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Addressing the mismatch between natural language descriptions and the corresponding SQL queries is a key challenge for text-to-SQL translation.
Semantics and Quantification in Natural Language Question Answering
W.A. Woods. 1978 · 1978
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An Efficient Easily Adaptable System for Interpreting Natural Language Queries
David H D Warren and Fernando C N Pereira. 1982 · 1982
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Natural language interfaces to databases – an introduction
I Androutsopoulos, G D Ritchie, and P Thanisch. 1995 · 1995
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Automated Construction of Database Interfaces: Intergrating Statistical and Relational Learning for Semantic Parsing
Lappoon R Tang and Raymond J Mooney. 2000 · 2000
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Towards a Theory of Natural Language Interfaces to Databases
Ana-Maria Popescu, Oren Etzioni, and Henry Kautz. 2003 · 2003
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Modern natural language interfaces to databases: composing statistical parsing with semantic tractability
Ana-Maria Popescu, Alex Armanasu, Oren Etzioni, David Ko, and Alexander Yates. 2004 · 2004
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Constructing a Generic Natural Language Interface for an XML Database
Yunyao Li, Huahai Yang, and H V Jagadish. 2006 · 2006
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Automatic Generation and Reranking of SQL-derived Answers to NL Questions
Alessandra Giordani and Alessandro Moschitti. 2012 · 2012
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Learning contextual representations for semantic parsing with generation-augmented pre-training
Peng Shi, Patrick Ng, Zhiguo Wang, Henghui Zhu, Alexander Hanbo Li, Jun Wang, Cícero Nogueira dos Santos, and Bing Xiang. 2020 · 2012
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Constructing an interactive natural language interface for relational databases
Fei Li and H. V. Jagadish. 2014 · 2014
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Schema-free SQL
Fei Li, Tianyin Pan, and Hosagrahar V. Jagadish. 2014 · 2014
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Learning a Neural Semantic Parser from User Feedback
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Jayant Krishnamurthy, and Luke Zettlemoyer. 2017 · 2017
Cited alongside, same era.
SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning
Xiaojun Xu, Chang Liu, and Dawn Song. 2017 · 2017
Cited alongside, same era.
SQLizer: Query Synthesis from Natural Language
Navid Yaghmazadeh, Yuepeng Wang, Isil Dillig, and Thomas Dillig. 2017 · 2017
Cited alongside, same era.
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
Victor Zhong, Caiming Xiong, and Richard Socher. 2017 · 2017
Cited alongside, same era.
Coarse-to-Fine Decoding for Neural Semantic Parsing
Li Dong and Mirella Lapata. 2018 · 2018
Cited alongside, same era.
Improving text-to-SQL evaluation methodology
Catherine Finegan-Dollak, Jonathan K. Kummerfeld, Li Zhang, Karthik Ramanathan, Sesh Sadasivam, Rui Zhang, and Dragomir Radev. 2018 · 2018
Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation
Jiaqi Guo, Zecheng Zhan, Yan Gao, Yan Xiao, Jian-Guang Lou, Ting Liu, and Dongmei Zhang. 2019 · 2019
Later among the works it cites.
X-SQL: REINFORCE CONTEXT INTO SCHEMA REPRESENTATION
Pengcheng He, Yi Mao, Kaushik Chakrabarti, and Weizhu Chen. 2019 · 2019
Later among the works it cites.
Clause-Wise and Recursive Decoding for Complex and Cross-Domain Text-to-SQL Generation
Dongjun Lee. 2019 · 2019
Later among the works it cites.
Editing-based SQL query generation for cross-domain context-dependent questions
Rui Zhang, Tao Yu, Heyang Er, Sungrok Shim, Eric Xue, Xi Victoria Lin, Tianze Shi, Caiming Xiong, Richard Socher, and Dragomir Radev. 2019 · 2019
Later among the works it cites.
Neural approaches for natural language interfaces to databases: A survey
Radu Cristian Alexandru Iacob, Florin Brad, Elena-Simona Apostol, Ciprian-Octavian Truică, Ionel Alexandru Hosu, and Traian Rebedea. 2020 · 2020
Later among the works it cites.
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Cited alongside, same era.
Robust Text-to-SQL Generation with Execution-Guided Decoding
Chenglong Wang, Kedar Tatwawadi, Marc Brockschmidt, Po-Sen Huang, Yi Mao, Oleksandr Polozov, and Rishabh Singh. 2018 · 2018
Cited alongside, same era.
SyntaxSQLNet: Syntax tree networks for complex and cross-domain text-to-SQL task
Tao Yu, Michihiro Yasunaga, Kai Yang, Rui Zhang, Dongxu Wang, Zifan Li, and Dragomir Radev. 2018a · 2018
Cited alongside, same era.
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 · 2018
Cited alongside, same era.
Global Reasoning over Database Structures for Text-to-SQL Parsing
Ben Bogin, Matt Gardner, and Jonathan Berant. 2019b · 2019
Cited alongside, same era.
Representing schema structure with graph neural networks for text-to-SQL parsing
Ben Bogin, Jonathan Berant, and Matt Gardner. 2019a
Cited in the paper.
Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing
Xi Victoria Lin, Richard Socher, and Caiming Xiong. 2020 · 2020
Later among the works it cites.
RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers
Bailin Wang, Richard Shin, Xiaodong Liu, Oleksandr Polozov, and Matthew Richardson. 2020 · 2020
Later among the works it cites.
Grounded adaptation for zero-shot executable semantic parsing
Victor Zhong, Mike Lewis, Sida I Wang, and Luke Zettlemoyer. 2020 · 2020
Later among the works it cites.
Unlocking compositional generalization in pre-trained models using intermediate representations
Jonathan Herzig, Peter Shaw, Ming-Wei Chang, Kelvin Guu, Panupong Pasupat, and Yuan Zhang. 2021 · 2021
Closest in time.
SmBoP: Semi-autoregressive bottom-up semantic parsing
Ohad Rubin and Jonathan Berant. 2021 · 2021
Closest in time.