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Recent progress in language model pre-training has achieved a great success via leveraging large-scale unstructured textual data.
Compositional semantic parsing on semi-structured tables
Panupong Pasupat and Percy Liang · 2015
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Neural programmer: Inducing latent programs with gradient descent
Arvind Neelakantan, Quoc V. Le, and Ilya Sutskever · 2016
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Search-based neural structured learning for sequential question answering
Mohit Iyyer, Wen-tau Yih, and Ming-Wei Chang · 2017
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Learning a natural language interface with neural programmer
Arvind Neelakantan, Quoc V. Le, Martín Abadi, Andrew McCallum, and Dario Amodei · 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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Macro grammars and holistic triggering for efficient semantic parsing
Yuchen Zhang, Panupong Pasupat, and Percy Liang · 2017
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Seq2sql: Generating structured queries from natural language using reinforcement learning
Victor Zhong, Caiming Xiong, and Richard Socher · 2017
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Weakly supervised semantic parsing with abstract examples
Omer Goldman, Veronica Latcinnik, Ehud Nave, Amir Globerson, and Jonathan Berant · 2018
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Memory augmented policy optimization for program synthesis and semantic parsing
Chen Liang, Mohammad Norouzi, Jonathan Berant, Quoc V Le, and Ni Lao · 2018
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Robust text-to-sql generation with execution-guided decoding
Chenglong Wang, Kedar Tatwawadi, Marc Brockschmidt, Po-Sen Huang, Yi Xin Mao, Oleksandr Polozov, and Rishabh Singh · 2018
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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
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Learning to generalize from sparse and underspecified rewards
Rishabh Agarwal, Chen Liang, Dale Schuurmans, and Mohammad Norouzi · 2019
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Iterative search for weakly supervised semantic parsing
Pradeep Dasigi, Matt Gardner, Shikhar Murty, Luke Zettlemoyer, and Eduard Hovy · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Using database rule for weak supervised text-to-sql generation
Tonglei Guo and Huilin Gao · 2019
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A split-and-recombine approach for follow-up query analysis
Qian Liu, Bei Chen, Haoyan Liu, Jian-Guang Lou, Lei Fang, Bin Zhou, and Dongmei Zhang · 2019
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A discrete hard EM approach for weakly supervised question answering
Sewon Min, Danqi Chen, Hannaneh Hajishirzi, and Luke Zettlemoyer · 2019
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Answering conversational questions on structured data without logical forms
Thomas Mueller, Francesco Piccinno, Peter Shaw, Massimo Nicosia, and Yasemin Altun · 2019
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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
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Knowledge-aware conversational semantic parsing over web tables
Yibo Sun, Duyu Tang, Nan Duan, Jingjing Xu, X. Feng, and Bing Qin · 2019
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Learning semantic parsers from denotations with latent structured alignments and abstract programs
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
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Learn to combine linguistic and symbolic information for table-based fact verification
Qi Shi, Yu Zhang, Qingyu Yin, and Ting Liu · 2020
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On the potential of lexico-logical alignments for semantic parsing to SQL queries
Tianze Shi, Chen Zhao, Jordan Boyd-Graber, Hal Daumé III, and Lillian Lee · 2020
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Program enhanced fact verification with verbalization and graph attention network
Xiaoyu Yang, Feng Nie, Yufei Feng, Quan Liu, Zhigang Chen, and Xiaodan Zhu · 2020
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TaBERT: Pretraining for joint understanding of textual and tabular data
Pengcheng Yin, Graham Neubig, Wen-tau Yih, and Sebastian Riedel · 2020
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Bailin Wang, Ivan Titov, and Mirella Lapata · 2019
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Unilmv2: Pseudo-masked language models for unified language model pre-training
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Tabfact: A large-scale dataset for table-based fact verification
Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou, and William Yang Wang · 2020
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TURL: table understanding through representation learning
Xiang Deng, Huan Sun, Alyssa Lees, You Wu, and Cong Yu · 2020
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Understanding tables with intermediate pre-training
Julian Eisenschlos, Syrine Krichene, and Thomas Müller · 2020
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TaPas: Weakly supervised table parsing via pre-training
Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Müller, Francesco Piccinno, and Julian Eisenschlos · 2020
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Table fact verification with structure-aware transformer
Hongzhi Zhang, Yingyao Wang, Sirui Wang, Xuezhi Cao, Fuzheng Zhang, and Zhongyuan Wang · 2020
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Grounded adaptation for zero-shot executable semantic parsing
Victor Zhong, Mike Lewis, Sida I. Wang, and Luke Zettlemoyer · 2020
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LogicalFactChecker: Leveraging logical operations for fact checking with graph module network
Wanjun Zhong, Duyu Tang, Zhangyin Feng, Nan Duan, Ming Zhou, Ming Gong, Linjun Shou, Daxin Jiang, Jiahai Wang, and Jian Yin · 2020
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Structure-grounded pretraining for text-to-SQL
Xiang Deng, Ahmed Hassan Awadallah, Christopher Meek, Oleksandr Polozov, Huan Sun, and Matthew Richardson · 2021
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Weakly supervised semantic parsing by learning from mistakes
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Learning contextual representations for semantic parsing with generation-augmented pre-training
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Logic-level evidence retrieval and graph-based verification network for table-based fact verification
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Learning to synthesize data for semantic parsing
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TUTA: tree-based transformers for generally structured table pre-training
Zhiruo Wang, Haoyu Dong, Ran Jia, Jia Li, Zhiyi Fu, Shi Han, and Dongmei Zhang · 2021
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