Title: DOUBLE-HARD DEBIASING: TAILORING WORD EMBEDDINGS FOR GENDER BIAS MITIGATION. Abstract: Gender bias in word embeddings has been widely investigated. However, recent work has shown that existing approaches, including the well-known Hard Debias algorithm which projects word embeddings to a subspace orthogonal to an inferred gender direction, are insufficient to deliver gender-neutral word embeddings. In our work, we discover that semantic-agnostic corpus statistics such as word frequency are important factors that limit the debiasing performance. We propose a simple but effective processing technique, Double-Hard Debias, to attenuate the effect due to such noise. We experiment with Word2Vec and GloVe embeddings and demonstrate on several benchmarks that our approach preserves the distributional semantics while effectively reducing gender bias to a larger extent than previous debiasing techniques. 