TAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing

TAR (Table-aware Pre-training with Abstract Reasoning) is a pre-training framework for context-dependent text-to-SQL parsing. It leverages SQL knowledge and utilizes abstract reasoning to better understand the context of a natural language query and generate accurate SQL queries.

The TAR model works by first pre-training on a large corpus of text and SQL pairs to learn the general patterns and structures of SQL queries. It then fine-tunes on a smaller dataset of context-dependent text-to-SQL examples to adapt to specific contexts and improve accuracy.

One unique aspect of TAR is its use of table-aware pre-training, which allows the model to incorporate information from the table schema into the pre-training process. This helps the model better understand the relationships between tables and columns, and improves its ability to generate accurate SQL queries.

TAR also incorporates abstract reasoning, which allows the model to make inferences and understand implicit relationships between words and concepts. This helps the model handle more complex queries and improves its overall performance.

Overall, TAR is a promising approach to improving context-dependent text-to-SQL parsing, and has shown strong results on several benchmark datasets.