论文标题
循环中的人：如何通过手动标记每个班级的几个文档来有效地创建连贯的主题
Human in the loop: How to effectively create coherent topics by manually labeling only a few documents per class
论文作者
论文摘要
在稀疏标签分配下进行精确建模的几种方法显着改善。但是，在自然语言处理中几乎没有射击建模的应用仅在文档分类领域。随着最近的性能改进，有监督的几种方法与简单的主题提取方法相结合，对无监督的主题建模方法构成了重大挑战。我们的研究表明，在生成连贯的主题方面，几乎没有射击的学习与简单的主题提取方法相结合，可以超越无监督的主题建模技术，即使使用每个班级只有几个标记的文档。
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.
