  The conventional success of textual classification relies on annotated data,
and the new paradigm of pre-trained language models (PLMs) still requires a few
labeled data for downstream tasks. However, in real-world applications, label
noise inevitably exists in training data, damaging the effectiveness,
robustness, and generalization of the models constructed on such data.
Recently, remarkable achievements have been made to mitigate this dilemma in
visual data, while only a few explore textual data. To fill this gap, we
present SelfMix, a simple yet effective method, to handle label noise in text
classification tasks. SelfMix uses the Gaussian Mixture Model to separate
samples and leverages semi-supervised learning. Unlike previous works requiring
multiple models, our method utilizes the dropout mechanism on a single model to
reduce the confirmation bias in self-training and introduces a textual-level
mixup training strategy. Experimental results on three text classification
benchmarks with different types of text show that the performance of our
proposed method outperforms these strong baselines designed for both textual
and visual data under different noise ratios and noise types. Our code is
available at https://github.com/noise-learning/SelfMix.
