  Since the introduction of BERT and RoBERTa, research on Semantic Textual
Similarity (STS) has made groundbreaking progress. Particularly, the adoption
of contrastive learning has substantially elevated state-of-the-art performance
across various STS benchmarks. However, contrastive learning categorizes text
pairs as either semantically similar or dissimilar, failing to leverage
fine-grained annotated information and necessitating large batch sizes to
prevent model collapse. These constraints pose challenges for researchers
engaged in STS tasks that require nuanced similarity levels or those with
limited computational resources, compelling them to explore alternatives like
Sentence-BERT. Nonetheless, Sentence-BERT tackles STS tasks from a
classification perspective, overlooking the progressive nature of semantic
relationships, which results in suboptimal performance. To bridge this gap,
this paper presents an innovative regression framework and proposes two simple
yet effective loss functions: Translated ReLU and Smooth K2 Loss. Experimental
analyses demonstrate that our method achieves convincing performance across
seven established STS benchmarks, especially when supplemented with
task-specific training data.
