Misc,
A Translate-Edit Model for Natural Language Question to SQL Query Generation on Multi-relational Healthcare Data
(2019)cite arxiv:1908.01839.
Abstract
Electronic health record (EHR) data contains most of the important patient
health information and is typically stored in a relational database with
multiple tables. One important way for doctors to make use of EHR data is to
retrieve intuitive information by posing a sequence of questions against it.
However, due to a large amount of information stored in it, effectively
retrieving patient information from EHR data in a short time is still a
challenging issue for medical experts since it requires a good understanding of
a query language to get access to the database. We tackle this challenge by
developing a deep learning based approach that can translate a natural language
question on multi-relational EHR data into its corresponding SQL query, which
is referred to as a Question-to-SQL generation task. Most of the existing
methods cannot solve this problem since they primarily focus on tackling the
questions related to a single table under the table-aware assumption. While in
our problem, it is possible that questions asked by clinicians are related to
multiple unspecified tables. In this paper, we first create a new question to
query dataset designed for healthcare to perform the Question-to-SQL generation
task, named MIMICSQL, based on a publicly available electronic medical
database. To address the challenge of generating queries on multi-relational
databases from natural language questions, we propose a TRanslate-Edit Model
for Question-to-SQL query (TREQS), which adopts the sequence-to-sequence model
to directly generate SQL query for a given question, and further edits it with
an attentive-copying mechanism and task-specific look-up tables. Both
quantitative and qualitative experimental results indicate the flexibility and
efficiency of our proposed method in tackling challenges that are unique in
MIMICSQL.
