Detection and Removal of Gender Bias from Word Embeddings

Word embeddings are the vector representation of words which act as an input (features) to other downstream tasks and ML models. There are several popular methods for learning word embeddings; among them, the Continous-Bag-of-Words and Glove models are the two most popular techniques. These embeddings act as an input to several NLP applications, i.e. sentiment analysis, document clustering, question answering, paraphrase detection, etc. Large organizations like Google and Facebook have trained these models on large web-scale corpora and made them readily available. Word embeddings encode the words such that words with similar meanings lie close to each other in the embedding space.