  Multilingual Neural Machine Translation approaches are based on the use of
task-specific models and the addition of one more language can only be done by
retraining the whole system. In this work, we propose a new training schedule
that allows the system to scale to more languages without modification of the
previous components based on joint training and language-independent
encoder/decoder modules allowing for zero-shot translation. This work in
progress shows close results to the state-of-the-art in the WMT task.
