Self-supervised sentence representation learning is the task of constructing
an embedding space for sentences without relying on human annotation efforts.
One straightforward approach is to finetune a pretrained language model (PLM)
with a representation learning method such as contrastive learning. While this
approach achieves impressive performance on larger PLMs, the performance
rapidly degrades as the number of parameters decreases. In this paper, we
propose a framework called Self-supervised Cross-View Training (SCT) to narrow
the performance gap between large and small PLMs. To evaluate the effectiveness
of SCT, we compare it to 5 baseline and state-of-the-art competitors on seven
Semantic Textual Similarity (STS) benchmarks using 5 PLMs with the number of
parameters ranging from 4M to 340M. The experimental results show that STC
outperforms the competitors for PLMs with less than 100M parameters in 18 of 21
cases.

我们提出了一种称为自监督跨视图训练（SCT）的框架，以缩小大型和小型预训练语言模型（PLM）之间性能差距，并在 7 个语义文本相似性（STS）基准测试中通过与 5 个基准和最先进的竞争对手的比较，在参数数量从 4M 到 340M 范围内的 5 个 PLMs 上证明 STC 在 21 个案例中的 18 个中胜过竞争对手，对于参数少于 100M 的 PLMs 表现出色。