  Multilingual machine translation (MMT), trained on a mixture of parallel and
monolingual data, is key for improving translation in low-resource language
pairs. However, the literature offers conflicting results on the performance of
different methods of including monolingual data. To resolve this, we examine
how denoising autoencoding (DAE) and backtranslation (BT) impact MMT under
different data conditions and model scales. Unlike prior studies, we use a
realistic dataset of 100 translation directions and consider many domain
combinations of monolingual and test data. We find that monolingual data
generally helps MMT, but models are surprisingly brittle to domain mismatches,
especially at smaller model scales. BT is beneficial when the parallel,
monolingual, and test data sources are similar but can be detrimental
otherwise, while DAE is less effective than previously reported. Next, we
analyze the impact of scale (from 90M to 1.6B parameters) and find it is
important for both methods, particularly DAE. As scale increases, DAE
transitions from underperforming the parallel-only baseline at 90M to
converging with BT performance at 1.6B, and even surpassing it in low-resource.
These results offer new insights into how to best use monolingual data in MMT.

Computation and Language