AnaMeta: A Table Understanding Dataset of Field Metadata Knowledge Shared by Multi-dimensional Data Analysis Tasks
This work addresses the need for accurate field metadata understanding in tabular data analysis across various domains, but it is incremental as it builds on existing methods with new data and interfaces.
The authors tackled the problem of understanding field semantics in tabular data analysis by introducing the AnaMeta dataset with 467k tables and derived supervision labels for four types of field metadata, and they proposed the KDF framework to improve metadata understanding, achieving benchmark improvements.
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.