Multi-label few-shot aspect category detection aims at identifying multiple aspect categories from sentences with a limited number of training instances. The representation of sentences and categories is a key issue in this task. Most of current methods extract keywords for the sentence representations and the category representations. Sentences often contain many category-independent words, which leads to suboptimal performance of keyword-based methods. Instead of directly extracting keywords, we propose a label-guided prompt method to represent sentences and categories. To be specific, we design label-specific prompts to represent sentences by combining crucial contextual and semantic information. Further, the label is introduced into a prompt to obtain category descriptions by utilizing a large language model. This kind of category descriptions contain the characteristics of the aspect categories, guiding the construction of discriminative category prototypes. Experimental results on two public datasets show that our method outperforms current state-of-the-art methods with a 3.86% - 4.75% improvement in the Macro-F1 score.

翻译：多标签少样本方面类别检测旨在从有限训练实例的句子中识别多个方面类别。句子和类别的表示是该任务的关键问题。当前大多数方法提取关键词用于句子表示和类别表示。句子通常包含许多与类别无关的词语，这导致基于关键词的方法性能欠佳。我们提出一种标签引导提示方法来表示句子和类别，而非直接提取关键词。具体而言，我们设计标签特定的提示，通过结合关键上下文和语义信息来表示句子。此外，通过利用大语言模型将标签引入提示中以获得类别描述。此类类别描述包含方面类别的特征，指导判别性类别原型的构建。在两个公开数据集上的实验结果表明，我们的方法优于当前最先进的方法，Macro-F1分数提高了3.86%至4.75%。