{"id":16459808,"url":"https://nextplusplus.github.io/TAT-DQA/","last_synced_at":"2025-10-27T09:31:21.207Z","repository":{"id":101298845,"uuid":"525828750","full_name":"NExTplusplus/TAT-DQA","owner":"NExTplusplus","description":"TAT-DQA: Towards Complex Document Understanding By Discrete Reasoning","archived":false,"fork":false,"pushed_at":"2024-09-17T04:25:26.000Z","size":1055,"stargazers_count":19,"open_issues_count":1,"forks_count":1,"subscribers_count":5,"default_branch":"master","last_synced_at":"2024-10-12T11:03:40.633Z","etag":null,"topics":["document-understanding","question-answering","vqa"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/NExTplusplus.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-08-17T14:28:04.000Z","updated_at":"2024-10-10T07:34:03.000Z","dependencies_parsed_at":null,"dependency_job_id":"c486f19b-9053-48c8-9a70-f5ade240c712","html_url":"https://github.com/NExTplusplus/TAT-DQA","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NExTplusplus%2FTAT-DQA","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NExTplusplus%2FTAT-DQA/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NExTplusplus%2FTAT-DQA/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NExTplusplus%2FTAT-DQA/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/NExTplusplus","download_url":"https://codeload.github.com/NExTplusplus/TAT-DQA/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":238471999,"owners_count":19478141,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["document-understanding","question-answering","vqa"],"created_at":"2024-10-11T11:00:53.471Z","updated_at":"2025-10-27T09:31:15.828Z","avatar_url":"https://github.com/NExTplusplus.png","language":null,"funding_links":[],"categories":["LLM Leaderboard"],"sub_categories":[],"readme":"TAT-DQA: Towards Complex Document Understanding By Discrete Reasoning\n====================\n\n**TAT-DQA** is a large-scale Document VQA dataset, which is constructed by extending the [TAT-QA](https://github.com/NExTplusplus/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.\n\n\nYou can download our TAT-DQA dataset via [TAT-DQA Dataset](https://drive.google.com/drive/folders/1SGpZyRWqycMd_dZim1ygvWhl5KdJYDR2).\n               \nFor more information, please refer to our [TAT-DQA Website](https://nextplusplus.github.io/TAT-DQA/) or read our ACM MM 2022 paper [PDF](https://arxiv.org/pdf/2207.11871.pdf).\n\n\n\n### Updates \n\n**${\\color{red}Jan 2024}$**: We release the ground truth for the TAT-DQA test set [TAT-DQA Dataset](https://drive.google.com/drive/folders/1SGpZyRWqycMd_dZim1ygvWhl5KdJYDR2), to facilitate future research on this task!\n\n**${\\color{red}May 2023}$**: Source Code released! You are welcome to use the [Doc2SoarGraph repo](https://github.com/fengbinzhu/Doc2SoarGraph) to explore the TAT-DQA dataset and start your research!\n\n### Citation\n\n__Please kindly cite our work if you use our dataset or codes, thank you.__\n```bash\n\n@inproceedings{zhu2022towards,\n  title={Towards complex document understanding by discrete reasoning},\n  author={Zhu, Fengbin and Lei, Wenqiang and Feng, Fuli and Wang, Chao and Zhang, Haozhou and Chua, Tat-Seng},\n  booktitle={Proceedings of the 30th ACM International Conference on Multimedia},\n  pages={4857--4866},\n  year={2022}\n}\n\n@inproceedings{zhu2024doc2soargraph,\n    title = \"{D}oc2{S}oar{G}raph: Discrete Reasoning over Visually-Rich Table-Text Documents via Semantic-Oriented Hierarchical Graphs\",\n    author = \"Zhu, Fengbin  and\n      Wang, Chao  and\n      Feng, Fuli  and\n      Ren, Zifeng  and\n      Li, Moxin  and\n      Chua, Tat-Seng\",\n    editor = \"Calzolari, Nicoletta  and\n      Kan, Min-Yen  and\n      Hoste, Veronique  and\n      Lenci, Alessandro  and\n      Sakti, Sakriani  and\n      Xue, Nianwen\",\n    booktitle = \"Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)\",\n    year = \"2024\",\n    address = \"Torino, Italia\",\n    publisher = \"ELRA and ICCL\",\n    url = \"https://aclanthology.org/2024.lrec-main.456\",\n    pages = \"5119--5131\"\n}\n```\n\n### License\n\nThe TAT-DQA dataset is under the license of [Creative Commons (CC BY) Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/)\n\n### Any Questions?\n\nFor any issues please create an issue [here](https://github.com/nextplusplus/TAT-DQA/issues) or kindly email us at:\nFengbin Zhu [zhfengbin@gmail.com](mailto:zhfengbin@gmail.com), thank you.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/nextplusplus.github.io%2FTAT-DQA%2F","html_url":"https://awesome.ecosyste.ms/projects/nextplusplus.github.io%2FTAT-DQA%2F","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/nextplusplus.github.io%2FTAT-DQA%2F/lists"}