Short Text Clustering with Transformers
This addresses the problem of clustering short texts for NLP applications, but it is incremental as it builds on existing Transformer and clustering techniques.
The paper tackled short text clustering by using sentence vector representations from Transformers with various clustering methods, and showed that iterative classification enhancement improved initial clustering performance.
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.