Labeling training data has become one of the major roadblocks to using
machine learning. Among various weak supervision paradigms, programmatic weak
supervision (PWS) has achieved remarkable success in easing the manual labeling
bottleneck by programmatically synthesizing training labels from multiple
potentially noisy supervision sources. This paper presents a comprehensive
survey of recent advances in PWS. In particular, we give a brief introduction
of the PWS learning paradigm, and review representative approaches for each
component within PWS's learning workflow. In addition, we discuss complementary
learning paradigms for tackling limited labeled data scenarios and how these
related approaches can be used in conjunction with PWS. Finally, we identify
several critical challenges that remain under-explored in the area to hopefully
inspire future research directions in the field.

本文综述了最近在程序化弱监督（PWS）方面取得的重要进展，特别介绍了该学习范例的简要概述并回顾了在该流程内的各个组成部分的代表性方法，还讨论了解决有限标记数据方案的补充学习范式及相关方法如何与 PWS 结合使用，最后鉴定了一些在该领域中仍未被探讨的关键挑战，希望能够激发未来的研究方向。