Improving Text-to-SQL Queries with Rephrased Questions: A Comparative Study

The article discusses the development of Text2SQL models, which are capable of converting natural language into SQL queries. The authors explain that traditional approaches to Text2SQL relied on rule-based and template-based methods, but with the advent of deep learning, especially sequence-to-sequence models with attention mechanisms, the field has seen significant progress. They highlight the importance of standardized benchmarks for evaluating Text2SQL models, such as the WikiSQL and ATIS datasets, which have been instrumental in advancing the capabilities of semantic parsing models. The authors then introduce a new dataset called CoSQL, which is particularly noteworthy due to its complex and cross-domain nature, and highlights the involvement of 7 Yale students who meticulously annotated the data.
To evaluate the performance of Text2SQL models, the authors propose a novel distance-to-maximum-nodes normalization technique that considers the complexity of queries in addition to accuracy. This approach allows for a more comprehensive and meaningful evaluation of Text2SQL conversion techniques, as it accounts for variations in query complexity and structure. The article concludes by presenting the results of experiments conducted on the CoSQL dataset using different Text2SQL models, which demonstrate the effectiveness of the proposed normalization technique.