Semi-supervised text classification (SSTC) has gained increasing attention
due to its ability to leverage unlabeled data. However, existing approaches
based on pseudo-labeling suffer from the issues of pseudo-label bias and error
accumulation. In this paper, we propose JointMatch, a holistic approach for
SSTC that addresses these challenges by unifying ideas from recent
semi-supervised learning and the task of learning with noise. JointMatch
adaptively adjusts classwise thresholds based on the learning status of
different classes to mitigate model bias towards current easy classes.
Additionally, JointMatch alleviates error accumulation by utilizing two
differently initialized networks to teach each other in a cross-labeling
manner. To maintain divergence between the two networks for mutual learning, we
introduce a strategy that weighs more disagreement data while also allowing the
utilization of high-quality agreement data for training. Experimental results
on benchmark datasets demonstrate the superior performance of JointMatch,
achieving a significant 5.13% improvement on average. Notably, JointMatch
delivers impressive results even in the extremely-scarce-label setting,
obtaining 86% accuracy on AG News with only 5 labels per class. We make our
code available at this https URL

提出了一种名为 JointMatch 的新方法，用于半监督文本分类，通过结合最近半监督学习和学习带有噪声任务的理念，解决了伪标签偏见和错误积累的问题。JointMatch 通过根据不同类别的学习状态自适应调整类别阈值，减轻了模型对当前容易类别的偏见。此外，JointMatch 通过利用两个初始化不同的网络以交互标记的方式互相教导，减轻了错误的积累问题。实验结果表明，JointMatch 在基准数据集上表现优越，在平均上提高了 5.13% 的性能。“尤其值得注意的是，即使在极度稀缺标签的情况下，JointMatch 在 AG News 上的准确率达到了 86%，每个类别仅有 5 个标签。我们将我们的代码发布在此 https URL。

JointMatch: 一种用于半监督文本分类的多样化和协作伪标签统一方法

JointMatch: A Unified Approach for Diverse and Collaborative  Pseudo-Labeling to Semi-Supervised Text Classification

Semi-supervised text classification-based paradigms (SSTC) typically employ
the spirit of self-training. The key idea is to train a deep classifier on
limited labeled texts and then iteratively predict the unlabeled texts as their
pseudo-labels for further training. However, the performance is largely
affected by the accuracy of pseudo-labels, which may not be significant in
real-world scenarios. This paper presents a Rank-aware Negative Training (RNT)
framework to address SSTC in learning with noisy label manner. To alleviate the
noisy information, we adapt a reasoning with uncertainty-based approach to rank
the unlabeled texts based on the evidential support received from the labeled
texts. Moreover, we propose the use of negative training to train RNT based on
the concept that ``the input instance does not belong to the complementary
label''. A complementary label is randomly selected from all labels except the
label on-target. Intuitively, the probability of a true label serving as a
complementary label is low and thus provides less noisy information during the
training, resulting in better performance on the test data. Finally, we
evaluate the proposed solution on various text classification benchmark
datasets. Our extensive experiments show that it consistently overcomes the
state-of-the-art alternatives in most scenarios and achieves competitive
performance in the others. The code of RNT is publicly available
at:this https URL

本文提出了一种 RNT 框架，采用未标注样本自动筛选和基于负样本训练的方法，解决了半监督文本分类中标注噪声和模型泛化问题。实验证明，该方法在多个数据集上具有显著的优势。

半监督文本分类的排名感知负训练

Rank-Aware Negative Training for Semi-Supervised Text Classification

Consistency training regularizes a model by enforcing predictions of original
and perturbed inputs to be similar. Previous studies have proposed various
augmentation methods for the perturbation but are limited in that they are
agnostic to the training model. Thus, the perturbed samples may not aid in
regularization due to their ease of classification from the model. In this
context, we propose an augmentation method of adding a discrete noise that
would incur the highest divergence between predictions. This virtual
adversarial discrete noise obtained by replacing a small portion of tokens
while keeping original semantics as much as possible efficiently pushes a
training model's decision boundary. Experimental results show that our proposed
method outperforms other consistency training baselines with text editing,
paraphrasing, or a continuous noise on semi-supervised text classification
tasks and a robustness benchmark

通过添加离散噪音的扰动方法，可以更有效地将模型的决策边界推向具有抵抗干扰能力的点，并在半监督文本分类任务和鲁棒性基准测试中，优于其他基于文本编辑、释义或连续噪音的一致性训练基线方法。