mlsanityData to 7 Oct 2026

Toward Gender-Inclusive Coreference Resolution

arXiv 1910.13913 ↗First posted 30 Oct 2019cs.CLcited by 84

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Abstract (arXiv)

Correctly resolving textual mentions of people fundamentally entails making inferences about those people. Such inferences raise the risk of systemic biases in coreference resolution systems, including biases that can harm binary and non-binary trans and cis stakeholders. To better understand such biases, we foreground nuanced conceptualizations of gender from sociology and sociolinguistics, and develop two new datasets for interrogating bias in crowd annotations and in existing coreference resolution systems. Through these studies, conducted on English text, we confirm that without acknowledging and building systems that recognize the complexity of gender, we build systems that lead to many potential harms.

Key facts. Cited by 84 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Oct 2019.

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Citing papers per year: 2021: 14; 2022: 17; 2023: 20; 2024: 21; 2025: 9; 2026: 3.

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  1. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  2. 2021Ethical and social risks of harm from Language Modelscited by 957
  3. 2021BBQ: A Hand-Built Bias Benchmark for Question Answeringcited by 683
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  5. 2023Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Modelscited by 186
  6. 2021Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologiescited by 128
  7. 2021Gender Bias in Machine Translationcited by 116
  8. 2024Large Language Models are Geographically Biasedcited by 91
  9. 2021A Survey on Gender Bias in Natural Language Processingcited by 89
  10. 2022Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?cited by 89
  11. 2023KoLA: Carefully Benchmarking World Knowledge of Large Language Modelscited by 85
  12. 2021Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metricscited by 83
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  1. 2021On Measures of Biases and Harms in NLPcited by 81
  2. 2022Theories of "Gender" in NLP Bias Researchcited by 62
  3. 2023SocialCounterfactuals: Probing and Mitigating Intersectional Social Biases in Vision-Language Models with Counterfactual Examplescited by 53
  4. 2023VisoGender: A dataset for benchmarking gender bias in image-text pronoun resolutioncited by 52
  5. 2023MISGENDERED: Limits of Large Language Models in Understanding Pronounscited by 43
  6. 2023Queer In AI: A Case Study in Community-Led Participatory AIcited by 33
  7. 2022Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desideratacited by 28
  8. 2023Sociodemographic Bias in Language Models: A Survey and Forward Pathcited by 27
  9. 2024Uncovering Bias in Large Vision-Language Models at Scale with Counterfactualscited by 27
  10. 2023Stereotypes and Smut: The (Mis)representation of Non-cisgender Identities by Text-to-Image Modelscited by 26
  11. 2024Position: Measure Dataset Diversity, Don't Just Claim Itcited by 26
  12. 2022Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translationcited by 23
  13. 2021CAPE: Context-Aware Private Embeddings for Private Language Learningcited by 22
  14. 2023Gender Neutralization for an Inclusive Machine Translation: from Theoretical Foundations to Open Challengescited by 22
  15. 2024AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filterscited by 21
  16. 2023Hi Guys or Hi Folks? Benchmarking Gender-Neutral Machine Translation with the GeNTE Corpuscited by 20
  17. 2023What about em? How Commercial Machine Translation Fails to Handle (Neo-)Pronounscited by 19
  18. 2024Robust Pronoun Fidelity with English LLMs: Are they Reasoning, Repeating, or Just Biased?cited by 17
  19. 2022How Conservative are Language Models? Adapting to the Introduction of Gender-Neutral Pronounscited by 16
  20. 2023Fairness in Language Models Beyond English: Gaps and Challengescited by 16
  21. 2021Extending Challenge Sets to Uncover Gender Bias in Machine Translation: Impact of Stereotypical Verbs and Adjectivescited by 14
  22. 2024Quite Good, but Not Enough: Nationality Bias in Large Language Models -- A Case Study of ChatGPTcited by 13
  23. 2021The Arabic Parallel Gender Corpus 2.0: Extensions and Analysescited by 12
  24. 2022How Gender Debiasing Affects Internal Model Representations, and Why It Matterscited by 12
  25. 2023In-Contextual Gender Bias Suppression for Large Language Modelscited by 12
  26. 2024A Prompt Response to the Demand for Automatic Gender-Neutral Translationcited by 12
  27. 2024The Emotion Dynamics of Literary Novelscited by 12
  28. 2021How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translationcited by 11
  29. 2022Conditional Supervised Contrastive Learning for Fair Text Classificationcited by 11
  30. 2023"One-Size-Fits-All"? Examining Expectations around What Constitute "Fair" or "Good" NLG System Behaviorscited by 11
  31. 2024Uncovering Bias in Large Vision-Language Models with Counterfactualscited by 9
  32. 2024WinoPron: Revisiting English Winogender Schemas for Consistency, Coverage, and Grammatical Casecited by 9
  33. 2022Mitigating Gender Bias in Machine Translation through Adversarial Learningcited by 8
  34. 2022Towards WinoQueer: Developing a Benchmark for Anti-Queer Bias in Large Language Modelscited by 8
  35. 2024The GPT-WritingPrompts Dataset: A Comparative Analysis of Character Portrayal in Short Storiescited by 8
  36. 2022Trustworthy Social Bias Measurementcited by 7
  37. 2023Logic Against Bias: Textual Entailment Mitigates Stereotypical Sentence Reasoningcited by 7
  38. 2023The Impact of Debiasing on the Performance of Language Models in Downstream Tasks is Underestimatedcited by 7

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