  This paper improves contrastive learning for sentence embeddings from two
perspectives: handling dropout noise and addressing feature corruption.
Specifically, for the first perspective, we identify that the dropout noise
from negative pairs affects the model's performance. Therefore, we propose a
simple yet effective method to deal with such type of noise. Secondly, we
pinpoint the rank bottleneck of current solutions to feature corruption and
propose a dimension-wise contrastive learning objective to address this issue.
Both proposed methods are generic and can be applied to any contrastive
learning based models for sentence embeddings. Experimental results on standard
benchmarks demonstrate that combining both proposed methods leads to a gain of
1.8 points compared to the strong baseline SimCSE configured with BERT base.
Furthermore, applying the proposed method to DiffCSE, another strong
contrastive learning based baseline, results in a gain of 1.4 points.
