While a substantial body of prior work has explored adversarial example
generation for natural language understanding tasks, these examples are often
unrealistic and diverge from the real-world data distributions. In this work,
we introduce a two-stage adversarial example generation framework
(NaturalAdversaries), for designing adversaries that are effective at fooling a
given classifier and demonstrate natural-looking failure cases that could
plausibly occur during in-the-wild deployment of the models.
At the first stage a token attribution method is used to summarize a given
classifier's behaviour as a function of the key tokens in the input. In the
second stage a generative model is conditioned on the key tokens from the first
stage. NaturalAdversaries is adaptable to both black-box and white-box
adversarial attacks based on the level of access to the model parameters. Our
results indicate these adversaries generalize across domains, and offer
insights for future research on improving robustness of neural text
classification models.

本篇论文提出了一种两阶段对抗性样本生成框架（NaturalAdversaries），利用 token attribution 方法和生成模型，生成真实的自然语言文本，可以模拟在实际场景下模型的错误分类情况，概括了当前神经文本分类模型鲁棒性改进方向的研究前景。

自然对手：自然对手是否能像人工对手一样有效？

NaturalAdversaries: Can Naturalistic Adversaries Be as Effective as Artificial Adversaries?

While end-to-end neural machine translation (NMT) has achieved impressive
progress, noisy input usually leads models to become fragile and unstable.
Generating adversarial examples as the augmented data has been proved to be
useful to alleviate this problem. Existing methods for adversarial example
generation (AEG) are word-level or character-level, which ignore the ubiquitous
phrase structure. In this paper, we propose a Phrase-level Adversarial Example
Generation (PAEG) framework to enhance the robustness of the translation model.
Our method further improves the gradient-based word-level AEG method by
adopting a phrase-level substitution strategy. We verify our method on three
benchmarks, including LDC Chinese-English, IWSLT14 German-English, and WMT14
English-German tasks. Experimental results demonstrate that our approach
significantly improves translation performance and robustness to noise compared
to previous strong baselines.

本研究提出了一种基于短语级别替代策略的对抗样本生成框架（PAEG）以提高神经机器翻译从业者的鲁棒性和性能表现。实验证明，相较于单纯的字级替代方法，我们的方法可以显著提高翻译性能和抗噪性。