  Most existing methods for automatic bilingual dictionary induction rely on
prior alignments between the source and target languages, such as parallel
corpora or seed dictionaries. For many language pairs, such supervised
alignments are not readily available. We propose an unsupervised approach for
learning a bilingual dictionary for a pair of languages given their
independently-learned monolingual word embeddings. The proposed method exploits
local and global structures in monolingual vector spaces to align them such
that similar words are mapped to each other. We show empirically that the
performance of bilingual correspondents learned using our proposed unsupervised
method is comparable to that of using supervised bilingual correspondents from
a seed dictionary.
