  This paper describes Facebook AI's submission to the WAT 2019 Myanmar-English
translation task. Our baseline systems are BPE-based transformer models. We
explore methods to leverage monolingual data to improve generalization,
including self-training, back-translation and their combination. We further
improve results by using noisy channel re-ranking and ensembling. We
demonstrate that these techniques can significantly improve not only a system
trained with additional monolingual data, but even the baseline system trained
exclusively on the provided small parallel dataset. Our system ranks first in
both directions according to human evaluation and BLEU, with a gain of over 8
BLEU points above the second best system.
