Papers with Dice loss
Dice Loss for Data-imbalanced NLP Tasks (2020.acl-main)
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| Challenge: | Using dice loss, we find that data imbalance is a common issue in many NLP tasks . data imbalance affects the performance of many tasks, such as tagging and machine reading comprehension . |
| Approach: | They propose to use dice loss to replace the standard cross-entropy objective for data-imbalanced NLP tasks. |
| Outcome: | The proposed training objective achieves significant performance boost on a wide range of data imbalanced tasks. |
Sentence-Level Resampling for Named Entity Recognition (2022.naacl-main)
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| Challenge: | named entity recognition (NER) tasks are often dominated by the majority of non-entity tokens in text . a data imbalance problem is causing the NER models to ignore named entities . |
| Approach: | They propose a set of sentence-level resampling methods to reduce data imbalance . they use a training sentence to compute the importance of each training sentence based on its tokens and entities . |
| Outcome: | The proposed methods outperform sub-sentence-level resampling, data augmentation, and loss functions on multiple corpora. |
Losses that Cook: Topological Optimal Transport for Structured Recipe Generation (2026.findings-acl)
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| Challenge: | Existing work on cooking recipes relies on cross-entropy, but it does not address holistic composition of ingredient sets and numerical aspects of recipes. |
| Approach: | They propose a topological loss that represents ingredient lists as point clouds in embedding space . they show that the Dice loss excels in time/temperature precision . |
| Outcome: | The proposed model improves ingredient- and action-level metrics while preserving time/temperature precision. |