Interpreting the Robustness of Neural NLP Models to Textual Perturbations

Abstract

Modern Natural Language Processing (NLP) models are known to be sensitive to input perturbations and their performance can decrease when applied to real-world, noisy data. However, it is still unclear why models are less robust to some perturbations than others. In this work, we test the hypothesis that the extent to which a model is affected by an unseen textual perturbation (robustness) can be explained by the learnability of the perturbation (defined as how well the model learns to identify the perturbation with a small amount of evidence). We further give a causal justification for the learnability metric. We conduct extensive experiments with four prominent NLP models — TextRNN, BERT, RoBERTa and XLNet — over eight types of textual perturbations on three datasets. We show that a model which is better at identifying a perturbation (higher learnability) becomes worse at ignoring such a perturbation at test time (lower robustness), providing empirical support for our hypothesis.

Publication
Findings of the Association for Computational Linguistics: ACL 2022
Yunxiang Zhang
Remote Undergraduate Intern (Aug ‘21)

Remote Undergraduate Intern

Liangming Pan
Doctoral Alumni (‘22)

Doctoral Alumni (‘22)

Samson Tan
Doctoral Alumni (‘22)

Doctoral Alumni (‘22)

Min-Yen Kan
Min-Yen Kan
Associate Professor

WING lead; interests include Digital Libraries, Information Retrieval and Natural Language Processing.