Extend Adversarial Policy Against Neural Machine Translation via Unknown Token

• URL: http://arxiv.org/abs/2501.12183v1
• Date: Tue, 21 Jan 2025 14:43:04 GMT
• Title: Extend Adversarial Policy Against Neural Machine Translation via Unknown Token
• Authors: Wei Zou, Shujian Huang, Jiajun Chen,
• Abstract summary: We propose the DexChar policy' that introduces character perturbations for the existing mainstream adversarial policy based on token substitution. We also improve the self-supervised matching that provides feedback in RL to cater to the semantic constraints required during training adversaries.
• Score: 66.40609413186122
• License:
• 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.

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