Abstract:Machine translation (MT) systems continue to produce gender-biased translations. In a time where self-expression is paramount, mistranslations based on default behaviour and stereotyping can lead to harm for users of these systems. To better understand how these systems translate gender in the absence of clear gender cues, we need benchmarking resources that reflect gender-ambiguous scenarios in a natural way. To this end, we present GAND, a gender-ambiguous natural data benchmarking resource for MT consisting of English source sentences, specifically designed to analyse the influence of contextual cues on gender in translation. We leverage GAND to conduct an interpretability analysis: we translate a subset of GAND into two grammatical gender languages and extend these with manually crafted contrastive translations. A following feature attribution analysis reveals source words in context that inform the gender translation of an ambiguous referent entity in the target translation.
Comments: Accepted at EAMT2026: Technical Track
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2607.22546 [cs.CL]
(or arXiv:2607.22546v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.22546
arXiv-issued DOI via DataCite
Submission history
From: Janiça Hackenbuchner [view email] [v1] Wed, 6 May 2026 11:58:20 UTC (5,089 KB)
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