mlsanityData to 14 Oct 2026

Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution

arXiv 2607.22546 ↗First posted 6 May 2026cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 30 of its 48 references that are in the map. First posted on arXiv 6 May 2026.

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References · 30

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18 more
  1. 2020Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpuscited by 57
  2. 2022MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translationcited by 38
  3. 2021First the worst: Finding better gender translations during beam searchcited by 29
  4. 2021Gender Bias Amplification During Speed-Quality Optimization in Neural Machine Translationcited by 28
  5. 2023Gender Neutralization for an Inclusive Machine Translation: from Theoretical Foundations to Open Challengescited by 22
  6. 2020Gender Coreference and Bias Evaluation at WMT 2020cited by 21
  7. 2023Hi Guys or Hi Folks? Benchmarking Gender-Neutral Machine Translation with the GeNTE Corpuscited by 20
  8. 2023A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translationcited by 19
  9. 2023GATE: A Challenge Set for Gender-Ambiguous Translation Examplescited by 18
  10. 2025Mind the Inclusivity Gap: Multilingual Gender-Neutral Translation Evaluation with mGeNTEcited by 12
  11. 2023Gender, names and other mysteries: Towards the ambiguous for gender-inclusive translationcited by 7
  12. 2025Are We Paying Attention to Her? Investigating Gender Disambiguation and Attention in Machine Translationcited by 7
  13. 2024MiTTenS: A Dataset for Evaluating Gender Mistranslationcited by 5
  14. 2025Voice, Bias, and Coreference: An Interpretability Study of Gender in Speech Translationcited by 5
  15. 2025GAMBIT+: A Challenge Set for Evaluating Gender Bias in Machine Translation Quality Estimation Metricscited by 4
  16. 2025Gender Bias in English-to-Greek Machine Translationcited by 3
  17. 2025The Unheard Alternative: Contrastive Explanations for Speech-to-Text Modelscited by 3
  18. 2025What Triggers my Model? Contrastive Explanations Inform Gender Choices by Translation Modelscited by 2

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