Joint Multiclass Debiasing of Word Embeddings
Table 2: Measurements of quality tasks after debiasing for GloVe embeddings.
['[EMPTY]', 'REG', 'HW', 'SW  [ITALIC] λ=0.2', 'SW  [ITALIC] λ=0.4', 'SW  [ITALIC] λ=0.6', 'SW  [ITALIC] λ=0.8', 'SW  [ITALIC] λ=1', 'CPT']
As expected, a significant quality drop appears with HardWEAT, most notably on the RG65 dataset. With SoftWEAT, greater λ induce larger modifications of the original embeddings, which corresponds to a greater drop in embedding quality. Extended results can be found in the online appendix.