GAND: a resource on gender-ambiguous natural data and contrastive attribution for evaluating gender bias in machine translation
Read the original at arxiv.org→arXiv:2607.22546v1 Announce Type: new Abstract: Machine translation (MT) systems continue to produce gender-biased translations. In a time where self-expression is paramount, mistranslations based on default...
Original headline: "Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution"
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- Jul 28, 04:00 UTC arXiv cs.CL lead source Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution