We tackle the challenges of text-to-SQL by developing a deep learning based
TRanslate-Edit Model for Question-to-SQL (TREQS) generation, which adapts the widely used
sequence-to-sequence model to directly generate the SQL query for a given question, and further
performs the required edits using an attentive-copying mechanism and task-specific look-up tables.
Based on the widely used publicly available electronic medical database, we create a new large-scale
Question-SQL pair dataset, named MIMICSQL, in order to perform the Question-to-SQL generation task
in healthcare domain