Papers › Mask Attention Networks: Rethinking and Strengthen Transformer
Mask Attention Networks: Rethinking and Strengthen Transformer
Zhihao Fan, Yeyun Gong, Dayiheng Liu, Zhongyu Wei, Siyuan Wang, Jian Jiao, Nan Duan, Ruofei Zhang, Xuanjing Huang
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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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Abstractive Text Summarization | CNN / Daily Mail | Mask Attention Network | ROUGE-1 | 40.98 | #36 of 53 | Archive leaderboard | report |
| Abstractive Text Summarization | CNN / Daily Mail | Mask Attention Network | ROUGE-2 | 18.29 | #36 of 53 | Archive leaderboard | report |
| Abstractive Text Summarization | CNN / Daily Mail | Mask Attention Network | ROUGE-L | 37.88 | #36 of 53 | Archive leaderboard | report |
| Machine Translation | IWSLT2014 German-English | Mask Attention Network (small) | BLEU score | 36.3 | #15 of 34 | Archive leaderboard | report |
| Machine Translation | IWSLT2014 German-English | Mask Attention Network (small) | Number of Params | 37M | #15 of 34 | Archive leaderboard | report |
| Machine Translation | WMT2014 English-German | Mask Attention Network (big) | BLEU score | 30.4 | #11 of 91 | Archive leaderboard | report |
| Machine Translation | WMT2014 English-German | Mask Attention Network (big) | Number of Params | 215M | #11 of 91 | Archive leaderboard | report |
| Machine Translation | WMT2014 English-German | Mask Attention Network (base) | BLEU score | 29.1 | #32 of 91 | Archive leaderboard | report |
| Machine Translation | WMT2014 English-German | Mask Attention Network (base) | Number of Params | 63M | #32 of 91 | Archive leaderboard | report |
| Text Summarization | GigaWord | Mask Attention Network | ROUGE-1 | 38.28 | #21 of 41 | Archive leaderboard | report |
| Text Summarization | GigaWord | Mask Attention Network | ROUGE-2 | 19.46 | #21 of 41 | Archive leaderboard | report |
| Text Summarization | GigaWord | Mask Attention Network | ROUGE-L | 35.46 | #21 of 41 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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