  Standard accuracy metrics indicate that reading comprehension systems are
making rapid progress, but the extent to which these systems truly understand
language remains unclear. To reward systems with real language understanding
abilities, we propose an adversarial evaluation scheme for the Stanford
Question Answering Dataset (SQuAD). Our method tests whether systems can answer
questions about paragraphs that contain adversarially inserted sentences, which
are automatically generated to distract computer systems without changing the
correct answer or misleading humans. In this adversarial setting, the accuracy
of sixteen published models drops from an average of $75\%$ F1 score to $36\%$;
when the adversary is allowed to add ungrammatical sequences of words, average
accuracy on four models decreases further to $7\%$. We hope our insights will
motivate the development of new models that understand language more precisely.
