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)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.403.pdf
- Data
- FEVER, MultiNLI
Export citation
@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
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.