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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★ 2 cited
Can Rationalization Improve Robustness?
Howard Chen, Jacqueline He, Karthik Narasimhan +1
A growing line of work has investigated the development of neural NLP models that can produce rationales--subsets of input that can explain their model predictions. In this paper,…