MABEL: Attenuating Gender Bias using Textual Entailment Data
MABEL: Attenuating Gender Bias using Textual Entailment Data
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 using Entailment Labels), an intermediate pre-training approach for mitigating gender bias in contextualized representations. Key to our approach is the use of a contrastive …