Exposing diverse subword segmentations to neural machine translation (NMT)
models often improves the robustness of machine translation as NMT models can
experience various subword candidates. However, the diversification of subword
segmentations mostly relies on the pre-trained subword language models from
which erroneous segmentations of unseen words are less likely to be sampled. In
this paper, we present adversarial subword regularization (ADVSR) to study
whether gradient signals during training can be a substitute criterion for
exposing diverse subword segmentations. We experimentally show that our
model-based adversarial samples effectively encourage NMT models to be less
sensitive to segmentation errors and improve the performance of NMT models in
low-resource and out-domain datasets.

该研究通过提出对抗子单词规范化技术（ADVSR）探讨了在训练过程中是否可以使用梯度信号作为多样化子单词分割的替代标准，实验证明该技术可以有效地减少分割错误对神经机器翻译模型的影响，从而提高模型在低资源和外部数据集上的性能。