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arXiv · 2204.09371

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

Dawei Zhu·Michael A. Hedderich·Fangzhou Zhai·David Ifeoluwa Adelani·Dietrich Klakow
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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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Dawei Zhu, Michael A. Hedderich, Fangzhou Zhai, David Ifeoluwa Adelani, Dietrich Klakow. 2022-04-20. Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text Classification. https://arxiv.org/abs/2204.09371

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