  Tabular data analysis is performed every day across various domains. It
requires an accurate understanding of field semantics to correctly operate on
table fields and find common patterns in daily analysis. In this paper, we
introduce the AnaMeta dataset, a collection of 467k tables with derived
supervision labels for four types of commonly used field metadata:
measure/dimension dichotomy, common field roles, semantic field type, and
default aggregation function. We evaluate a wide range of models for inferring
metadata as the benchmark. We also propose a multi-encoder framework, called
KDF, which improves the metadata understanding capability of tabular models by
incorporating distribution and knowledge information. Furthermore, we propose
four interfaces for incorporating field metadata into downstream analysis
tasks.
