Zhengbao Jiang, Yi Mao, Pengcheng He, Graham Neubig, Weizhu Chen. OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering. In Marine Carpuat, Marie-Catherine de Marneffe, Iván Vladimir Meza Ruíz, editors, Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022, Seattle, WA, United States, July 10-15, 2022. pages 932-942, Association for Computational Linguistics, 2022. [doi]
@inproceedings{JiangMHNC22,
title = {OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering},
author = {Zhengbao Jiang and Yi Mao and Pengcheng He and Graham Neubig and Weizhu Chen},
year = {2022},
url = {https://aclanthology.org/2022.naacl-main.68},
researchr = {https://researchr.org/publication/JiangMHNC22},
cites = {0},
citedby = {0},
pages = {932-942},
booktitle = {Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022, Seattle, WA, United States, July 10-15, 2022},
editor = {Marine Carpuat and Marie-Catherine de Marneffe and Iván Vladimir Meza Ruíz},
publisher = {Association for Computational Linguistics},
isbn = {978-1-955917-71-1},
}