  We present an approach to neural machine translation (NMT) that supports
multiple domains in a single model and allows switching between the domains
when translating. The core idea is to treat text domains as distinct languages
and use multilingual NMT methods to create multi-domain translation systems, we
show that this approach results in significant translation quality gains over
fine-tuning. We also explore whether the knowledge of pre-specified text
domains is necessary, turns out that it is after all, but also that when it is
not known quite high translation quality can be reached.
