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

• URL: http://arxiv.org/abs/2412.16963v2
• Date: Sun, 23 Feb 2025 02:53:21 GMT
• Title: LH-Mix: Local Hierarchy Correlation Guided Mixup over Hierarchical Prompt Tuning
• Abstract summary: Hierarchical text classification (HTC) aims to assign one or more labels in the hierarchy for each text.<n>To address this, incorporating a text-specific local hierarchy is essential.<n>We first integrate local hierarchies into a manual depth-level prompt to capture parent-child relationships.<n>We then apply Mixup to this hierarchical prompt tuning scheme to improve the latent correlation within sibling/peer relationships.
• Score: 26.98015572633289
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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