Generic sentence embeddings provide a coarse-grained approximation of semantic textual similarity but ignore specific aspects that make texts similar. Conversely, aspect-based sentence embeddings provide similarities between texts based on certain predefined aspects. Thus, similarity predictions of texts are more targeted to specific requirements and more easily explainable. In this paper, we present AspectCSE, an approach for aspect-based contrastive learning of sentence embeddings. Results indicate that AspectCSE achieves an average improvement of 3.97% on information retrieval tasks across multiple aspects compared to the previous best results. We also propose using Wikidata knowledge graph properties to train models of multi-aspect sentence embeddings in which multiple specific aspects are simultaneously considered during similarity predictions. We demonstrate that multi-aspect embeddings outperform single-aspect embeddings on aspect-specific information retrieval tasks. Finally, we examine the aspect-based sentence embedding space and demonstrate that embeddings of semantically similar aspect labels are often close, even without explicit similarity training between different aspect labels.

翻译：通用句子嵌入提供了语义文本相似度的粗粒度近似，但忽略了使文本相似的具体方面。相反，方面感知句子嵌入基于特定预定义方面提供文本间的相似度。因此，文本的相似度预测更具针对性，且更易于解释。本文提出AspectCSE，一种基于方面的句子嵌入对比学习方法。结果表明，与先前最佳结果相比，AspectCSE在多方面信息检索任务上平均提升3.97%。我们还提出利用Wikidata知识图谱属性训练多方面的句子嵌入模型，在相似度预测中同时考虑多个特定方面。我们证明，在特定方面的信息检索任务中，多方面嵌入优于单方面嵌入。最后，我们分析了方面感知句子嵌入空间，并表明即使未对不同方面标签进行显式相似度训练，语义相似的方面标签的嵌入也往往彼此接近。