task_categories:text2text-generation annotations_creators:crowdsourced language_creators:crowdsourced multilinguality:monolingual size_categories:10K<n<100K source_datasets:original language:en license:unknown question-generation region:us
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 processing 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.
@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},
}
---
pretty_name: InquisitiveQg
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: inquisitive
tags:
- question-generation
dataset_info:
  features:
  - name: id
    dtype: int32
  - name: article_id
    dtype: int32
  - name: article
    dtype: string
  - name: sentence_id
    dtype: int32
  - name: sentence
    dtype: string
  - name: span
    dtype: string
  - name: question
    dtype: string
  - name: span_start_position
    dtype: int32
  - name: span_end_position
    dtype: int32
  config_name: plain_text
  splits:
  - name: train
    num_bytes: 66099232
    num_examples: 15931
  - name: validation
    num_bytes: 8904329
    num_examples: 1991
  - name: test
    num_bytes: 7167203
    num_examples: 1894
  download_size: 7085941
  dataset_size: 82170764
---

# Dataset Card for InquisitiveQg

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

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- **Paper:** [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]()
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### Dataset Summary

[More Information Needed]

### Supported Tasks and Leaderboards

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### Languages

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## Dataset Structure

### Data Instances

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### Data Fields

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### Data Splits

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## Dataset Creation

### Curation Rationale

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### Source Data

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#### Initial Data Collection and Normalization

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#### Who are the source language producers?

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### Annotations

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#### Annotation process

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#### Who are the annotators?

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### Personal and Sensitive Information

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## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

[More Information Needed]

### Contributions

Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.