Summary of Iae: Irony-based Adversarial Examples For Sentiment Analysis Systems, by Xiaoyin Yi and Jiacheng Huang
IAE: Irony-based Adversarial Examples for Sentiment Analysis Systems
by Xiaoyin Yi, Jiacheng Huang
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Irony-based Adversarial Examples (IAE) method transforms straightforward sentences into ironic ones to create adversarial text. This approach exploits the rhetorical device of irony, requiring a deeper understanding of context to detect. The IAE method is challenging due to the need to accurately locate evaluation words, substitute them with appropriate collocations, and expand the text with suitable ironic elements while maintaining semantic coherence. The research demonstrates that several state-of-the-art deep learning models on sentiment analysis tasks significantly deteriorate when subjected to IAE attacks. This finding underscores the susceptibility of current NLP systems to adversarial manipulation through irony. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Adversarial examples can trick artificial intelligence (AI) into making mistakes. In this study, researchers created a new way to make AI think something is true when it’s actually false. They used “irony” – saying the opposite of what you mean – to create fake text that can fool AI systems. This method is tricky because it requires understanding the meaning behind words and sentences. The researchers tested their approach on several AI models and found that they were easily tricked into making wrong decisions. This shows how vulnerable current AI systems are to attacks using irony. |
Keywords
» Artificial intelligence » Deep learning » Nlp
