Understand
In this work, we present X-SQL, a new network architecture for the problem of parsing natural language to SQL query.
- X-SQL proposes to enhance the structural schema representation with the contextual output from BERT-style pre-training model, and together with type information to learn a new schema representation for down-stream tasks.
- We evaluated X-SQL on the WikiSQL dataset and show its new state-of-the-art performance.
Built on
Multi-task deep neural networks for natural language understanding
Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. 2019 · 1901
Earlier work this paper cites.
SQLNet: Generating structured queries from natural language without reinforcement learning
Xiaojun Xu, Chang Liu, and Dawn Song. 2017 · 2017
Earlier work this paper cites.
Seq2SQL: Generating structured queries from natural language using reinforcement learning
Victor Zhong, Caiming Xiong, and Richard Socher. 2017 · 2017
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Earlier work this paper cites.
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Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task
Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, and Dragomir Radev. 2018b
Cited in the paper.
Then
Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018a · 2018
Later among the works it cites.
TypeSQL: Knowledge-based type-aware neural text-to-SQL generation
Tao Yu, Zifan Li, Zilin Zhang, Rui Zhang, and Dragomir Radev. 2018a · 2018
Later among the works it cites.
A comprehensive exploration on WikiSQL with table-aware word contextualization
Wonseok Hwang, Jinyeung Yim, Seunghyun Park, and Minjoon Seo. 2019 · 2019
Closest in time.
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