  The task of Question Answering has gained prominence in the past few decades
for testing the ability of machines to understand natural language. Large
datasets for Machine Reading have led to the development of neural models that
cater to deeper language understanding compared to information retrieval tasks.
Different components in these neural architectures are intended to tackle
different challenges. As a first step towards achieving generalization across
multiple domains, we attempt to understand and compare the peculiarities of
existing end-to-end neural models on the Stanford Question Answering Dataset
(SQuAD) by performing quantitative as well as qualitative analysis of the
results attained by each of them. We observed that prediction errors reflect
certain model-specific biases, which we further discuss in this paper.
