2019
DOI: 10.31224/osf.io/du2vs
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Exploring and Improving Robustness of Multi Task Deep Neural Networks via Domain Agnostic Defenses

Abstract: In this paper, we explore the robustness of the Multi-Task Deep Neural Networks (MT-DNN) against non-targeted adversarial attacks across Natural Language Understanding (NLU) tasks as well as some possible ways to defend against them. Liu et al., have shown that the Multi-Task Deep Neural Network [5], due to the regularization effect produced when training as a result of it's cross task data, is more robust than a vanilla BERT model trained only on one task (1.1%-1.5% absolute difference). We further show that … Show more

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