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Generic sentence embeddings provide a coarse-grained approximation ofsemantic textual similarity but ignore specific aspects that make textssimilar. Conversely, aspect-based sentence embeddings provide similaritiesbetween texts based on certain predefined aspects. Thus, similarity predictionsof texts are more targeted to specific requirements and more easilyexplainable. In this paper, we present AspectCSE, an approach for aspect-basedcontrastive learning of sentence embeddings. Results indicate that AspectCSEachieves an average improvement of 3.97% on information retrieval tasks acrossmultiple aspects compared to the previous best results. We also propose usingWikidata knowledge graph properties to train models of multi-aspect sentenceembeddings in which multiple specific aspects are simultaneously consideredduring similarity predictions. We demonstrate that multi-aspect embeddingsoutperform single-aspect embeddings on aspect-specific information retrievaltasks. Finally, we examine the aspect-based sentence embedding space anddemonstrate that embeddings of semantically similar aspect labels are oftenclose, even without explicit similarity training between different aspectlabels.

AspectCSE: Sentence Embeddings for Aspect-based Semantic Textual Similarity Using Contrastive Learning and Structured Knowledge