  Existing curriculum learning approaches to Neural Machine Translation (NMT)
require sampling sufficient amounts of "easy" samples from training data at the
early training stage. This is not always achievable for low-resource languages
where the amount of training data is limited. To address such limitation, we
propose a novel token-wise curriculum learning approach that creates sufficient
amounts of easy samples. Specifically, the model learns to predict a short
sub-sequence from the beginning part of each target sentence at the early stage
of training, and then the sub-sequence is gradually expanded as the training
progresses. Such a new curriculum design is inspired by the cumulative effect
of translation errors, which makes the latter tokens more difficult to predict
than the beginning ones. Extensive experiments show that our approach can
consistently outperform baselines on 5 language pairs, especially for
low-resource languages. Combining our approach with sentence-level methods
further improves the performance on high-resource languages.
