Abstract
Generating adversarial examples contributes to mainstream neural machine translation~(NMT) robustness. However, popular adversarial policies are apt for fixed tokenization, hindering its efficacy for common character perturbations involving versatile tokenization. Based on existing adversarial generation via reinforcement learning~(RL), we propose the `DexChar policy' that introduces character perturbations for the existing mainstream adversarial policy based on token substitution. Furthermore, we improve the self-supervised matching that provides feedback in RL to cater to the semantic constraints required during training adversaries. Experiments show that our method is compatible with the scenario where baseline adversaries fail, and can generate high-efficiency adversarial examples for analysis and optimization of the system.
Abstract (translated)
生成对抗样本有助于主流神经机器翻译（NMT）的稳健性。然而，流行的对抗策略适用于固定的分词方式，这阻碍了其在涉及多样化分词的常见字符扰动中的有效性。基于现有的通过强化学习（RL）进行对抗生成方法，我们提出了“DexChar 策略”，该策略引入了基于词汇替换的现有主流对抗策略下的字符扰动。此外，我们改进了自我监督匹配以提供训练对手时所需的语义约束方面的反馈。实验表明，我们的方法在基线对抗样本失效的情况下仍然有效，并且能够生成用于分析和优化系统的高效对抗样例。
URL
https://arxiv.org/abs/2501.12183
