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Contextual word embeddings such as BERT have achieved state of the art performance in numerous NLP tasks.
Evaluating the underlying gender bias in contextualized word embeddings
Christine Basta, Marta R Costa-jussà, and Noe Casas. 2019 · 1904
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Trait-based and sex-based discrimination in occupational prestige, occupational salary, and hiring
Peter Glick. 1991 · 1991
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Measuring individual differences in implicit cognition: The implicit association test
Anthony Greenwald, Debbie E. McGhee, and Jordan L. K. Schwartz. 1998 · 1998
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Algorithms for non-negative matrix factorization
Daniel Lee and Hyunjune Seung. 2001 · 2001
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
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GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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Social media use in hiring: Assessing the risks
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Big data’s disparate impact
Solon Barocas and Andrew D Selbst. 2016 · 2016
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. 2016 · 2016
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Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J Bryson, and Arvind Narayanan. 2017 · 2017
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Incorporating dialectal variability for socially equitable language identification
David Jurgens, Yulia Tsvetkov, and Dan Jurafsky. 2017 · 2017
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Beyond parity: Fairness objectives for collaborative filtering
Sirui Yao and Bert Huang. 2017 · 2017
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Men also like shopping: Reducing gender bias amplification using corpus-level constraints
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2017 · 2017
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Automated essay scoring in the presence of biased ratings
Evelin Amorim, Marcia Cançado, and Adriano Veloso. 2018 · 2018
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Word embeddings quantify 100 years of gender and ethnic stereotypes
Learning gender-neutral word embeddings
Jieyu Zhao, Yichao Zhou, Zeyu Li, Wei Wang, and Kai-Wei Chang. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Entity-centric contextual affective analysis
Anjalie Field and Yulia Tsvetkov. 2019 · 2019
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Feature-wise bias amplification
Klas Leino, Matt Fredrikson, Emily Black, Shayak Sen, and Anupam Datta. 2019 · 2019
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Black is to criminal as caucasian is to police: Detecting and removing multiclass bias in word embeddings
Thomas Manzini, Yao Chong, Yulia Tsvetkov, and Alan W Black. 2019 · 2019
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On measuring social biases in sentence encoders
Chandler May, Alex Wang, Shikha Bordia, Samuel R. Bowman, and Rachel Rudinger. 2019 · 2019
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Nikhil Garg, Londa Schiebinger, Dan Jurafsky, and James Zou. 2018 · 2018
Cited alongside, same era.
Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Cited alongside, same era.
Gender bias in coreference resolution
Rachel Rudinger, Jason Naradowsky, Brian Leonard, and Benjamin Van Durme. 2018 · 2018
Cited alongside, same era.
Mind the gap: A balanced corpus of gendered ambiguous pronouns
Kellie Webster, Marta Recasens, Vera Axelrod, and Jason Baldridge. 2018 · 2018
Cited alongside, same era.
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What are the biases in my word embedding?
Nathaniel Swinger, Maria De-Arteaga, Neil Heffernan IV, Mark Leiserson, and Adam Kalai. 2019 · 2019
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What do you learn from context? probing for sentence structure in contextualized word representations
Ian Tenney, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak, R. Thomas McCoy, Najoung Kim, Benjamin Van Durme, Samuel R. Bowman, Dipanjan Das, and Ellie Pavlick. 2019 · 2019
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Gender bias in contextualized word embeddings
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Ryan Cotterell, Vicente Ordonez, and Kai-Wei Chang. 2019 · 2019
Closest in time.