Smoothed Contrastive Learning for Unsupervised Sentence Embedding
Xing Wu, Chaochen Gao, Yipeng Su, Jizhong Han, Zhongyuan Wang, Songlin Hu
Correct Metadata for
Abstract
Unsupervised contrastive sentence embedding models, e.g., unsupervised SimCSE, use the InfoNCE loss function in training. Theoretically, we expect to use larger batches to get more adequate comparisons among samples and avoid overfitting. However, increasing batch size leads to performance degradation when it exceeds a threshold, which is probably due to the introduction of false-negative pairs through statistical observation. To alleviate this problem, we introduce a simple smoothing strategy upon the InfoNCE loss function, termed Gaussian Smoothed InfoNCE (GS-InfoNCE). In other words, we add random Gaussian noise as an extension to the negative pairs without increasing the batch size. Through experiments on the semantic text similarity tasks, though simple, the proposed smoothing strategy brings improvements to unsupervised SimCSE.- Anthology ID:
- 2022.coling-1.434
- 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:
- 4902–4906
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.434/
- DOI:
- Bibkey:
- Cite (ACL):
- Xing Wu, Chaochen Gao, Yipeng Su, Jizhong Han, Zhongyuan Wang, and Songlin Hu. 2022. Smoothed Contrastive Learning for Unsupervised Sentence Embedding. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4902–4906, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Smoothed Contrastive Learning for Unsupervised Sentence Embedding (Wu et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.434.pdf
- Code
- caskcsg/sentemb + additional community code
Export citation
@inproceedings{wu-etal-2022-smoothed,
title = "Smoothed Contrastive Learning for Unsupervised Sentence Embedding",
author = "Wu, Xing and
Gao, Chaochen and
Su, Yipeng and
Han, Jizhong and
Wang, Zhongyuan and
Hu, Songlin",
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-1.434/",
pages = "4902--4906",
abstract = "Unsupervised contrastive sentence embedding models, e.g., unsupervised SimCSE, use the InfoNCE loss function in training. Theoretically, we expect to use larger batches to get more adequate comparisons among samples and avoid overfitting. However, increasing batch size leads to performance degradation when it exceeds a threshold, which is probably due to the introduction of false-negative pairs through statistical observation. To alleviate this problem, we introduce a simple smoothing strategy upon the InfoNCE loss function, termed Gaussian Smoothed InfoNCE (GS-InfoNCE). In other words, we add random Gaussian noise as an extension to the negative pairs without increasing the batch size. Through experiments on the semantic text similarity tasks, though simple, the proposed smoothing strategy brings improvements to unsupervised SimCSE."
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%0 Conference Proceedings %T Smoothed Contrastive Learning for Unsupervised Sentence Embedding %A Wu, Xing %A Gao, Chaochen %A Su, Yipeng %A Han, Jizhong %A Wang, Zhongyuan %A Hu, Songlin %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 wu-etal-2022-smoothed %X Unsupervised contrastive sentence embedding models, e.g., unsupervised SimCSE, use the InfoNCE loss function in training. Theoretically, we expect to use larger batches to get more adequate comparisons among samples and avoid overfitting. However, increasing batch size leads to performance degradation when it exceeds a threshold, which is probably due to the introduction of false-negative pairs through statistical observation. To alleviate this problem, we introduce a simple smoothing strategy upon the InfoNCE loss function, termed Gaussian Smoothed InfoNCE (GS-InfoNCE). In other words, we add random Gaussian noise as an extension to the negative pairs without increasing the batch size. Through experiments on the semantic text similarity tasks, though simple, the proposed smoothing strategy brings improvements to unsupervised SimCSE. %U https://aclanthology.org/2022.coling-1.434/ %P 4902-4906
Markdown (Informal)
[Smoothed Contrastive Learning for Unsupervised Sentence Embedding](https://aclanthology.org/2022.coling-1.434/) (Wu et al., COLING 2022)
- Smoothed Contrastive Learning for Unsupervised Sentence Embedding (Wu et al., COLING 2022)
ACL
- Xing Wu, Chaochen Gao, Yipeng Su, Jizhong Han, Zhongyuan Wang, and Songlin Hu. 2022. Smoothed Contrastive Learning for Unsupervised Sentence Embedding. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4902–4906, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.