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Common studies of gender bias in NLP focus either on extrinsic bias measured by model performance on a downstream task or on intrinsic bias found in models' internal representations.
Roberta: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
Intrinsic bias metrics do not correlate with application bias
Seraphina Goldfarb-Tarrant, Rebecca Marchant, Ricardo Muñoz Sánchez, Mugdha Pandya, and Adam Lopez. 2021 · 1940
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Comparing effect sizes in follow-up studies: Roc area, cohen’s d, and r
Marnie E Rice and Grant T Harris. 2005 · 2005
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Deberta: Decoding-enhanced bert with disentangled attention
Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. 2020 · 2006
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011 · 2011
Earlier work this paper cites.
Ontonotes : A large training corpus for enhanced processing
R. Weischedel, E. Hovy, M. Marcus, and Martha Palmer. 2013 · 2013
Earlier work this paper cites.
Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai. 2016 · 2016
Earlier work this paper cites.
Women through the glass ceiling: gender asymmetries in wikipedia
Claudia Wagner, Eduardo Graells-Garrido, David Garcia, and Filippo Menczer. 2016 · 2016
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The problem with bias: Allocative versus representational harms in machine learning
Solon Barocas, Kate Crawford, Aaron Shapiro, and Hanna Wallach. 2017 · 2017
Earlier work this paper cites.
Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, J. Bryson, and A. Narayanan. 2017 · 2017
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The trouble with bias. keynote at neurips
Kate Crawford. 2017 · 2017
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Examining gender and race bias in two hundred sentiment analysis systems
Svetlana Kiritchenko and Saif Mohammad. 2018 · 2018
Earlier work this paper cites.
Higher-order coreference resolution with coarse-to-fine inference
Kenton Lee, Luheng He, and Luke Zettlemoyer. 2018a · 2018
Earlier work this paper cites.
Gender bias in coreference resolution
Rachel Rudinger, Jason Naradowsky, Brian Leonard, and Benjamin Van Durme. 2018 · 2018
Earlier work this paper cites.
Mind the GAP: A balanced corpus of gendered ambiguous pronouns
Kellie Webster, Marta Recasens, Vera Axelrod, and Jason Baldridge. 2018 · 2018
Earlier work this paper cites.
Gender bias in coreference resolution: Evaluation and debiasing methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2018 · 2018
Earlier work this paper cites.
Fairness and Machine Learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan. 2019 · 2019
Earlier work this paper cites.
Evaluating the underlying gender bias in contextualized word embeddings
Christine Basta, Marta R. Costa-jussà, and Noe Casas. 2019 · 2019
Cited alongside, same era.
Analysis Methods in Neural Language Processing: A Survey
Yonatan Belinkov and James Glass. 2019 · 2019
Cited alongside, same era.
Bias in bios: A case study of semantic representation bias in a high-stakes setting
Maria De-Arteaga, Alexey Romanov, H. Wallach, J. Chayes, C. Borgs, A. Chouldechova, S. C. Geyik, K. Kenthapadi, and A. Kalai. 2019 · 2019
Cited alongside, same era.
Understanding undesirable word embedding associations
Kawin Ethayarajh, David Duvenaud, and Graeme Hirst. 2019 · 2019
Cited alongside, same era.
Lipstick on a pig: Debiasing methods cover up systematic gender biases in word embeddings but do not remove them
Hila Gonen and Yoav Goldberg. 2019b · 2019
Cited alongside, same era.
Designing and interpreting probes with control tasks
Evaluating gender bias in natural language inference
Shanya Sharma, Manan Dey, and Koustuv Sinha. 2020 · 2020
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Information-theoretic probing with minimum description length
Elena Voita and Ivan Titov. 2020 · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
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Revealing the myth of higher-order inference in coreference resolution
Liyan Xu and Jinho D. Choi. 2020 · 2020
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Extensive study on the underlying gender bias in contextualized word embeddings
Christine Basta, Marta R Costa-jussà, and Noe Casas. 2021 · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
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John Hewitt and Percy Liang. 2019 · 2019
Cited alongside, same era.
Measuring bias in contextualized word representations
Keita Kurita, Nidhi Vyas, Ayush Pareek, Alan W Black, and Yulia Tsvetkov. 2019 · 2019
Cited alongside, same era.
On measuring social biases in sentence encoders
Chandler May, Alex Wang, Shikha Bordia, Samuel R. Bowman, and Rachel Rudinger. 2019 · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
Cited alongside, same era.
What’s in a name? reducing bias in bios without access to protected attributes
Alexey Romanov, Maria De-Arteaga, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, Anna Rumshisky, and Adam Tauman Kalai. 2019 · 2019
Cited alongside, same era.
Evaluating gender bias in machine translation
Gabriel Stanovsky, Noah A. Smith, and Luke Zettlemoyer. 2019 · 2019
Cited alongside, same era.
Assessing social and intersectional biases in contextualized word representations
Yi Chern Tan and L. Elisa Celis. 2019 · 2019
Cited alongside, same era.
Probing classifiers: Promises, shortcomings, and alternatives
Yonatan Belinkov. 2021 · 2021
Later among the works it cites.
On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. 2021 · 2021
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Bold: Dataset and metrics for measuring biases in open-ended language generation
J. Dhamala, Tony Sun, Varun Kumar, Satyapriya Krishna, Yada Pruksachatkun, Kai-Wei Chang, and Rahul Gupta. 2021 · 2021
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Detecting emergent intersectional biases: Contextualized word embeddings contain a distribution of human-like biases
Wei Guo and Aylin Caliskan. 2021 · 2021
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On transferability of bias mitigation effects in language model fine-tuning
Xisen Jin, Francesco Barbieri, Brendan Kennedy, Aida Mostafazadeh Davani, Leonardo Neves, and Xiang Ren. 2021 · 2021
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Everything is relative: Understanding fairness with optimal transport
Kweku Kwegyir-Aggrey, Rebecca Santorella, and Sarah M. Brown. 2021 · 2021
Later among the works it cites.
Debiasing methods in natural language understanding make bias more accessible
Michael Mendelson and Yonatan Belinkov. 2021a · 2021
Later among the works it cites.
Debiasing methods in natural language understanding make bias more accessible
Michael Mendelson and Yonatan Belinkov. 2021b · 2021
Later among the works it cites.
Men are elected, women are married: Events gender bias on wikipedia
Jiao Sun and Nanyun Peng. 2021 · 2021
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A framework for understanding sources of harm throughout the machine learning life cycle
Harini Suresh and John Guttag. 2021 · 2021
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Yang Trista Cao, Yada Pruksachatkun, Kai-Wei Chang, Rahul Gupta, Varun Kumar, Jwala Dhamala, and Aram Galstyan. 2022 · 2022
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