Papers with uncertainty-aware bootstrap learning
Uncertainty-Aware Bootstrap Learning for Joint Extraction on Distantly-Supervised Data (2023.acl-short)
Copied to clipboard
| Challenge: | Existing approaches to extract entity pairs and their relations from labeled data are noisy and expensive. |
| Approach: | They propose a bootstrap learning approach that is motivated by intuition that the higher the uncertainty of an instance, the more likely the model confidence is inconsistent with the ground truths. |
| Outcome: | The proposed method outperforms baselines and related methods on two large datasets. |