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Minji Tang

2 accepted papers

2023

NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in Natural Language Processing

ACL 2023findings

Large-scale datasets in the real world inevitably involve label noise. Deep models can gradually overfit noisy labels and thus degrade model generalization. To mitigate the effects of label noise, learning with noisy labels (LNL) methods are designed to achieve better generalization performance. Due…

2022

STGN: an Implicit Regularization Method for Learning with Noisy Labels in Natural Language Processing

EMNLP 2022main

Noisy labels are ubiquitous in natural language processing (NLP) tasks. Existing work, namely learning with noisy labels in NLP, is often limited to dedicated tasks or specific training procedures, making it hard to be widely used. To address this issue, SGD noise has been explored to provide a more…