  Machine translation systems are very sensitive to the domains they were
trained on. Several domain adaptation techniques have been deeply studied. We
propose a new technique for neural machine translation (NMT) that we call
domain control which is performed at runtime using a unique neural network
covering multiple domains. The presented approach shows quality improvements
when compared to dedicated domains translating on any of the covered domains
and even on out-of-domain data. In addition, model parameters do not need to be
re-estimated for each domain, making this effective to real use cases.
Evaluation is carried out on English-to-French translation for two different
testing scenarios. We first consider the case where an end-user performs
translations on a known domain. Secondly, we consider the scenario where the
domain is not known and predicted at the sentence level before translating.
Results show consistent accuracy improvements for both conditions.
