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This work presents a sparse-attention Transformer architecture for modeling documents that contain large tables.
Uncovering the relational web
Michael J. Cafarella, Alon Halevy, Daisy Zhe Wang, Eugene Wu, and Yang Zhang. 2008 · 2008
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Semantic parsing on Freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013 · 2013
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Learning dependency-based compositional semantics
Percy Liang, Michael I. Jordan, and Dan Klein. 2013 · 2013
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Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally. 2015 · 2015
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Compositional semantic parsing on semi-structured tables
Panupong Pasupat and Percy Liang. 2015 · 2015
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Compression of neural machine translation models via pruning
Abigail See, Minh-Thang Luong, and Christopher D. Manning. 2016 · 2016
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Reading Wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017 · 2017
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From language to programs: Bridging reinforcement learning and maximum marginal likelihood
Kelvin Guu, Panupong Pasupat, Evan Liu, and Percy Liang. 2017 · 2017
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Search-based neural structured learning for sequential question answering
Mohit Iyyer, Wen-tau Yih, and Ming-Wei Chang. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Giving BERT a calculator: Finding operations and arguments with reading comprehension
Daniel Andor, Luheng He, Kenton Lee, and Emily Pitler. 2019 · 2019
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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.
Attention is not Explanation
Sarthak Jain and Byron C. Wallace. 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.
Latent retrieval for weakly supervised open domain question answering
Kenton Lee, Ming-Wei Chang, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Efficient transformers: A survey
Yi Tay, Mostafa Dehghani, Dara Bahri, and Donald Metzler. 2020 · 2020
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Linformer: Self-attention with linear complexity
Sinong Wang, Belinda Z. Li, Madian Khabsa, Han Fang, and Hao Ma. 2020 · 2020
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Pretrained encyclopedia: Weakly supervised knowledge-pretrained language model
Wenhan Xiong, Jingfei Du, William Yang Wang, and Veselin Stoyanov. 2020 · 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 · 2020
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Big bird: Transformers for longer sequences
Manzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, et al. 2020 · 2020
Later among the works it cites.
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Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter. 2019 · 2019
Cited alongside, same era.
ETC: Encoding long and structured inputs in transformers
Joshua Ainslie, Santiago Ontanon, Chris Alberti, Vaclav Cvicek, Zachary Fisher, Philip Pham, Anirudh Ravula, Sumit Sanghai, Qifan Wang, and Li Yang. 2020 · 2020
Cited alongside, same era.
Understanding tables with intermediate pre-training
Julian Eisenschlos, Syrine Krichene, and Thomas Müller. 2020 · 2020
Cited alongside, same era.
TaPas: Weakly supervised table parsing via pre-training
Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Müller, Francesco Piccinno, and Julian Eisenschlos. 2020 · 2020
Cited alongside, same era.
AxCell: Automatic extraction of results from machine learning papers
Marcin Kardas, Piotr Czapla, Pontus Stenetorp, Sebastian Ruder, Sebastian Riedel, Ross Taylor, and Robert Stojnic. 2020 · 2020
Cited alongside, same era.
Reformer: The efficient transformer
Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya. 2020 · 2020
Cited alongside, same era.
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. 2020a
Cited in the paper.
Table fact verification with structure-aware transformer
Hongzhi Zhang, Yingyao Wang, Sirui Wang, Xuezhi Cao, Fuzheng Zhang, and Zhongyuan Wang. 2020 · 2020
Later among the works it cites.
Open question answering over tables and text
Wenhu Chen, Ming-Wei Chang, Eva Schlinger, William Yang Wang, and William W. Cohen. 2021 · 2021
Closest in time.
Open domain question answering over tables via dense retrieval
Jonathan Herzig, Thomas Müller, Syrine Krichene, and Julian Eisenschlos. 2021 · 2021
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DoT: An efficient double transformer for NLP tasks with tables
Syrine Krichene, Thomas Müller, and Julian Eisenschlos. 2021 · 2021
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Multimodal{qa}: complex question answering over text, tables and images
Alon Talmor, Ori Yoran, Amnon Catav, Dan Lahav, Yizhong Wang, Akari Asai, Gabriel Ilharco, Hannaneh Hajishirzi, and Jonathan Berant. 2021 · 2021
Closest in time.