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How Gender Debiasing of NLP Models Affects Internal Model Representations, and Why It Matters
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Hadas Orgad, M.Sc. Thesis Seminar
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Wednesday, 23.3.2022, 10:00
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Zoom Lecture: 98412403331
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Advisor:  Dr. Yonatan Belinkov
Common studies of gender bias in natural language processing (NLP) focus either on extrinsic bias which is measured by model performance on a specific task or on intrinsic bias which is measured on a models' internal representations. However, the relationship between extrinsic and intrinsic bias is relatively unknown. In this work, we illuminate this relationship by measuring both quantities together: we debias a model during downstream fine-tuning, which reduces extrinsic bias, and measure the effect on intrinsic bias, which we measure with information-theoretic probing. Through experiments on two tasks and multiple bias metrics, we show that our intrinsic bias metric is a better indicator of debiasing than the standard metric, and can also expose cases of superficial debiasing. Our framework provides a comprehensive perspective on bias in NLP models, which can be applied to deploy NLP systems in a more informed manner.
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