  In this work we approach the task of learning multilingual word
representations in an offline manner by fitting a generative latent variable
model to a multilingual dictionary. We model equivalent words in different
languages as different views of the same word generated by a common latent
variable representing their latent lexical meaning. We explore the task of
alignment by querying the fitted model for multilingual embeddings achieving
competitive results across a variety of tasks. The proposed model is robust to
noise in the embedding space making it a suitable method for distributed
representations learned from noisy corpora.
