  Several deep learning models have been proposed for question answering.
However, due to their single-pass nature, they have no way to recover from
local maxima corresponding to incorrect answers. To address this problem, we
introduce the Dynamic Coattention Network (DCN) for question answering. The DCN
first fuses co-dependent representations of the question and the document in
order to focus on relevant parts of both. Then a dynamic pointing decoder
iterates over potential answer spans. This iterative procedure enables the
model to recover from initial local maxima corresponding to incorrect answers.
On the Stanford question answering dataset, a single DCN model improves the
previous state of the art from 71.0% F1 to 75.9%, while a DCN ensemble obtains
80.4% F1.
