  This paper presents MixText, a semi-supervised learning method for text
classification, which uses our newly designed data augmentation method called
TMix. TMix creates a large amount of augmented training samples by
interpolating text in hidden space. Moreover, we leverage recent advances in
data augmentation to guess low-entropy labels for unlabeled data, hence making
them as easy to use as labeled data.By mixing labeled, unlabeled and augmented
data, MixText significantly outperformed current pre-trained and fined-tuned
models and other state-of-the-art semi-supervised learning methods on several
text classification benchmarks. The improvement is especially prominent when
supervision is extremely limited. We have publicly released our code at
https://github.com/GT-SALT/MixText.
