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.

本文提出一种名为 ISESL-SQL 的框架，通过具有迭代性的语义增强架构图方法，构建了问题单词和数据库模式之间的联系，提高文本到 SQL 系统的泛化能力。实验结果表明，ISESL-SQL 优于其他基准模型，并展示了其在多种数据库上的鲁棒性和泛化能力。