Weakly Supervised Data Denoising

In natural language processing, weakly supervised data denoising is a technique that leverages a small amount of labeled data and a large amount of unlabeled data to identify and correct noisy labels in the training data. Its primary goal is to enhance the robustness and generalization capability of models, reducing performance degradation caused by data noise. This method has significant application value in the preprocessing stage of large-scale datasets, effectively improving the accuracy and reliability of subsequent tasks.