On Attention Redundancy: A Comprehensive Study

Yuchen Bian, Jiaji Huang, Xingyu Cai, Jiahong Yuan, Kenneth Church


Abstract
Multi-layer multi-head self-attention mechanism is widely applied in modern neural language models. Attention redundancy has been observed among attention heads but has not been deeply studied in the literature. Using BERT-base model as an example, this paper provides a comprehensive study on attention redundancy which is helpful for model interpretation and model compression. We analyze the attention redundancy with Five-Ws and How. (What) We define and focus the study on redundancy matrices generated from pre-trained and fine-tuned BERT-base model for GLUE datasets. (How) We use both token-based and sentence-based distance functions to measure the redundancy. (Where) Clear and similar redundancy patterns (cluster structure) are observed among attention heads. (When) Redundancy patterns are similar in both pre-training and fine-tuning phases. (Who) We discover that redundancy patterns are task-agnostic. Similar redundancy patterns even exist for randomly generated token sequences. (“Why”) We also evaluate influences of the pre-training dropout ratios on attention redundancy. Based on the phase-independent and task-agnostic attention redundancy patterns, we propose a simple zero-shot pruning method as a case study. Experiments on fine-tuning GLUE tasks verify its effectiveness. The comprehensive analyses on attention redundancy make model understanding and zero-shot model pruning promising.
Anthology ID:
2021.naacl-main.72
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
930–945
Language:
URL:
https://aclanthology.org/2021.naacl-main.72
DOI:
10.18653/v1/2021.naacl-main.72
Bibkey:
Cite (ACL):
Yuchen Bian, Jiaji Huang, Xingyu Cai, Jiahong Yuan, and Kenneth Church. 2021. On Attention Redundancy: A Comprehensive Study. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 930–945, Online. Association for Computational Linguistics.
Cite (Informal):
On Attention Redundancy: A Comprehensive Study (Bian et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.72.pdf
Video:
 https://aclanthology.org/2021.naacl-main.72.mp4