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Automatically Identifying Gender Issues in Machine Translation using Perturbations

arXiv 2004.14065 ↗First posted 29 Apr 2020cs.CLcited by 28

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Abstract (arXiv)

The successful application of neural methods to machine translation has realized huge quality advances for the community. With these improvements, many have noted outstanding challenges, including the modeling and treatment of gendered language. While previous studies have identified issues using synthetic examples, we develop a novel technique to mine examples from real world data to explore challenges for deployed systems. We use our method to compile an evaluation benchmark spanning examples for four languages from three language families, which we publicly release to facilitate research. The examples in our benchmark expose where model representations are gendered, and the unintended consequences these gendered representations can have in downstream application.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Apr 2020.

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Citing papers per year: 2021: 10; 2022: 5; 2023: 7; 2024: 2; 2025: 4.

28 papers.

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  1. 2023From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Modelscited by 257
  2. 2021Societal Biases in Language Generation: Progress and Challengescited by 201
  3. 2021Gender Bias in Machine Translationcited by 116
  4. 2023Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensionscited by 111
  5. 2021Trustworthy AI: A Computational Perspectivecited by 93
  6. 2021Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metricscited by 83
  7. 2021Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translationcited by 62
  8. 2022Theories of "Gender" in NLP Bias Researchcited by 62
  9. 2022Evaluation Gaps in Machine Learning Practicecited by 48
  10. 2023Sociodemographic Bias in Language Models: A Survey and Forward Pathcited by 27
  11. 2023It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performancecited by 25
  12. 2021Investigating Failures of Automatic Translation in the Case of Unambiguous Gendercited by 20
16 more
  1. 2023What about em? How Commercial Machine Translation Fails to Handle (Neo-)Pronounscited by 19
  2. 2023A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translationcited by 19
  3. 2024What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-centered Studycited by 15
  4. 2024Does Liking Yellow Imply Driving a School Bus? Semantic Leakage in Language Modelscited by 14
  5. 2021The Arabic Parallel Gender Corpus 2.0: Extensions and Analysescited by 12
  6. 2021How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translationcited by 11
  7. 2021How to Write a Bias Statement: Recommendations for Submissions to the Workshop on Gender Bias in NLPcited by 5
  8. 2025Assumed Identities: Quantifying Gender Bias in Machine Translation of Gender-Ambiguous Occupational Termscited by 5
  9. 2022Controlling Extra-Textual Attributes about Dialogue Participants -- A Case Study of English-to-Polish Neural Machine Translationcited by 3
  10. 2023That was the last straw, we need more: Are Translation Systems Sensitive to Disambiguating Context?cited by 3
  11. 2025Gender Bias in English-to-Greek Machine Translationcited by 3
  12. 2025Uncertainty Quantification for Evaluating Machine Translation Biascited by 2
  13. 2025From Charts to Fair Narratives: Uncovering and Mitigating Geo-Economic Biases in Chart-to-Textcited by 2
  14. 2022Screening Gender Transfer in Neural Machine Translationcited by 1
  15. 2022The User-Aware Arabic Gender Rewritercited by 1
  16. 2021Investigating Cross-Linguistic Gender Bias in Hindi-English Across Domainscited by 0