  Most multi-domain machine translation models rely on domain-annotated data.
Unfortunately, domain labels are usually unavailable in both training processes
and real translation scenarios. In this work, we propose a label-free
multi-domain machine translation model which requires only a few or no
domain-annotated data in training and no domain labels in inference. Our model
is composed of three parts: a backbone model, a domain discriminator taking
responsibility to discriminate data from different domains, and a set of
experts that transfer the decoded features from generic to specific. We design
a stage-wise training strategy and train the three parts sequentially. To
leverage the extra domain knowledge and improve the training stability, in the
discriminator training stage, domain differences are modeled explicitly with
clustering and distilled into the discriminator through a multi-classification
task. Meanwhile, the Gumbel-Max sampling is adopted as the routing scheme in
the expert training stage to achieve the balance of each expert in
specialization and generalization. Experimental results on the
German-to-English translation task show that our model significantly improves
BLEU scores on six different domains and even outperforms most of the models
trained with domain-annotated data.
