Given a database schema, Text-to-SQL aims to translate a natural language
question into the corresponding SQL query. Under the setup of cross-domain,
traditional semantic parsing models struggle to adapt to unseen database
schemas. To improve the model generalization capability for rare and unseen
schemas, we propose a new architecture, ShadowGNN, which processes schemas at
abstract and semantic levels. By ignoring names of semantic items in databases,
abstract schemas are exploited in a well-designed graph projection neural
network to obtain delexicalized representation of question and schema. Based on
the domain-independent representations, a relation-aware transformer is
utilized to further extract logical linking between question and schema.
Finally, a SQL decoder with context-free grammar is applied. On the challenging
Text-to-SQL benchmark Spider, empirical results show that ShadowGNN outperforms
state-of-the-art models. When the annotated data is extremely limited (only
10\% training set), ShadowGNN gets over absolute 5\% performance gain, which
shows its powerful generalization ability. Our implementation will be
open-sourced at https://github.com/WowCZ/shadowgnn.

该研究提出了一种新的基于 ShadowGNN 的文本到 SQL 转化模型，通过抽象和语义级别处理数据库模式以提高模型对不常见和没有看到过的模式的泛化能力，并在 Text-to-SQL 基准测试中表现优异。