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2019

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

Guo, Tong, Gao, Huilin

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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.

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

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Liang C, Berant J, Le Q, et al. Neural symbolic machines: Learning semantic parsers on freebase with weak supervision[J]. arXiv preprint arXiv:1611.00020, 2016
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Pasupat P, Liang P. Inferring Logical Forms From Denotations[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016: 23-32
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Then

Wang C, Tatwawadi K, Brockschmidt M, et al. Robust text-to-sql generation with execution-guided decoding[J]. arXiv preprint arXiv:1807.03100, 2018
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2018
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Yu T, Li Z, Zhang Z, et al. TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation[J]. arXiv preprint arXiv:1804.09769, 2018
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Dong, Li, and Mirella Lapata. ”Coarse-to-Fine Decoding for Neural Semantic Parsing.” arXiv preprint arXiv:1805.04793 (2018)
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Liang C, Norouzi M, Berant J, et al. Memory augmented policy optimization for program synthesis and semantic parsing[C]//Advances in Neural Information Processing Systems. 2018: 9994-10006
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