  Sparse language vectors from linguistic typology databases and learned
embeddings from tasks like multilingual machine translation have been
investigated in isolation, without analysing how they could benefit from each
other's language characterisation. We propose to fuse both views using singular
vector canonical correlation analysis and study what kind of information is
induced from each source. By inferring typological features and language
phylogenies, we observe that our representations embed typology and strengthen
correlations with language relationships. We then take advantage of our
multi-view language vector space for multilingual machine translation, where we
achieve competitive overall translation accuracy in tasks that require
information about language similarities, such as language clustering and
ranking candidates for multilingual transfer. With our method, which is also
released as a tool, we can easily project and assess new languages without
expensive retraining of massive multilingual or ranking models, which are major
disadvantages of related approaches.
