  A recent research line has obtained strong results on bilingual lexicon
induction by aligning independently trained word embeddings in two languages
and using the resulting cross-lingual embeddings to induce word translation
pairs through nearest neighbor or related retrieval methods. In this paper, we
propose an alternative approach to this problem that builds on the recent work
on unsupervised machine translation. This way, instead of directly inducing a
bilingual lexicon from cross-lingual embeddings, we use them to build a
phrase-table, combine it with a language model, and use the resulting machine
translation system to generate a synthetic parallel corpus, from which we
extract the bilingual lexicon using statistical word alignment techniques. As
such, our method can work with any word embedding and cross-lingual mapping
technique, and it does not require any additional resource besides the
monolingual corpus used to train the embeddings. When evaluated on the exact
same cross-lingual embeddings, our proposed method obtains an average
improvement of 6 accuracy points over nearest neighbor and 4 points over CSLS
retrieval, establishing a new state-of-the-art in the standard MUSE dataset.
