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One of the recent best attempts at Text-to-SQL is the pre-trained language model.
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; Levy, O.; Lewis, M.; Zettlemoyer, L.; and Stoyanov, V. 2019 · 1907
Earlier work this paper cites.
Learning to Parse Database Queries Using Inductive Logic Programming
Zelle, J. M.; and Mooney, R. J. 1996 · 1996
Earlier work this paper cites.
Long Short-Term Memory
Hochreiter, S.; and Schmidhuber, J. 1997 · 1997
Earlier work this paper cites.
Automatic Generation and Reranking of SQL-derived Answers to NL Questions
Giordani, A.; and Moschitti, A. 2012 · 2012
Earlier work this paper cites.
Learning a Neural Semantic Parser from User Feedback
Iyer, S.; Konstas, I.; Cheung, A.; Krishnamurthy, J.; and Zettlemoyer, L. 2017 · 2017
Earlier work this paper cites.
Neural Semantic Parsing with Type Constraints for Semi-Structured Tables
Krishnamurthy, J.; Dasigi, P.; and Gardner, M. 2017 · 2017
Earlier work this paper cites.
Focal Loss for Dense Object Detection
Lin, T.; Goyal, P.; Girshick, R. B.; He, K.; and Dollár, P. 2017 · 2017
Earlier work this paper cites.
Attention is All you Need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; and Polosukhin, I. 2017 · 2017
Earlier work this paper cites.
A Syntactic Neural Model for General-Purpose Code Generation
Yin, P.; and Neubig, G. 2017 · 2017
Earlier work this paper cites.
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
Zhong, V.; Xiong, C.; and Socher, R. 2017 · 2017
Earlier work this paper cites.
An Encoder-Decoder Framework Translating Natural Language to Database Queries
Cai, R.; Xu, B.; Zhang, Z.; Yang, X.; Li, Z.; and Liang, Z. 2018 · 2018
Earlier work this paper cites.
Modeling Relational Data with Graph Convolutional Networks
Schlichtkrull, M. S.; Kipf, T. N.; Bloem, P.; van den Berg, R.; Titov, I.; and Welling, M. 2018 · 2018
Earlier work this paper cites.
Self-Attention with Relative Position Representations
Shaw, P.; Uszkoreit, J.; and Vaswani, A. 2018 · 2018
Earlier work this paper cites.
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
Shazeer, N.; and Stern, M. 2018 · 2018
Earlier work this paper cites.
Robust Text-to-SQL Generation with Execution-Guided Decoding
Wang, C.; Tatwawadi, K.; Brockschmidt, M.; Huang, P.-S.; Mao, Y.; Polozov, O.; and Singh, R. 2018 · 2018
Earlier work this paper cites.
TypeSQL: Knowledge-Based Type-Aware Neural Text-to-SQL Generation
Yu, T.; Li, Z.; Zhang, Z.; Zhang, R.; and Radev, D. R. 2018a · 2018
Earlier work this paper cites.
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task
Yu, T.; Zhang, R.; Yang, K.; Yasunaga, M.; Wang, D.; Li, Z.; Ma, J.; Li, I.; Yao, Q.; Roman, S.; Zhang, Z.; and Radev, D. R. 2018c · 2018
Cited alongside, same era.
Global Reasoning over Database Structures for Text-to-SQL Parsing
Bogin, B.; Gardner, M.; and Berant, J. 2019 · 2019
Cited alongside, same era.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.; Lee, K.; and Toutanova, K. 2019 · 2019
Cited alongside, same era.
Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation
Guo, J.; Zhan, Z.; Gao, Y.; Xiao, Y.; Lou, J.; Liu, T.; and Zhang, D. 2019 · 2019
Cited alongside, same era.
Decoupled Weight Decay Regularization
Loshchilov, I.; and Hutter, F. 2019 · 2019
Cited alongside, same era.
Structure-Grounded Pretraining for Text-to-SQL
Deng, X.; Awadallah, A. H.; Meek, C.; Polozov, O.; Sun, H.; and Richardson, M. 2021 · 2021
Later among the works it cites.
Towards Robustness of Text-to-SQL Models against Synonym Substitution
Gan, Y.; Chen, X.; Huang, Q.; Purver, M.; Woodward, J. R.; Xie, J.; and Huang, P. 2021a · 2021
Later among the works it cites.
Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalization
Gan, Y.; Chen, X.; and Purver, M. 2021 · 2021
Later among the works it cites.
SmBoP: Semi-autoregressive Bottom-up Semantic Parsing
Rubin, O.; and Berant, J. 2021 · 2021
Later among the works it cites.
PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models
Scholak, T.; Schucher, N.; and Bahdanau, D. 2021 · 2021
Later among the works it cites.
Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both?
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Chen, D.; Lin, Y.; Li, W.; Li, P.; Zhou, J.; and Sun, X. 2020 · 2020
Cited alongside, same era.
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
Lewis, M.; Liu, Y.; Goyal, N.; Ghazvininejad, M.; Mohamed, A.; Levy, O.; Stoyanov, V.; and Zettlemoyer, L. 2020 · 2020
Cited alongside, same era.
Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing
Lin, X. V.; Socher, R.; and Xiong, C. 2020 · 2020
Cited alongside, same era.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C.; Shazeer, N.; Roberts, A.; Lee, K.; Narang, S.; Matena, M.; Zhou, Y.; Li, W.; and Liu, P. J. 2020 · 2020
Cited alongside, same era.
Exploring Unexplored Generalization Challenges for Cross-Database Semantic Parsing
Suhr, A.; Chang, M.; Shaw, P.; and Lee, K. 2020 · 2020
Cited alongside, same era.
RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers
Wang, B.; Shin, R.; Liu, X.; Polozov, O.; and Richardson, M. 2020a · 2020
Cited alongside, same era.
Relational Graph Attention Network for Aspect-based Sentiment Analysis
Wang, K.; Shen, W.; Yang, Y.; Quan, X.; and Wang, R. 2020b · 2020
Cited alongside, same era.
Shaw, P.; Chang, M.; Pasupat, P.; and Toutanova, K. 2021 · 2021
Later among the works it cites.
Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training
Shi, P.; Ng, P.; Wang, Z.; Zhu, H.; Li, A. H.; Wang, J.; dos Santos, C. N.; and Xiang, B. 2021 · 2021
Later among the works it cites.
GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing
Yu, T.; Wu, C.; Lin, X. V.; Wang, B.; Tan, Y. C.; Yang, X.; Radev, D. R.; Socher, R.; and Xiong, C. 2021 · 2021
Later among the works it cites.
When Do You Need Billions of Words of Pretraining Data?
Zhang, Y.; Warstadt, A.; Li, X.; and Bowman, S. R. 2021 · 2021
Later among the works it cites.
Towards Generalizable and Robust Text-to-SQL Parsing
Gao, C.; Li, B.; Zhang, W.; Lam, W.; Li, B.; Huang, F.; Si, L.; and Li, Y. 2022 · 2022
Later among the works it cites.
S 2 SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers
Hui, B.; Geng, R.; Wang, L.; Qin, B.; Li, Y.; Li, B.; Sun, J.; and Li, Y. 2022 · 2022
Later among the works it cites.
RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL
Qi, J.; Tang, J.; He, Z.; Wan, X.; Cheng, Y.; Zhou, C.; Wang, X.; Zhang, Q.; and Lin, Z. 2022 · 2022
Later among the works it cites.
SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers
Qin, B.; Wang, L.; Hui, B.; Li, B.; Wei, X.; Li, B.; Huang, F.; Si, L.; Yang, M.; and Li, Y. 2022 · 2022
Later among the works it cites.
Natural SQL: Making SQL Easier to Infer from Natural Language Specifications
Gan, Y.; Chen, X.; Xie, J.; Purver, M.; Woodward, J. R.; Drake, J. H.; and Zhang, Q. 2021b · 2042
Closest in time.
Optimizing Deeper Transformers on Small Datasets
Xu, P.; Kumar, D.; Yang, W.; Zi, W.; Tang, K.; Huang, C.; Cheung, J. C. K.; Prince, S. J. D.; and Cao, Y. 2021 · 2089
Closest in time.