@inproceedings{wang-etal-2022-learning,
title = "Learning Adaptive Axis Attentions in Fine-tuning: Beyond Fixed Sparse Attention Patterns",
author = "Wang, Zihan and
Gu, Jiuxiang and
Kuen, Jason and
Zhao, Handong and
Morariu, Vlad and
Zhang, Ruiyi and
Nenkova, Ani and
Sun, Tong and
Shang, Jingbo",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-acl.74",
doi = "10.18653/v1/2022.findings-acl.74",
pages = "916--925",
abstract = "We present a comprehensive study of sparse attention patterns in Transformer models. We first question the need for pre-training with sparse attention and present experiments showing that an efficient fine-tuning only approach yields a slightly worse but still competitive model. Then we compare the widely used local attention pattern and the less-well-studied global attention pattern, demonstrating that global patterns have several unique advantages. We also demonstrate that a flexible approach to attention, with different patterns across different layers of the model, is beneficial for some tasks. Drawing on this insight, we propose a novel Adaptive Axis Attention method, which learns{---}during fine-tuning{---}different attention patterns for each Transformer layer depending on the downstream task. Rather than choosing a fixed attention pattern, the adaptive axis attention method identifies important tokens{---}for each task and model layer{---}and focuses attention on those. It does not require pre-training to accommodate the sparse patterns and demonstrates competitive and sometimes better performance against fixed sparse attention patterns that require resource-intensive pre-training.",
}
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<abstract>We present a comprehensive study of sparse attention patterns in Transformer models. We first question the need for pre-training with sparse attention and present experiments showing that an efficient fine-tuning only approach yields a slightly worse but still competitive model. Then we compare the widely used local attention pattern and the less-well-studied global attention pattern, demonstrating that global patterns have several unique advantages. We also demonstrate that a flexible approach to attention, with different patterns across different layers of the model, is beneficial for some tasks. Drawing on this insight, we propose a novel Adaptive Axis Attention method, which learns—during fine-tuning—different attention patterns for each Transformer layer depending on the downstream task. Rather than choosing a fixed attention pattern, the adaptive axis attention method identifies important tokens—for each task and model layer—and focuses attention on those. It does not require pre-training to accommodate the sparse patterns and demonstrates competitive and sometimes better performance against fixed sparse attention patterns that require resource-intensive pre-training.</abstract>
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%0 Conference Proceedings
%T Learning Adaptive Axis Attentions in Fine-tuning: Beyond Fixed Sparse Attention Patterns
%A Wang, Zihan
%A Gu, Jiuxiang
%A Kuen, Jason
%A Zhao, Handong
%A Morariu, Vlad
%A Zhang, Ruiyi
%A Nenkova, Ani
%A Sun, Tong
%A Shang, Jingbo
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Findings of the Association for Computational Linguistics: ACL 2022
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F wang-etal-2022-learning
%X We present a comprehensive study of sparse attention patterns in Transformer models. We first question the need for pre-training with sparse attention and present experiments showing that an efficient fine-tuning only approach yields a slightly worse but still competitive model. Then we compare the widely used local attention pattern and the less-well-studied global attention pattern, demonstrating that global patterns have several unique advantages. We also demonstrate that a flexible approach to attention, with different patterns across different layers of the model, is beneficial for some tasks. Drawing on this insight, we propose a novel Adaptive Axis Attention method, which learns—during fine-tuning—different attention patterns for each Transformer layer depending on the downstream task. Rather than choosing a fixed attention pattern, the adaptive axis attention method identifies important tokens—for each task and model layer—and focuses attention on those. It does not require pre-training to accommodate the sparse patterns and demonstrates competitive and sometimes better performance against fixed sparse attention patterns that require resource-intensive pre-training.
%R 10.18653/v1/2022.findings-acl.74
%U https://aclanthology.org/2022.findings-acl.74
%U https://doi.org/10.18653/v1/2022.findings-acl.74
%P 916-925
Markdown (Informal)
[Learning Adaptive Axis Attentions in Fine-tuning: Beyond Fixed Sparse Attention Patterns](https://aclanthology.org/2022.findings-acl.74) (Wang et al., Findings 2022)
ACL
- Zihan Wang, Jiuxiang Gu, Jason Kuen, Handong Zhao, Vlad Morariu, Ruiyi Zhang, Ani Nenkova, Tong Sun, and Jingbo Shang. 2022. Learning Adaptive Axis Attentions in Fine-tuning: Beyond Fixed Sparse Attention Patterns. In Findings of the Association for Computational Linguistics: ACL 2022, pages 916–925, Dublin, Ireland. Association for Computational Linguistics.