Mitigating the Diminishing Effect of Elastic Weight Consolidation

Canasai Kruengkrai; Junichi Yamagishi

Mitigating the Diminishing Effect of Elastic Weight Consolidation

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Abstract

Elastic weight consolidation (EWC, Kirkpatrick et al. 2017) is a promising approach to addressing catastrophic forgetting in sequential training. We find that the effect of EWC can diminish when fine-tuning large-scale pre-trained language models on different datasets. We present two simple objective functions to mitigate this problem by rescaling the components of EWC. Experiments on natural language inference and fact-checking tasks indicate that our methods require much smaller values for the trade-off parameters to achieve results comparable to EWC.

Anthology ID:: 2022.coling-1.403
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:: 4568–4574
Language:
URL:: https://aclanthology.org/2022.coling-1.403/
DOI:
Bibkey:
Cite (ACL):: Canasai Kruengkrai and Junichi Yamagishi. 2022. Mitigating the Diminishing Effect of Elastic Weight Consolidation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4568–4574, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):: Mitigating the Diminishing Effect of Elastic Weight Consolidation (Kruengkrai & Yamagishi, COLING 2022)
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PDF:: https://aclanthology.org/2022.coling-1.403.pdf

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@inproceedings{kruengkrai-yamagishi-2022-mitigating,
    title = "Mitigating the Diminishing Effect of Elastic Weight Consolidation",
    author = "Kruengkrai, Canasai  and
      Yamagishi, Junichi",
    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-1.403/",
    pages = "4568--4574",
    abstract = "Elastic weight consolidation (EWC, Kirkpatrick et al. 2017) is a promising approach to addressing catastrophic forgetting in sequential training. We find that the effect of EWC can diminish when fine-tuning large-scale pre-trained language models on different datasets. We present two simple objective functions to mitigate this problem by rescaling the components of EWC. Experiments on natural language inference and fact-checking tasks indicate that our methods require much smaller values for the trade-off parameters to achieve results comparable to EWC."
}

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%0 Conference Proceedings
%T Mitigating the Diminishing Effect of Elastic Weight Consolidation
%A Kruengkrai, Canasai
%A Yamagishi, Junichi
%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 kruengkrai-yamagishi-2022-mitigating
%X Elastic weight consolidation (EWC, Kirkpatrick et al. 2017) is a promising approach to addressing catastrophic forgetting in sequential training. We find that the effect of EWC can diminish when fine-tuning large-scale pre-trained language models on different datasets. We present two simple objective functions to mitigate this problem by rescaling the components of EWC. Experiments on natural language inference and fact-checking tasks indicate that our methods require much smaller values for the trade-off parameters to achieve results comparable to EWC.
%U https://aclanthology.org/2022.coling-1.403/
%P 4568-4574

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Markdown (Informal)

[Mitigating the Diminishing Effect of Elastic Weight Consolidation](https://aclanthology.org/2022.coling-1.403/) (Kruengkrai & Yamagishi, COLING 2022)

Mitigating the Diminishing Effect of Elastic Weight Consolidation (Kruengkrai & Yamagishi, COLING 2022)

ACL

Canasai Kruengkrai and Junichi Yamagishi. 2022. Mitigating the Diminishing Effect of Elastic Weight Consolidation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4568–4574, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.