  In this paper, we propose two novel methods for domain adaptation for the
attention-only neural machine translation (NMT) model, i.e., the Transformer.
Our methods focus on training a single translation model for multiple domains
by either learning domain specialized hidden state representations or predictor
biases for each domain. We combine our methods with a previously proposed
black-box method called mixed fine tuning, which is known to be highly
effective for domain adaptation. In addition, we incorporate multilingualism
into the domain adaptation framework. Experiments show that multilingual
multi-domain adaptation can significantly improve both resource-poor in-domain
and resource-rich out-of-domain translations, and the combination of our
methods with mixed fine tuning achieves the best performance.
