  Interacting with relational databases through natural language helps users of
any background easily query and analyze a vast amount of data. This requires a
system that understands users' questions and converts them to SQL queries
automatically. In this paper we present a novel approach, TypeSQL, which views
this problem as a slot filling task. Additionally, TypeSQL utilizes type
information to better understand rare entities and numbers in natural language
questions. We test this idea on the WikiSQL dataset and outperform the prior
state-of-the-art by 5.5% in much less time. We also show that accessing the
content of databases can significantly improve the performance when users'
queries are not well-formed. TypeSQL gets 82.6% accuracy, a 17.5% absolute
improvement compared to the previous content-sensitive model.
