With the rapid development of neural network applications in NLP, model robustness problem is gaining more attention. Different from computer vision, the discrete nature of texts makes it more challenging to explore robustness in NLP. Therefore, in this paper, we aim to connect discrete perturbations with continuous perturbations, therefore we can use such connections as a bridge to help understand discrete perturbations in NLP models. Specifically, we first explore how to connect and measure the correlation between discrete perturbations and continuous perturbations. Then we design a regression task as a PerturbScore to learn the correlation automatically. Through experimental results, we find that we can build a connection between discrete and continuous perturbations and use the proposed PerturbScore to learn such correlation, surpassing previous methods used in discrete perturbation measuring. Further, the proposed PerturbScore can be well generalized to different datasets, perturbation methods, indicating that we can use it as a powerful tool to study model robustness in NLP.

翻译：随着神经网络在自然语言处理领域的快速发展，模型鲁棒性问题日益受到关注。与计算机视觉不同，文本的离散特性使得探究自然语言处理中的鲁棒性更具挑战性。因此，本文旨在连接离散扰动与连续扰动，利用这种关联作为桥梁，帮助理解自然语言处理模型中的离散扰动。具体而言，我们首先探索如何连接并衡量离散扰动与连续扰动之间的相关性，随后设计了一个回归任务作为PerturbScore来自动学习这种相关性。实验结果表明，我们能够建立离散扰动与连续扰动之间的关联，并通过所提出的PerturbScore学习这种相关性，超越了以往用于离散扰动度量的方法。此外，所提出的PerturbScore能够良好地泛化至不同数据集与扰动方法，表明其可作为研究自然语言处理中模型鲁棒性的有力工具。