@inproceedings{brarda-etal-2017-sequential,
title = "Sequential Attention: A Context-Aware Alignment Function for Machine Reading",
author = "Brarda, Sebastian and
Yeres, Philip and
Bowman, Samuel",
editor = "Blunsom, Phil and
Bordes, Antoine and
Cho, Kyunghyun and
Cohen, Shay and
Dyer, Chris and
Grefenstette, Edward and
Hermann, Karl Moritz and
Rimell, Laura and
Weston, Jason and
Yih, Scott",
booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2610",
doi = "10.18653/v1/W17-2610",
pages = "75--80",
abstract = "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.",
}
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%0 Conference Proceedings
%T Sequential Attention: A Context-Aware Alignment Function for Machine Reading
%A Brarda, Sebastian
%A Yeres, Philip
%A Bowman, Samuel
%Y Blunsom, Phil
%Y Bordes, Antoine
%Y Cho, Kyunghyun
%Y Cohen, Shay
%Y Dyer, Chris
%Y Grefenstette, Edward
%Y Hermann, Karl Moritz
%Y Rimell, Laura
%Y Weston, Jason
%Y Yih, Scott
%S Proceedings of the 2nd Workshop on Representation Learning for NLP
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F brarda-etal-2017-sequential
%X 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.
%R 10.18653/v1/W17-2610
%U https://aclanthology.org/W17-2610
%U https://doi.org/10.18653/v1/W17-2610
%P 75-80
Markdown (Informal)
[Sequential Attention: A Context-Aware Alignment Function for Machine Reading](https://aclanthology.org/W17-2610) (Brarda et al., RepL4NLP 2017)
ACL