TexttoSQL

Text-to-SQL is a natural language processing task that translates a user’s natural language question or request into a structured SQL query that can be executed against a database. The goal is to allow non-technical users to retrieve information without writing SQL themselves, by expressing what they want in ordinary language and letting the system generate the corresponding query.

Inputs typically include a natural language query paired with a database schema, and the output is a SQL statement that may include selection, filtering, aggregation, sorting, and multi-table joins. The challenge is to produce a correct and efficient query that faithfully captures the user’s intent while respecting the schema’s structure and constraints.

Approaches to text-to-SQL range from traditional semantic parsing, which builds interpretable mappings from language to formal representations, to modern neural models that learn to generate SQL directly from NL and schema information. Neural methods often involve schema linking to connect mentions in the query to specific tables and columns, and may use execution-guided decoding to prune invalid queries. Transformer-based and sequence-to-sequence architectures are common, sometimes augmented with retrieval or weak supervision signals.

Prominent datasets include WikiSQL, which focuses on single-database and relatively simple queries, and Spider, which provides cross-domain, complex queries across multiple databases. Evaluation typically uses exact-match accuracy against reference SQL and, in some setups, execution accuracy by running the generated queries.

Key challenges include handling nested and multi-join queries, precise schema grounding, generalization to unseen databases, disambiguation of user intent, and maintaining robustness with limited training data. Text-to-SQL research supports more accessible data analysis and has influenced broader natural language interfaces to databases.