This is the official repository of:
- TUTA:Tree-based Transformers for Generally Structured Table Pre-training
- ForTaP:Using Formulae for Numerical-Reasoning-Aware Table Pretraining.
TUTA is a unified pretrained model for understanding generally structured tables.
Based on TUTA, ForTaP further endows the model with stronger numerical-reasoning skills by pretraining on spreadsheet formulas.
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2022-03-22: We released ForTaP code.
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2022-03-08: ForTaP was accepted by ACL 2022. You may find ForTaP paper here.
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2022-01-09: We updated cell type classification code for TUTA.
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2021-10-29: We released TUTA code.
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2021-9-2: We released HiTab, a large dataset on question answering and data-to-text over complex hierarchical tables.
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2021-8-17: TUTA was accepted by KDD 2021.
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2020-10-21: We released our TUTA paper on arXiv.
Detailed implementations and usages of the pretrain models are shown in their folders:
If you find TUTA and ForTaP useful in your research, please consider citing following papers:
@inproceedings{wang2021tuta,
title={TUTA: Tree-based Transformers for Generally Structured Table Pre-training},
author={Wang, Zhiruo and Dong, Haoyu and Jia, Ran and Li, Jia and Fu, Zhiyi and Han, Shi and Zhang, Dongmei},
booktitle={Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining},
pages={1780--1790},
year={2021}
}
@article{cheng2021fortap,
title={FORTAP: Using Formulae for Numerical-Reasoning-Aware Table Pretraining},
author={Cheng, Zhoujun and Dong, Haoyu and Cheng, Fan and Jia, Ran and Wu, Pengfei and Han, Shi and Zhang, Dongmei},
journal={arXiv preprint arXiv:2109.07323},
year={2021}
}
If you have any problems regarding the paper or code, please feel free to submit issues in this repository. Or you can reach us by emails.
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