  Measuring, evaluating and reducing Gender Bias has come to the forefront with
newer and improved language embeddings being released every few months. But
could this bias vary from domain to domain? We see a lot of work to study these
biases in various embedding models but limited work has been done to debias
Indic languages. We aim to measure and study this bias in Hindi language, which
is a higher-order language (gendered) with reference to English, a lower-order
language. To achieve this, we study the variations across domains to quantify
if domain embeddings allow us some insight into Gender bias for this pair of
Hindi-English model. We will generate embeddings in four different corpora and
compare results by implementing different metrics like with pre-trained State
of the Art Indic-English translation model, which has performed better at many
NLP tasks than existing models.
