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- license: c-uda
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ annotations_creators:
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+ - expert-generated
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+ - crowdsourced
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+ language:
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+ - en
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+ language_creators:
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+ - found
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+ - crowdsourced
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+ - expert-generated
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+ license:
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+ - c-uda
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+ multilinguality:
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+ - monolingual
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+ paperswithcode_id: totto
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+ pretty_name: HiTab
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+ size_categories:
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+ - 10K<n<100K
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+ source_datasets:
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+ - original
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+ - extended|totto
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+ tags: []
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+ task_categories:
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+ - table-to-text
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+ task_ids: []
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  ---
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+
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+ # Dataset Card for HiTab
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+
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+ ## Dataset Description
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+ - **Homepage:** https://www.microsoft.com/en-us/research/publication/hitab-a-hierarchical-table-dataset-for-question-answering-and-natural-language-generation/
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+ - **Repository:** https://github.com/microsoft/HiTab/blob/main/LICENSE
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+ - **Paper:** https://aclanthology.org/2022.acl-long.78/
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+
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+ ### Dataset Summary
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+ HiTab is a dataset for question answering and data-to-text over hierarchical tables . It contains 10,672 samples and 3,597 tables from statistical reports (StatCan, NSF) and Wikipedia (ToTTo). 98.1% of the tables in HiTab are with hierarchies.
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+
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+ ### Supported Tasks and Leaderboards
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+ Table-to-Text Generation, Question Answering
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+
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+ ### Languages
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+ English
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+
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+ ### Data Instances
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+ 3,597 tables, 10,686 sentences
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+
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+ ### Data Splits
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+ train, test, validation
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+
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+ ## Dataset Creation
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+ During the dataset annotation process, annotators first manually collect tables and descriptive sentences highly-related to tables on statistical websites written by professional analysts. Then these descriptions are revised to questions to preserve the original meanings and analyses.
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+
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+ ### Licensing Information
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+
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+ This dataset follows the Computational Use of Data Agreement v1.0.
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+
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+ ### Citation Information
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+
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+ ```
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+ @article{cheng2021hitab,
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+ title={HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation},
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+ author={Cheng, Zhoujun and Dong, Haoyu and Wang, Zhiruo and Jia, Ran and Guo, Jiaqi and Gao, Yan and Han, Shi and Lou, Jian-Guang and Zhang, Dongmei},
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+ journal={arXiv preprint arXiv:2108.06712},
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+ year={2021}
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+ }