TAT-DQA is a large-scale Document VQA dataset, which is constructed by extending the TAT-QA. It aims to stimulate progress of QA research over more complex and realistic visually-rich documents with rich tabular and textual content, especially those requiring numerical reasoning.
You can download our TAT-DQA dataset via TAT-DQA Dataset.
For more information, please refer to our TAT-DQA Website or read our ACM MM 2022 paper PDF.
Please kindly cite our work if you use our dataset or codes, thank you.
@inproceedings{zhu2022towards,
title={Towards complex document understanding by discrete reasoning},
author={Zhu, Fengbin and Lei, Wenqiang and Feng, Fuli and Wang, Chao and Zhang, Haozhou and Chua, Tat-Seng},
booktitle={Proceedings of the 30th ACM International Conference on Multimedia},
pages={4857--4866},
year={2022}
}
@misc{zhu2023doc2soargraph,
title={Doc2SoarGraph: Discrete Reasoning over Visually-Rich Table-Text Documents with Semantic-Oriented Hierarchical Graphs},
author={Fengbin Zhu and Chao Wang and Fuli Feng and Zifeng Ren and Moxin Li and Tat-Seng Chua},
year={2023},
eprint={2305.01938},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
The TAT-DQA dataset is under the license of Creative Commons (CC BY) Attribution 4.0 International
For any issues please create an issue here or kindly email us at: Fengbin Zhu zhfengbin@gmail.com, thank you.