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INQUISITIVE Dataset

A dataset of about 20k questions that are elicited from readers as they naturally read through a document sentence by sentence. Compared to existing datasets, INQUISITIVE questions target more towards high-level (semantic and discourse) comprehension of text. Because these questions are generated while the readers are pro-cessing the information, the questions directly communicate gaps between the reader’s and writer’s knowledge about the events described in the text, and are not necessarily answered in the document itself. This type of question reflects a real-world scenario: if one has questions during reading, some of them are answered by the text later on, the rest are not, but any of them would help further the reader’s understanding at the particular point when they asked it. This resource could enable question generation models to simulate human-like curiosity and cognitive processing, which may open up a new realm of applications.

Citation:

@InProceedings{ko2020inquisitive,
  author    = {Ko, Wei-Jen and Chen, Te-Yuan and Huang, Yiyan and Durrett, Greg and Li, Junyi Jessy},
  title     = {Inquisitive Question Generation for High Level Text Comprehension},
  booktitle = {Proceedings of EMNLP},
  year      = {2020},
}

Data split

Validation: 1~100, 1051~1100

Test: 101~150, 501~550, 1101~1150

The remaining articles are the training set.

Article Sources

WSJ: 51~259, 551~590, 696~900, 1446~1491

Newsela: 1~50, 260~550, 901~1050, 1492~1500

AP: 591~695, 1051~1445

Since the articles are copyrighted, please send me an email to ask for the articles (wjko@outlook.com). Please obtain permission from newsela (https://newsela.com/data) first before emailing me.

Note on span

The Span_Start_Position and Span_End_Position are calculated by counting the white spaces. Note that in some sentences there are consecutive white spaces.

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