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Recent research showed promising results on combining pretrained language models (LMs) with canonical utterance for few-shot semantic parsing.
Good-enough compositional data augmentation
Jacob Andreas. 2019 · 1904
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fairseq: A fast, extensible toolkit for sequence modeling
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019 · 1904
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Compositional generalization through meta sequence-to-sequence learning
Brenden M Lake. 2019 · 1906
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H Miller, and Sebastian Riedel. 2019 · 1909
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 1910
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Learning to parse database queries using inductive logic programming
John M Zelle and Raymond J Mooney. 1996 · 1996
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Unnatural language processing: Bridging the gap between synthetic and natural language data
Alana Marzoev, Samuel Madden, M Frans Kaashoek, Michael Cafarella, and Jacob Andreas. 2020 · 2004
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 2005
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Zero-shot transfer learning with synthesized data for multi-domain dialogue state tracking
Giovanni Campagna, Agata Foryciarz, Mehrad Moradshahi, and Monica S Lam. 2020 · 2005
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Compositional generalization in semantic parsing: Pre-training vs. specialized architectures
Daniel Furrer, Marc van Zee, Nathan Scales, and Nathanael Schärli. 2020 · 2007
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Compositional generalization via neural-symbolic stack machines
Xinyun Chen, Chen Liang, Adams Wei Yu, Dawn Song, and Denny Zhou. 2020 · 2008
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Span-based semantic parsing for compositional generalization
Jonathan Herzig and Jonathan Berant. 2020 · 2009
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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
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Learning to recombine and resample data for compositional generalization
Ekin Akyürek, Afra Feyza Akyürek, and Jacob Andreas. 2020 · 2010
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Improving compositional generalization in semantic parsing
Inbar Oren, Jonathan Herzig, Nitish Gupta, Matt Gardner, and Jonathan Berant. 2020 · 2010
Cited alongside, same era.
Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2010
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Autoqa: From databases to qa semantic parsers with only synthetic training data
Silei Xu, Sina J Semnani, Giovanni Campagna, and Monica S Lam. 2020 · 2010
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Compositional generalization via semantic tagging
Hao Zheng and Mirella Lapata. 2020 · 2010
Cited alongside, same era.
Semantic parsing for task oriented dialog using hierarchical representations
Sonal Gupta, Rushin Shah, Mrinal Mohit, Anuj Kumar, and Mike Lewis. 2018 · 2018
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Genie: A generator of natural language semantic parsers for virtual assistant commands
Giovanni Campagna, Silei Xu, Mehrad Moradshahi, Richard Socher, and Monica S Lam. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
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Grounded adaptation for zero-shot executable semantic parsing
Victor Zhong, Mike Lewis, Sida I Wang, and Luke Zettlemoyer. 2020 · 2019
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Dbpal: A fully pluggable nl2sql training pipeline
Nathaniel Weir, Prasetya Utama, Alex Galakatos, Andrew Crotty, Amir Ilkhechi, Shekar Ramaswamy, Rohin Bhushan, Nadja Geisler, Benjamin Hättasch, Steffen Eger, et al. 2020 · 2020
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Tianyu Gao, Adam Fisch, and Danqi Chen. 2020 · 2012
Cited alongside, same era.
Semantic parsing via paraphrasing
Jonathan Berant and Percy Liang. 2014 · 2014
Cited alongside, same era.
Information extraction over structured data: Question answering with freebase
Xuchen Yao and Benjamin Van Durme. 2014 · 2014
Cited alongside, same era.
Semantic parsing for single-relation question answering
Wen-tau Yih, Xiaodong He, and Christopher Meek. 2014 · 2014
Cited alongside, same era.
Building a semantic parser overnight
Yushi Wang, Jonathan Berant, and Percy Liang. 2015 · 2015
Cited alongside, same era.
Data recombination for neural semantic parsing
Robin Jia and Percy Liang. 2016 · 2016
Cited alongside, same era.
Learning a neural semantic parser from user feedback
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Jayant Krishnamurthy, and Luke Zettlemoyer. 2017 · 2017
Cited alongside, same era.
Coarse-to-fine decoding for neural semantic parsing
Li Dong and Mirella Lapata. 2018 · 2018
Cited alongside, same era.
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
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Reframing instructional prompts to gptk’s language
Swaroop Mishra, Daniel Khashabi, Chitta Baral, Yejin Choi, and Hannaneh Hajishirzi. 2021 · 2021
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Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, and Christopher D Manning. 2021 · 2021
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Learning how to ask: Querying lms with mixtures of soft prompts
Guanghui Qin and Jason Eisner. 2021 · 2021
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The power of prompt tuning for low-resource semantic parsing
Nathan Schucher, Siva Reddy, and Harm de Vries. 2021 · 2021
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Constrained language models yield few-shot semantic parsers
Richard Shin, Christopher H Lin, Sam Thomson, Charles Chen, Subhro Roy, Emmanouil Antonios Platanios, Adam Pauls, Dan Klein, Jason Eisner, and Benjamin Van Durme. 2021 · 2021
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Robust fine-tuning of zero-shot models
Mitchell Wortsman, Gabriel Ilharco, Mike Li, Jong Wook Kim, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, and Ludwig Schmidt. 2021 · 2021
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Frustratingly simple but surprisingly strong: Using language-independent features for zero-shot cross-lingual semantic parsing
Jingfeng Yang, Federico Fancellu, Bonnie Webber, and Diyi Yang. 2021 · 2021
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