Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations (2022.lrec-1)
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| Challenge: | toxicity classifiers rely on lexical cues, so creative language use can be detrimental to utility of current corpora and state-of-the-art models. |
| Approach: | They propose to use model-agnostic adversarial behavior to enhance toxic content classification models. |
| Outcome: | The proposed model-agnostic adversarial behavior and augmentation for cyberbullying detection are robust against word-level perturbations at a slight trade-off in overall task performance. |
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Ashima Suvarna, Christina A Chance, Karolina Naranjo, Hamid Palangi, Sophie Hao, Thomas Hartvigsen, Saadia Gabriel
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