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README.md
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---
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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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# Dataset Card for HiTab
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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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### 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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### Supported Tasks and Leaderboards
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Table-to-Text Generation, Question Answering
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### Languages
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English
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### Data Instances
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3,597 tables, 10,686 sentences
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### Data Splits
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train, test, validation
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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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### Licensing Information
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This dataset follows the Computational Use of Data Agreement v1.0.
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### Citation Information
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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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}
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