A Survey on Programmatic Weak Supervision

• URL: http://arxiv.org/abs/2202.05433v2
• Date: Mon, 14 Feb 2022 05:45:58 GMT
• Title: A Survey on Programmatic Weak Supervision
• Authors: Jieyu Zhang, Cheng-Yu Hsieh, Yue Yu, Chao Zhang, Alexander Ratner
• Abstract summary: We give brief introduction of the PWS learning paradigm and review representative approaches for each PWS's learning workflow. We identify several critical challenges that remain underexplored in the area to hopefully inspire future directions in the field.
• Score: 74.13976343129966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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