Papers by Laure Berti-Equille
R&R: Metric-guided Adversarial Sentence Generation (2022.findings-aacl)
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| Challenge: | Existing methods prioritize misclassification by maximizing each perturbation’s effectiveness at misleading a text classifier. |
| Approach: | They propose a rewrite and rollback framework for adversarial attack that optimizes a critique score which combines fluency, similarity, and misclassification metrics. |
| Outcome: | The proposed framework outperforms current state-of-the-art in attack success rate by +16.2%, +12.8%, and +14.0% on the classifiers respectively. |