275 research outputs found
Sequential Attention: A Context-Aware Alignment Function for Machine Reading
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.Comment: To appear in ACL 2017 2nd Workshop on Representation Learning for
NLP. Contains additional experiments in section 4 and a revised Figure
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