  Relational database management systems (RDBMSs) are powerful because they are
able to optimize and answer queries against any relational database. A natural
language interface (NLI) for a database, on the other hand, is tailored to
support that specific database. In this work, we introduce a general purpose
transfer-learnable NLI with the goal of learning one model that can be used as
NLI for any relational database. We adopt the data management principle of
separating data and its schema, but with the additional support for the
idiosyncrasy and complexity of natural languages. Specifically, we introduce an
automatic annotation mechanism that separates the schema and the data, where
the schema also covers knowledge about natural language. Furthermore, we
propose a customized sequence model that translates annotated natural language
queries to SQL statements. We show in experiments that our approach outperforms
previous NLI methods on the WikiSQL dataset and the model we learned can be
applied to another benchmark dataset OVERNIGHT without retraining.
