Fetching the paper…
Reading the bibliography…
Many text corpora exhibit socially problematic biases, which can be propagated or amplified in the models trained on such data.
Equalizing gender biases in neural machine translation with word embeddings techniques
Joel Escudé Font and Marta R. Costa-Jussà. 2019 · 1901
Earlier work this paper cites.
Building a large annotated corpus of english: The penn treebank
Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz. 1993 · 1993
Earlier work this paper cites.
Recurrent neural network based language model
Tomas Mikolov, Martin Karafiát, Lukás Burget, Jan Cernocký, and Sanjeev Khudanpur. 2010 · 2010
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
Earlier work this paper cites.
Linguistic models for analyzing and detecting biased language
Marta Recasens, Cristian Danescu-Niculescu-Mizil, and Dan Jurafsky. 2013 · 2013
Earlier work this paper cites.
Dependency-based word embeddings
Omer Levy and Yoav Goldberg. 2014 · 2014
Earlier work this paper cites.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Earlier work this paper cites.
Demographic factors improve classification performance
Dirk Hovy. 2015 · 2015
Cited alongside, same era.
Face recognition vendor test (FRVT) performance of automated gender classification algorithms
Mei Ngan and Patrick Grother. 2015 · 2015
Cited alongside, same era.
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
Cited alongside, same era.
Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2016 · 2016
Cited alongside, same era.
Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan. 2017 · 2017
Cited alongside, same era.
Algorithmic bias? an empirical study into apparent gender-based discrimination in the display of stem career ads
Anja Lambrecht and Catherine E Tucker. 2018 · 2018
Later among the works it cites.
Regularizing and optimizing LSTM language models
Stephen Merity, Nitish Shirish Keskar, and Richard Socher. 2018 · 2018
Later among the works it cites.
Gender bias in coreference resolution
Rachel Rudinger, Jason Naradowsky, Brian Leonard, and Benjamin Van Durme. 2018 · 2018
Later among the works it cites.
Learning gender-neutral word embeddings
Jieyu Zhao, Yichao Zhou, Zeyu Li, Wei Wang, and Kai-Wei Chang. 2018 · 2018
Later among the works it cites.
Lipstick on a pig: Debiasing methods cover up systematic gender biases in word embeddings but do not remove them
Hila Gonen and Yoav Goldberg. 2019 · 2019
Closest in time.
On measuring social bias in sentence encoders
Chandler May, Alex Wang, Shikha Bordia, Samuel R. Bowman, and Rachel Rudinger. 2019 · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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
Cited alongside, same era.
Closest in time.