论文标题
通过领域不可知论探索和改善多任务深度神经网络的鲁棒性
Exploring and Improving Robustness of Multi Task Deep Neural Networks via Domain Agnostic Defenses
论文作者
论文摘要
在本文中，我们探讨了多任务深神经网络（MT-DNN）的鲁棒性，以针对自然语言理解（NLU）任务（NLU）任务的非目标对抗性攻击以及一些可能防御它们的可能方法。 Liu等人已经表明，由于其交叉任务数据训练时产生的正则化效应，多任务深神经网络比仅在一项任务上训练的香草BERT模型（1.1％-1.5％的绝对差异）更强大。我们进一步表明，尽管MT-DNN概括了更好的概括，使其在域和任务之间易于转移，但它仍然可以妥协，因为仅在2次攻击（1个字符和2个字符）之后，SNLI和SCITAIL任务的精度下降了42.05％和32.24％。最后，我们提出了一个域名防御，该防御能够恢复模型的准确性（分别为36.75％和25.94％），而不是通用防御或现成的咒语检查器。
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.
