标题： 带有监督对比学习的学习标签层次结构 显示英文标题

标题： Learning Label Hierarchy with Supervised Contrastive Learning

摘要： 监督对比学习（SCL）框架将每个类别视为独立的，因此认为所有类别同样重要。 这忽略了标签层次结构存在的常见情况，在这种情况下，同一类别下的细粒度类别比非常不同的类别更具相似性。 本文引入了一类标签感知的SCL方法（LASCL），通过利用类之间的相似性将层次信息融入SCL，从而创建一个更结构化和具有区分性的特征空间。 这是通过首先根据其类别的接近程度调整实例之间的距离来实现的，使用缩放的实例-实例对比。 引入了额外的实例-中心对比，以将类内示例移近由一组可学习标签参数表示的中心。 学习到的标签参数可以直接用作最近邻分类器，无需进一步微调。 这样，生成的特征表示在簇内紧凑性和簇间分离性方面都有所改进。 在三个数据集上的实验表明，所提出的LASCL在区分多标签中的单个标签的文本分类中表现良好，优于基线监督方法。 我们的代码是公开的。 显示英文摘要

摘要： Supervised contrastive learning (SCL) frameworks treat each class as independent and thus consider all classes to be equally important. This neglects the common scenario in which label hierarchy exists, where fine-grained classes under the same category show more similarity than very different ones. This paper introduces a family of Label-Aware SCL methods (LASCL) that incorporates hierarchical information to SCL by leveraging similarities between classes, resulting in creating a more well-structured and discriminative feature space. This is achieved by first adjusting the distance between instances based on measures of the proximity of their classes with the scaled instance-instance-wise contrastive. An additional instance-center-wise contrastive is introduced to move within-class examples closer to their centers, which are represented by a set of learnable label parameters. The learned label parameters can be directly used as a nearest neighbor classifier without further finetuning. In this way, a better feature representation is generated with improvements of intra-cluster compactness and inter-cluster separation. Experiments on three datasets show that the proposed LASCL works well on text classification of distinguishing a single label among multi-labels, outperforming the baseline supervised approaches. Our code is publicly available.