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1906.04571
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Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology
Annual Meeting of the Association for Computational Linguistics (ACL), 2019
11 June 2019
Ran Zmigrod
Sabrina J. Mielke
Hanna M. Wallach
Robert Bamler
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Papers citing
"Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology"
50 / 201 papers shown
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246
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280
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200
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146
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Joke Daems
Luna De Bruyne
316
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480
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354
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271
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460
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Ruizhe Chen
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406
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Usman Naseem
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Hakim Hacid
Flora D. Salim
Imran Razzak
325
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Xin Xu
Wei Xu
Ningyu Zhang
Julian McAuley
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393
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Jacob Mitchell Springer
Aditi Raghunathan
Maarten Sap
359
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Amalia Todirascu
399
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Daiki Shirafuji
Makoto Takenaka
Shinya Taguchi
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312
12
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16 Dec 2024
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345
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405
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245
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527
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REFINE-LM: Mitigating Language Model Stereotypes via Reinforcement Learning
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Rameez Qureshi
Naim Es-Sebbani
Luis Galárraga
Yvette Graham
Miguel Couceiro
Zied Bouraoui
252
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Shachi H. Kumar
Saurav Sahay
Sahisnu Mazumder
Eda Okur
R. Manuvinakurike
Nicole Beckage
Hsuan Su
Hung-yi Lee
L. Nachman
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301
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FairFlow: An Automated Approach to Model-based Counterfactual Data Augmentation For NLP
E. Tokpo
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218
6
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Riccardo Cantini
Giada Cosenza
A. Orsino
Domenico Talia
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467
16
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Do Multilingual Large Language Models Mitigate Stereotype Bias?
Shangrui Nie
Michael Fromm
Charles F Welch
Rebekka Görge
Akbar Karimi
Joan Plepi
Nazia Afsan Mowmita
Nicolas Flores-Herr
Mehdi Ali
Lucie Flek
379
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Social Bias Evaluation for Large Language Models Requires Prompt Variations
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Naoaki Okazaki
361
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235
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Henning Wachsmuth
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Gene Louis Kim
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