Fei Wang, Kexuan Sun, Muhao Chen, Jay Pujara, and Pedro Szekely. Retrieving complex tables with multi-granular graph representation learning. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '21, page 1472-1482, New York, NY, USA, 2021b. Association for Computing Machinery. ISBN 9781450380379. doi: 10.1145/3404835.3462909. URL https://doi.org/10.1145/3404835.3462909.
Daheng Wang, Prashant Shiralkar, Colin Lockard, Binxuan Huang, Xin Luna Dong, and Meng Jiang. Tcn: Table convolutional network for web table interpretation. In Proceedings of the Web Conference 2021, pages 4020-4032, 2021c.
Peng Shi, Patrick Ng, Feng Nan, Henghui Zhu, Jun Wang, Jiarong Jiang, Alexander Hanbo Li, Rishav Chakravarti, Donald Weidner, Bing Xiang, et al. Generation-focused table-based intermediate pre-training for free-form question answering. 2022.
Jonathan Herzig, Thomas Müller, Syrine Krichene, and Julian Martin Eisenschlos. Open domain question answering over tables via dense retrieval. arXiv preprint arXiv:2103.12011, 2021.
Michael Glass, Mustafa Canim, Alfio Gliozzo, Saneem Chemmengath, Vishwajeet Kumar, Rishav Chakravarti, Avi Sil, Feifei Pan, Samarth Bharadwaj, and Nicolas Rodolfo Fauceglia. Capturing row and column semantics in transformer based question answering over tables. arXiv preprint arXiv:2104.08303, 2021.
Ankur P Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, and Dipanjan Das. Totto: A controlled table-to-text generation dataset. arXiv preprint arXiv:2004.14373, 2020.
Ori Yoran, Alon Talmor, and Jonathan Berant. Turning tables: Generating examples from semi-structured tables for endowing language models with reasoning skills. arXiv preprint arXiv:2107.07261, 2021.
Fei Wang, Zhewei Xu, Pedro Szekely, and Muhao Chen. Robust (controlled) table-to-text generation with structure-aware equivariance learning. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5037-5048, Seattle, United States, July 2022. Association for Computational Linguistics. doi: 10.18653/v1/2022.nacl-main.371. URL https://aclanthology.org/2022.nacl-main.371.
Ewa Andrejczuk, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, and Yasemin Altun. Table-to-text generation and pre-training with tabt5. arXiv preprint arXiv:2210.09162, 2022.
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140):1-67, 2020. URL http://jmlr.org/papers/v21/20-074.html.
Sameer Agarwal, Kristin Branson, and Serge Belongie. Higher order learning with graphs. In Proceedings of the 23rd international conference on Machine learning, pages 17-24, 2006.
Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, and Partha Talukdar. Hypergen: A new method for training graph convolutional networks on hypergraphs. Advances in neural information processing systems, 32, 2019.
Devanshu Arya, Deepak K Gupta, Stevan Rudinac, and Marcel Worring. Hypersage: Generalizing inductive representation learning on hypergraphs. arXiv preprint arXiv:2010.04558, 2020.
László Babai, Paul Erdős's, and Stanley M. Selkow. Random graph isomorphism. SIAM Journal on Computing, 9(3):628-635, 1980. doi: 10.1137/0209047. URL https://doi.org/10.1137/0209047.
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826, 2018.