mlsanityData to 7 Oct 2026

Easy Adaptation to Mitigate Gender Bias in Multilingual Text Classification

arXiv 2204.05459 ↗First posted 12 Apr 2022cs.CL cs.SIcited by 5

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Abstract (arXiv)

Existing approaches to mitigate demographic biases evaluate on monolingual data, however, multilingual data has not been examined. In this work, we treat the gender as domains (e.g., male vs. female) and present a standard domain adaptation model to reduce the gender bias and improve performance of text classifiers under multilingual settings. We evaluate our approach on two text classification tasks, hate speech detection and rating prediction, and demonstrate the effectiveness of our approach with three fair-aware baselines.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 21 of its 40 references that are in the map. First posted on arXiv 12 Apr 2022.

Builds on

21 of 40 references.

References · 21

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9 more
  1. 2020Towards Debiasing Sentence Representationscited by 170
  2. 2018Reducing Gender Bias in Abusive Language Detectioncited by 152
  3. 2018Counterfactual Fairness in Text Classification through Robustnesscited by 150
  4. 2018The Frontiers of Fairness in Machine Learningcited by 139
  5. 2021Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metricscited by 83
  6. 2020Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognitioncited by 43
  7. 2020Demographics Should Not Be the Reason of Toxicity: Mitigating Discrimination in Text Classifications with Instance Weightingcited by 41
  8. 2021Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classificationcited by 20
  9. 2021The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classificationcited by 18

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Cited by year

Citing papers per year: 2023: 2; 2024: 2; 2025: 1.

5 papers.

  1. 2023Fairness in Language Models Beyond English: Gaps and Challengescited by 16
  2. 2023Erasure of Unaligned Attributes from Neural Representationscited by 6
  3. 2024Learn it or Leave it: Module Composition and Pruning for Continual Learningcited by 3
  4. 2025Social Bias in Multilingual Language Models: A Surveycited by 1
  5. 2024UniPSDA: Unsupervised Pseudo Semantic Data Augmentation for Zero-Shot Cross-Lingual Natural Language Understandingcited by 0