RECPARSER: A Recursive Semantic Parsing Framework for Text-to-SQL Task

Neural semantic parsers usually fail to parse long and complicated utterances into nested SQL queries, due to the large search space. In this paper, we propose a novel recursive semantic parsing framework called RECPARSER to generate the nested SQL query layer-by-layer. It decomposes the complicated nested SQL query generation problem into several progressive non-nested SQL query generation problems. Furthermore, we propose a novel Question Decomposer module to explicitly encourage RECPARSER to focus on different components of an utterance when predicting SQL queries of different layers. Experiments on the Spider dataset show that our approach is more effective compared to the previous works at predicting the nested SQL queries. In addition, we achieve an overall accuracy that is comparable with state-of-the-art approaches.

Authors

• Yu Zeng University of Science and Technology of China
• Yan Gao Xiaohongshu Inc
• Jiaqi Guo
• Bei Chen
• Qian Liu Sea AI Lab
• Jian-Guang Lou Microsoft Research Asia
• Fei Teng Department of EEE, Imperial College London, UK
• Dongmei Zhang Peking University

Keywords

• Natural Language Processing: Natural Language Generation
• Natural Language Processing: Natural Language Processing
• Natural Language Processing: Natural Language Semantics