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性的粗粒度近似，但忽略了使文本相似的具体方面。相反，基于方面的句子嵌入能根据预定义的特定方面提供文本间的相似性。因此，文本的相似性预测更具针对性，且更易于解释。本文提出了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在多个方面的信息检索任务上实现了平均3.97%的提升。我们还提出利用维基数据知识图谱属性来训练多方面句子嵌入模型，使得在相似性预测中能同时考虑多个特定方面。我们证明了多方面嵌入在特定方面的信息检索任务上优于单方面嵌入。最后，我们考察了基于方面的句子嵌入空间，并证明了语义相似的方面标签的嵌入往往彼此接近，即使不同方面标签之间没有进行显式的相似性训练。