Are Data Augmentation Methods in Named Entity Recognition Applicable for Uncertainty Estimation? (2024.emnlp-main)
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| Challenge: | Named Entity Recognition (NER) is a key task in NLP to find mentions of named entities and classify them into predefined categories. |
| Approach: | They investigated the impact of data augmentation on confidence calibration and uncertainty estimation in Named Entity Recognition (NER) tasks. |
| Outcome: | The data augmentation improves calibration and uncertainty in cross-genre and cross-lingual setting, especially in-domain setting. |
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| Challenge: | Recent studies in deep learning have shown significant progress in named entity recognition (NER) . however, most existing works assume clean data annotation, while real-world data typically involve a large amount of noises. |
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A Survey of Data Augmentation Approaches for NLP (2021.findings-acl)
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Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, Eduard Hovy
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