Learning Gender-Neutral Word Embeddings
Table 2: Results on the benchmark datasets. Performance is measured in accuracy and in Spearman rank correlation for word analogy and word similarity tasks, respectively.
['Embeddings', 'Analogy Google', 'Analogy MSR', 'Similarity WS353-ALL', 'Similarity RG-65', 'Similarity MTurk-287', 'Similarity MTurk-771', 'Similarity RW', 'Similarity MEN-TR-3k']
In addition, we evaluate the word embeddings on the benchmark tasks to ensure their quality. The word similarity tasks measure how well a word embedding model captures the similarity between words comparing to human annotated rating scores. Embeddings are tested on multiple datasets: WS353-ALL Finkelstein et al. RG-65 Rubenstein and Goodenough The analogy tasks are to answer the question “A is to B as C is to ?” by finding a word vector w that is closest to wA−wB+wC in the embedding space. Google Mikolov et al. datasets are utilized for this evaluation. Compared with others, GN-GloVe achieves a higher accuracy in the similarity tasks and its analogy score slightly drops indicating that GN-GloVe is capable of preserving proximity among words.