We study two methods for language clustering: (1) using prior knowledge, where we cluster languages according to language family, and (2) using language embedding, in which we represent each language by an embedding vector and cluster them in the embedding space.
we obtain the embedding vectors of all the languages by training a universal neural machine translation model.