Alleviating the Inequality of Attention Heads for Neural Machine Translation

Zewei Sun; Shujian Huang (书剑 黄); Xinyu Dai; Jiajun Chen

Alleviating the Inequality of Attention Heads for Neural Machine Translation

Zewei Sun, Shujian Huang, Xinyu Dai, Jiajun Chen

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Abstract

Recent studies show that the attention heads in Transformer are not equal. We relate this phenomenon to the imbalance training of multi-head attention and the model dependence on specific heads. To tackle this problem, we propose a simple masking method: HeadMask, in two specific ways. Experiments show that translation improvements are achieved on multiple language pairs. Subsequent empirical analyses also support our assumption and confirm the effectiveness of the method.

Anthology ID:: 2022.coling-1.466
Volume:: Proceedings of the 29th International Conference on Computational Linguistics
Month:: October
Year:: 2022
Address:: Gyeongju, Republic of Korea
Editors:: Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
Venue:: COLING
SIG:
Publisher:: International Committee on Computational Linguistics
Note:
Pages:: 5246–5250
Language:
URL:: https://aclanthology.org/2022.coling-1.466/
DOI:
Bibkey:
Cite (ACL):: Zewei Sun, Shujian Huang, Xinyu Dai, and Jiajun Chen. 2022. Alleviating the Inequality of Attention Heads for Neural Machine Translation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5246–5250, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):: Alleviating the Inequality of Attention Heads for Neural Machine Translation (Sun et al., COLING 2022)
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PDF:: https://aclanthology.org/2022.coling-1.466.pdf

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@inproceedings{sun-etal-2022-alleviating,
    title = "Alleviating the Inequality of Attention Heads for Neural Machine Translation",
    author = "Sun, Zewei  and
      Huang, Shujian  and
      Dai, Xinyu  and
      Chen, Jiajun",
    editor = "Calzolari, Nicoletta  and
      Huang, Chu-Ren  and
      Kim, Hansaem  and
      Pustejovsky, James  and
      Wanner, Leo  and
      Choi, Key-Sun  and
      Ryu, Pum-Mo  and
      Chen, Hsin-Hsi  and
      Donatelli, Lucia  and
      Ji, Heng  and
      Kurohashi, Sadao  and
      Paggio, Patrizia  and
      Xue, Nianwen  and
      Kim, Seokhwan  and
      Hahm, Younggyun  and
      He, Zhong  and
      Lee, Tony Kyungil  and
      Santus, Enrico  and
      Bond, Francis  and
      Na, Seung-Hoon",
    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2022.coling-1.466/",
    pages = "5246--5250",
    abstract = "Recent studies show that the attention heads in Transformer are not equal. We relate this phenomenon to the imbalance training of multi-head attention and the model dependence on specific heads. To tackle this problem, we propose a simple masking method: HeadMask, in two specific ways. Experiments show that translation improvements are achieved on multiple language pairs. Subsequent empirical analyses also support our assumption and confirm the effectiveness of the method."
}

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%0 Conference Proceedings
%T Alleviating the Inequality of Attention Heads for Neural Machine Translation
%A Sun, Zewei
%A Huang, Shujian
%A Dai, Xinyu
%A Chen, Jiajun
%Y Calzolari, Nicoletta
%Y Huang, Chu-Ren
%Y Kim, Hansaem
%Y Pustejovsky, James
%Y Wanner, Leo
%Y Choi, Key-Sun
%Y Ryu, Pum-Mo
%Y Chen, Hsin-Hsi
%Y Donatelli, Lucia
%Y Ji, Heng
%Y Kurohashi, Sadao
%Y Paggio, Patrizia
%Y Xue, Nianwen
%Y Kim, Seokhwan
%Y Hahm, Younggyun
%Y He, Zhong
%Y Lee, Tony Kyungil
%Y Santus, Enrico
%Y Bond, Francis
%Y Na, Seung-Hoon
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F sun-etal-2022-alleviating
%X Recent studies show that the attention heads in Transformer are not equal. We relate this phenomenon to the imbalance training of multi-head attention and the model dependence on specific heads. To tackle this problem, we propose a simple masking method: HeadMask, in two specific ways. Experiments show that translation improvements are achieved on multiple language pairs. Subsequent empirical analyses also support our assumption and confirm the effectiveness of the method.
%U https://aclanthology.org/2022.coling-1.466/
%P 5246-5250

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Markdown (Informal)

[Alleviating the Inequality of Attention Heads for Neural Machine Translation](https://aclanthology.org/2022.coling-1.466/) (Sun et al., COLING 2022)

Alleviating the Inequality of Attention Heads for Neural Machine Translation (Sun et al., COLING 2022)

ACL

Zewei Sun, Shujian Huang, Xinyu Dai, and Jiajun Chen. 2022. Alleviating the Inequality of Attention Heads for Neural Machine Translation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5246–5250, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.