A Causal Inference Method for Reducing Gender Biasin Word Embedding Relations
Table 2: Result of gender-biased word relation tasks
['[EMPTY]', '[BOLD] GloVe', '[BOLD] Post-Processing  [BOLD] Hard-GloVe', '[BOLD] Post-Processing  [BOLD] GP-GloVe', '[BOLD] Post-Processing  [BOLD] HSR-GloVe', '[BOLD] WV-Learning  [BOLD] GN-GloVe']
Clustering male- and female-biased words (GBWR-Clustering) This task takes the top 500 male-biased words and the top 500 female-biased words according to the original embedding and clusters them into two clusters using k-means [lloyd1982least]. We could see that HSR-GloVe is the runner-up among all post-processing methods. Specifically, HSR brings about 6.72% reduction of the correlation coefficient compared to the runner-up method, and it achieves 16.89% reduction compared to the original GloVe embedding. Again, our proposed HSR-GloVe has the lowest Pearson correlation coefficient and outperforms all other methods. Moreover, our method further reduces the correlation coefficient by 4.98% compared to the runner-up method, and it results in 17.02% reduction compared to the original GloVe embedding. the p-values of the three tasks for each algorithm are reported in the appendix. HSR-GloVe and Hard-GloVe have the best performance that only one of the three p-values is significant.