Exploring and Improving Robustness of Multi Task Deep Neural Networks via Domain Agnostic Defenses
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| Authors | Kashyap Coimbatore Murali |
| Journal/Conference Name | arXiv preprint |
| Paper Category | Artificial Intelligence |
| Paper 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, due to the regularization effect produced when training as a result of its 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 although the MT-DNN has generalized better, making it easily transferable across domains and tasks, it can still be compromised as after only 2 attacks (1-character and 2-character) the accuracy drops by 42.05% and 32.24% for the SNLI and SciTail tasks. Finally, we propose a domain agnostic defense which restores the model's accuracy (36.75% and 25.94% respectively) as opposed to a general-purpose defense or an off-the-shelf spell checker. |
| Date of publication | 2020 |
| Code Programming Language | Python |
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