Content Enhanced BERT-based Text-to-SQL Generation
This work addresses SQL query generation from natural language for database applications, representing an incremental improvement over existing BERT-based methods.
The paper tackles the text-to-SQL problem by incorporating table content features into a BERT-based model, achieving state-of-the-art results on the WikiSQL dataset with a 3.7% improvement in both logic form and execution accuracy over the baseline.
We present a simple methods to leverage the table content for the BERT-based model to solve the text-to-SQL problem. Based on the observation that some of the table content match some words in question string and some of the table header also match some words in question string, we encode two addition feature vector for the deep model. Our methods also benefit the model inference in testing time as the tables are almost the same in training and testing time. We test our model on the WikiSQL dataset and outperform the BERT-based baseline by 3.7% in logic form and 3.7% in execution accuracy and achieve state-of-the-art.