<cite>Artetxe et al. (2017)</cite> use a very small, automatically-generated seed lexicon of identical numerals as the initialization in an iterative self-learning framework to learn a linear mapping between monolingual embedding spaces; Zhang et al. (2017) use an adversarial training method to learn a similar mapping. These recent advances in unsupervised bilingual lexicon induction show promise for use in low-resource contexts. However, none of them make use of linguistic features of the languages themselves (with the arguable exception of syntactic/semantic information encoded in the word embeddings).