In summary, previous work on text adversaries change semantics, only generate local (instancespecific) adversaries (Zhao et al., 2018;Iyyer et al., 2018), or are tailored for white-box models (Ebrahimi et al., 2018) or specific tasks (Jia and Liang, 2017).
sEAs expose oversensitivity for specific predictions of black-box models for a variety of tasks, while sEARs are intuitive and actionable global rules that induce a high number of high-quality adversaries.