  We propose DecaProp (Densely Connected Attention Propagation), a new densely
connected neural architecture for reading comprehension (RC). There are two
distinct characteristics of our model. Firstly, our model densely connects all
pairwise layers of the network, modeling relationships between passage and
query across all hierarchical levels. Secondly, the dense connectors in our
network are learned via attention instead of standard residual skip-connectors.
To this end, we propose novel Bidirectional Attention Connectors (BAC) for
efficiently forging connections throughout the network. We conduct extensive
experiments on four challenging RC benchmarks. Our proposed approach achieves
state-of-the-art results on all four, outperforming existing baselines by up to
$2.6\%-14.2\%$ in absolute F1 score.
