--- title: "Who cites It's All in the Name: Mitigating — 76 citing papers" description: "76 papers of the Elamur Research library cite «It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data…" canonical: "https://research.elamur.ai/free/en/papers/maudslay-2019-it-s-all-name-mitigating/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/maudslay-2019-it-s-all-name-mitigating/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [It's All in the Name: Mitigating Gender Bias with…](https://md.research.elamur.ai/free/en/papers/maudslay-2019-it-s-all-name-mitigating/) / cited by # Who cites «It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution» 76 papers of our library cite it (of the 141,840 whose bibliographies we read) · 13 with a page here, the most cited first · [← back to «It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution»](https://md.research.elamur.ai/free/en/papers/maudslay-2019-it-s-all-name-mitigating/) 1. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,703 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/) 2. **[Language (Technology) is Power: A Critical Survey of “Bias” in NLP](https://md.research.elamur.ai/free/en/papers/blodgett-2020-language-technology-is-power-critical/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Su Lin Blodgett, Solon Barocas, Hal Daumé III, et al.*2020 · 1,703 citations* 3. **[Bias and Fairness in Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/gallegos-2023-bias-fairness-large-language-models/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, et al.*2023 · 1,074 citations* 4. [**A Survey of Data Augmentation Approaches for NLP arXiv:2105.03075**Steven Y. Feng, Varun Gangal, Jason Wei, et al.*2021 · 937 citations*](https://md.research.elamur.ai/free/en/papers/feng-2021-survey-data-augmentation-approaches-nlp/) 5. **[Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation](https://md.research.elamur.ai/free/en/papers/dinan-2020-queens-are-powerful-too-mitigating/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Emily Dinan, Angela Fan, Adina Williams, et al.*2020 · 266 citations* 6. [**Measuring and Reducing Gendered Correlations in Pre-trained Models arXiv:2010.06032**Kellie Webster, Xuezhi Wang, Ian Tenney, et al.*2020 · 253 citations*](https://md.research.elamur.ai/free/en/papers/webster-2020-measuring-reducing-gendered-correlations-pre/) 7. **[Debiasing Pre-trained Contextualised Embeddings](https://md.research.elamur.ai/free/en/papers/kaneko-2021-debiasing-pre-trained-contextualised-embeddings/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Masahiro Kaneko, Danushka Bollegala*2021 · 174 citations* 8. **[Sustainable Modular Debiasing of Language Models](https://md.research.elamur.ai/free/en/papers/lauscher-2021-sustainable-modular-debiasing-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Anne Lauscher, Tobias Lueken, Goran Glavaš*2021 · 165 citations* 9. **[Does Gender Matter? Towards Fairness in Dialogue Systems](https://md.research.elamur.ai/free/en/papers/liu-2020-does-gender-matter-fairness-dialogue/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Haochen Liu, Jamell Dacon, Wenqi Fan, et al.*2020 · 132 citations* 10. **[Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics](https://md.research.elamur.ai/free/en/papers/czarnowska-2021-quantifying-social-biases-nlp-generalization/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Paula Czarnowska, Yogarshi Vyas, Kashif Shah*2021 · 131 citations* 11. **[Unmasking Contextual Stereotypes: Measuring and Mitigating BERT's Gender Bias](https://md.research.elamur.ai/free/en/papers/bartl-2020-unmasking-contextual-stereotypes-measuring-mitigating/) [Workshop on Gender Bias in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/gebnlp/)**Marion Bartl, Malvina Nissim, Albert Gatt*2020 · 115 citations* 12. **[The Arabic Parallel Gender Corpus 2.0: Extensions and Analyses](https://md.research.elamur.ai/free/en/papers/alhafni-2022-arabic-parallel-gender-corpus-2/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Bashar Alhafni, Nizar Habash, Houda Bouamor*2022 · 21 citations* 13. **[The User-Aware Arabic Gender Rewriter](https://md.research.elamur.ai/free/en/papers/alhafni-2023-user-aware-arabic-gender-rewriter/) [Workshop on Gender-Inclusive Translation Technologies](https://md.research.elamur.ai/free/en/venues/gitt/)**Bashar Alhafni, Ossama Obeid, Nizar Habash*2023 · 5 citations* 63 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/maudslay-2019-it-s-all-name-mitigating/) — It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution