论文标题
通过增强的抽象培训对可编程弦变换的鲁棒性
Robustness to Programmable String Transformations via Augmented Abstract Training
论文作者
论文摘要
自然语言处理任务的深神经网络容易受到对抗输入扰动的影响。在本文中，我们提出了一种多功能语言，用于编程指定字符串转换，例如插入，删除，替换，掉期等 - 与手头任务有关。然后，我们提出了一种对对抗训练模型的方法，该模型可用于此类用户定义的字符串变换。我们的方法结合了基于搜索的技术对对抗训练的优势和基于抽象的技术。具体而言，我们展示了如何将一组用户定义的字符串转换分解为两个组件规范，一种是从搜索中受益的，另一种则从抽象中受益。我们使用我们的技术来训练AG和SST2数据集上的模型，并表明所得模型对于模仿拼写错误和其他具有含义的转换的用户定义转换的组合非常强大。
Deep neural networks for natural language processing tasks are vulnerable to adversarial input perturbations. In this paper, we present a versatile language for programmatically specifying string transformations -- e.g., insertions, deletions, substitutions, swaps, etc. -- that are relevant to the task at hand. We then present an approach to adversarially training models that are robust to such user-defined string transformations. Our approach combines the advantages of search-based techniques for adversarial training with abstraction-based techniques. Specifically, we show how to decompose a set of user-defined string transformations into two component specifications, one that benefits from search and another from abstraction. We use our technique to train models on the AG and SST2 datasets and show that the resulting models are robust to combinations of user-defined transformations mimicking spelling mistakes and other meaning-preserving transformations.
