  Many modern Neural Machine Translation (NMT) systems are trained on
nonhomogeneous datasets with several distinct dimensions of variation (e.g.
domain, source, generation method, style, etc.). We describe and empirically
evaluate multidimensional tagging (MDT), a simple yet effective method for
passing sentence-level information to the model. Our human and BLEU evaluation
results show that MDT can be applied to the problem of multi-domain adaptation
and significantly reduce training costs without sacrificing the translation
quality on any of the constituent domains.
