4 citations · 7 across the 3 of their papers we have counts for
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cs.CL2022★ 3 cited
MABEL: Attenuating Gender Bias using Textual Entailment Data
Jacqueline He, Mengzhou Xia, Christiane Fellbaum +1
Pre-trained language models encode undesirable social biases, which are further exacerbated in downstream use. To this end, we propose MABEL (a Method for Attenuating Gender Bias u…
cs.CL2022★ 4 cited
Mitigating Gender Bias in Machine Translation through Adversarial Learning
Eve Fleisig, Christiane Fellbaum
Machine translation and other NLP systems often contain significant biases regarding sensitive attributes, such as gender or race, that worsen system performance and perpetuate har…