  Neural machine translation has meant a revolution of the field. Nevertheless,
post-editing the outputs of the system is mandatory for tasks requiring high
translation quality. Post-editing offers a unique opportunity for improving
neural machine translation systems, using online learning techniques and
treating the post-edited translations as new, fresh training data. We review
classical learning methods and propose a new optimization algorithm. We
thoroughly compare online learning algorithms in a post-editing scenario.
Results show significant improvements in translation quality and effort
reduction.
