Adversarial examples expose the vulnerabilities of natural language
processing (NLP) models, and can be used to evaluate and improve their
robustness. Existing techniques of generating such examples are typically
driven by local heuristic rules that are agnostic to the context, often
resulting in unnatural and ungrammatical outputs. This paper presents CLARE, a
ContextuaLized AdversaRial Example generation model that produces fluent and
grammatical outputs through a mask-then-infill procedure. CLARE builds on a
pre-trained masked language model and modifies the inputs in a context-aware
manner. We propose three contextualized perturbations, Replace, Insert and
Merge, allowing for generating outputs of varied lengths. With a richer range
of available strategies, CLARE is able to attack a victim model more
efficiently with fewer edits. Extensive experiments and human evaluation
demonstrate that CLARE outperforms the baselines in terms of attack success
rate, textual similarity, fluency and grammaticality.

本文提出了一种基于上下文的 CLARE 模型，通过掩码插值过程生成流畅和语法正确的对抗样例，旨在发现自然语言处理模型的脆弱性，并通过三个上下文化扰动方法 —— 替换、插入、合并，产生不同长度的文本以提高攻击效率，并通过实验和人类评估证明了相对于基线模型，CLARE 模型在攻击成功率、文本相似性、流畅性和语法正确性方面表现更好。