A Causal Inference Method for Reducing Gender Biasin Word Embedding Relations
Table 5: Average F1 score of coreference resolution 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']
The results of GloVe, Hard-GloVe, and GN-GloVe are collected from \citeauthorzhao2018learning \shortcitezhao2018learning. From the table, we could see that HSR-GloVe performs the best on the WinoBias task by achieving the lowest difference (WinoBias-Diff) between PRO and ANTI datasets. Specifically, HSR-GloVe reduces WinoBias-Diff by 87.26% compared to the runner-up method Hard-GloVe and by 93.38% compared to the original GloVe embedding. Furthermore, HSR-GloVe has an on-par performance on the OntoNotes 5.0 benchmark dataset compared to other baselines.