Using Database Rule for Weak Supervised Text-to-SQL Generation

2019/07/01 by Tong Guo, Guo, Tong, Huilin Gao +1
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Topic Modeling #Web Data Mining and Analysis

paper · pdf · doi:10.48550/arxiv.1907.00620

openalex publication_date 2019/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present a simple way to do the task of text-to-SQL problem with weak supervision. We call it Rule-SQL. Given the question and the answer from the database table without the SQL logic form, Rule-SQL use the rules based on table column names and question string for the SQL exploration first and then use the explored SQL for supervised training. We design several rules for reducing the exploration search space. For the deep model, we leverage BERT for the representation layer and separate the model to SELECT, AGG and WHERE parts. The experiment result on WikiSQL outperforms the strong baseline of full supervision and is comparable to the start-of-the-art weak supervised mothods.

Citations

• ImageNet classification with deep convolutional neural networks
• Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
• SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning

Related

• Weakly Supervised Text-to-SQL Parsing through Question Decomposition
• SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data
• Synthesizing Text-to-SQL Data from Weak and Strong LLMs
• DB-Explore: Automated Database Exploration and Instruction Synthesis for Text-to-SQL
• Content Enhanced BERT-based Text-to-SQL Generation