Finding relevant and high-quality datasets to train machine learning models is a major bottleneck for practitioners. Furthermore, to address ambitious real-world use-cases there is usually the requirement that the data come labelled with high-quality annotations that can facilitate the training of a supervised model. Manually labelling data with high-quality labels is generally a time-consuming and challenging task and often this turns out to be the bottleneck in a machine learning project. Weak Supervised Learning (WSL) approaches have been developed to alleviate the annotation burden by offering an automatic way of assigning approximate labels (pseudo-labels) to unlabelled data based on heuristics, distant supervision and knowledge bases. We apply probabilistic generative latent variable models (PLVMs), trained on heuristic labelling representations of the original dataset, as an accurate, fast and cost-effective way to generate pseudo-labels. We show that the PLVMs achieve state-of-the-art performance across four datasets. For example, they achieve 22% points higher F1 score than Snorkel in the class-imbalanced Spouse dataset. PLVMs are plug-and-playable and are a drop-in replacement to existing WSL frameworks (e.g. Snorkel) or they can be used as benchmark models for more complicated algorithms, giving practitioners a compelling accuracy boost.

翻译：寻找相关且高质量的机器学习模型训练数据集，是实践者面临的主要瓶颈。此外，为应对具有挑战性的实际应用场景，通常要求数据附带高质量标注以支持监督模型训练。人工标注高质量标签通常耗时且困难，往往成为机器学习项目的瓶颈。弱监督学习方法通过基于启发式规则、远程监督和知识库，自动为未标注数据分配近似标签（伪标签），从而减轻标注负担。我们采用基于原始数据集启发式标注表示训练的生成式概率潜变量模型，以精确、快速且经济高效的方式生成伪标签。实验表明，该模型在四个数据集上均达到最优性能。例如，在类别不平衡的Spouse数据集中，其F1分数比Snorkel高22个百分点。该模型即插即用，可无缝替代现有弱监督学习框架（如Snorkel），也可作为复杂算法的基准模型，显著提升实践者的模型精度。