task_categories:question-answering task_ids:open-domain-qa task_ids:extractive-qa annotations_creators:expert-generated language_creators:found multilinguality:monolingual size_categories:10K<n<100K language:en-US license:bsd-3-clause arxiv:1707.03904 region:us
We present two new large-scale datasets aimed at evaluating systems designed to comprehend a natural language query and extract its answer from a large corpus of text. The Quasar-S dataset consists of 37000 cloze-style (fill-in-the-gap) queries constructed from definitions of software entity tags on the popular website Stack Overflow. The posts and comments on the website serve as the background corpus for answering the cloze questions. The Quasar-T dataset consists of 43000 open-domain trivia questions and their answers obtained from various internet sources. ClueWeb09 serves as the background corpus for extracting these answers. We pose these datasets as a challenge for two related subtasks of factoid Question Answering: (1) searching for relevant pieces of text that include the correct answer to a query, and (2) reading the retrieved text to answer the query.
@article{dhingra2017quasar,
  title={Quasar: Datasets for Question Answering by Search and Reading},
  author={Dhingra, Bhuwan and Mazaitis, Kathryn and Cohen, William W},
  journal={arXiv preprint arXiv:1707.03904},
  year={2017}
}
---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en-US
license:
- bsd-3-clause
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- 
task_categories:
- question-answering
task_ids:
- open-domain-qa
- extractive-qa
paperswithcode_id: quasar-1
---

# Dataset Card Creation Guide

## 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

- **Homepage:** N/A
- **Repository:** [GitHub](https://github.com/bdhingra/quasar)
- **Paper:** [Quasar: Datasets for Question Answering by Search and Reading](https://arxiv.org/abs/1707.03904)
- **Leaderboard:** N/A
- **Point of Contact:** -

### Dataset Summary

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### 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

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### Discussion of Biases

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### Other Known Limitations

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## Additional Information

### Dataset Curators

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### Licensing Information

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### Citation Information

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### Contributions
