  Few-shot methods for accurate modeling under sparse label-settings have
improved significantly. However, the applications of few-shot modeling in
natural language processing remain solely in the field of document
classification. With recent performance improvements, supervised few-shot
methods, combined with a simple topic extraction method pose a significant
challenge to unsupervised topic modeling methods. Our research shows that
supervised few-shot learning, combined with a simple topic extraction method,
can outperform unsupervised topic modeling techniques in terms of generating
coherent topics, even when only a few labeled documents per class are used.
