  Debiasing word embeddings has been largely limited to individual and
independent social categories. However, real-world corpora typically present
multiple social categories that possibly correlate or intersect with each
other. For instance, "hair weaves" is stereotypically associated with African
American females, but neither African American nor females alone. Therefore,
this work studies biases associated with multiple social categories: joint
biases induced by the union of different categories and intersectional biases
that do not overlap with the biases of the constituent categories. We first
empirically observe that individual biases intersect non-trivially (i.e., over
a one-dimensional subspace). Drawing from the intersectional theory in social
science and the linguistic theory, we then construct an intersectional subspace
to debias for multiple social categories using the nonlinear geometry of
individual biases. Empirical evaluations corroborate the efficacy of our
approach. Data and implementation code can be downloaded at
https://github.com/GitHubLuCheng/Implementation-of-JoSEC-COLING-22.
