@inproceedings{fan-etal-2021-mask,
title = "Mask Attention Networks: Rethinking and Strengthen Transformer",
author = "Fan, Zhihao and
Gong, Yeyun and
Liu, Dayiheng and
Wei, Zhongyu and
Wang, Siyuan and
Jiao, Jian and
Duan, Nan and
Zhang, Ruofei and
Huang, Xuanjing",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.135",
doi = "10.18653/v1/2021.naacl-main.135",
pages = "1692--1701",
abstract = "Transformer is an attention-based neural network, which consists of two sublayers, namely, Self-Attention Network (SAN) and Feed-Forward Network (FFN). Existing research explores to enhance the two sublayers separately to improve the capability of Transformer for text representation. In this paper, we present a novel understanding of SAN and FFN as Mask Attention Networks (MANs) and show that they are two special cases of MANs with static mask matrices. However, their static mask matrices limit the capability for localness modeling in text representation learning. We therefore introduce a new layer named dynamic mask attention network (DMAN) with a learnable mask matrix which is able to model localness adaptively. To incorporate advantages of DMAN, SAN, and FFN, we propose a sequential layered structure to combine the three types of layers. Extensive experiments on various tasks, including neural machine translation and text summarization demonstrate that our model outperforms the original Transformer.",
}
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<abstract>Transformer is an attention-based neural network, which consists of two sublayers, namely, Self-Attention Network (SAN) and Feed-Forward Network (FFN). Existing research explores to enhance the two sublayers separately to improve the capability of Transformer for text representation. In this paper, we present a novel understanding of SAN and FFN as Mask Attention Networks (MANs) and show that they are two special cases of MANs with static mask matrices. However, their static mask matrices limit the capability for localness modeling in text representation learning. We therefore introduce a new layer named dynamic mask attention network (DMAN) with a learnable mask matrix which is able to model localness adaptively. To incorporate advantages of DMAN, SAN, and FFN, we propose a sequential layered structure to combine the three types of layers. Extensive experiments on various tasks, including neural machine translation and text summarization demonstrate that our model outperforms the original Transformer.</abstract>
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%0 Conference Proceedings
%T Mask Attention Networks: Rethinking and Strengthen Transformer
%A Fan, Zhihao
%A Gong, Yeyun
%A Liu, Dayiheng
%A Wei, Zhongyu
%A Wang, Siyuan
%A Jiao, Jian
%A Duan, Nan
%A Zhang, Ruofei
%A Huang, Xuanjing
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 jun
%I Association for Computational Linguistics
%C Online
%F fan-etal-2021-mask
%X Transformer is an attention-based neural network, which consists of two sublayers, namely, Self-Attention Network (SAN) and Feed-Forward Network (FFN). Existing research explores to enhance the two sublayers separately to improve the capability of Transformer for text representation. In this paper, we present a novel understanding of SAN and FFN as Mask Attention Networks (MANs) and show that they are two special cases of MANs with static mask matrices. However, their static mask matrices limit the capability for localness modeling in text representation learning. We therefore introduce a new layer named dynamic mask attention network (DMAN) with a learnable mask matrix which is able to model localness adaptively. To incorporate advantages of DMAN, SAN, and FFN, we propose a sequential layered structure to combine the three types of layers. Extensive experiments on various tasks, including neural machine translation and text summarization demonstrate that our model outperforms the original Transformer.
%R 10.18653/v1/2021.naacl-main.135
%U https://aclanthology.org/2021.naacl-main.135
%U https://doi.org/10.18653/v1/2021.naacl-main.135
%P 1692-1701
Markdown (Informal)
[Mask Attention Networks: Rethinking and Strengthen Transformer](https://aclanthology.org/2021.naacl-main.135) (Fan et al., NAACL 2021)
ACL
- Zhihao Fan, Yeyun Gong, Dayiheng Liu, Zhongyu Wei, Siyuan Wang, Jian Jiao, Nan Duan, Ruofei Zhang, and Xuanjing Huang. 2021. Mask Attention Networks: Rethinking and Strengthen Transformer. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1692–1701, Online. Association for Computational Linguistics.