  Sentence embedding models aim to provide general purpose embeddings for
sentences. Most of the models studied in this paper claim to perform well on
STS tasks - but they do not report on their suitability for clustering. This
paper looks at four recent sentence embedding models (Universal Sentence
Encoder (Cer et al., 2018), Sentence-BERT (Reimers and Gurevych, 2019), LASER
(Artetxe and Schwenk, 2019), and DeCLUTR (Giorgi et al., 2020)). It gives a
brief overview of the ideas behind their implementations. It then investigates
how well topic classes in two text classification datasets (Amazon Reviews (Ni
et al., 2019) and News Category Dataset (Misra, 2018)) map to clusters in their
corresponding sentence embedding space. While the performance of the resulting
classification model is far from perfect, it is better than random. This is
interesting because the classification model has been constructed in an
unsupervised way. The topic classes in these real life topic classification
datasets can be partly reconstructed by clustering the corresponding sentence
embeddings.
