Previous unsupervised sentence embedding studies have focused on data
augmentation methods such as dropout masking and rule-based sentence
transformation methods. However, these approaches have a limitation of
controlling the fine-grained semantics of augmented views of a sentence. This
results in inadequate supervision signals for capturing a semantic similarity
of similar sentences. In this work, we found that using neighbor sentences
enables capturing a more accurate semantic similarity between similar
sentences. Based on this finding, we propose RankEncoder, which uses relations
between an input sentence and sentences in a corpus for training unsupervised
sentence encoders. We evaluate RankEncoder from three perspectives: 1) the
semantic textual similarity performance, 2) the efficacy on similar sentence
pairs, and 3) the universality of RankEncoder. Experimental results show that
RankEncoder achieves 80.07% Spearman's correlation, a 1.1% absolute improvement
compared to the previous state-of-the-art performance. The improvement is even
more significant, a 1.73% improvement, on similar sentence pairs. Also, we
demonstrate that RankEncoder is universally applicable to existing unsupervised
sentence encoders.

本文提出了一种基于相邻句子的无监督句子编码器 RankEncoder，利用相邻句子约束进行训练，在语义文本相似度性能、相似句子对的有效性和普适性等方面都较好地表现，达到了 80.07% 的斯皮尔曼等级相关性，相较于最先进技术性能有 1.1% 的绝对提升，并且在相似句子对方面提升更为显着，为 1.73%，同时证明了 RankEncoder 可以普遍适用于现有的无监督句子编码器。