  Neural Machine Translation (NMT) models are typically trained on
heterogeneous data that are concatenated and randomly shuffled. However, not
all of the training data are equally useful to the model. Curriculum training
aims to present the data to the NMT models in a meaningful order. In this work,
we introduce a two-stage curriculum training framework for NMT where we
fine-tune a base NMT model on subsets of data, selected by both deterministic
scoring using pre-trained methods and online scoring that considers prediction
scores of the emerging NMT model. Through comprehensive experiments on six
language pairs comprising low- and high-resource languages from WMT'21, we have
shown that our curriculum strategies consistently demonstrate better quality
(up to +2.2 BLEU improvement) and faster convergence (approximately 50% fewer
updates).
