Unsupervised sentence representation learning aims to transform input
sentences into fixed-length vectors enriched with intricate semantic
information while obviating the reliance on labeled data. Recent progress
within this field, propelled by contrastive learning and prompt engineering,
has significantly bridged the gap between unsupervised and supervised
strategies. Nonetheless, the potential utilization of Chain-of-Thought, remains
largely untapped within this trajectory. To unlock latent capabilities within
pre-trained models, such as BERT, we propose a two-stage approach for sentence
representation: comprehension and summarization. Subsequently, the output of
the latter phase is harnessed as the vectorized representation of the input
sentence. For further performance enhancement, we meticulously refine both the
contrastive learning loss function and the template denoising technique for
prompt engineering. Rigorous experimentation substantiates our method,
CoT-BERT, transcending a suite of robust baselines without necessitating other
text representation models or external databases.

利用无监督的句子表示学习方法，通过对输入句子进行转换，得到富含复杂语义信息的定长向量表示，消除了对标注数据的依赖。本文提出了一种两阶段的方法，通过理解和总结对预训练模型（如 BERT）的潜在能力进行开发，并通过精细调整对比学习损失函数和模板去噪技术来进一步提高性能。实验证明，所提出的 CoT-BERT 方法在无需其他文本表示模型或外部数据库的情况下超越了一系列强大的基准模型。