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.

提出了一种基于重要性感知数据增强算法的文档级神经机器翻译方法，通过估计隐藏状态和训练梯度的法线来增加训练数据，并在常用文档级神经机器翻译基准数据集上获得了显著优于强基线模型和其他数据增强方法的结果，包括句子级和文档级 BLEU。