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Semantic parsing is an important NLP problem, particularly for voice assistants such as Alexa and Google Assistant.
Co-training and self-training for word sense disambiguation
Rada Mihalcea · 2004
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Effective self-training for parsing
David McClosky, Eugene Charniak, and Mark Johnson · 2006
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Confidence driven unsupervised semantic parsing
Dan Goldwasser, Roi Reichart, James Clarke, and Dan Roth · 2011
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Semantic parsing on Freebase from question-answer pairs
J. Berant, A. Chou, R. Frostig, and P. Liang · 2013
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Semantic parsing via paraphrasing
Jonathan Berant and Percy Liang · 2014
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Building a semantic parser overnight
Yushi Wang, Jonathan Berant, and Percy Liang · 2015
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Neural semantic parsing with type constraints for semi-structured tables
Jayant Krishnamurthy, Pradeep Dasigi, and Matt Gardner · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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John Wieting and Kevin Gimpel · 2017
Cited alongside, same era.
A syntactic neural model for general-purpose code generation
Pengcheng Yin and Graham Neubig · 2017
Cited alongside, same era.
Confidence modeling for neural semantic parsing
Li Dong, Chris Quirk, and Mirella Lapata · 2018
Cited alongside, same era.
Semantic parsing with dual learning
Ruisheng Cao, Su Zhu, Chen Liu, Jieyu Li, and Kai Yu · 2019
Cited alongside, same era.
Improving semantic parsing for task oriented dialog
Arash Einolghozati, Panupong Pasupat, Sonal Gupta, Rushin Shah, Mrinal Mohit, Mike Lewis, and Luke Zettlemoyer · 2019
Cited alongside, same era.
Don’t parse, generate! a sequence to sequence architecture for task-oriented semantic parsing
Subendhu Rongali, Luca Soldaini, Emilio Monti, and Wael Hamza · 2020
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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
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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
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A survey of data augmentation approaches for nlp
Steven Y Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard Hovy · 2021
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It’s not just size that matters: Small language models are also few-shot learners
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer · 2019
Cited alongside, same era.
Grammatical sequence prediction for real-time neural semantic parsing
Chunyang Xiao, Christoph Teichmann, and Konstantine Arkoudas · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Timo Schick and Hinrich Schütze · 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
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Shan Wu, Bo Chen, Chunlei Xin, Xianpei Han, Le Sun, Weipeng Zhang, Jiansong Chen, Fan Yang, and Xunliang Cai · 2021
Later among the works it cites.