Weakly supervised learning is a popular approach for training machine learning models in low-resource settings. Instead of requesting high-quality yet costly human annotations, it allows training models with noisy annotations obtained from various weak sources. Recently, many sophisticated approaches have been proposed for robust training under label noise, reporting impressive results. In this paper, we revisit the setup of these approaches and find that the benefits brought by these approaches are significantly overestimated. Specifically, we find that the success of existing weakly supervised learning approaches heavily relies on the availability of clean validation samples which, as we show, can be leveraged much more efficiently by simply training on them. After using these clean labels in training, the advantages of using these sophisticated approaches are mostly wiped out. This remains true even when reducing the size of the available clean data to just five samples per class, making these approaches impractical. To understand the true value of weakly supervised learning, we thoroughly analyze diverse NLP datasets and tasks to ascertain when and why weakly supervised approaches work. Based on our findings, we provide recommendations for future research.

翻译：弱监督学习是一种在低资源环境下训练机器学习模型的流行方法。它无需获取高质量但昂贵的人工标注，而是允许利用从各种弱监督源获得的含噪标注来训练模型。近年来，针对标签噪声下的鲁棒训练提出了许多复杂方法，并报告了令人印象深刻的结果。本文重新审视了这些方法的设定，发现这些方法带来的好处被显著高估。具体而言，我们发现现有弱监督学习方法的成功严重依赖于干净验证样本的可用性，而正如我们所展示的，通过简单地在这些样本上进行训练，可以更高效地利用它们。在训练中使用了这些干净标签后，使用这些复杂方法的优势基本消失。即使将可用干净数据的规模减少到每类仅五个样本，这一结论仍然成立，使得这些方法不切实际。为理解弱监督学习的真正价值，我们全面分析了多种自然语言处理数据集和任务，以确定弱监督方法何时以及为何有效。基于研究结果，我们为未来研究提供了建议。