Self-Supervised Contrastive Learning with Adversarial Perturbations for Defending Word Substitution-based Attacks

TL;DR

This paper introduces a self-supervised contrastive learning method using adversarial perturbations to enhance BERT's robustness against word substitution attacks without relying on labeled data.

Contribution

It proposes a novel adversarial contrastive learning approach that improves language model robustness without labeled data, outperforming traditional adversarial training methods.

Findings

Improves BERT robustness against four attack types

Combining with adversarial training yields higher robustness

Effective using only unlabeled large text datasets

Abstract

In this paper, we present an approach to improve the robustness of BERT language models against word substitution-based adversarial attacks by leveraging adversarial perturbations for self-supervised contrastive learning. We create a word-level adversarial attack generating hard positives on-the-fly as adversarial examples during contrastive learning. In contrast to previous works, our method improves model robustness without using any labeled data. Experimental results show that our method improves robustness of BERT against four different word substitution-based adversarial attacks, and combining our method with adversarial training gives higher robustness than adversarial training alone. As our method improves the robustness of BERT purely with unlabeled data, it opens up the possibility of using large text datasets to train robust language models against word substitution-based adversarial attacks.