Utilizing Local Hierarchy with Adversarial Training for Hierarchical Text Classification

arXiv:2402.18825v1 Announce Type: new
Abstract: 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 …

abstract adversarial adversarial training arxiv classification cs.cl focus ground-truth hierarchical labels text text classification training truth type