  Text-to-SQL semantic parsing faces challenges in generalizing to cross-domain
and complex queries. Recent research has employed a question decomposition
strategy to enhance the parsing of complex SQL queries. However, this strategy
encounters two major obstacles: (1) existing datasets lack question
decomposition; (2) due to the syntactic complexity of SQL, most complex queries
cannot be disentangled into sub-queries that can be readily recomposed. To
address these challenges, we propose a new modular Query Plan Language (QPL)
that systematically decomposes SQL queries into simple and regular sub-queries.
We develop a translator from SQL to QPL by leveraging analysis of SQL server
query optimization plans, and we augment the Spider dataset with QPL programs.
Experimental results demonstrate that the modular nature of QPL benefits
existing semantic-parsing architectures, and training text-to-QPL parsers is
more effective than text-to-SQL parsing for semantically equivalent queries.
The QPL approach offers two additional advantages: (1) QPL programs can be
paraphrased as simple questions, which allows us to create a dataset of
(complex question, decomposed questions). Training on this dataset, we obtain a
Question Decomposer for data retrieval that is sensitive to database schemas.
(2) QPL is more accessible to non-experts for complex queries, leading to more
interpretable output from the semantic parser.
