  Document-level neural machine translation (DocNMT) aims to generate
translations that are both coherent and cohesive, in contrast to its
sentence-level counterpart. However, due to its longer input length and limited
availability of training data, DocNMT often faces the challenge of data
sparsity. To overcome this issue, we propose a novel Importance-Aware Data
Augmentation (IADA) algorithm for DocNMT that augments the training data based
on token importance information estimated by the norm of hidden states and
training gradients. We conduct comprehensive experiments on three widely-used
DocNMT benchmarks. Our empirical results show that our proposed IADA
outperforms strong DocNMT baselines as well as several data augmentation
approaches, with statistical significance on both sentence-level and
document-level BLEU.
