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Tabular data analysis is performed every day across various domains. Itrequires an accurate understanding of field semantics to correctly operate ontable fields and find common patterns in daily analysis. In this paper, weintroduce the AnaMeta dataset, a collection of 467k tables with derivedsupervision labels for four types of commonly used field metadata:measure/dimension dichotomy, common field roles, semantic field type, anddefault aggregation function. We evaluate a wide range of models for inferringmetadata as the benchmark. We also propose a multi-encoder framework, calledKDF, which improves the metadata understanding capability of tabular models byincorporating distribution and knowledge information. Furthermore, we proposefour interfaces for incorporating field metadata into downstream analysistasks.

AnaMeta: A Table Understanding Dataset of Field Metadata Knowledge Shared by Multi-dimensional Data Analysis Tasks