Having done that, we map these word embeddings to a cross-lingual space using the unsupervised mode in VecMap<cite> (Artetxe et al., 2018b)</cite> , which builds an initial solution based on the intralingual similarity distribution of the embeddings and iteratively improves it through self-learning.