  In this paper we propose a neural network model with a novel Sequential
Attention layer that extends soft attention by assigning weights to words in an
input sequence in a way that takes into account not just how well that word
matches a query, but how well surrounding words match. We evaluate this
approach on the task of reading comprehension (on the Who did What and CNN
datasets) and show that it dramatically improves a strong baseline--the
Stanford Reader--and is competitive with the state of the art.
