  Inferring meta information about tables, such as column headers or
relationships between columns, is an active research topic in data management
as we find many tables are missing some of this information. In this paper, we
study the problem of annotating table columns (i.e., predicting column types
and the relationships between columns) using only information from the table
itself. We develop a multi-task learning framework (called Doduo) based on
pre-trained language models, which takes the entire table as input and predicts
column types/relations using a single model. Experimental results show that
Doduo establishes new state-of-the-art performance on two benchmarks for the
column type prediction and column relation prediction tasks with up to 4.0% and
11.9% improvements, respectively. We report that Doduo can already outperform
the previous state-of-the-art performance with a minimal number of tokens, only
8 tokens per column. We release a toolbox
(https://github.com/megagonlabs/doduo) and confirm the effectiveness of Doduo
on a real-world data science problem through a case study.
