Adversarial Augmentation Policy Search for Domain and Cross-Lingual Generalization in Reading Comprehension (2020.findings-emnlp)
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| Challenge: | Reading comprehension models often overfit to nuances of training datasets and fail at adversarial evaluation. |
| Approach: | They propose a method that introduces multiple points of confusion within the context and shows dependence on insertion location of the distractor. |
| Outcome: | The proposed methods improve robustness against adversarial evaluation but weak generalization to the source domain and new domains and languages. |
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