Sequential Attention: A Context-Aware Alignment Function for Machine Reading
Sebastian Brarda, Philip Yeres, Samuel R. Bowman · 2017
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.