  Online texts -- across genres, registers, domains, and styles -- are riddled
with human stereotypes, expressed in overt or subtle ways. Word embeddings,
trained on these texts, perpetuate and amplify these stereotypes, and propagate
biases to machine learning models that use word embeddings as features. In this
work, we propose a method to debias word embeddings in multiclass settings such
as race and religion, extending the work of (Bolukbasi et al., 2016) from the
binary setting, such as binary gender. Next, we propose a novel methodology for
the evaluation of multiclass debiasing. We demonstrate that our multiclass
debiasing is robust and maintains the efficacy in standard NLP tasks.
