Feature Representation Learning for NL2SQL Generation Based on Coupling
  and Decoupling
Chenduo Hao Xu Zhang Chuanbao Gao Deyu Zhou
The NL2SQL task involves parsing natural language statements into SQL
queries. While most state-of-the-art methods treat NL2SQL as a slot-filling
task and use feature representation learning techniques, they overlook explicit
correlation features between the SELECT and WHERE clauses and implicit
correlation features between sub-tasks within a single clause. To address this
issue, we propose the Clause Feature Correlation Decoupling and Coupling
(CFCDC) model, which uses a feature representation decoupling method to
separate the SELECT and WHERE clauses at the parameter level. Next, we
introduce a multi-task learning architecture to decouple implicit correlation
feature representation between different SQL tasks in a specific clause.
Moreover, we present an improved feature representation coupling module to
integrate the decoupled tasks in the SELECT and WHERE clauses and predict the
final SQL query. Our proposed CFCDC model demonstrates excellent performance on
the WikiSQL dataset, with significant improvements in logic precision and
execution accuracy. The source code for the model will be publicly available on
GitHub