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--- |
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license: cc-by-4.0 |
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task_categories: |
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- question-answering |
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language: |
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- en |
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tags: |
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- finance |
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- table-text |
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- visual-document-QA |
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- numerical-reasoning |
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size_categories: |
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- 10K<n<100K |
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--- |
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# TAT-DQA |
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- [**Project Page**](https://nextplusplus.github.io/TAT-DQA/) |
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- [**Paper - MM 22**](https://dl.acm.org/doi/abs/10.1145/3503161.3548422) |
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- [**Paper - Arxiv**](https://arxiv.org/abs/2207.11871) |
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- [**Github**](https://github.com/NExTplusplus/TAT-DQA) |
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- [**Leaderboard**](https://nextplusplus.github.io/TAT-DQA/#leaderboard) |
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**TAT-DQA** is a large-scale Document VQA dataset, which is constructed by extending the TAT-QA. It aims to stimulate the progress of QA research over more complex and realistic **visually-rich documents** with rich tabular and textual content, especially those requiring numerical reasoning. |
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The unique features of TAT-DQA include: |
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- The documents in TAT-DQA dataset are sampled from real-world high-quality financial reports and each document contains both tabular and textual data; |
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- The average number of words of each document in TAT-DQA is around 550, which is significantly larger than all existing Document VQA datasets. |
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- Around 85% of the documents in the dataset have only one page while 15% has multiple pages. |
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- Similar to TAT-QA, the answer forms are diverse, including single span, multiple spans and free-form and various numerical reasoning capabilities are usually required, including addition (+), subtraction (-), multiplication (x), division (/), counting, comparison, sorting, and their compositions; |
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In total, TAT-DQA contains 16,558 questions associated with 2,758 documents ( 3,067 document pages ) sampled from real-world financial reports. |
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## Citation |
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```python |
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@inproceedings{zhu2022towards, |
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title={Towards complex document understanding by discrete reasoning}, |
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author={Zhu, Fengbin and Lei, Wenqiang and Feng, Fuli and Wang, Chao and Zhang, Haozhou and Chua, Tat-Seng}, |
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booktitle={Proceedings of the 30th ACM International Conference on Multimedia}, |
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pages={4857--4866}, |
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year={2022} |
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} |
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``` |
