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.

通过建立离散和连续扰动之间的联系，使用提出的 PerturbScore 可以学习这种相关性，从而超越了以往用于测量离散扰动的方法，并且可很好地应用于不同数据集和扰动方法，因此可以作为研究 NLP 模型稳健性的强大工具。

PerturbScore：连接自然语言处理中的离散与连续扰动

PerturbScore: Connecting Discrete and Continuous Perturbations in NLP

In recent times, deep artificial neural networks have achieved many successes
in pattern recognition. Part of this success can be attributed to the reliance
on big data to increase generalization. However, in the field of time series
recognition, many datasets are often very small. One method of addressing this
problem is through the use of data augmentation. In this paper, we survey data
augmentation techniques for time series and their application to time series
classification with neural networks. We propose a taxonomy and outline the four
families in time series data augmentation, including transformation-based
methods, pattern mixing, generative models, and decomposition methods.
Furthermore, we empirically evaluate 12 time series data augmentation methods
on 128 time series classification datasets with six different types of neural
networks. Through the results, we are able to analyze the characteristics,
advantages and disadvantages, and recommendations of each data augmentation
method. This survey aims to help in the selection of time series data
augmentation for neural network applications.

本论文调研了时间序列数据增强技术及其在神经网络时间序列分类中的应用，其中提出了四种基于变换、模式混合、生成模型和分解方法的方法， 并在 128 个时间序列分类数据集上使用六种不同类型的神经网络对 12 种时间序列数据增强方法进行了实证评估，以分析每种数据增强方法的特点、优缺点和建议，并旨在帮助选择神经网络应用的时间序列数据增强方法