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Content Enhanced BERT-based Text-to-SQL Generation

16 Oct 2019arXiv:1910.07179archive 2025-07-28

Tong Guo, Huilin Gao

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.

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Code

guotong1988/NL2SQL-RULE officialmentioned in papermentioned on GitHubpytorch report
guotong1988/NL2SQL-BERT officialmentioned on GitHubpytorch report
realsonalkumar/Mish-Mash-Hackathon mentioned on GitHubpytorch report
shivam017arora/Conversational-BI mentioned on GitHubpytorch report
yangyucheng000/University mindspore report

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Tasks

Code GenerationSemantic ParsingText to SQLText-To-SQL

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Code Generation WikiSQL NL2SQL-RULE Exact Match Accuracy 83.7 #1 of 10 Archive leaderboard report
Code Generation WikiSQL NL2SQL-RULE Execution Accuracy 89.2 #1 of 10 Archive leaderboard report
Semantic Parsing WikiSQL NL2SQL-BERT Accuracy 89 #1 of 5 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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