OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddings
Table S4: TPR-Gap-RMS metric of different debiasing method when applied on word embeddings.
['Method', 'RoBERTa', 'RoBERTa LP', 'RoBERTa HD', 'RoBERTa INLP', 'RoBERTa OSCaR']
Lower scores imply lesser gendered bias in professions. We refer readers to [ravfogel2020null] for detailed definition. We follow the same experiment steps, except that we apply different debiasing algorithms to the input word embeddings (instead of the CLS token). This allows us to compare debiasing methods with static use of contextualized embeddings (i.e. without fine-tuning). We see that RoBERTa and RoBERTa HD perform on par while both linear projection and iterative projection methods and our method OSCaR perform close to each other.