Papers with noise-detection method
Label Noise in Context (2020.acl-demos)
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| Challenge: | Label noise—incorrectly or ambiguously labeled training examples—can negatively impact model performance. |
| Approach: | They propose a noise-detection method that uses an example's neighborhood within the training set to reduce false positives and provide an explanation as to why the ex ample was flagged as noise. |
| Outcome: | The proposed method outperforms the state-of-the-art on precision and F0.5-score on short-text classification datasets. |