Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text Classification

• URL: http://arxiv.org/abs/2204.09371v1
• Date: Wed, 20 Apr 2022 10:24:19 GMT
• Title: Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text Classification
• Authors: Dawei Zhu, Michael A. Hedderich, Fangzhou Zhai, David Ifeoluwa Adelani, Dietrich Klakow
• Abstract summary: Wrong labels in training data occur when human annotators make mistakes or when the data is generated via weak or distant supervision. It has been shown that complex noise-handling techniques are required to prevent models from fitting this label noise. We show in this work that, for text classification tasks with modern NLP models like BERT, over a variety of noise types, existing noisehandling methods do not always improve its performance, and may even deteriorate it.
• Score: 23.554544399110508
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Incorrect labels in training data occur when human annotators make mistakes or when the data is generated via weak or distant supervision. It has been shown that complex noise-handling techniques - by modeling, cleaning or filtering the noisy instances - are required to prevent models from fitting this label noise. However, we show in this work that, for text classification tasks with modern NLP models like BERT, over a variety of noise types, existing noisehandling methods do not always improve its performance, and may even deteriorate it, suggesting the need for further investigation. We also back our observations with a comprehensive analysis.

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