Previous contrastive learning methods for sentence representations often
focus on insensitive transformations to produce positive pairs, but neglect the
role of sensitive transformations that are harmful to semantic representations.
Therefore, we propose an Equivariant Self-Contrastive Learning (ESCL) method to
make full use of sensitive transformations, which encourages the learned
representations to be sensitive to certain types of transformations with an
additional equivariant learning task. Meanwhile, in order to improve
practicability and generality, ESCL simplifies the implementations of
traditional equivariant contrastive methods to share model parameters from the
perspective of multi-task learning. We evaluate our ESCL on semantic textual
similarity tasks. The proposed method achieves better results while using fewer
learning parameters compared to previous methods.

本文提出了一种称为 ESCL 的方法，该方法通过等变学习任务鼓励学习到的表示对某些类型的转换敏感，从而利用敏感的变换，以改善语义文本相似度任务的对比学习，并且通过在多任务学习的角度上共享模型参数来简化算法实现。最终的结果表明，该方法与以前的方法相比在使用更少的学习参数的情况下可以获得更好的结果。