Learning Gender-Neutral Word Embeddings
Table 3: F1 score (%) on the coreference system.
['Embeddings', 'OntoNotes-test', 'PRO', 'ANTI', 'Avg', 'Diff']
We train the end-to-end coreference resolution model Lee et al. For the WinoBias dataset, we also report the average (Avg) and absolute difference (Diff) of F1 scores on two subsets. A smaller Diff value indicates less bias in a system. Results show that GN-GloVe achieves comparable performance as Glove and Hard-GloVe on the OntoNotes dataset while distinctly reducing the bias on the WinoBias dataset. When only the w(a) potion of the embedding is used in representing words, GN-GloVe(w(a)) further reduces the bias in coreference resolution.