Papers by Diego Ortego
Prototypical Extreme Multi-label Classification with a Dynamic Margin Loss (2025.naacl-long)
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| Challenge: | Recent work in XMC addresses this problem using deep encoders that project text descriptions to an embedding space suitable for recovering the closest labels. |
| Approach: | They propose a method that uses a shallow transformer encoder to combine text-based embeddings, label centroids and learnable free vectors to improve XMC efficiency. |
| Outcome: | The proposed method achieves state-of-the-art in several public benchmarks of different sizes and domains while keeping the model efficient. |