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Tables in Web documents are pervasive and can be directly used to answer many of the queries searched on the Web, motivating their integration in question answering.
A BERT baseline for the natural questions
Chris Alberti, Kenton Lee, and Michael Collins. 2019 · 1901
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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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Methods for exploring and mining tables on wikipedia
Chandra Sekhar Bhagavatula, Thanapon Noraset, and Doug Downey. 2013 · 2013
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Compositional semantic parsing on semi-structured tables
Panupong Pasupat and Percy Liang. 2015 · 2015
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Neural semantic parsing with type constraints for semi-structured tables
Jayant Krishnamurthy, Pradeep Dasigi, and Matt Gardner. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Self-attention with relative position representations
Peter Shaw, Jakob Uszkoreit, and Ashish Vaswani. 2018 · 2018
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Open domain question answering using early fusion of knowledge bases and text
Haitian Sun, Bhuwan Dhingra, Manzil Zaheer, Kathryn Mazaitis, Ruslan Salakhutdinov, and William W Cohen. 2018 · 2018
Cited alongside, same era.
Transformer-XL: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc Le, and Ruslan Salakhutdinov. 2019 · 2019
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
A comprehensive exploration on WikiSQL with table-aware word contextualization
Wonseok Hwang, Jinyeung Yim, Seunghyun Park, and Minjoon Seo. 2019 · 2019
Cited alongside, same era.
Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Matthew Kelcey, Jacob Devlin, Kenton Lee, Kristina N. Toutanova, Llion Jones, Ming-Wei Chang, Andrew Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
Cited alongside, same era.
Hybridqa: A dataset of multi-hop question answering over tabular and textual data
Wenhu Chen, Hanwen Zha, Zhiyu Chen, Wenhan Xiong, Hong Wang, and William Wang. 2020 · 2020
Later among the works it cites.
InfoTabS: Inference on tables as semi-structured data
Vivek Gupta, Maitrey Mehta, Pegah Nokhiz, and Vivek Srikumar. 2020 · 2020
Later among the works it cites.
TaPas: Weakly supervised table parsing via pre-training
Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno, and Julian Martin Eisenschlos. 2020 · 2020
Later among the works it cites.
RikiNet: Reading wikipedia pages for natural question answering
Dayiheng Liu, Yeyun Gong, Jie Fu, Yu Yan, Jiusheng Chen, Daxin Jiang, Jiancheng Lv, and Nan Duan. 2020 · 2020
Later among the works it cites.
ETC: Encoding long and structured inputs in transformers
Anirudh Ravula, Chris Alberti, Joshua Ainslie, Li Yang, Philip Minh Pham, Qifan Wang, Santiago Ontanon, Sumit Kumar Sanghai, Vaclav Cvicek, and Zach Fisher. 2020 · 2020
Later among the works it cites.
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Answering conversational questions on structured data without logical forms
Thomas Müeller, Francesco Piccinno, Peter Shaw, Massimo Nicosia, and Yasemin Altun. 2019 · 2019
Cited alongside, same era.
Generating logical forms from graph representations of text and entities
Peter Shaw, Philip Massey, Angelica Chen, Francesco Piccinno, and Yasemin Altun. 2019 · 2019
Cited alongside, same era.
TaBERT: Pretraining for joint understanding of textual and tabular data
Pengcheng Yin, Graham Neubig, Wen-tau Yih, and Sebastian Riedel. 2020 · 2020
Later among the works it cites.