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
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.
Topics & Concepts
Computer scienceNoise (video)Noisy dataArtificial intelligenceVariety (cybernetics)Training setNoise measurementMachine learningSpeech recognitionPattern recognition (psychology)Noise reductionImage (mathematics)Machine Learning and Data ClassificationAnomaly Detection Techniques and ApplicationsImbalanced Data Classification Techniques