Fetching the paper…
Reading the bibliography…
Despite recent progress in text-to-SQL parsing, current semantic parsers are still not accurate enough for practical use.
Note on the sampling error of the difference between correlated proportions or percentages
Quinn McNemar. 1947 · 1947
Earlier work this paper cites.
Ohio supercomputer center
Ohio Supercomputer Center. 1987 · 1987
Earlier work this paper cites.
Learning to parse database queries using inductive logic programming
John M. Zelle and Raymond J. Mooney. 1996 · 1996
Earlier work this paper cites.
Automated construction of database interfaces: Integrating statistical and relational learning for semantic parsing
Lappoon R. Tang and Raymond J. Mooney. 2000 · 2000
Earlier work this paper cites.
A systematic review of software development cost estimation studies
Magne Jorgensen and Martin Shepperd. 2007 · 2007
Earlier work this paper cites.
How long will it take to fix this bug?
Cathrin Weiss, Rahul Premraj, Thomas Zimmermann, and Andreas Zeller. 2007 · 2007
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.
Adafactor: Adaptive learning rates with sublinear memory cost
Noam Shazeer and Mitchell Stern. 2018 · 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
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. 2018 · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
Earlier work this paper cites.
On learning meaningful code changes via neural machine translation
Michele Tufano, Jevgenija Pantiuchina, Cody Watson, Gabriele Bavota, and Denys Poshyvanyk. 2019 · 2019
Earlier work this paper cites.
Model-based interactive semantic parsing: A unified framework and a text-to-SQL case study
Ziyu Yao, Yu Su, Huan Sun, and Wen-tau Yih. 2019 · 2019
Earlier work this paper cites.
CodeBERT: A pre-trained model for programming and natural languages
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, and Ming Zhou. 2020 · 2020
Earlier work this paper cites.
Codesearchnet challenge: Evaluating the state of semantic code search
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt. 2020 · 2020
Earlier work this paper cites.
“what do you mean by that?” a parser-independent interactive approach for enhancing text-to-SQL
Yuntao Li, Bei Chen, Qian Liu, Yan Gao, Jian-Guang Lou, Yan Zhang, and Dongmei Zhang. 2020 · 2020
Earlier work this paper cites.
Bridging textual and tabular data for cross-domain text-to-SQL semantic parsing
Xi Victoria Lin, Richard Socher, and Caiming Xiong. 2020 · 2020
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
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. 2020 · 2020
Cited alongside, same era.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
Cited alongside, same era.
An imitation game for learning semantic parsers from user interaction
Ziyu Yao, Yiqi Tang, Wen-tau Yih, Huan Sun, and Yu Su. 2020 · 2020
UniXcoder: Unified cross-modal pre-training for code representation
Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, and Jian Yin. 2022 · 2022
Later among the works it cites.
Exploring the secrets behind the learning difficulty of meaning representations for semantic parsing
Zhenwen Li, Jiaqi Guo, Qian Liu, Jian-Guang Lou, and Tao Xie. 2022 · 2022
Later among the works it cites.
Language models of code are few-shot commonsense learners
Aman Madaan, Shuyan Zhou, Uri Alon, Yiming Yang, and Graham Neubig. 2022 · 2022
Later among the works it cites.
Towards transparent interactive semantic parsing via step-by-step correction
Lingbo Mo, Ashley Lewis, Huan Sun, and Michael White. 2022 · 2022
Later among the works it cites.
GraphQ IR: Unifying the semantic parsing of graph query languages with one intermediate representation
Lunyiu Nie, Shulin Cao, Jiaxin Shi, Jiuding Sun, Qi Tian, Lei Hou, Juanzi Li, and Jidong Zhai. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Photon: A robust cross-domain text-to-SQL system
Jichuan Zeng, Xi Victoria Lin, Steven C.H. Hoi, Richard Socher, Caiming Xiong, Michael Lyu, and Irwin King. 2020 · 2020
Cited alongside, same era.
Unified pre-training for program understanding and generation
Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021 · 2021
Cited alongside, same era.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba. 2021 · 2021
Cited alongside, same era.
Structure-grounded pretraining for text-to-SQL
Xiang Deng, Ahmed Hassan Awadallah, Christopher Meek, Oleksandr Polozov, Huan Sun, and Matthew Richardson. 2021 · 2021
Cited alongside, same era.
NL-EDIT: Correcting semantic parse errors through natural language interaction
Ahmed Elgohary, Christopher Meek, Matthew Richardson, Adam Fourney, Gonzalo Ramos, and Ahmed Hassan Awadallah. 2021 · 2021
Cited alongside, same era.
GraphCodeBERT: Pre-training code representations with data flow
Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, and Ming Zhou. 2021 · 2021
Cited alongside, same era.
On the stability of fine-tuning BERT: Misconceptions, explanations, and strong baselines
Marius Mosbach, Maksym Andriushchenko, and Dietrich Klakow. 2021 · 2021
Cited alongside, same era.
Using developer discussions to guide fixing bugs in software
Sheena Panthaplackel, Milos Gligoric, Junyi Jessy Li, and Raymond Mooney. 2022 · 2022
Later among the works it cites.
Progprompt: Generating situated robot task plans using large language models
Ishika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Jesse Thomason, and Animesh Garg. 2022 · 2022
Later among the works it cites.
Code4struct: Code generation for few-shot structured prediction from natural language
Xingyao Wang, Sha Li, and Heng Ji. 2022 · 2022
Later among the works it cites.
UnifiedSKG: Unifying and multi-tasking structured knowledge grounding with text-to-text language models
Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, and Tao Yu. 2022 · 2022
Later among the works it cites.
Error detection for text-to-sql semantic parsing
Shijie Chen, Ziru Chen, Huan Sun, and Yu Su. 2023 · 2023
Closest in time.
Incoder: A generative model for code infilling and synthesis
Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Scott Yih, Luke Zettlemoyer, and Mike Lewis. 2023 · 2023
Closest in time.
Codegen: An open large language model for code with multi-turn program synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. 2023 · 2023
Closest in time.
Coditt5: Pretraining for source code and natural language editing
Jiyang Zhang, Sheena Panthaplackel, Pengyu Nie, Junyi Jessy Li, and Milos Gligoric. 2023 · 2023
Closest in time.
Natural SQL: Making SQL easier to infer from natural language specifications
Yujian Gan, Xinyun Chen, Jinxia Xie, Matthew Purver, John R. Woodward, John Drake, and Qiaofu Zhang. 2021b · 2042
Closest in time.
Speak to your parser: Interactive text-to-SQL with natural language feedback
Ahmed Elgohary, Saghar Hosseini, and Ahmed Hassan Awadallah. 2020 · 2077
Closest in time.