  When training multilingual machine translation (MT) models that can translate
to/from multiple languages, we are faced with imbalanced training sets: some
languages have much more training data than others. Standard practice is to
up-sample less resourced languages to increase representation, and the degree
of up-sampling has a large effect on the overall performance. In this paper, we
propose a method that instead automatically learns how to weight training data
through a data scorer that is optimized to maximize performance on all test
languages. Experiments on two sets of languages under both one-to-many and
many-to-one MT settings show our method not only consistently outperforms
heuristic baselines in terms of average performance, but also offers flexible
control over the performance of which languages are optimized.
