Fetching the paper…
Reading the bibliography…
Natural language to SQL (NL2SQL) aims to parse a natural language with a given database into a SQL query, which widely appears in practical Internet applications.
ELECTRA: pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. 2020 · 2003
Earlier work this paper cites.
Optimizing deeper transformers on small datasets: An application on text-to-sql semantic parsing
Peng Xu, Wei Yang, Wenjie Zi, Keyi Tang, Chengyang Huang, Jackie Chi Kit Cheung, and Yanshuai Cao. 2020 · 2012
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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.
A syntactic neural model for general-purpose code generation
Pengcheng Yin and Graham Neubig. 2017 · 2017
Earlier work this paper cites.
Deep learning using rectified linear units (relu)
Abien Fred Agarap. 2018 · 2018
Earlier work this paper cites.
Coarse-to-fine decoding for neural semantic parsing
Li Dong and Mirella Lapata. 2018 · 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, et al. 2018 · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Tree-structured decoding for solving math word problems
Qianying Liu, Wenyv Guan, Sujian Li, and Daisuke Kawahara. 2019 · 2019
Cited alongside, same era.
A goal-driven tree-structured neural model for math word problems
Zhipeng Xie and Shichao Sun. 2019 · 2019
Cited alongside, same era.
Graph-to-tree learning for solving math word problems
Jipeng Zhang, Lei Wang, Roy Ka-Wei Lee, Yi Bin, Yan Wang, Jie Shao, and Ee-Peng Lim. 2020 · 2020
Later among the works it cites.
Lgesql: Line graph enhanced text-to-sql model with mixed local and non-local relations
Ruisheng Cao, Lu Chen, Zhi Chen, Su Zhu, and Kai Yu. 2021 · 2021
Closest in time.
Structure-grounded pretraining for text-to-sql
Xiang Deng, Ahmed Hassan, Christopher Meek, Oleksandr Polozov, Huan Sun, and Matthew Richardson. 2021 · 2021
Closest in time.
SmBoP: Semi-autoregressive bottom-up semantic parsing
Ohad Rubin and Jonathan Berant. 2021 · 2021
Closest in time.
Learning contextual representations for semantic parsing with generation-augmented pre-training
Peng Shi, Patrick Ng, Zhiguo Wang, Henghui Zhu, Alexander Hanbo Li, Jun Wang, Cicero Nogueira dos Santos, and Bing Xiang. 2021 · 2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Tao Yu, Chien-Sheng Wu, Xi Victoria Lin, Yi Chern Tan, Xinyi Yang, Dragomir Radev, Caiming Xiong, et al. 2020 · 2020
Cited alongside, same era.
RAT-SQL: Relation-aware schema encoding and linking for text-to-SQL parsers
Bailin Wang, Richard Shin, Xiaodong Liu, Oleksandr Polozov, and Matthew Richardson. 2020a
Cited in the paper.
RAT-SQL: Relation-aware schema encoding and linking for text-to-SQL parsers
Bailin Wang, Richard Shin, Xiaodong Liu, Oleksandr Polozov, and Matthew Richardson. 2020b
Cited in the paper.
Compositional generalization for neural semantic parsing via span-level supervised attention
Pengcheng Yin, Hao Fang, Graham Neubig, Adam Pauls, Emmanouil Antonios Platanios, Yu Su, Sam Thomson, and Jacob Andreas. 2021 · 2021
Closest in time.