In this paper, we show that their method of adversarial training failed because the specificity of the <cite>AddSent</cite> algorithm along with the lack of naturally-occurring counterexamples allow models to learn superficial clues regarding what is a 'distractor' and subsequently ignore it; thus significantly limiting their robustness.