@inproceedings{guan-etal-2020-far,
title = "How Far Does {BERT} Look At: Distance-based Clustering and Analysis of {BERT}{'}s Attention",
author = "Guan, Yue and
Leng, Jingwen and
Li, Chao and
Chen, Quan and
Guo, Minyi",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.342",
doi = "10.18653/v1/2020.coling-main.342",
pages = "3853--3860",
abstract = "Recent research on the multi-head attention mechanism, especially that in pre-trained models such as BERT, has shown us heuristics and clues in analyzing various aspects of the mechanism. As most of the research focus on probing tasks or hidden states, previous works have found some primitive patterns of attention head behavior by heuristic analytical methods, but a more systematic analysis specific on the attention patterns still remains primitive. In this work, we clearly cluster the attention heatmaps into significantly different patterns through unsupervised clustering on top of a set of proposed features, which corroborates with previous observations. We further study their corresponding functions through analytical study. In addition, our proposed features can be used to explain and calibrate different attention heads in Transformer models.",
}
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<abstract>Recent research on the multi-head attention mechanism, especially that in pre-trained models such as BERT, has shown us heuristics and clues in analyzing various aspects of the mechanism. As most of the research focus on probing tasks or hidden states, previous works have found some primitive patterns of attention head behavior by heuristic analytical methods, but a more systematic analysis specific on the attention patterns still remains primitive. In this work, we clearly cluster the attention heatmaps into significantly different patterns through unsupervised clustering on top of a set of proposed features, which corroborates with previous observations. We further study their corresponding functions through analytical study. In addition, our proposed features can be used to explain and calibrate different attention heads in Transformer models.</abstract>
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%0 Conference Proceedings
%T How Far Does BERT Look At: Distance-based Clustering and Analysis of BERT’s Attention
%A Guan, Yue
%A Leng, Jingwen
%A Li, Chao
%A Chen, Quan
%A Guo, Minyi
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F guan-etal-2020-far
%X Recent research on the multi-head attention mechanism, especially that in pre-trained models such as BERT, has shown us heuristics and clues in analyzing various aspects of the mechanism. As most of the research focus on probing tasks or hidden states, previous works have found some primitive patterns of attention head behavior by heuristic analytical methods, but a more systematic analysis specific on the attention patterns still remains primitive. In this work, we clearly cluster the attention heatmaps into significantly different patterns through unsupervised clustering on top of a set of proposed features, which corroborates with previous observations. We further study their corresponding functions through analytical study. In addition, our proposed features can be used to explain and calibrate different attention heads in Transformer models.
%R 10.18653/v1/2020.coling-main.342
%U https://aclanthology.org/2020.coling-main.342
%U https://doi.org/10.18653/v1/2020.coling-main.342
%P 3853-3860
Markdown (Informal)
[How Far Does BERT Look At: Distance-based Clustering and Analysis of BERT’s Attention](https://aclanthology.org/2020.coling-main.342) (Guan et al., COLING 2020)
ACL