Papers › HILL: Hierarchy-aware Information Lossless Contrastive Learning for Hierarchical Text...
HILL: Hierarchy-aware Information Lossless Contrastive Learning for Hierarchical Text Classification
He Zhu, Junran Wu, Ruomei Liu, Yue Hou, Ze Yuan, Shangzhe Li, YiCheng Pan, Ke Xu
Existing self-supervised methods in natural language processing (NLP), especially hierarchical text classification (HTC), mainly focus on self-supervised contrastive learning, extremely relying on human-designed augmentation rules to generate contrastive samples, which can potentially corrupt or distort the original information. In this paper, we tend to investigate the feasibility of a contrastive learning scheme in which the semantic and syntactic information inherent in the input sample is adequately reserved in the contrastive samples and fused during the learning process. Specifically, we propose an information lossless contrastive learning strategy for HTC, namely \textbf{H}ierarchy-aware \textbf{I}nformation \textbf{L}ossless contrastive \textbf{L}earning (HILL), which consists of a text encoder representing the input document, and a structure encoder directly generating the positive sample. The structure encoder takes the document embedding as input, extracts the essential syntactic information inherent in the label hierarchy with the principle of structural entropy minimization, and injects the syntactic information into the text representation via hierarchical representation learning. Experiments on three common datasets are conducted to verify the superiority of HILL.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Hierarchical Multi-label Classification | New York Times Annotated Corpus | HILL | Macro F1 | 69.96 | #3 of 7 | Archive leaderboard | report |
| Hierarchical Multi-label Classification | New York Times Annotated Corpus | HILL | Micro F1 | 80.47 | #3 of 7 | Archive leaderboard | report |
| Hierarchical Multi-label Classification | RCV1-v2 | HILL | Macro F1 | 70.12 | #5 of 7 | Archive leaderboard | report |
| Hierarchical Multi-label Classification | RCV1-v2 | HILL | Micro F1 | 87.31 | #5 of 7 | Archive leaderboard | report |
| Hierarchical Multi-label Classification | WOS | HILL | Macro F1 | 81.77 | #1 of 3 | Archive leaderboard | report |
| Hierarchical Multi-label Classification | WOS | HILL | Micro F1 | 87.28 | #1 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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