Semantic Decomposition of Question and SQL for Text-to-SQL Parsing
Ben Eyal, Amir Bachar, Ophir Haroche, Moran Mahabi, Michael Elhadad

TL;DR
This paper introduces QPL, a modular language for decomposing complex SQL queries into simpler sub-queries, improving text-to-SQL parsing, especially for cross-domain and complex queries, by augmenting datasets and enabling question decomposition.
Contribution
The paper proposes a new Query Plan Language (QPL) for systematic SQL decomposition, along with a translation method and dataset augmentation, enhancing semantic parsing and interpretability.
Findings
QPL improves parsing accuracy on complex queries
Training with QPL yields more effective question decomposers
QPL-based methods are more interpretable and schema-sensitive
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · Web Data Mining and Analysis
