A Survey on Programmatic Weak Supervision
It provides a comprehensive overview for researchers and practitioners dealing with limited labeled data, but is incremental as a survey.
This paper surveys programmatic weak supervision (PWS), a method to address the labeling bottleneck in machine learning by synthesizing training labels from noisy sources, summarizing recent advances and identifying future challenges.
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.