Jump To Hyperspace: Comparing Euclidean and Hyperbolic Loss Functions for Hierarchical Multi-Label Text Classification (2025.coling-main)
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| Challenge: | Hierarchical Multi-Label Text Classification (HMTC) is a challenging machine learning task . a recent study evaluated the effectiveness of Euclidean and hyperbolic loss functions on HMTC . |
| Approach: | They evaluate label-aware and contrastive losses in the Euclidean and hyperbolic space . they find contrastive loss functions are less effective when deployed in the hyperbolical space compared to non-hyperbolic ones . |
| Outcome: | The proposed model improves on four commonly used HMTC datasets. |
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