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Tables are an important form of structured data for both human and machine readers alike, providing answers to questions that cannot, or cannot easily, be found in texts.
Learning semantic annotations for tabular data
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Answering table queries on the web using column keywords
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Finding related tables
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Applying webtables in practice
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task
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Ad hoc table retrieval using semantic similarity
Shuo Zhang and Krisztian Balog. 2018 · 2018
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Besnik Fetahu, Avishek Anand, and Maria Koutraki. 2019 · 2019
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Tabular cell classification using pre-trained cell embeddings
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Web table extraction, retrieval, and augmentation: A survey
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Improved table retrieval using multiple context embeddings for attributes
Mohamed Trabelsi, Brian D Davison, and Jeff Heflin. 2019 · 2019
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Table2vec: Neural word and entity embeddings for table population and retrieval
Li Zhang, Shuo Zhang, and Krisztian Balog. 2019 · 2019
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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
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Tapas: Weakly supervised table parsing via pre-training
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Dense passage retrieval for open-domain question answering
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Ambigqa: Answering ambiguous open-domain questions
Sewon Min, Julian Michael, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2020 · 2020
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Tabbie: Pretrained representations of tabular data
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Dot: An efficient double transformer for nlp tasks with tables
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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 · 2021
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Open domain question answering with a unified knowledge interface
Kaixin Ma, Hao Cheng, Xiaodong Liu, Eric Nyberg, and Jianfeng Gao. 2021 · 2021
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Qa dataset explosion: A taxonomy of nlp resources for question answering and reading comprehension
Anna Rogers, Matt Gardner, and Isabelle Augenstein. 2021 · 2021
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A graph-based approach for inferring semantic descriptions of wikipedia tables
Binh Vu, Craig A Knoblock, Pedro Szekely, Minh Pham, and Jay Pujara. 2021 · 2021
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Representations for question answering from documents with tables and text
Vicky Zayats, Kristina Toutanova, and Mari Ostendorf. 2021 · 2021
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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 · 2022
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Tableformer: Robust transformer modeling for table-text encoding
Jingfeng Yang, Aditya Gupta, Shyam Upadhyay, Luheng He, Rahul Goel, and Shachi Paul. 2022 · 2022
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