Click here to flash read.
Relational databases play an important role in this Big Data era. However, it
is challenging for non-experts to fully unleash the analytical power of
relational databases, since they are not familiar with database languages such
as SQL. Many techniques have been proposed to automatically generate SQL from
natural language, but they suffer from two issues: (1) they still make many
mistakes, particularly for complex queries, and (2) they do not provide a
flexible way for non-expert users to validate and refine the incorrect queries.
To address these issues, we introduce a new interaction mechanism that allows
users directly edit a step-by-step explanation of an incorrect SQL to fix SQL
errors. Experiments on the Spider benchmark show that our approach outperforms
three SOTA approaches by at least 31.6% in terms of execution accuracy. A user
study with 24 participants further shows that our approach helped users solve
significantly more SQL tasks with less time and higher confidence,
demonstrating its potential to expand access to databases, particularly for
non-experts.
No creative common's license