Quoref
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下载链接:
https://modelscope.cn/datasets/OmniData/Quoref
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资源简介:
displayName: Quoref
labelTypes:
- Text
license:
- CC BY 4.0
mediaTypes:
- Text
paperUrl: https://arxiv.org/pdf/1908.05803v2.pdf
publishDate: "2019"
publishUrl: https://allenai.org/data/quoref
publisher:
- University of Washington
- Allen Institute for Artificial Intelligence
tags:
- Text
taskTypes:
- Machine Reading Comprehension
- Reading Comprehension
- Coreference Resolution
---
# 数据集介绍
## 简介
Quoref 是一个 QA 数据集,用于测试阅读理解系统的共指推理能力。在这个跨度选择基准包含来自维基百科的 4.7K 段落中的 24K 问题,系统必须先解决硬共指,然后才能在段落中选择适当的跨度来回答问题。
## 引文
```
"@article{dasigi2019quoref,
title={Quoref: A reading comprehension dataset with questions requiring coreferential reasoning},
author={Dasigi, Pradeep and Liu, Nelson F and Marasovi{\'c}, Ana and Smith, Noah A and Gardner, Matt},
journal={arXiv preprint arXiv:1908.05803},
year={2019}
}"
```
## Download dataset
:modelscope-code[]{type="git"}
displayName: Quoref
labelTypes:
- 文本(Text)
license:
- 知识共享署名4.0(CC BY 4.0)
mediaTypes:
- 文本(Text)
paperUrl: https://arxiv.org/pdf/1908.05803v2.pdf
publishDate: "2019"
publishUrl: https://allenai.org/data/quoref
publisher:
- 华盛顿大学(University of Washington)
- 艾伦人工智能研究所(Allen Institute for Artificial Intelligence)
tags:
- 文本(Text)
taskTypes:
- 机器阅读理解(Machine Reading Comprehension)
- 阅读理解(Reading Comprehension)
- 共指消解(Coreference Resolution)
---
# 数据集介绍
## 简介
Quoref 是一个问答(QA)数据集,旨在评测阅读理解系统的共指推理能力。该跨度选择基准包含源自维基百科的4.7K段文本中的24K个问题,系统需先完成共指消解的难例推理,方能在段落中选取合适的文本跨度以回答对应问题。
## 引文
"@article{dasigi2019quoref,
title={Quoref: A reading comprehension dataset with questions requiring coreferential reasoning},
author={Dasigi, Pradeep and Liu, Nelson F and Marasović, Ana and Smith, Noah A and Gardner, Matt},
journal={arXiv preprint arXiv:1908.05803},
year={2019}
}"
## 下载数据集
:modelscope-code[]{type="git"}
提供机构:
maas
创建时间:
2024-07-02



