| Challenge: | Recent advances in event detection focus on wordwise classification with one NIL class for tokens do not trigger any event. |
| Approach: | They propose a cost-sensitive regularization method which penalizes more on mislabeling . they propose two estimators which can effectively measure such label confusion based on instance-level statistics . |
| Outcome: | The proposed method can improve the performance of different models in English and Chinese event detection. |
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| Challenge: | Current neural event detection approaches focus on trigger-centric representations, which work well on distilling discrimination knowledge, but poorly on learning generalization knowledge. |
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Similar but not the Same: Word Sense Disambiguation Improves Event Detection via Neural Representation Matching (D18-1)
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Learning from Noisy Labels for Entity-Centric Information Extraction (2021.emnlp-main)
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| Challenge: | Recent information extraction approaches can easily overfit noisy labels and suffer from performance degradation. |
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Fine-Grained Event Trigger Detection (2021.eacl-main)
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LABO: Towards Learning Optimal Label Regularization via Bi-level Optimization (2023.findings-acl)
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Selective Labeling: How to Radically Lower Data-Labeling Costs for Document Extraction Models (2023.emnlp-main)
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Adaptive Label Smoothing with Self-Knowledge in Natural Language Generation (2022.emnlp-main)
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| Challenge: | Overconfidence in model generalization and calibration has been shown to impair model generalisation and calibration. |
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