Generating Adversarial Examples for Low-Resource NMT via Multi-Reward Reinforcement Learning

Weak robustness and noise adaptability are major issues for Low-Resource Neural Machine Translation (NMT) models. Adversarial example is currently a major tool to improve model robustness and how to generate an adversarial examples that can degrade the performance of the model and ensure semantic consistency is a challenging task. In this paper, we adopt multi-reward reinforcement learning to generate adversarial examples for low-resource NMT. Specifically, utilizing gradient ascent to modify the source sentence, the discriminator and changes estimate are used to determine whether the generated adversarial examples maintain semantic consistency and the overall modifications of adversarial examples. Furthermore, we also install a language model reward to measure the fluency of adversarial examples. Experimental results on low-resource translation tasks show that our method highly aggressive to the model while maintaining semantic constraints greatly. Moreover, the model performance is significantly improved after fine-tuning with adversarial examples.