Yoshihiko Suhara, Jinfeng Li, Yuliang Li, Dan Zhang, Cagatay Demiralp, Chen Chen, Wang-Chiew Tan
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
toolbox1 and confirm the effectiveness of Doduo on a real-world
data science problem through a case study.