  Recent Weak Supervision (WS) approaches have had widespread success in easing
the bottleneck of labeling training data for machine learning by synthesizing
labels from multiple potentially noisy supervision sources. However, proper
measurement and analysis of these approaches remain a challenge. First,
datasets used in existing works are often private and/or custom, limiting
standardization. Second, WS datasets with the same name and base data often
vary in terms of the labels and weak supervision sources used, a significant
"hidden" source of evaluation variance. Finally, WS studies often diverge in
terms of the evaluation protocol and ablations used. To address these problems,
we introduce a benchmark platform, WRENCH, for thorough and standardized
evaluation of WS approaches. It consists of 22 varied real-world datasets for
classification and sequence tagging; a range of real, synthetic, and
procedurally-generated weak supervision sources; and a modular, extensible
framework for WS evaluation, including implementations for popular WS methods.
We use WRENCH to conduct extensive comparisons over more than 120 method
variants to demonstrate its efficacy as a benchmark platform. The code is
available at https://github.com/JieyuZ2/wrench.
