@inproceedings{elbayad-etal-2018-pervasive,
title = "Pervasive Attention: 2{D} Convolutional Neural Networks for Sequence-to-Sequence Prediction",
author = "Elbayad, Maha and
Besacier, Laurent and
Verbeek, Jakob",
editor = "Korhonen, Anna and
Titov, Ivan",
booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K18-1010",
doi = "10.18653/v1/K18-1010",
pages = "97--107",
abstract = "Current state-of-the-art machine translation systems are based on encoder-decoder architectures, that first encode the input sequence, and then generate an output sequence based on the input encoding. Both are interfaced with an attention mechanism that recombines a fixed encoding of the source tokens based on the decoder state. We propose an alternative approach which instead relies on a single 2D convolutional neural network across both sequences. Each layer of our network re-codes source tokens on the basis of the output sequence produced so far. Attention-like properties are therefore pervasive throughout the network. Our model yields excellent results, outperforming state-of-the-art encoder-decoder systems, while being conceptually simpler and having fewer parameters.",
}
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%0 Conference Proceedings
%T Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction
%A Elbayad, Maha
%A Besacier, Laurent
%A Verbeek, Jakob
%Y Korhonen, Anna
%Y Titov, Ivan
%S Proceedings of the 22nd Conference on Computational Natural Language Learning
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F elbayad-etal-2018-pervasive
%X Current state-of-the-art machine translation systems are based on encoder-decoder architectures, that first encode the input sequence, and then generate an output sequence based on the input encoding. Both are interfaced with an attention mechanism that recombines a fixed encoding of the source tokens based on the decoder state. We propose an alternative approach which instead relies on a single 2D convolutional neural network across both sequences. Each layer of our network re-codes source tokens on the basis of the output sequence produced so far. Attention-like properties are therefore pervasive throughout the network. Our model yields excellent results, outperforming state-of-the-art encoder-decoder systems, while being conceptually simpler and having fewer parameters.
%R 10.18653/v1/K18-1010
%U https://aclanthology.org/K18-1010
%U https://doi.org/10.18653/v1/K18-1010
%P 97-107
Markdown (Informal)
[Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction](https://aclanthology.org/K18-1010) (Elbayad et al., CoNLL 2018)
ACL