  In this work we look into adding a new language to a multilingual NMT system
in an unsupervised fashion. Under the utilization of pre-trained cross-lingual
word embeddings we seek to exploit a language independent multilingual sentence
representation to easily generalize to a new language. While using
cross-lingual embeddings for word lookup we decode from a yet entirely unseen
source language in a process we call blind decoding. Blindly decoding from
Portuguese using a basesystem containing several Romance languages we achieve
scores of 36.4 BLEU for Portuguese-English and 12.8 BLEU for Russian-English.
In an attempt to train the mapping from the encoder sentence representation to
a new target language we use our model as an autoencoder. Merely training to
translate from Portuguese to Portuguese while freezing the encoder we achieve
26 BLEU on English-Portuguese, and up to 28 BLEU when adding artificial noise
to the input. Lastly we explore a more practical adaptation approach through
non-iterative backtranslation, exploiting our model's ability to produce high
quality translations through blind decoding. This yields us up to 34.6 BLEU on
English-Portuguese, attaining near parity with a model adapted on real
bilingual data.
