Evaluating Text Classification Robustness to Part-of-Speech Adversarial Examples
This addresses robustness issues in text classification for safety-critical applications, though it is incremental as it builds on existing adversarial example research.
The paper investigates which parts of speech most impact text classifiers, revealing a bias in CNN algorithms against certain parts of speech tokens in review datasets, highlighting a vulnerability in their linguistic processing.
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.