@inproceedings{nguyen-etal-2020-differentiable,
title = "Differentiable Window for Dynamic Local Attention",
author = "Nguyen, Thanh-Tung and
Nguyen, Xuan-Phi and
Joty, Shafiq and
Li, Xiaoli",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.589",
doi = "10.18653/v1/2020.acl-main.589",
pages = "6589--6599",
abstract = "We propose Differentiable Window, a new neural module and general purpose component for dynamic window selection. While universally applicable, we demonstrate a compelling use case of utilizing Differentiable Window to improve standard attention modules by enabling more focused attentions over the input regions. We propose two variants of Differentiable Window, and integrate them within the Transformer architecture in two novel ways. We evaluate our proposed approach on a myriad of NLP tasks, including machine translation, sentiment analysis, subject-verb agreement and language modeling. Our experimental results demonstrate consistent and sizable improvements across all tasks.",
}
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%0 Conference Proceedings
%T Differentiable Window for Dynamic Local Attention
%A Nguyen, Thanh-Tung
%A Nguyen, Xuan-Phi
%A Joty, Shafiq
%A Li, Xiaoli
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F nguyen-etal-2020-differentiable
%X We propose Differentiable Window, a new neural module and general purpose component for dynamic window selection. While universally applicable, we demonstrate a compelling use case of utilizing Differentiable Window to improve standard attention modules by enabling more focused attentions over the input regions. We propose two variants of Differentiable Window, and integrate them within the Transformer architecture in two novel ways. We evaluate our proposed approach on a myriad of NLP tasks, including machine translation, sentiment analysis, subject-verb agreement and language modeling. Our experimental results demonstrate consistent and sizable improvements across all tasks.
%R 10.18653/v1/2020.acl-main.589
%U https://aclanthology.org/2020.acl-main.589
%U https://doi.org/10.18653/v1/2020.acl-main.589
%P 6589-6599
Markdown (Informal)
[Differentiable Window for Dynamic Local Attention](https://aclanthology.org/2020.acl-main.589) (Nguyen et al., ACL 2020)
ACL
- Thanh-Tung Nguyen, Xuan-Phi Nguyen, Shafiq Joty, and Xiaoli Li. 2020. Differentiable Window for Dynamic Local Attention. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6589–6599, Online. Association for Computational Linguistics.