2024/08/15 by Anahita Samadi, Samadi, Anahita, Allison Sullivan +1
Computer Science · #Adversarial Robustness in Machine Learning #Computation and Language (cs.CL) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Machine Learning (cs.LG) #Natural Language Processing Techniques
paper · pdf · doi:10.48550/arxiv.2408.08374
openalex publication_date 2024/08/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.