Appeared in the proceedings of EMNLP–IJCNLP 2019 (Hong Kong, November).This clarified version was prepared in December 2019.It’s All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution
Table 2: Word similarity Results
['Method', '[ITALIC] rs  [BOLD] Gigaword', '[ITALIC] rs  [BOLD] Wikipedia']
the SimLex-999 Spearman rank-order correlation coefficients rs (all are significant, p<0.01). Surprisingly, the WED40 and 70 methods outperform the unmitigated embedding, although the difference in result is small (0.386 and 0.395 vs. 0.385 on Gigaword, 0.371 and 0.367 vs. 0.368 on Wikipedia). nWED70, on the other hand, performs worse than the unmitigated embedding (0.384 vs. 0.385 on Gigaword, 0.367 vs. 0.368 on Wikipedia). CDA and CDS methods do not match the quality of the unmitigated space, but once again the difference is small. author=simone,color=blue!40,size=,fancyline, caption=,todo: author=simone,color=blue!40,size=,fancyline,caption=,Second Part of Reaction to Reviewer 4.It should be noted that since SimLex-999 was produced by human raters, it will reflect the human biases these methods were designed to remove, so worse performance might result from successful bias mitigation.