Gender-preserving Debiasing for Pre-trained Word Embeddings
Table 3: Number of word-pairs in the original (Orig) and balanced (Bal) similarity benchmarks.
['Datasets', '#Orig', '#Bal']
To address this issue, we balance the original datasets with respect to gender by including extra word pairs generated from the opposite sex with the same human ratings. For instance, if the word-pair (baby, mother) exists in the dataset, we add a new pair (baby, father) to the dataset. Ideally, we should re-annotate this balanced version of the dataset to obtain human similarity ratings. However, such a re-annotation exercise would be costly and inconsistent with the original ratings. Therefore, we resort to a proxy where we reassign the human rating for the original word-pair to its derived opposite gender version.