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Semantic parsing datasets are expensive to collect.
Rat-sql: Relation-aware schema encoding and linking for text-to-sql parsers
Bailin Wang, Richard Shin, Xiaodong Liu, Oleksandr Polozov, and Matthew Richardson. 2019 · 1911
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998 · 1998
Earlier work this paper cites.
Speak to your parser: Interactive text-to-sql with natural language feedback
Ahmed Elgohary, Saghar Hosseini, and Ahmed Hassan Awadallah. 2020 · 2005
Earlier work this paper cites.
Grappa: Grammar-augmented pre-training for table semantic parsing
Tao Yu, Chien-Sheng Wu, Xi Victoria Lin, Bailin Wang, Yi Chern Tan, Xinyi Yang, Dragomir Radev, Richard Socher, and Caiming Xiong. 2020 · 2009
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012 · 2012
Earlier work this paper cites.
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. 2020 · 2012
Earlier work this paper cites.
Building a semantic parser overnight
Yushi Wang, Jonathan Berant, and Percy Liang. 2015 · 2015
Earlier work this paper cites.
Data recombination for neural semantic parsing
Robin Jia and Percy Liang. 2016 · 2016
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.
Tagged back-translation
Isaac Caswell, Ciprian Chelba, and David Grangier. 2019 · 2019
Cited alongside, same era.
A survey on image data augmentation for deep learning
Connor Shorten and Taghi M Khoshgoftaar. 2019 · 2019
Cited alongside, same era.
EDA: Easy data augmentation techniques for boosting performance on text classification tasks
Jason Wei and Kai Zou. 2019a · 2019
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 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.
Lgesql: Line graph enhanced text-to-sql model with mixed local and non-local relations
Ruisheng Cao, Lu Chen, Zhi Chen, Yanbin Zhao, Su Zhu, and Kai Yu. 2021 · 2021
Closest in time.
Picard: Parsing incrementally for constrained auto-regressive decoding from language models
Torsten Scholak, Nathan Schucher, and Dzmitry Bahdanau. 2021 · 2021
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Learning to synthesize data for semantic parsing
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Jacob Andreas. 2020 · 2020
Cited alongside, same era.
Eda: Easy data augmentation techniques for boosting performance on text classification tasks
Jason Wei and Kai Zou. 2019b
Cited in the paper.
Optimizing deeper transformers on small datasets
Peng Xu, Dhruv Kumar, Wei Yang, Wenjie Zi, Keyi Tang, Chenyang Huang, Jackie Chi Kit Cheung, Simon J. D. Prince, and Yanshuai Cao. 2021a
Cited in the paper.
Peng Xu, Wenjie Zi, Hamidreza Shahidi, Ákos Kádár, Keyi Tang, Wei Yang, Jawad Ateeq, Harsh Barot, Meidan Alon, and Yanshuai Cao. 2021b
Cited in the paper.
Bailin Wang, Wenpeng Yin, Xi Victoria Lin, and Caiming Xiong. 2021 · 2021
Closest in time.
Gp: Context-free grammar pre-training for text-to-sql parsers
Liang Zhao, Hexin Cao, and Yunsong Zhao. 2021 · 2021
Closest in time.