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        <title>Surprisingly Easy Hard-Attention for Sequence to Sequence Learning</title>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>In this paper we show that a simple beam approximation of the joint distribution between attention and output is an easy, accurate, and efficient attention mechanism for sequence to sequence learning. The method combines the advantage of sharp focus in hard attention and the implementation ease of soft attention. On five translation tasks we show effortless and consistent gains in BLEU compared to existing attention mechanisms.</abstract>
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%0 Conference Proceedings
%T Surprisingly Easy Hard-Attention for Sequence to Sequence Learning
%A Shankar, Shiv
%A Garg, Siddhant
%A Sarawagi, Sunita
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F shankar-etal-2018-surprisingly
%X In this paper we show that a simple beam approximation of the joint distribution between attention and output is an easy, accurate, and efficient attention mechanism for sequence to sequence learning. The method combines the advantage of sharp focus in hard attention and the implementation ease of soft attention. On five translation tasks we show effortless and consistent gains in BLEU compared to existing attention mechanisms.
%R 10.18653/v1/D18-1065
%U https://aclanthology.org/D18-1065
%U https://doi.org/10.18653/v1/D18-1065
%P 640-645

Markdown (Informal)

[Surprisingly Easy Hard-Attention for Sequence to Sequence Learning](https://aclanthology.org/D18-1065) (Shankar et al., EMNLP 2018)

• Surprisingly Easy Hard-Attention for Sequence to Sequence Learning (Shankar et al., EMNLP 2018)

ACL

• Shiv Shankar, Siddhant Garg, and Sunita Sarawagi. 2018. Surprisingly Easy Hard-Attention for Sequence to Sequence Learning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 640–645, Brussels, Belgium. Association for Computational Linguistics.