AdvExpander: Generating Natural Language Adversarial Examples by Expanding Text

TL;DR

AdvExpander introduces a novel text expansion method for generating adversarial examples, complementing substitution-based techniques, to better expose vulnerabilities in NLP models through linguistically guided modifications.

Contribution

It proposes a new text expansion approach using linguistic rules and a CVAE-based generator to craft label-preserving adversarial examples, revealing robustness issues missed by previous methods.

Findings

Effective in three classification tasks

Generates valid adversarial examples through text expansion

Reveals new robustness vulnerabilities in models

Abstract

Adversarial examples are vital to expose the vulnerability of machine learning models. Despite the success of the most popular substitution-based methods which substitutes some characters or words in the original examples, only substitution is insufficient to uncover all robustness issues of models. In this paper, we present AdvExpander, a method that crafts new adversarial examples by expanding text, which is complementary to previous substitution-based methods. We first utilize linguistic rules to determine which constituents to expand and what types of modifiers to expand with. We then expand each constituent by inserting an adversarial modifier searched from a CVAE-based generative model which is pre-trained on a large scale corpus. To search adversarial modifiers, we directly search adversarial latent codes in the latent space without tuning the pre-trained parameters. To ensure that our adversarial examples are label-preserving for text matching, we also constrain the modifications with a heuristic rule. Experiments on three classification tasks verify the effectiveness of AdvExpander and the validity of our adversarial examples. AdvExpander crafts a new type of adversarial examples by text expansion, thereby promising to reveal new robustness issues.