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Recent works have found evidence of gender bias in models of machine translation and coreference resolution using mostly synthetic diagnostic datasets.
Automatically identifying gender issues in machine translation using perturbations
Hila Gonen and Kellie Webster. 2020 · 1995
Earlier work this paper cites.
The winograd schema challenge
Hector Levesque, Ernest Davis, and Leora Morgenstern. 2012 · 2012
Earlier work this paper cites.
CoNLL-2012 shared task: Modeling multilingual unrestricted coreference in OntoNotes
Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Olga Uryupina, and Yuchen Zhang. 2012 · 2012
Earlier work this paper cites.
Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan. 2017 · 2017
Earlier work this paper cites.
Social bias in elicited natural language inferences
Rachel Rudinger, Chandler May, and Benjamin Van Durme. 2017 · 2017
Earlier work this paper cites.
Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru. 2018 · 2018
Earlier work this paper cites.
AllenNLP: A deep semantic natural language processing platform
Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Peters, Michael Schmitz, and Luke Zettlemoyer. 2018 · 2018
Earlier work this paper cites.
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. 2018 · 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
Cited alongside, same era.
BERT for coreference resolution: Baselines and analysis
Mandar Joshi, Omer Levy, Luke Zettlemoyer, and Daniel Weld. 2019 · 2019
Cited alongside, same era.
Social data: Biases, methodological pitfalls, and ethical boundaries
Alexandra Olteanu, Carlos Castillo, Fernando Diaz, and Emre Kıcıman. 2019 · 2019
Cited alongside, same era.
Assessing gender bias in machine translation: a case study with google translate
Marcelo O. R. Prates, Pedro H. C. Avelar, and L. Lamb. 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.
Neural machine translation doesn’t translate gender coreference right unless you make it
Danielle Saunders, Rosie Sallis, and Bill Byrne. 2020 · 2020
Later among the works it cites.
Syntactic search by example
Micah Shlain, Hillel Taub-Tabib, Shoval Sadde, and Yoav Goldberg. 2020 · 2020
Later among the works it cites.
Multilingual translation with extensible multilingual pretraining and finetuning
Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, and Angela Fan. 2020 · 2020
Later among the works it cites.
OPUS-MT — Building open translation services for the World
Jörg Tiedemann and Santhosh Thottingal. 2020 · 2020
Later among the works it cites.
CORD-19: The COVID-19 open research dataset
Lucy Lu Wang, Kyle Lo, Yoganand Chandrasekhar, Russell Reas, Jiangjiang Yang, Doug Burdick, Darrin Eide, Kathryn Funk, Yannis Katsis, Rodney Michael Kinney, Yunyao Li, Ziyang Liu, William Merrill, Paul Mooney, Dewey A. Murdick, Devvret Rishi, Jerry Sheehan, Zhihong Shen, Brandon Stilson, Alex D. Wade, Kuansan Wang, Nancy Xin Ru Wang, Christopher Wilhelm, Boya Xie, Douglas M. Raymond, Daniel S. Weld, Oren Etzioni, and Sebastian Kohlmeier. 2020 · 2020
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Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, and Armand Joulin. 2020 · 2020
Cited alongside, same era.
Type B reflexivization as an unambiguous testbed for multilingual multi-task gender bias
Ana Valeria González, Maria Barrett, Rasmus Hvingelby, Kellie Webster, and Anders Søgaard. 2020 · 2020
Cited alongside, same era.
spaCy: Industrial-strength Natural Language Processing in Python
Matthew Honnibal, Ines Montani, Sofie Van Landeghem, and Adriane Boyd. 2020 · 2020
Cited alongside, same era.
SpanBERT: Improving pre-training by representing and predicting spans
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S. Weld, Luke Zettlemoyer, and Omer Levy. 2020 · 2020
Cited alongside, same era.
Gender coreference and bias evaluation at WMT 2020
Tom Kocmi, Tomasz Limisiewicz, and Gabriel Stanovsky. 2020 · 2020
Cited alongside, same era.
Multilingual denoising pre-training for neural machine translation
Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, and Luke Zettlemoyer. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
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
Closest in time.
The gap on gap: Tackling the problem of differing data distributions in bias-measuring datasets
Vid Kocijan, Oana-Maria Camburu, and Thomas Lukasiewicz. 2021 · 2021
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A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2021 · 2021
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Investigating failures of automatic translation in the case of unambiguous gender
Adithya Renduchintala and Adina Williams. 2021 · 2021
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A framework for understanding sources of harm throughout the machine learning life cycle
Harini Suresh and John V. Guttag. 2021 · 2021
Closest in time.