LH-Mix: Local Hierarchy Correlation Guided Mixup over Hierarchical Prompt Tuning

TL;DR

LH-Mix introduces a novel hierarchical prompt tuning method that leverages local hierarchy correlations and Mixup to enhance text classification accuracy in hierarchical structures.

Contribution

The paper proposes LH-Mix, a new approach that integrates local hierarchies into prompt tuning and applies a hierarchy-guided Mixup to improve correlation modeling.

Findings

Achieves superior performance on three benchmark datasets.

Effectively captures parent-child and sibling correlations.

Demonstrates robustness across diverse hierarchical text classification tasks.

Abstract

Hierarchical text classification (HTC) aims to assign one or more labels in the hierarchy for each text. Many methods represent this structure as a global hierarchy, leading to redundant graph structures. To address this, incorporating a text-specific local hierarchy is essential. However, existing approaches often model this local hierarchy as a sequence, focusing on explicit parent-child relationships while ignoring implicit correlations among sibling/peer relationships. In this paper, we first integrate local hierarchies into a manual depth-level prompt to capture parent-child relationships. We then apply Mixup to this hierarchical prompt tuning scheme to improve the latent correlation within sibling/peer relationships. Notably, we propose a novel Mixup ratio guided by local hierarchy correlation to effectively capture intrinsic correlations. This Local Hierarchy Mixup (LH-Mix) model demonstrates remarkable performance across three widely-used datasets.