HILL: Hierarchy-aware Information Lossless Contrastive Learning for Hierarchical Text Classification (2024.naacl-long)
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| Challenge: | Existing self-supervised methods in natural language processing rely on augmentation rules to generate contrastive samples. |
| Approach: | They propose a hierarchy-aware information lossless contrastive learning scheme that uses syntactic information reserved in the input sample and fused during the learning process. |
| Outcome: | The proposed learning scheme is superior to existing methods in hierarchical text classification . the proposed learning system is based on a structure encoder and a text encoder . |
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| Challenge: | Existing methods to improve hierarchical text classification are expensive and lack high-quality labeled data. |
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| Challenge: | Hierarchical text classification is a complex subtask under multi-label text classification . the relevance of document sections can vary based on the hierarchy level, necessitating a dynamic document representation. |
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| Challenge: | Existing approaches to hierarchical multi-label text classification (HMTC) ignore the correlation between similar samples and introduce noise . |
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