Investigation of Sentence-BERT Sentence Vectors Using Image Generation Models

Abstract

We have verified that the sentence vectors output by Sentence-BERT capture the meaning of sentences using k-means and UMAP. As a result, we confirmed that the sentence vectors generated by Sentence-BERT capture the meaning of sentences very well. In this study, we examine the properties and characteristics of the sentence vectors that are considered to capture the meaning of sentences. We visualize the sentence vectors by imaging the sentences, and examine the output results when changes are made to the sentence vectors. As a result, we confirmed that there is a difference in the information expressed in each dimension as a feature of the sentence vector, although the roles are not completely divided.