Common approaches rely on fixed-length embedding vectors from language models as
sentence embeddings for downstream tasks such as semantic textual similarity (STS). Such …

This paper improves contrastive learning for sentence embeddings from two perspectives:
handling dropout noise and addressing feature corruption. Specifically, for the first …