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Developing methods to adversarially challenge NLP systems is a promisingavenue for improving both model performance and interpretability. Here, wedescribe the approach of the team "longhorns" on Task 1 of the The FirstWorkshop on Dynamic Adversarial Data Collection (DADC), which asked teams tomanually fool a model on an Extractive Question Answering task. Our teamfinished first, with a model error rate of 62%. We advocate for a systematic,linguistically informed approach to formulating adversarial questions, and wedescribe the results of our pilot experiments, as well as our officialsubmission.

longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks