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Since a vast number of tables can be easily collected from web pages, spreadsheets, PDFs, and various other document types, a flurry of table pre-training frameworks have been proposed following the success of text and images, and they have achieved new state-of-the-arts on various tasks such as table question answering, table type recognition, column relation classification, table search, formula prediction, etc.
Uncovering the relational web
Michael J Cafarella, Alon Y Halevy, Yang Zhang, Daisy Zhe Wang, and Eugene Wu · 2008
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Learning to sportscast: a test of grounded language acquisition
David L Chen and Raymond J Mooney · 2008
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A critical review of the literature on spreadsheet errors
Stephen G Powell, Kenneth R Baker, and Barry Lawson · 2008
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Learning semantic correspondences with less supervision
Percy Liang, Michael I Jordan, and Dan Klein · 2009
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The kbgen challenge
Eva Banik, Claire Gardent, and Eric Kow · 2013
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Automatic web spreadsheet data extraction
Zhe Chen and Michael Cafarella · 2013
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
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Fuse: a reproducible, extendable, internet-scale corpus of spreadsheets
Titus Barik, Kevin Lubick, Justin Smith, John Slankas, and Emerson Murphy-Hill · 2015
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Tabel: entity linking in web tables
Chandra Sekhar Bhagavatula, Thanapon Noraset, and Doug Downey · 2015
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Building the dresden web table corpus: A classification approach
Julian Eberius, Katrin Braunschweig, and Others · 2015
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Enron’s spreadsheets and related emails: A dataset and analysis
Felienne Hermans and Emerson Murphy-Hill · 2015
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Compositional semantic parsing on semi-structured tables
Panupong Pasupat and Percy Liang · 2015
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Building a semantic parser overnight
Yushi Wang, Jonathan Berant, and Percy Liang · 2015
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Fine-grained analysis of sentence embeddings using auxiliary prediction tasks
Yossi Adi, Einat Kermany, Yonatan Belinkov, Ofer Lavi, and Yoav Goldberg · 2016
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Custodes: automatic spreadsheet cell clustering and smell detection using strong and weak features
Shing-Chi Cheung, Wanjun Chen, Yepang Liu, and Chang Xu · 2016
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Cacheck: detecting and repairing cell arrays in spreadsheets
Wensheng Dou, Chang Xu, Shing-Chi Cheung, and Jun Wei · 2016
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Bag of tricks for efficient text classification
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov · 2016
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Neural text generation from structured data with application to the biography domain
Rémi Lebret, David Grangier, and Michael Auli · 2016
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Assessing the ability of lstms to learn syntax-sensitive dependencies
Tal Linzen, Emmanuel Dupoux, and Yoav Goldberg · 2016
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Crowd-sourcing nlg data: Pictures elicit better data
Jekaterina Novikova, Oliver Lemon, and Verena Rieser · 2016
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Table cell search for question answering
Huan Sun, Hao Ma, Xiaodong He, Wen-tau Yih, Yu Su, and Xifeng Yan · 2016
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What do neural machine translation models learn about morphology?
Yonatan Belinkov, Nadir Durrani, Fahim Dalvi, Hassan Sajjad, and James Glass · 2017
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Understanding the semantic structures of tables with a hybrid deep neural network architecture
Kyosuke Nishida, Kugatsu Sadamitsu, Ryuichiro Higashinaka, and Yoshihiro Matsuo · 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
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Challenges in data-to-document generation
Sam Wiseman, Stuart M Shieber, and Alexander M Rush · 2017
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Entitables: Smart assistance for entity-focused tables
Shuo Zhang and Krisztian Balog · 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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Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, Luis Ceze, et al · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Tabvec: Table vectors for classification of web tables
Majid Ghasemi-Gol and Pedro Szekely · 2018
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Auto-detect: Data-driven error detection in tables
Zhipeng Huang and Yeye He · 2018
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Table recognition in spreadsheets via a graph representation
Elvis Koci, Maik Thiele, Wolfgang Lehner, and Oscar Romero · 2018
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Data synthesis based on generative adversarial networks
Noseong Park, Mahmoud Mohammadi, Kshitij Gorde, Sushil Jajodia, Hongkyu Park, and Youngmin Kim · 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, et al · 2018
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Ad hoc table retrieval using semantic similarity
Shuo Zhang and Krisztian Balog · 2018
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Colnet: Embedding the semantics of web tables for column type prediction
Jiaoyan Chen, Ernesto Jiménez-Ruiz, Ian Horrocks, and Charles Sutton · 2019
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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 · 2019
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Complicated table structure recognition
Zewen Chi, Heyan Huang, Heng-Da Xu, Houjin Yu, Wanxuan Yin, and Xian-Ling Mao · 2019
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What does bert look at? an analysis of bert’s attention
Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D Manning · 2019
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Semantic structure extraction for spreadsheet tables with a multi-task learning architecture
Haoyu Dong, Shijie Liu, Zhouyu Fu, Shi Han, and Dongmei Zhang · 2019
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Tablesense: Spreadsheet table detection with convolutional neural networks
Haoyu Dong, Shijie Liu, Shi Han, Zhouyu Fu, and Dongmei Zhang · 2019
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Tablenet: An approach for determining fine-grained relations for wikipedia tables
Besnik Fetahu, Avishek Anand, and Maria Koutraki · 2019
Cited alongside, same era.
Icdar 2019 competition on table detection and recognition (ctdar)
Liangcai Gao, Yilun Huang, Hervé Déjean, Jean-Luc Meunier, Qinqin Yan, Yu Fang, Florian Kleber, and Eva Lang · 2019
Cited alongside, same era.
Tabular cell classification using pre-trained cell embeddings
Majid Ghasemi Gol, Jay Pujara, and Pedro Szekely · 2019
Cited alongside, same era.
Tabular cell classification using pre-trained cell embeddings
Majid Ghasemi Gol, Jay Pujara, and Pedro Szekely · 2019
Cited alongside, same era.
Generating titles for web tables
Braden Hancock, Hongrae Lee, and Cong Yu · 2019
Cited alongside, same era.
Vizml: A machine learning approach to visualization recommendation
Kevin Hu, Michiel A Bakker, Stephen Li, Tim Kraska, and César Hidalgo · 2019
Auto-suggest: Learning-to-recommend data preparation steps using data science notebooks
Cong Yan and Yeye He · 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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Vime: Extending the success of self-and semi-supervised learning to tabular domain
Jinsung Yoon, Yao Zhang, James Jordon, and Mihaela van der Schaar · 2020
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Grappa: Grammar-augmented pre-training for table semantic parsing
Tao Yu, Chien-Sheng Wu, Xi Victoria Lin, Yi Chern Tan, Xinyi Yang, Dragomir Radev, Caiming Xiong, et al · 2020
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Web table extraction, retrieval, and augmentation: A survey
Shuo Zhang and Krisztian Balog · 2020
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A graph representation of semi-structured data for web question answering
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Cited alongside, same era.
Deco: A dataset of annotated spreadsheets for layout and table recognition
Elvis Koci, Maik Thiele, Josephine Rehak, Oscar Romero, and Wolfgang Lehner · 2019
Cited alongside, same era.
Roberta: A robustly optimized bert pretraining approach, 2019
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Cited alongside, same era.
Content-based table retrieval for web queries
Yibo Sun, Zhao Yan, Duyu Tang, Nan Duan, and Bing Qin · 2019
Cited alongside, same era.
A multiscale visualization of attention in the transformer model
Jesse Vig · 2019
Cited alongside, same era.
Table2vec: Neural word and entity embeddings for table population and retrieval
Li Zhang, Shuo Zhang, and Krisztian Balog · 2019
Cited alongside, same era.
Xingyao Zhang, Linjun Shou, Jian Pei, Ming Gong, Lijie Wen, and Daxin Jiang · 2020
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Table2analysis: Modeling and recommendation of common analysis patterns for multi-dimensional data
Mengyu Zhou, Wang Tao, Ji Pengxin, Han Shi, and Zhang Dongmei · 2020
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Docformer: End-to-end transformer for document understanding
Srikar Appalaraju, Bhavan Jasani, Bhargava Urala Kota, Yusheng Xie, and R Manmatha · 2021
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Tabnet: Attentive interpretable tabular learning
Sercan Ö Arik and Tomas Pfister · 2021
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Flap: Table-to-text generation with feature indication and numerical reasoning pretraining
Anonymous Authors · 2021
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Spreadsheetcoder: Formula prediction from semi-structured context
Xinyun Chen, Petros Maniatis, Rishabh Singh, Charles Sutton, Hanjun Dai, Max Lin, and Denny Zhou · 2021
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Finqa: A dataset of numerical reasoning over financial data
Zhiyu Chen, Wenhu Chen, Charese Smiley, Sameena Shah, Iana Borova, Dylan Langdon, Reema Moussa, Matt Beane, Ting-Hao Huang, Bryan Routledge, et al · 2021
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Fortap: Using formulae for numerical-reasoning-aware table pretraining
Zhoujun Cheng, Haoyu Dong, Fan Cheng, Ran Jia, Pengfei Wu, Shi Han, and Dongmei Zhang · 2021
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Hitab: A hierarchical table dataset for question answering and natural language generation
Zhoujun Cheng, Haoyu Dong, Zhiruo Wang, Ran Jia, Jiaqi Guo, Yan Gao, Shi Han, Jian-Guang Lou, and Dongmei Zhang · 2021
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Structure-grounded pretraining for text-to-sql
Xiang Deng, Ahmed Hassan, Christopher Meek, Oleksandr Polozov, Huan Sun, and Matthew Richardson · 2021
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Tabularnet: A neural network architecture for understanding semantic structures of tabular data
Lun Du, Fei Gao, Xu Chen, Ran Jia, Junshan Wang, Jiang Zhang, Shi Han, and Dongmei Zhang · 2021
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Mate: Multi-view attention for table transformer efficiency
Julian Eisenschlos, Maharshi Gor, Thomas Mueller, and William Cohen · 2021
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Gittables: A large-scale corpus of relational tables
Madelon Hulsebos, Çağatay Demiralp, and Paul Groth · 2021
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Tabbie: Pretrained representations of tabular data
Hiroshi Iida, Dung Thai, Varun Manjunatha, and Mohit Iyyer · 2021
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Dot: An efficient double transformer for nlp tasks with tables
Syrine Krichene, Thomas Müller, and Julian Martin Eisenschlos · 2021
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Markuplm: Pre-training of text and markup language for visually-rich document understanding
Junlong Li, Yiheng Xu, Lei Cui, and Furu Wei · 2021
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Tapex: Table pre-training via learning a neural sql executor
Qian Liu, Bei Chen, Jiaqi Guo, Zeqi Lin, and Jian-guang Lou · 2021
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Learning to reason for text generation from scientific tables
Nafise Sadat Moosavi, Andreas Rücklé, Dan Roth, and Iryna Gurevych · 2021
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Learning contextual representations for semantic parsing with generation-augmented pre-training
Peng Shi, Patrick Ng, Zhiguo Wang, Henghui Zhu, Alexander Hanbo Li, Jun Wang, Cicero Nogueira dos Santos, and Bing Xiang · 2021
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Saint: Improved neural networks for tabular data via row attention and contrastive pre-training
Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild, C Bayan Bruss, and Tom Goldstein · 2021
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Towards table-to-text generation with numerical reasoning
Lya Hulliyyatus Suadaa, Hidetaka Kamigaito, Kotaro Funakoshi, Manabu Okumura, and Hiroya Takamura · 2021
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Rpt: relational pre-trained transformer is almost all you need towards democratizing data preparation
Nan Tang, Ju Fan, Fangyi Li, Jianhong Tu, Xiaoyong Du, Guoliang Li, Sam Madden, and Mourad Ouzzani · 2021
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Representing numbers in nlp: a survey and a vision
Avijit Thawani, Jay Pujara, Filip Ilievski, and Pedro Szekely · 2021
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Tcn: Table convolutional network for web table interpretation
Daheng Wang, Prashant Shiralkar, Colin Lockard, Binxuan Huang, Xin Luna Dong, and Meng Jiang · 2021
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Retrieving complex tables with multi-granular graph representation learning
Fei Wang, Kexuan Sun, Muhao Chen, Jay Pujara, and Pedro Szekely · 2021
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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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Structure-aware pre-training for table-to-text generation
Xinyu Xing and Xiaojun Wan · 2021
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Ori Yoran, Alon Talmor, and Jonathan Berant · 2021
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Representations for question answering from documents with tables and text
Vicky Zayats, Kristina Toutanova, and Mari Ostendorf · 2021
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Semantic table structure identification in spreadsheets
Yakun Zhang, Xiao Lv, Haoyu Dong, Wensheng Dou, Shi Han, Dongmei Zhang, Jun Wei, and Dan Ye · 2021
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Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context
Xinyi Zheng, Douglas Burdick, Lucian Popa, Xu Zhong, and Nancy Xin Ru Wang · 2021
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Table2charts: Recommending charts by learning shared table representations
Mengyu Zhou, Qingtao Li, Xinyi He, Yuejiang Li, Yibo Liu, Wei Ji, Shi Han, Yining Chen, Daxin Jiang, and Dongmei Zhang · 2021
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Tat-qa: A question answering benchmark on a hybrid of tabular and textual content in finance
Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, and Tat-Seng Chua · 2021
Later among the works it cites.
Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I Wang, et al · 2022
Closest in time.
Tableformer: Robust transformer modeling for table-text encoding
Jingfeng Yang, Aditya Gupta, Shyam Upadhyay, Luheng He, Rahul Goel, and Shachi Paul · 2022
Closest in time.