A Causal Inference Method for Reducing Gender Biasin Word Embedding Relations
Table 1: Result of gender direction 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']
We could see that, while Hard-GloVe has the least bias-by-projection among all methods, our method is the runner-up and outperforms the word-vector-learning method GN-GloVe. It is observed that our method is the best-performing post-processing method on SemBias.