In the short text, the extremely short length, feature sparsity, and high
ambiguity pose huge challenges to classification tasks. Recently, as an
effective method for tuning Pre-trained Language Models for specific downstream
tasks, prompt-learning has attracted a vast amount of attention and research.
The main intuition behind the prompt-learning is to insert the template into
the input and convert the text classification tasks into equivalent cloze-style
tasks. However, most prompt-learning methods expand label words manually or
only consider the class name for knowledge incorporating in cloze-style
prediction, which will inevitably incur omissions and bias in short text
classification tasks. In this paper, we propose a simple short text
classification approach that makes use of prompt-learning based on
knowledgeable expansion. Taking the special characteristics of short text into
consideration, the method can consider both the short text itself and class
name during expanding label words space. Specifically, the top $N$ concepts
related to the entity in the short text are retrieved from the open Knowledge
Graph like Probase, and we further refine the expanded label words by the
distance calculation between selected concepts and class labels. Experimental
results show that our approach obtains obvious improvement compared with other
fine-tuning, prompt-learning, and knowledgeable prompt-tuning methods,
outperforming the state-of-the-art by up to 6 Accuracy points on three
well-known datasets.

本文介绍了基于知识拓展的 Prompt-learning 方法来解决短文本分类中的标签扩展问题，并在三个著名数据集上实验，结果比其他方法均取得了显著提高。