Hierarchical text classification (HTC) is a challenging subtask of multi-label classification due to its complex taxonomic structure. Nearly all recent HTC works focus on how the labels are structured but ignore the sub-structure of ground-truth labels according to each input text which contains fruitful label co-occurrence information. In this work, we introduce this local hierarchy with an adversarial framework. We propose a HiAdv framework that can fit in nearly all HTC models and optimize them with the local hierarchy as auxiliary information. We test on two typical HTC models and find that HiAdv is effective in all scenarios and is adept at dealing with complex taxonomic hierarchies. Further experiments demonstrate that the promotion of our framework indeed comes from the local hierarchy and the local hierarchy is beneficial for rare classes which have insufficient training data.

翻译：层次文本分类（HTC）是多标签分类中一项具有挑战性的子任务，原因在于其复杂的分类结构。几乎所有近期HTC研究都聚焦于标签的组织结构，却忽略了根据每个输入文本所包含的丰富标签共现信息而存在的真实标签子结构。在本工作中，我们引入这种局部层次结构，并结合对抗框架进行创新。我们提出了HiAdv框架，该框架几乎可适用于所有HTC模型，并利用局部层次结构作为辅助信息对其进行优化。我们在两个典型HTC模型上进行了测试，发现HiAdv在所有场景下均有效，且擅长处理复杂的分层结构。进一步的实验证明，我们框架的性能提升确实来源于局部层次结构，并且该局部层次结构对于训练数据不足的稀有类别尤为有益。