@inproceedings{yang-etal-2018-modeling,
title = "Modeling Localness for Self-Attention Networks",
author = "Yang, Baosong and
Tu, Zhaopeng and
Wong, Derek F. and
Meng, Fandong and
Chao, Lidia S. and
Zhang, Tong",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1475/",
doi = "10.18653/v1/D18-1475",
pages = "4449--4458",
abstract = "Self-attention networks have proven to be of profound value for its strength of capturing global dependencies. In this work, we propose to model localness for self-attention networks, which enhances the ability of capturing useful local context. We cast localness modeling as a learnable Gaussian bias, which indicates the central and scope of the local region to be paid more attention. The bias is then incorporated into the original attention distribution to form a revised distribution. To maintain the strength of capturing long distance dependencies while enhance the ability of capturing short-range dependencies, we only apply localness modeling to lower layers of self-attention networks. Quantitative and qualitative analyses on Chinese-English and English-German translation tasks demonstrate the effectiveness and universality of the proposed approach."
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<abstract>Self-attention networks have proven to be of profound value for its strength of capturing global dependencies. In this work, we propose to model localness for self-attention networks, which enhances the ability of capturing useful local context. We cast localness modeling as a learnable Gaussian bias, which indicates the central and scope of the local region to be paid more attention. The bias is then incorporated into the original attention distribution to form a revised distribution. To maintain the strength of capturing long distance dependencies while enhance the ability of capturing short-range dependencies, we only apply localness modeling to lower layers of self-attention networks. Quantitative and qualitative analyses on Chinese-English and English-German translation tasks demonstrate the effectiveness and universality of the proposed approach.</abstract>
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%0 Conference Proceedings
%T Modeling Localness for Self-Attention Networks
%A Yang, Baosong
%A Tu, Zhaopeng
%A Wong, Derek F.
%A Meng, Fandong
%A Chao, Lidia S.
%A Zhang, Tong
%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 yang-etal-2018-modeling
%X Self-attention networks have proven to be of profound value for its strength of capturing global dependencies. In this work, we propose to model localness for self-attention networks, which enhances the ability of capturing useful local context. We cast localness modeling as a learnable Gaussian bias, which indicates the central and scope of the local region to be paid more attention. The bias is then incorporated into the original attention distribution to form a revised distribution. To maintain the strength of capturing long distance dependencies while enhance the ability of capturing short-range dependencies, we only apply localness modeling to lower layers of self-attention networks. Quantitative and qualitative analyses on Chinese-English and English-German translation tasks demonstrate the effectiveness and universality of the proposed approach.
%R 10.18653/v1/D18-1475
%U https://aclanthology.org/D18-1475/
%U https://doi.org/10.18653/v1/D18-1475
%P 4449-4458
Markdown (Informal)
[Modeling Localness for Self-Attention Networks](https://aclanthology.org/D18-1475/) (Yang et al., EMNLP 2018)
ACL
- Baosong Yang, Zhaopeng Tu, Derek F. Wong, Fandong Meng, Lidia S. Chao, and Tong Zhang. 2018. Modeling Localness for Self-Attention Networks. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4449–4458, Brussels, Belgium. Association for Computational Linguistics.