Exploring the Impact of Negative Samples of Contrastive Learning: A Case Study of Sentence Embedding

Rui Cao, Yihao Wang, Yuxin Liang, Ling Gao, Jie Zheng, Jie Ren, Zheng Wang


Abstract
Contrastive learning is emerging as a powerful technique for extracting knowledge from unlabeled data. This technique requires a balanced mixture of two ingredients: positive (similar) and negative (dissimilar) samples. This is typically achieved by maintaining a queue of negative samples during training. Prior works in the area typically uses a fixed-length negative sample queue, but how the negative sample size affects the model performance remains unclear. The opaque impact of the number of negative samples on performance when employing contrastive learning aroused our in-depth exploration. This paper presents a momentum contrastive learning model with negative sample queue for sentence embedding, namely MoCoSE. We add the prediction layer to the online branch to make the model asymmetric and together with EMA update mechanism of the target branch to prevent the model from collapsing. We define a maximum traceable distance metric, through which we learn to what extent the text contrastive learning benefits from the historical information of negative samples. Our experiments find that the best results are obtained when the maximum traceable distance is at a certain range, demonstrating that there is an optimal range of historical information for a negative sample queue. We evaluate the proposed unsupervised MoCoSE on the semantic text similarity (STS) task and obtain an average Spearman’s correlation of 77.27%. Source code is available here.
Anthology ID:
2022.findings-acl.248
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venues:
ACL | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3138–3152
Language:
URL:
https://aclanthology.org/2022.findings-acl.248
DOI:
10.18653/v1/2022.findings-acl.248
Bibkey:
Cite (ACL):
Rui Cao, Yihao Wang, Yuxin Liang, Ling Gao, Jie Zheng, Jie Ren, and Zheng Wang. 2022. Exploring the Impact of Negative Samples of Contrastive Learning: A Case Study of Sentence Embedding. In Findings of the Association for Computational Linguistics: ACL 2022, pages 3138–3152, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Exploring the Impact of Negative Samples of Contrastive Learning: A Case Study of Sentence Embedding (Cao et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-acl.248.pdf
Code
 xbdxwyh/mocose