  We propose a machine reading comprehension model based on the
compare-aggregate framework with two-staged attention that achieves
state-of-the-art results on the MovieQA question answering dataset. To
investigate the limitations of our model as well as the behavioral difference
between convolutional and recurrent neural networks, we generate adversarial
examples to confuse the model and compare to human performance. Furthermore, we
assess the generalizability of our model by analyzing its differences to human
inference,
