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The task of text-to-SQL parsing, which aims at converting natural language questions into executable SQL queries, has garnered increasing attention in recent years, as it can assist end users in efficiently extracting vital information from databases without the need for technical background.
Learning to Parse Database Queries Using Inductive Logic Programming
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Catastrophic forgetting in connectionist networks
French, R. M. 1999 · 1999
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Compositional Semantic Parsing on Semi-Structured Tables
Pasupat, P.; and Liang, P. 2015 · 2015
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Learning a Neural Semantic Parser from User Feedback
Iyer, S.; Konstas, I.; Cheung, A.; Krishnamurthy, J.; and Zettlemoyer, L. 2017 · 2017
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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
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Sqlnet: Generating structured queries from natural language without reinforcement learning
Xu, X.; Liu, C.; and Song, D. 2017 · 2017
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SQLizer: query synthesis from natural language
Yaghmazadeh, N.; Wang, Y.; Dillig, I.; and Dillig, T. 2017 · 2017
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Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
Zhong, V.; Xiong, C.; and Socher, R. 2017 · 2017
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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
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Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
Shazeer, N.; and Stern, M. 2018 · 2018
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The Web as a Knowledge-Base for Answering Complex Questions
Talmor, A.; and Berant, J. 2018 · 2018
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Graph Attention Networks
Velickovic, P.; Cucurull, G.; Casanova, A.; Romero, A.; Liò, P.; and Bengio, Y. 2018 · 2018
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Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing
Bogin, B.; Berant, J.; and Gardner, M. 2019 · 2019
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.-W.; 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.-G.; Liu, T.; and Zhang, D. 2019 · 2019
Cited alongside, same era.
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
Clark, K.; Luong, M.; Le, Q. V.; and Manning, C. D. 2020 · 2020
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Hierarchical Graph Network for Multi-hop Question Answering
Fang, Y.; Sun, S.; Gan, Z.; Pillai, R.; Wang, S.; and Liu, J. 2020 · 2020
Cited alongside, same era.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
DART: Open-Domain Structured Data Record to Text Generation
Nan, L.; Radev, D.; Zhang, R.; Rau, A.; Sivaprasad, A.; Hsieh, C.; Tang, X.; Vyas, A.; Verma, N.; Krishna, P.; Liu, Y.; Irwanto, N.; Pan, J.; Rahman, F.; Zaidi, A.; Mutuma, M.; Tarabar, Y.; Gupta, A.; Yu, T.; Tan, Y. C.; Lin, X. V.; Xiong, C.; Socher, R.; and Rajani, N. F. 2021 · 2021
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SmBoP: Semi-autoregressive Bottom-up Semantic Parsing
Rubin, O.; and Berant, J. 2021 · 2021
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PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models
Scholak, T.; Schucher, N.; and Bahdanau, D. 2021 · 2021
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Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both?
Shaw, P.; Chang, M.-W.; Pasupat, P.; and Toutanova, K. 2021 · 2021
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SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing
Yu, T.; Zhang, R.; Polozov, A.; Meek, C.; and Awadallah, A., Hassan. 2021 · 2021
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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.
Transformers: State-of-the-Art Natural Language Processing
Wolf, T.; Debut, L.; Sanh, V.; Chaumond, J.; Delangue, C.; Moi, A.; Cistac, P.; Rault, T.; Louf, R.; Funtowicz, M.; Davison, J.; Shleifer, S.; von Platen, P.; Ma, C.; Jernite, Y.; Plu, J.; Xu, C.; Le Scao, T.; Gugger, S.; Drame, M.; Lhoest, Q.; and Rush, A. 2020 · 2020
Cited alongside, same era.
Grounded Adaptation for Zero-shot Executable Semantic Parsing
Zhong, V.; Lewis, M.; Wang, S. I.; and Zettlemoyer, L. 2020 · 2020
Cited alongside, same era.
SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL
Cai, R.; Yuan, J.; Xu, B.; and Hao, Z. 2021 · 2021
Cited alongside, same era.
LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations
Cao, R.; Chen, L.; Chen, Z.; Zhao, Y.; Zhu, S.; and Yu, K. 2021 · 2021
Cited alongside, same era.
ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser
Chen, Z.; Chen, L.; Zhao, Y.; Cao, R.; Xu, Z.; Zhu, S.; and Yu, K. 2021 · 2021
Cited alongside, same era.
Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalization
Gan, Y.; Chen, X.; and Purver, M. 2021 · 2021
Cited alongside, same era.
STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing
Cai, Z.; Li, X.; Hui, B.; Yang, M.; Li, B.; Li, B.; Cao, Z.; Li, W.; Huang, F.; Si, L.; and Li, Y. 2022 · 2022
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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
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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
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Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing
Wang, L.; Qin, B.; Hui, B.; Li, B.; Yang, M.; Wang, B.; Li, B.; Huang, F.; Si, L.; and Li, Y. 2022 · 2022
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UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
Xie, T.; Wu, C. H.; Shi, P.; Zhong, R.; Scholak, T.; Yasunaga, M.; Wu, C.-S.; Zhong, M.; Yin, P.; Wang, S. I.; Zhong, V.; Wang, B.; Li, C.; Boyle, C.; Ni, A.; Yao, Z.; Radev, D.; Xiong, C.; Kong, L.; Zhang, R.; Smith, N. A.; Zettlemoyer, L.; and Yu, T. 2022 · 2022
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Ontology-enhanced Prompt-tuning for Few-shot Learning
Ye, H.; Zhang, N.; Deng, S.; Chen, X.; Chen, H.; Xiong, F.; Chen, X.; and Chen, H. 2022 · 2022
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