To mitigate the hubness problem, we adopt the Cross-Domain Similarity Local Scaling (CSLS) metric proposed in (Conneau et al., 2017) that penalizes the similarity score of the hubs.
given two mapped embeddings W xy x denoted x and y, CSLS first computes the average cosine similarity of x and y for their k nearest neighbors denoted r T (x ) and r S (y) in the other language respectively, then the corrected similarity measure CSLS(•, •) is defined as: where cos(•, •) is the cosine similarity.