  Relational databases are among the most widely used architectures to store
massive amounts of data in the modern world. However, there is a barrier
between these databases and the average user. The user often lacks the
knowledge of a query language such as SQL required to interact with the
database. The NL2SQL task aims at finding deep learning approaches to solve
this problem by converting natural language questions into valid SQL queries.
Given the sensitive nature of some databases and the growing need for data
privacy, we have presented an approach with data privacy at its core. We have
passed RoBERTa embeddings and data-agnostic knowledge vectors into LSTM based
submodels to predict the final query. Although we have not achieved state of
the art results, we have eliminated the need for the table data, right from the
training of the model, and have achieved a test set execution accuracy of
76.7%. By eliminating the table data dependency while training we have created
a model capable of zero shot learning based on the natural language question
and table schema alone.
