Interpreting the Robustness of Neural NLP Models to Textual Perturbations (2022.findings-acl)
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| Challenge: | Modern Natural Language Processing models are sensitive to input perturbations and their performance can decrease when applied to noisy data. |
| Approach: | They propose to explain the extent to which a model is affected by an unseen textual perturbation by the learnability of the perturbation. |
| Outcome: | The proposed model is better at identifying a perturbation (higher learnability) but worse at ignoring it (lower robustness). |
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