评估文本分类对词性对抗样本的鲁棒性
Evaluating Text Classification Robustness to Part-of-Speech Adversarial Examples
AI总结：
为提升文本对抗样本质量，本文探究不同词性对文本分类器的影响，发现CNN算法在评论数据集上对特定词性词元存在明显偏差，暴露了其语言处理的关键漏洞。
摘要：
随着机器学习系统应用愈发广泛，尤其是在安全关键场景中，人们愈发需要确保这类系统即便遭遇对抗样本也能按预期运行。对抗样本是旨在干扰决策过程的输入，且设计为人类难以察觉。但对于基于文本的分类系统而言，对输入文本串的改动始终是可被感知的，因此文本对抗样本转而聚焦于保留语义。遗憾的是，近期研究表明这一目标往往难以实现。为提升文本对抗样本的质量，我们需要明确输入文本的哪些元素值得重点关注。针对这一问题，本文探究了哪些词性对文本分类器的影响最大。实验揭示了CNN（卷积神经网络）算法在评论数据集上对特定词性词元存在明显偏差，该发现凸显了CNN在语言处理能力方面的关键漏洞。
英文摘要：
As machine learning systems become more widely used, especially for safety critical applications, there is a growing need to ensure that these systems behave as intended, even in the face of adversarial examples. Adversarial examples are inputs that are designed to trick the decision making process, and are intended to be imperceptible to humans. However, for text-based classification systems, changes to the input, a string of text, are always perceptible. Therefore, text-based adversarial examples instead focus on trying to preserve semantics. Unfortunately, recent work has shown this goal is often not met. To improve the quality of text-based adversarial examples, we need to know what elements of the input text are worth focusing on. To address this, in this paper, we explore what parts of speech have the highest impact of text-based classifiers. Our experiments highlight a distinct bias in CNN algorithms against certain parts of speech tokens within review datasets. This finding underscores a critical vulnerability in the linguistic processing capabilities of CNNs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
