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
this https URL

提出了 MixText，这是一种用于文本分类的半监督学习方法，利用我们新设计的数据增强方法 TMix，在隐藏空间插值文本来创建大量的增强训练样本。混合标记的、未标记的和增强的数据，MixText 在几个文本分类基准测试中显著优于当前的预训练和微调模型以及其他最先进的半监督学习方法，尤其是在监督极度有限的情况下。我们已经在此 https URL 上公开发布了我们的代码。

MixText: 语言知识驱动的隐藏空间插值半监督文本分类

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

Semi-supervised learning has proven to be a powerful paradigm for leveraging
unlabeled data to mitigate the reliance on large labeled datasets. In this
work, we unify the current dominant approaches for semi-supervised learning to
produce a new algorithm, MixMatch, that works by guessing low-entropy labels
for data-augmented unlabeled examples and mixing labeled and unlabeled data
using MixUp. We show that MixMatch obtains state-of-the-art results by a large
margin across many datasets and labeled data amounts. For example, on CIFAR-10
with 250 labels, we reduce error rate by a factor of 4 (from 38% to 11%) and by
a factor of 2 on STL-10. We also demonstrate how MixMatch can help achieve a
dramatically better accuracy-privacy trade-off for differential privacy.
Finally, we perform an ablation study to tease apart which components of
MixMatch are most important for its success.

本研究提出了一种半监督学习算法 MixMatch，采用猜测低熵标签的方法，通过使用 MixUp 混合标记和未标记的数据来处理扩充后的未标注样例。实验结果表明，MixMatch 在许多数据集和标记数据量上都取得了大幅度的优越结果，同时也证明了 MixMatch 如何帮助实现更好的准确性和隐私权的权衡。最终，我们进行了消融研究来分离 MixMatch 的哪些组件对其成功最为重要。