Recent work has managed to learn cross-lingual word embeddings without parallel data by mapping monolingual embeddings to a shared space through adversarial training. However, their evaluation has focused on favorable conditions, using comparable corpora or closely-related languages, and we show that they often fail in more re-alistic scenarios. This work proposes an alternative approach based on a fully unsupervised initialization that ex-plicitly exploits the structural similarity of the embeddings, and a robust self-learning algorithm that iteratively im-proves this solution. Our method succeeds in all tested scenarios and obtains the best published results in standard datasets, even surpassing previous supervised systems. Our implementation is released as an open source project at github.com/artetxem/vecmap.
