  We analyze state-of-the-art deep learning models for three tasks: question
answering on (1) images, (2) tables, and (3) passages of text. Using the notion
of \emph{attribution} (word importance), we find that these deep networks often
ignore important question terms. Leveraging such behavior, we perturb questions
to craft a variety of adversarial examples. Our strongest attacks drop the
accuracy of a visual question answering model from $61.1\%$ to $19\%$, and that
of a tabular question answering model from $33.5\%$ to $3.3\%$. Additionally,
we show how attributions can strengthen attacks proposed by Jia and Liang
(2017) on paragraph comprehension models. Our results demonstrate that
attributions can augment standard measures of accuracy and empower
investigation of model performance. When a model is accurate but for the wrong
reasons, attributions can surface erroneous logic in the model that indicates
inadequacies in the test data.
