Contrastive learning has recently achieved compelling performance in unsupervised sentence representation. As an essential element, data augmentation protocols, however, have not been well explored. The pioneering work SimCSE resorting to a simple dropout mechanism (viewed as continuous augmentation) surprisingly dominates discrete augmentations such as cropping, word deletion, and synonym replacement as reported. To understand the underlying rationales, we revisit existing approaches and attempt to hypothesize the desiderata of reasonable data augmentation methods: balance of semantic consistency and expression diversity. We then develop three simple yet effective discrete sentence augmentation schemes: punctuation insertion, modal verbs, and double negation. They act as minimal noises at lexical level to produce diverse forms of sentences. Furthermore, standard negation is capitalized on to generate negative samples for alleviating feature suppression involved in contrastive learning. We experimented extensively with semantic textual similarity on diverse datasets. The results support the superiority of the proposed methods consistently.

翻译：对比学习近期在无监督句子表示领域取得了显著性能。然而，作为关键要素的数据增强策略尚未得到充分探索。开创性工作SimCSE采用简单的dropout机制（视为连续增强），意外地超越了报道中的裁剪、单词删除和同义词替换等离散增强方法。为理解其内在机理，我们重新审视现有方法，并尝试提出合理数据增强方法应满足的假想条件：语义一致性与表达多样性的平衡。基于此，我们开发了三种简单有效的离散句子增强方案：标点插入、情态动词与双重否定。这些方案在词汇层面引入最小噪声，生成多样化的句子形式。此外，利用标准否定生成负样本，以缓解对比学习中涉及的特征抑制问题。我们在多个数据集上对语义文本相似度进行了广泛实验，结果一致验证了所提方法的优越性。