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Few-shot methods for accurate modeling under sparse label-settings haveimproved significantly. However, the applications of few-shot modeling innatural language processing remain solely in the field of documentclassification. With recent performance improvements, supervised few-shotmethods, combined with a simple topic extraction method pose a significantchallenge to unsupervised topic modeling methods. Our research shows thatsupervised few-shot learning, combined with a simple topic extraction method,can outperform unsupervised topic modeling techniques in terms of generatingcoherent topics, even when only a few labeled documents per class are used.

Human in the loop: How to effectively create coherent topics by manually labeling only a few documents per class