On Sensitivity of Deep Learning Based Text Classification Algorithms to Practical Input Perturbations

TL;DR

This study evaluates how common input perturbations like adding or removing tokens affect the robustness of deep learning models such as CNN, LSTM, and BERT in text classification tasks, revealing notable sensitivities.

Contribution

The paper provides a systematic analysis of the impact of practical input perturbations on deep learning text classifiers, highlighting their vulnerabilities and offering guidance for robustness assessment.

Findings

BERT is more sensitive to token removal than addition.

LSTM shows slightly higher sensitivity to perturbations than CNN.

Deep learning models are vulnerable to minor input changes affecting performance.

Abstract

Text classification is a fundamental Natural Language Processing task that has a wide variety of applications, where deep learning approaches have produced state-of-the-art results. While these models have been heavily criticized for their black-box nature, their robustness to slight perturbations in input text has been a matter of concern. In this work, we carry out a data-focused study evaluating the impact of systematic practical perturbations on the performance of the deep learning based text classification models like CNN, LSTM, and BERT-based algorithms. The perturbations are induced by the addition and removal of unwanted tokens like punctuation and stop-words that are minimally associated with the final performance of the model. We show that these deep learning approaches including BERT are sensitive to such legitimate input perturbations on four standard benchmark datasets SST2, TREC-6, BBC News, and tweet_eval. We observe that BERT is more susceptible to the removal of tokens as compared to the addition of tokens. Moreover, LSTM is slightly more sensitive to input perturbations as compared to CNN based model. The work also serves as a practical guide to assessing the impact of discrepancies in train-test conditions on the final performance of models.