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.

本文介绍了一种基于对抗框架的本地层次结构，用于在几乎所有层次文本分类（HTC）模型中优化复杂的分类树形结构，实验证明该本地层次结构有助于处理复杂的分类树形结构并提高稀有类别的分类效果。