Divide and Denoise: Learning from Noisy Labels in Fine-Grained Entity Typing with Cluster-Wise Loss Correction (2022.acl-long)
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| Challenge: | Existing FET noise learning methods rely on prediction distributions in instance-independent manner, which causes confirmation bias. |
| Approach: | They propose a clustering-based loss correction framework to address confirmation bias in FET . they first train a coarse backbone model as a feature extractor and noise estimator . |
| Outcome: | The proposed framework achieves the best performance over existing systems on three public datasets and is stable to hyperparameters. |
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| Challenge: | Experimental results show that noise correction in fine-grained entity typing improves quality of training samples. |
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| Challenge: | Recent work on distantly supervised (DS) ultra-fine entity typing has received significant attention . however, DS data is noisy and often suffers from missing or wrong labeling issues resulting in low precision and low recall. |
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Fine-grained Entity Typing without Knowledge Base (2021.emnlp-main)
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| Challenge: | Existing work on fine-grained entity typing (FET) relies on knowledge bases as distant supervision, but lack of or incompleteness of KB can hinder training. |
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