Summary of Are Data Augmentation Methods in Named Entity Recognition Applicable For Uncertainty Estimation?, by Wataru Hashimoto et al.
Are Data Augmentation Methods in Named Entity Recognition Applicable for Uncertainty Estimation?
by Wataru Hashimoto, Hidetaka Kamigaito, Taro Watanabe
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel study examines how data augmentation affects confidence calibration and uncertainty estimation in Named Entity Recognition (NER) tasks using Deep Neural Networks (DNNs), including Pre-trained Language Models (PLMs). The research aims to improve NER’s applicability in safety-critical fields like healthcare and finance, where accurate predictions with calibrated confidence are crucial. The investigation finds that data augmentation enhances calibration and uncertainty estimation in cross-genre and cross-lingual settings, particularly in-domain setting. Additionally, the study reveals that lower perplexity of sentences generated by data augmentation improves NER’s calibration, and increasing the size of augmentation further boosts calibration and uncertainty. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how adding extra information to training data affects how well computers can recognize named entities like people, places, or organizations. The goal is to make these computers better at doing this job in important areas like healthcare and finance, where it’s really important to get things right. The study shows that adding more data makes the computers better at guessing when they’re not sure, which helps them be more useful. |
Keywords
» Artificial intelligence » Data augmentation » Named entity recognition » Ner » Perplexity
