Text classification is a fundamental problem in information retrieval with many real-world applications, such as predicting the topics of online articles and the categories of e-commerce product descriptions. However, low-resource text classification, with no or few labeled samples, presents a serious concern for supervised learning. Meanwhile, many text data are inherently grounded on a network structure, such as a hyperlink/citation network for online articles, and a user-item purchase network for e-commerce products. These graph structures capture rich semantic relationships, which can potentially augment low-resource text classification. In this paper, we propose a novel model called Graph-Grounded Pre-training and Prompting (G2P2) to address low-resource text classification in a two-pronged approach. During pre-training, we propose three graph interaction-based contrastive strategies to jointly pre-train a graph-text model; during downstream classification, we explore handcrafted discrete prompts and continuous prompt tuning for the jointly pre-trained model to achieve zero- and few-shot classification, respectively. Moreover, we explore the possibility of employing continuous prompt tuning for zero-shot inference. Specifically, we aim to generalize continuous prompts to unseen classes while leveraging a set of base classes. To this end, we extend G2P2 into G2P2$^*$, hinging on a new architecture of conditional prompt tuning. Extensive experiments on four real-world datasets demonstrate the strength of G2P2 in zero- and few-shot low-resource text classification tasks, and illustrate the advantage of G2P2$^*$ in dealing with unseen classes.

翻译：文本分类是信息检索中的基础问题，具有许多实际应用，例如预测在线文章的主题和电子商务产品描述的类别。然而，低资源文本分类（即很少或没有标注样本）对监督学习构成了严峻挑战。与此同时，许多文本数据本质上基于网络结构，例如在线文章的超链接/引用网络，以及电子商务产品的用户-商品购买网络。这些图结构捕获了丰富的语义关系，有望增强低资源文本分类。在本文中，我们提出了一种名为图基预训练与提示（G2P2）的新模型，采用双管齐下的方法解决低资源文本分类问题。在预训练阶段，我们提出了三种基于图交互的对比策略，以联合预训练图-文本模型；在下游分类阶段，我们探索了手工设计的离散提示和连续提示调优，分别用于联合预训练模型实现零样本和少样本分类。此外，我们探索了使用连续提示调优进行零样本推理的可能性。具体而言，我们旨在将连续提示泛化到未见类别，同时利用一组基类。为此，我们将G2P2扩展为G2P2$^*$，基于一种新的条件提示调优架构。在四个真实数据集上的大量实验证明了G2P2在零样本和少样本低资源文本分类任务中的优势，并展示了G2P2$^*$在处理未见类别方面的优越性。