Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking Graph

• URL: http://arxiv.org/abs/2208.03903v1
• Date: Mon, 8 Aug 2022 03:59:33 GMT
• Title: Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking Graph
• Authors: Aiwei Liu, Xuming Hu, Li Lin and Lijie Wen
• Abstract summary: The generalizability to new databases is of vital importance to Text-to- systems which aim to parse human utterances intosql statements. In this paper, we propose a framework named IS ESL to iteratively build a enhanced semantic schema-linking graph between question tokens and database schemas. Extensive experiments on three benchmarks demonstrate that IS ESL could consistently outperform the baselines and further investigations show its generalizability and robustness.
• Score: 6.13728903057727
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The generalizability to new databases is of vital importance to Text-to-SQL systems which aim to parse human utterances into SQL statements. Existing works achieve this goal by leveraging the exact matching method to identify the lexical matching between the question words and the schema items. However, these methods fail in other challenging scenarios, such as the synonym substitution in which the surface form differs between the corresponding question words and schema items. In this paper, we propose a framework named ISESL-SQL to iteratively build a semantic enhanced schema-linking graph between question tokens and database schemas. First, we extract a schema linking graph from PLMs through a probing procedure in an unsupervised manner. Then the schema linking graph is further optimized during the training process through a deep graph learning method. Meanwhile, we also design an auxiliary task called graph regularization to improve the schema information mentioned in the schema-linking graph. Extensive experiments on three benchmarks demonstrate that ISESL-SQL could consistently outperform the baselines and further investigations show its generalizability and robustness.

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