  A common use of machine translation in the industry is providing initial
translation hypotheses, which are later supervised and post-edited by a human
expert. During this revision process, new bilingual data are continuously
generated. Machine translation systems can benefit from these new data,
incrementally updating the underlying models under an online learning paradigm.
We conducted a user study on this scenario, for a neural machine translation
system. The experimentation was carried out by professional translators, with a
vast experience in machine translation post-editing. The results showed a
reduction in the required amount of human effort needed when post-editing the
outputs of the system, improvements in the translation quality and a positive
perception of the adaptive system by the users.
