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

06/29/2022

∙

by   Venelin Kovatchev, et al.

∙

0

∙

share

Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability. Here, we describe the approach of the team "longhorns" on Task 1 of the The First Workshop on Dynamic Adversarial Data Collection (DADC), which asked teams to manually fool a model on an Extractive Question Answering task. Our team finished first, with a model error rate of 62 linguistically informed approach to formulating adversarial questions, and we describe the results of our pilot experiments, as well as our official submission.