GP: Context-free Grammar Pre-training for Text-to-SQL Parsers

• URL: http://arxiv.org/abs/2101.09901v2
• Date: Sun, 28 Feb 2021 15:35:56 GMT
• Title: GP: Context-free Grammar Pre-training for Text-to-SQL Parsers
• Authors: Liang Zhao, Hexin Cao, Yunsong Zhao
• Abstract summary: Grammar Pre-training (GP) is proposed to decode deep relations between question and database. Experiments show that our method is easier to converge during training and has excellent robustness.
• Score: 7.652782364282768
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A new method for Text-to-SQL parsing, Grammar Pre-training (GP), is proposed to decode deep relations between question and database. Firstly, to better utilize the information of databases, a random value is added behind a question word which is recognized as a column, and the new sentence serves as the model input. Secondly, initialization of vectors for decoder part is optimized, with reference to the former encoding so that question information can be concerned. Finally, a new approach called flooding level is adopted to get the non-zero training loss which can generalize better results. By encoding the sentence with GRAPPA and RAT-SQL model, we achieve better performance on spider, a cross-DB Text-to-SQL dataset (72.8 dev, 69.8 test). Experiments show that our method is easier to converge during training and has excellent robustness.

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