Content Enhanced BERT-based Text-to-SQL Generation

2019/10/16 by Tong Guo, Guo, Tong, Huilin Gao +1 · 2 citations
Computer Science · Decision Sciences · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Scientific Computing and Data Management #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1910.07179

openalex publication_date 2019/10/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• A Comprehensive Exploration on WikiSQL with Table-Aware Word\n Contextualization

Cited by

• CLTR: An End-to-End, Transformer-Based System for Cell Level Table Retrieval and Table Question Answering
• Turing: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface

Related

• A Comprehensive Exploration on WikiSQL with Table-Aware Word\n Contextualization
• PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency
• Using Database Rule for Weak Supervised Text-to-SQL Generation
• Bertrand-DR: Improving Text-to-SQL using a Discriminative Re-ranker
• Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL