  This paper treats gender bias latent in word embeddings. Previous mitigation
attempts rely on the operationalisation of gender bias as a projection over a
linear subspace. An alternative approach is Counterfactual Data Augmentation
(CDA), in which a corpus is duplicated and augmented to remove bias, e.g. by
swapping all inherently-gendered words in the copy. We perform an empirical
comparison of these approaches on the English Gigaword and Wikipedia, and find
that whilst both successfully reduce direct bias and perform well in tasks
which quantify embedding quality, CDA variants outperform projection-based
methods at the task of drawing non-biased gender analogies by an average of 19%
across both corpora. We propose two improvements to CDA: Counterfactual Data
Substitution (CDS), a variant of CDA in which potentially biased text is
randomly substituted to avoid duplication, and the Names Intervention, a novel
name-pairing technique that vastly increases the number of words being treated.
CDA/S with the Names Intervention is the only approach which is able to
mitigate indirect gender bias: following debiasing, previously biased words are
significantly less clustered according to gender (cluster purity is reduced by
49%), thus improving on the state-of-the-art for bias mitigation.
