We then add Gaussian noise (Zheng et al., 2016) to further compensate the imperfect alignment of cross-lingual embeddings.
a combination of these methods allows us to build a transferable latent variable model that learns the distribution of training language inputs that is invariant to noise in the cross-  lingual word embeddings.