mlsanityData to 7 Oct 2026

GENder-IT: An Annotated English-Italian Parallel Challenge Set for Cross-Linguistic Natural Gender Phenomena

arXiv 2108.02854 ↗First posted 5 Aug 2021cs.CL cs.AI cs.CYcited by 16

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Abstract (arXiv)

Languages differ in terms of the absence or presence of gender features, the number of gender classes and whether and where gender features are explicitly marked. These cross-linguistic differences can lead to ambiguities that are difficult to resolve, especially for sentence-level MT systems. The identification of ambiguity and its subsequent resolution is a challenging task for which currently there aren't any specific resources or challenge sets available. In this paper, we introduce gENder-IT, an English--Italian challenge set focusing on the resolution of natural gender phenomena by providing word-level gender tags on the English source side and multiple gender alternative translations, where needed, on the Italian target side.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 12 of its 23 references that are in the map. First posted on arXiv 5 Aug 2021.

Builds on

12 of 23 references.

References · 12

  1. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
  2. 2018Gender Bias in Coreference Resolution: Evaluation and Debiasing Methodscited by 657
  3. 2018Gender Bias in Coreference Resolutioncited by 438
  4. 2019Evaluating Gender Bias in Machine Translationcited by 247
  5. 2018Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systemscited by 234
  6. 2019Getting Gender Right in Neural Machine Translationcited by 98
  7. 2020Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problemcited by 86
  8. 2021Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translationcited by 70
  9. 2020Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpuscited by 57
  10. 2016Personalized Machine Translation: Preserving Original Author Traitscited by 47
  11. 2020Neural Machine Translation Doesn't Translate Gender Coreference Right Unless You Make Itcited by 40
  12. 2019GeBioToolkit: Automatic Extraction of Gender-Balanced Multilingual Corpus of Wikipedia Biographiescited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 5; 2024: 4; 2025: 3; 2026: 1.

16 papers.

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  1. 2022Perturbation Augmentation for Fairer NLPcited by 51
  2. 2022MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translationcited by 38
  3. 2023Hi Guys or Hi Folks? Benchmarking Gender-Neutral Machine Translation with the GeNTE Corpuscited by 20
  4. 2023What about em? How Commercial Machine Translation Fails to Handle (Neo-)Pronounscited by 19
  5. 2023A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translationcited by 19
  6. 2023GATE: A Challenge Set for Gender-Ambiguous Translation Examplescited by 18
  7. 2024What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-centered Studycited by 15
  8. 2021The Arabic Parallel Gender Corpus 2.0: Extensions and Analysescited by 12
  9. 2023Angler: Helping Machine Translation Practitioners Prioritize Model Improvementscited by 11
  10. 2024Watching the Watchers: Exposing Gender Disparities in Machine Translation Quality Estimationcited by 10
  11. 2025Assumed Identities: Quantifying Gender Bias in Machine Translation of Gender-Ambiguous Occupational Termscited by 5
  12. 2024Gender Bias in Machine Translation and The Era of Large Language Modelscited by 4
4 more
  1. 2024Data Bias According to Bipol: Men are Naturally Right and It is the Role of Women to Follow Their Leadcited by 2
  2. 2025Uncertainty Quantification for Evaluating Machine Translation Biascited by 2
  3. 2025What Triggers my Model? Contrastive Explanations Inform Gender Choices by Translation Modelscited by 2
  4. 2026ConGA: Guidelines for Contextual Gender Annotation. A Framework for Annotating Gender in Machine Translationcited by 0