ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser

• URL: http://arxiv.org/abs/2104.04689v2
• Date: Wed, 14 Apr 2021 07:06:55 GMT
• Title: ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser
• Authors: Zhi Chen, Lu Chen, Yanbin Zhao, Ruisheng Cao, Zihan Xu, Su Zhu and Kai Yu
• Abstract summary: We propose a new architecture, ShadowGNN, which processes schemas at abstract and semantic levels. On the challenging Text-to-Spider benchmark, empirical results show that ShadowGNN outperforms state-of-the-art models.
• Score: 36.12921337235763
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 \url{https://github.com/WowCZ/shadowgnn}.

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