  Most data selection research in machine translation focuses on improving a
single domain. We perform data selection for multiple domains at once. This is
achieved by carefully introducing instance-level domain-relevance features and
automatically constructing a training curriculum to gradually concentrate on
multi-domain relevant and noise-reduced data batches. Both the choice of
features and the use of curriculum are crucial for balancing and improving all
domains, including out-of-domain. In large-scale experiments, the multi-domain
curriculum simultaneously reaches or outperforms the individual performance and
brings solid gains over no-curriculum training.
