Papers with noise-robust methods
Model-agnostic Methods for Text Classification with Inherent Noise (2020.coling-industry)
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| Challenge: | Text classification is a fundamental problem in natural language processing, but its performance relies on high-quality annotations. |
| Approach: | They propose to use model-agnostic methods to handle inherent noise in large scale text classification that can be easily incorporated into existing machine learning workflows with minimal interruption. |
| Outcome: | The proposed method outperforms baselines by up to 10% in classification accuracy while requiring no network modifications. |