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An increasing awareness of biased patterns in natural language processing resources, like BERT, has motivated many metrics to quantify `bias' and `fairness'.
Bad seeds: Evaluating lexical methods for bias measurement
Maria Antoniak and David Mimno. 2021 · 1904
Earlier work this paper cites.
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.
Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 1910
Earlier work this paper cites.
Assessing social and intersectional biases in contextualized word representations
Yi Chern Tan and L Elisa Celis. 2019 · 1911
Earlier work this paper cites.
On Information and Sufficiency
S. Kullback and R. A. Leibler. 1951 · 1951
Earlier work this paper cites.
CrowS-pairs: A challenge dataset for measuring social biases in masked language models
Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel R. Bowman. 2020 · 1967
Earlier work this paper cites.
Anaphora in natural language understanding: a survey
Graeme Hirst et al. 1981 · 1981
Earlier work this paper cites.
Measuring individual differences in implicit cognition: the implicit association test
Anthony G Greenwald, Debbie E McGhee, and Jordan LK Schwartz. 1998 · 1998
Earlier work this paper cites.
Fast-track women and the “choice” to stay home
Pamela Stone and Meg Lovejoy. 2004 · 2004
Earlier work this paper cites.
Multi-dimensional gender bias classification
Emily Dinan, Angela Fan, Ledell Wu, Jason Weston, Douwe Kiela, and Adina Williams. 2020 · 2005
Earlier work this paper cites.
Gender bias in multilingual embeddings and cross-lingual transfer
Jieyu Zhao, Subhabrata Mukherjee, Saghar Hosseini, Kai-Wei Chang, and Ahmed Hassan Awadallah. 2020 · 2005
Earlier work this paper cites.
Measuring and reducing gendered correlations in pre-trained models
Kellie Webster, Xuezhi Wang, Ian Tenney, Alex Beutel, Emily Pitler, Ellie Pavlick, Jilin Chen, Ed Chi, and Slav Petrov. 2020 · 2010
Earlier work this paper cites.
Intrinsic bias metrics do not correlate with application bias
Seraphina Goldfarb-Tarrant, Rebecca Marchant, Ricardo Muñoz Sanchez, Mugdha Pandya, and Adam Lopez. 2020 · 2012
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, G.s Corrado, and Jeffrey Dean. 2013 · 2013
Earlier work this paper cites.
OntoNotes Release 5.0
Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, and Ann Houston. 2013 · 2013
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Vader: A parsimonious rule-based model for sentiment analysis of social media text
Clayton Hutto and Eric Gilbert. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
Earlier work this paper cites.
Machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks
Julia Angwin and Jeff Larson. 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 Zou, Venkatesh Saligrama, and Adam 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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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
ChatEval: A tool for chatbot evaluation
João Sedoc, Daphne Ippolito, Arun Kirubarajan, Jai Thirani, Lyle Ungar, and Chris Callison-Burch. 2019 · 2019
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The role of protected class word lists in bias identification of contextualized word representations
João Sedoc and Lyle Ungar. 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
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Unmasking contextual stereotypes: Measuring and mitigating BERT’s gender bias
Marion Bartl, Malvina Nissim, and Albert Gatt. 2020 · 2020
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Evaluating bias in Dutch word embeddings
Rodrigo Alejandro Chávez Mulsa and Gerasimos Spanakis. 2020 · 2020
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RobBERT: a Dutch RoBERTa-based Language Model
Pieter Delobelle, Thomas Winters, and Bettina Berendt. 2020 · 2020
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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Gender bias in coreference resolution
Rachel Rudinger, Jason Naradowsky, Brian Leonard, and Benjamin Van Durme. 2018 · 2018
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Gender bias in coreference resolution: Evaluation and debiasing methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2018 · 2018
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Fairness and Machine Learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan. 2019 · 2019
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Evaluating the underlying gender bias in contextualized word embeddings
Christine Basta, Marta R. Costa-jussà, and Noe Casas. 2019 · 2019
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Identifying and reducing gender bias in word-level language models
Shikha Bordia and Samuel R. Bowman. 2019 · 2019
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Bias in bios: A case study of semantic representation bias in a high-stakes setting
Maria De-Arteaga, Alexey Romanov, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, and Adam Tauman Kalai. 2019 · 2019
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On measuring and mitigating biased inferences of word embeddings
Sunipa Dev, Tao Li, Jeff M Phillips, and Vivek Srikumar. 2020 · 2020
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Masked language model scoring
Julian Salazar, Davis Liang, Toan Q. Nguyen, and Katrin Kirchhoff. 2020 · 2020
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Investigating gender bias in language models using causal mediation analysis
Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer, and Stuart M Shieber. 2020 · 2020
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Multi-simlex: A large-scale evaluation of multilingual and crosslingual lexical semantic similarity
Ivan Vulic, Simon Baker, E. Ponti, Ulla Petti, Ira Leviant, Kelly Wing, Olga Majewska, Eden Bar, Matt Malone, T. Poibeau, Roi Reichart, and Anna Korhonen. 2020 · 2020
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On the dangers of stochastic parrots: Can language models be too big?
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021 · 2021
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Stereotyping norwegian salmon: An inventory of pitfalls in fairness benchmark datasets
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A survey on bias in deep nlp
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Unmasking the mask–evaluating social biases in masked language models
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Sustainable modular debiasing of language models
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Towards understanding and mitigating social biases in language models
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Stereoset: Measuring stereotypical bias in pretrained language models
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