  In this paper, we advance the current state-of-the-art method for debiasing
monolingual word embeddings so as to generalize well in a multilingual setting.
We consider different methods to quantify bias and different debiasing
approaches for monolingual as well as multilingual settings. We demonstrate the
significance of our bias-mitigation approach on downstream NLP applications.
Our proposed methods establish the state-of-the-art performance for debiasing
multilingual embeddings for three Indian languages - Hindi, Bengali, and Telugu
in addition to English. We believe that our work will open up new opportunities
in building unbiased downstream NLP applications that are inherently dependent
on the quality of the word embeddings used.
