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

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

1 Jul 2019arXiv:1907.00620archive 2025-07-28

Tong Guo, Huilin Gao

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.

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guotong1988/Rule-SQL officialmentioned in papermentioned on GitHubpytorch report

guotong1988/weak-supervised-Rule-Text2SQL mentioned on GitHubpytorch report

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Text to SQL Text-To-SQL

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Adam Attention Attention Dropout BERT Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Weight Decay WordPiece

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