  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 show that a multi-task learning approach (called Doduo), trained
using pre-trained language models on both tasks outperforms individual learning
approaches. 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 also establish that Doduo can already perform the previous
state-of-the-art performance with a minimal number of tokens, only 8 tokens per
column.
