Abstract
We introduce MultiMatch, a novel semi-supervised learning (SSL) algorithm combining the paradigms of co-training and consistency regularization with pseudo-labeling. At its core, MultiMatch features a three-fold pseudo-label weighting module designed for three key purposes: selecting and filtering pseudo-labels based on head agreement and model confidence, and weighting them according to the perceived classification difficulty. This novel module enhances and unifies three existing techniques -- heads agreement from Multihead Co-training, self-adaptive thresholds from FreeMatch, and Average Pseudo-Margins from MarginMatch -- resulting in a holistic approach that improves robustness and performance in SSL settings. Experimental results on benchmark datasets highlight the superior performance of MultiMatch, achieving state-of-the-art results on 9 out of 10 setups from 5 natural language processing datasets and ranking first according to the Friedman test among 19 methods. Furthermore, MultiMatch demonstrates exceptional robustness in highly imbalanced settings, outperforming the second-best approach by 3.26% -- and data imbalance is a key factor for many text classification tasks.
Abstract (translated)
我们介绍了MultiMatch，这是一种新颖的半监督学习（SSL）算法，结合了联合训练和一致性正则化与伪标签生成的范式。在核心部分，MultiMatch包含一个三重伪标签加权模块，旨在实现三个关键目标：根据头部一致性和模型置信度选择并过滤伪标签，并根据感知分类难度对它们进行加权。这一创新模块增强了并统一了三种现有技术——来自Multihead Co-training的头部一致性、FreeMatch中的自适应阈值以及MarginMatch中的平均伪边界，从而形成了一种全面提升半监督学习场景中鲁棒性和性能的整体方法。在基准数据集上的实验结果突显了MultiMatch的优越表现，在五个自然语言处理数据集中十个设置中有九个达到了最先进的水平，并且根据Friedman检验在19种方法中排名第一。此外，MultiMatch展示了其在高度不平衡环境中的卓越稳健性，超越了第二最佳的方法3.26%——而数据不平衡是许多文本分类任务的关键因素。
URL
https://arxiv.org/abs/2506.07801
