Text Classification is one of the fundamental tasks in natural language processing, which requires an agent to determine the most appropriate category for input sentences. Recently, deep neural networks have achieved impressive performance in this area, especially Pre-trained Language Models (PLMs). Usually, these methods concentrate on input sentences and corresponding semantic embedding generation. However, for another essential component: labels, most existing works either treat them as meaningless one-hot vectors or use vanilla embedding methods to learn label representations along with model training, underestimating the semantic information and guidance that these labels reveal. To alleviate this problem and better exploit label information, in this paper, we employ Self-Supervised Learning (SSL) in model learning process and design a novel self-supervised Relation of Relation (R2) classification task for label utilization from a one-hot manner perspective. Then, we propose a novel Relation of Relation Learning Network (R2-Net) for text classification, in which text classification and R2 classification are treated as optimization targets. Meanwhile, triplet loss is employed to enhance the analysis of differences and connections among labels. Moreover, considering that one-hot usage is still short of exploiting label information, we incorporate external knowledge from WordNet to obtain multi-aspect descriptions for label semantic learning and extend R2-Net to a novel Description-Enhanced Label Embedding network (DELE) from a label embedding perspective. ...

翻译：文本分类是自然语言处理中的基础任务之一，要求模型为输入句子确定最合适的类别。近年来，深度神经网络在该领域取得了令人瞩目的性能，尤其是预训练语言模型（PLMs）。通常，这些方法专注于输入句子及其对应的语义嵌入生成。然而，对于另一个关键组成部分——标签，大多数现有工作要么将其视为无意义的一热向量，要么使用简单嵌入方法随模型训练学习标签表示，低估了这些标签蕴含的语义信息和指导作用。为解决该问题并更好地利用标签信息，本文在模型学习过程中引入自监督学习（SSL），并从一热视角设计了一种新颖的自监督关系关系（R2）分类任务以利用标签。随后，我们提出一种新的关系关系学习网络（R2-Net）用于文本分类，将文本分类和R2分类作为优化目标。同时，采用三元组损失增强标签间差异与联系的分析。此外，考虑到一热使用仍不足以充分利用标签信息，我们整合了来自WordNet的外部知识，以获取多方面描述用于标签语义学习，并从标签嵌入视角将R2-Net扩展为一种新的描述增强标签嵌入网络（DELE）。……