  Unsupervised sentence embedding aims to obtain the most appropriate embedding
for a sentence to reflect its semantic. Contrastive learning has been
attracting developing attention. For a sentence, current models utilize diverse
data augmentation methods to generate positive samples, while consider other
independent sentences as negative samples. Then they adopt InfoNCE loss to pull
the embeddings of positive pairs gathered, and push those of negative pairs
scattered. Although these models have made great progress on sentence
embedding, we argue that they may suffer from feature suppression. The models
fail to distinguish and decouple textual similarity and semantic similarity.
And they may overestimate the semantic similarity of any pairs with similar
textual regardless of the actual semantic difference between them. This is
because positive pairs in unsupervised contrastive learning come with similar
and even the same textual through data augmentation. To alleviate feature
suppression, we propose contrastive learning for unsupervised sentence
embedding with soft negative samples (SNCSE). Soft negative samples share
highly similar textual but have surely and apparently different semantic with
the original samples. Specifically, we take the negation of original sentences
as soft negative samples, and propose Bidirectional Margin Loss (BML) to
introduce them into traditional contrastive learning framework, which merely
involves positive and negative samples. Our experimental results show that
SNCSE can obtain state-of-the-art performance on semantic textual similarity
(STS) task with average Spearman's correlation coefficient of 78.97% on
BERTbase and 79.23% on RoBERTabase. Besides, we adopt rank-based error analysis
method to detect the weakness of SNCSE for future study.
