From Lazy to Prolific: Tackling Missing Labels in Open Vocabulary Extreme Classification by Positive-Unlabeled Sequence Learning (2025.findings-naacl)
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| Challenge: | Extreme multi-label classification (OXMC) is a challenging and critical task in natural language processing. |
| Approach: | They propose to use PUSL to reframe OXMC as an infinite keyphrase generation task . they propose to adopt evaluation metrics to reliably assess OXML models with incomplete ground truths. |
| Outcome: | The proposed approach improves on a highly imbalanced e-commerce dataset with missing labels . it generates 30% more unique labels and 72% of its predictions align with actual user queries . |
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| Challenge: | Existing classification-based models are poorly per-form for tail labels and ignore semantic relations among labels. |
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| Challenge: | Existing approaches to extreme multi-label text classification face inherent challenges in terms of model, data, and evaluation. |
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| Challenge: | Experimental results show that extreme multi-label learning improves label prediction quality by 3% to 5% in three of the 5 tasks and is competitive in the others. |
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| Challenge: | Existing research has focused on fully supervised XMC, but real-world scenarios often lack supervision signals, highlighting the importance of zero-shot settings. |
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| Challenge: | Extreme Zero-shot XMC uses lightweight bi-encoders to identify pseudo labels . state-of-the-art methods rely on suboptimal labels for training . |
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