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- W4387559708 abstract "Hierarchical multi-label text classification (HMTC) aims at utilizing a label hierarchy in multi-label classification. Recent approaches to HMTC deal with the problem of imposing an overconstrained premise on the output space by using contrastive learning on generated samples in a semi-supervised manner to bring text and label embeddings closer. However, the generation of samples tends to introduce noise as it ignores the correlation between similar samples in the same batch. One solution to this issue is supervised contrastive learning, but it remains an underexplored topic in HMTC due to its complex structured labels. To overcome this challenge, we propose HJCL, a textbf{H}ierarchy-aware textbf{J}oint Supervised textbf{C}ontrastive textbf{L}earning method that bridges the gap between supervised contrastive learning and HMTC. Specifically, we employ both instance-wise and label-wise contrastive learning techniques and carefully construct batches to fulfill the contrastive learning objective." @default.
- W4387559708 created "2023-10-12" @default.
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- W4387559708 date "2023-10-08" @default.
- W4387559708 modified "2023-10-18" @default.
- W4387559708 title "Instances and Labels: Hierarchy-aware Joint Supervised Contrastive Learning for Hierarchical Multi-Label Text Classification" @default.
- W4387559708 doi "https://doi.org/10.48550/arxiv.2310.05128" @default.
- W4387559708 hasPublicationYear "2023" @default.
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