PerturbScore: Connecting Discrete and Continuous Perturbations in NLP
Linyang Li Ke Ren Yunfan Shao Pengyu Wang Xipeng Qiu
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.