  Learning multilingual and multi-domain translation model is challenging as
the heterogeneous and imbalanced data make the model converge inconsistently
over different corpora in real world. One common practice is to adjust the
share of each corpus in the training, so that the learning process is balanced
and low-resource cases can benefit from the high resource ones. However,
automatic balancing methods usually depend on the intra- and inter-dataset
characteristics, which is usually agnostic or requires human priors. In this
work, we propose an approach, MultiUAT, that dynamically adjusts the training
data usage based on the model's uncertainty on a small set of trusted clean
data for multi-corpus machine translation. We experiments with two classes of
uncertainty measures on multilingual (16 languages with 4 settings) and
multi-domain settings (4 for in-domain and 2 for out-of-domain on
English-German translation) and demonstrate our approach MultiUAT substantially
outperforms its baselines, including both static and dynamic strategies. We
analyze the cross-domain transfer and show the deficiency of static and
similarity based methods.
