  Interpreting the performance of deep learning models beyond test set accuracy
is challenging. Characteristics of individual data points are often not
considered during evaluation, and each data point is treated equally. We
examine the impact of a test set question's difficulty to determine if there is
a relationship between difficulty and performance. We model difficulty using
well-studied psychometric methods on human response patterns. Experiments on
Natural Language Inference (NLI) and Sentiment Analysis (SA) show that the
likelihood of answering a question correctly is impacted by the question's
difficulty. As DNNs are trained with more data, easy examples are learned more
quickly than hard examples.
