  Research shows that natural language processing models are generally
considered to be vulnerable to adversarial attacks; but recent work has drawn
attention to the issue of validating these adversarial inputs against certain
criteria (e.g., the preservation of semantics and grammaticality). Enforcing
constraints to uphold such criteria may render attacks unsuccessful, raising
the question of whether valid attacks are actually feasible. In this work, we
investigate this through the lens of human language ability. We report on
crowdsourcing studies in which we task humans with iteratively modifying words
in an input text, while receiving immediate model feedback, with the aim of
causing a sentiment classification model to misclassify the example. Our
findings suggest that humans are capable of generating a substantial amount of
adversarial examples using semantics-preserving word substitutions. We analyze
how human-generated adversarial examples compare to the recently proposed
TextFooler, Genetic, BAE and SememePSO attack algorithms on the dimensions
naturalness, preservation of sentiment, grammaticality and substitution rate.
Our findings suggest that human-generated adversarial examples are not more
able than the best algorithms to generate natural-reading, sentiment-preserving
examples, though they do so by being much more computationally efficient.
