Relational database management systems (RDBMSs) are powerful because they are able to optimize and answer queries against any relational database. A natural language interface (NLI) for a database, on the other hand, is tailored to support that specific database. In this work, we introduce a general purpose transfer-learnable NLI with the goal of learning one model that can be used as NLI for any relational database. We adopt the data management principle of separating data and its schema, but with the additional support for the idiosyncrasy and complexity of natural languages. Specifically, we introduce an automatic annotation mechanism that separates the schema and the data, where the schema also covers knowledge about natural language. Furthermore, we propose a customized sequence model that translates annotated natural language queries to SQL statements. We show in experiments that our approach outperforms previous NLI methods on the WikiSQL dataset and the model we learned can be applied to another benchmark dataset OVERNIGHT without retraining.
Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL
Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算机系）；Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）；College of Cyber Security, Jinan University（暨南大学网络安全学院）
Text-to-OverpassQL: A Natural Language Interface for Complex Geodata Querying of OpenStreetMap
School of Informatics, University of Edinburgh（爱丁堡大学信息学院）；Computational Linguistics, Heidelberg University（海德堡大学计算语言学系）；IWR, Heidelberg University（海德堡大学应用科学计算研究所）
Development of Semantic Web-based Imaging Database for Biological Morphome
RIKEN Center for Life Science Technologies (CLST)（日本理化学研究所生命科学技术中心）；RIKEN CLST-JEOL Collaboration Center（日本理化学研究所CLST-JEOL合作中心）；RIKEN Compass to Healthy Life Research Complex Program（日本理化学研究所健康生活研究综合体指南项目）；RIKEN BioResource Center (BRC)（日本理化学研究所生物资源中心）；Advanced Center for Computing and Communication (ACCC), RIKEN（日本理化学研究所高级计算与通信中心）
