Abstract
In this work we approach the task of learning multilingual word representations in an of-
fline 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