  Recent techniques for the task of short text clustering often rely on word
embeddings as a transfer learning component. This paper shows that sentence
vector representations from Transformers in conjunction with different
clustering methods can be successfully applied to address the task.
Furthermore, we demonstrate that the algorithm of enhancement of clustering via
iterative classification can further improve initial clustering performance
with different classifiers, including those based on pre-trained Transformer
language models.
