A linear map from a seed dictionary aligns two word-embedding spaces

Mikolov, Le & Sutskever train monolingual skip-gram embeddings separately per language (the headline English-Spanish result uses WMT11-trained English embeddings, not Google News) [mikolov-le-sutskever-2013-exploiting-similarities-for-mt] They fit a linear transformation from a small seed bilingual dictionary and map one embedding space onto the other [mikolov-le-sutskever-2013-exploiting-similarities-for-mt] The map achieves high real word-translation precision (around 90% precision at rank 5 for English-Spanish) [mikolov-le-sutskever-2013-exploiting-similarities-for-mt] It is a foundational supervised precursor to later unsupervised cross-lingual alignment work (MUSE) [mikolov-le-sutskever-2013-exploiting-similarities-for-mt]