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Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology

arXiv 1906.04571 ↗First posted 11 Jun 2019cs.CLcited by 200

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Abstract (arXiv)

Gender stereotypes are manifest in most of the world's languages and are consequently propagated or amplified by NLP systems. Although research has focused on mitigating gender stereotypes in English, the approaches that are commonly employed produce ungrammatical sentences in morphologically rich languages. We present a novel approach for converting between masculine-inflected and feminine-inflected sentences in such languages. For Spanish and Hebrew, our approach achieves F1 scores of 82% and 73% at the level of tags and accuracies of 90% and 87% at the level of forms. By evaluating our approach using four different languages, we show that, on average, it reduces gender stereotyping by a factor of 2.5 without any sacrifice to grammaticality.

Key facts. Cited by 200 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Jun 2019.

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Citing papers per year: 2021: 34; 2022: 38; 2023: 46; 2024: 38; 2025: 29; 2026: 15.

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  1. 2023Bias and Fairness in Large Language Models: A Surveycited by 639
  2. 2021A Survey of Data Augmentation Approaches for NLPcited by 409
  3. 2021Societal Biases in Language Generation: Progress and Challengescited by 202
  4. 2021Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyondcited by 178
  5. 2022Towards Faithful Model Explanation in NLP: A Surveycited by 130
  6. 2022Pretrained Language Models for Text Generation: A Surveycited by 122
  7. 2021Gender Bias in Machine Translationcited by 117
  8. 2021Debiasing Pre-trained Contextualised Embeddingscited by 107
  9. 2021Re-imagining Algorithmic Fairness in India and Beyondcited by 104
  10. 2021An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Modelscited by 97
  11. 2023A Survey on Fairness in Large Language Modelscited by 95
  12. 2021A Survey on Gender Bias in Natural Language Processingcited by 89
38 more
  1. 2021Data Augmentation Approaches in Natural Language Processing: A Surveycited by 82
  2. 2021On Measures of Biases and Harms in NLPcited by 81
  3. 2024Bias in Large Language Models: Origin, Evaluation, and Mitigationcited by 77
  4. 2023Fast Model Debias with Machine Unlearningcited by 72
  5. 2022Theories of "Gender" in NLP Bias Researchcited by 62
  6. 2021Counterfactual Interventions Reveal the Causal Effect of Relative Clause Representations on Agreement Predictioncited by 59
  7. 2024Fairness in Large Language Models: A Taxonomic Surveycited by 58
  8. 2022Perturbation Augmentation for Fairer NLPcited by 52
  9. 2022Debiasing isn't enough! -- On the Effectiveness of Debiasing MLMs and their Social Biases in Downstream Taskscited by 48
  10. 2022A Causal Lens for Controllable Text Generationcited by 45
  11. 2022Event knowledge in large language models: the gap between the impossible and the unlikelycited by 45
  12. 2024Thinking Fair and Slow: On the Efficacy of Structured Prompts for Debiasing Language Modelscited by 45
  13. 2023Faithful Explanations of Black-box NLP Models Using LLM-generated Counterfactualscited by 44
  14. 2021An Investigation of the (In)effectiveness of Counterfactually Augmented Datacited by 42
  15. 2022Optimizing Relevance Maps of Vision Transformers Improves Robustnesscited by 41
  16. 2022Probing Classifiers are Unreliable for Concept Removal and Detectioncited by 41
  17. 2022Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversalcited by 40
  18. 2024Social Bias Evaluation for Large Language Models Requires Prompt Variationscited by 40
  19. 2025FairSteer: Inference Time Debiasing for LLMs with Dynamic Activation Steeringcited by 40
  20. 2024Prompting Techniques for Reducing Social Bias in LLMs through System 1 and System 2 Cognitive Processescited by 39
  21. 2021Perceptual Score: What Data Modalities Does Your Model Perceive?cited by 35
  22. 2022Unified Detoxifying and Debiasing in Language Generation via Inference-time Adaptive Optimizationcited by 33
  23. 2024Decoding Biases: Automated Methods and LLM Judges for Gender Bias Detection in Language Modelscited by 33
  24. 2023On Evaluating and Mitigating Gender Biases in Multilingual Settingscited by 31
  25. 2021Towards Robust Classification Model by Counterfactual and Invariant Data Generationcited by 30
  26. 2021Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysiscited by 30
  27. 2022Choose Your Lenses: Flaws in Gender Bias Evaluationcited by 29
  28. 2021Gender Bias Amplification During Speed-Quality Optimization in Neural Machine Translationcited by 28
  29. 2023Language Models Get a Gender Makeover: Mitigating Gender Bias with Few-Shot Data Interventionscited by 28
  30. 2021Influence Tuning: Demoting Spurious Correlations via Instance Attribution and Instance-Driven Updatescited by 27
  31. 2023Survey of Social Bias in Vision-Language Modelscited by 26
  32. 2024Large Language Models as Recommender Systems: A Study of Popularity Biascited by 26
  33. 2021Substructure Substitution: Structured Data Augmentation for NLPcited by 25
  34. 2023Building Socio-culturally Inclusive Stereotype Resources with Community Engagementcited by 25
  35. 2024Causality for Large Language Modelscited by 25
  36. 2021Dictionary-based Debiasing of Pre-trained Word Embeddingscited by 24
  37. 2023Debiasing Algorithm through Model Adaptationcited by 24
  38. 2021Detect and Perturb: Neutral Rewriting of Biased and Sensitive Text via Gradient-based Decodingcited by 23

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