To address the above issues, we propose a new lexical mapping approach that combines the advantages of both discrete dictionary-based methods and continuous embedding-based methods.
we first project embeddings of different languages into the shared BWE space, then learn discrete word translations by looking for nearest neighbors in this projected space, and finally train a model on the translated data.