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This paper presents MixText, a semi-supervised learning method for textclassification, which uses our newly designed data augmentation method calledTMix. TMix creates a large amount of augmented training samples byinterpolating text in hidden space. Moreover, we leverage recent advances indata augmentation to guess low-entropy labels for unlabeled data, hence makingthem as easy to use as labeled data.By mixing labeled, unlabeled and augmenteddata, MixText significantly outperformed current pre-trained and fined-tunedmodels and other state-of-the-art semi-supervised learning methods on severaltext classification benchmarks. The improvement is especially prominent whensupervision is extremely limited. We have publicly released our code athttps://github.com/GT-SALT/MixText.

MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification