  Generative adversarial networks (GANs) have succeeded in inducing
cross-lingual word embeddings -- maps of matching words across languages --
without supervision. Despite these successes, GANs' performance for the
difficult case of distant languages is still not satisfactory. These
limitations have been explained by GANs' incorrect assumption that source and
target embedding spaces are related by a single linear mapping and are
approximately isomorphic. We assume instead that, especially across distant
languages, the mapping is only piece-wise linear, and propose a
multi-adversarial learning method. This novel method induces the seed
cross-lingual dictionary through multiple mappings, each induced to fit the
mapping for one subspace. Our experiments on unsupervised bilingual lexicon
induction show that this method improves performance over previous
single-mapping methods, especially for distant languages.
