Double-Hard Debias:Tailoring Word Embeddings for Gender Bias Mitigation
Table 5: Clustering Accuracy(%) of top 100/500/1000 male and female words. Lower accuracy means less gender cues captured. Double-Hard Word2Vec consistently achieves the lowest accuracy.
['[BOLD] Embeddings', '[BOLD] Top 100', '[BOLD] Top 500', '[BOLD] Top 1000']
Across three experiments where we cluster top 100/500/1000 male and female words, Double-Hard Word2Vec consistently achieves the lowest accuracy . Note that neighborhood metric reflects gender information that can be captured by the clustering algorithm. Experimental result validates that our method can further improve Hard Debias algorithm. While the original Word2Vec embeddings clearly locate separately into two groups corresponding to different genders, this phenomenon becomes less obvious after applying our debiasing method.