---
title: QuAC
canonical_url: "https://www.modelscope.cn/datasets/OmniData/QuAC"
md_url: "https://www.modelscope.cn/datasets/OmniData/QuAC.md"
repository: OmniData/QuAC
last_updated: 2024-07-02
license: "[CC BY-SA 4.0]"
storage_size: "73 MB"
domain:
  - publishDate
  - paperUrl
  - publishUrl
  - taskTypes
  - displayName
  - mediaTypes
  - labelTypes
  - publisher
tasks:
  - 2018
  - "https://aclanthology.org/D18-1241.pdf"
  - "https://quac.ai/"
  - "Multi-Turn Question Answering"
  - "QuAC (Question Answering in Context)"
  - Text
  - "University of Massachusetts Amherst"
downloads: 136
stars: 0
---

# QuAC

> QuAC - OmniData 在 ModelScope 开源的数据集。displayName: QuAC (Question Answering in Context) labelTypes: Text license: CC BY-SA 4.0 mediaTypes: Text paperUrl: https://aclanthology.org/D18-1241.pdf publishDate: "2018" publishUrl: https://quac.ai/ publisher: Stanford…

OmniData/QuAC 是 ModelScope 魔搭社区上的2018、https://aclanthology.org/D18-1241.pdf、https://quac.ai/数据集，涉及 publishDate、paperUrl、publishUrl 领域，存储大小 73 MB，采用 [CC BY-SA 4.0] 许可。

- **Repository**: OmniData/QuAC
- **License**: [CC BY-SA 4.0]
- **Tasks**: 2018, https://aclanthology.org/D18-1241.pdf, https://quac.ai/, Multi-Turn Question Answering, QuAC (Question Answering in Context), Text, University of Massachusetts Amherst
- **Domain**: publishDate, paperUrl, publishUrl, taskTypes, displayName, mediaTypes, labelTypes, publisher
- **Storage size**: 73 MB
- **Downloads**: 136
- **Stars**: 0
- **Last updated**: 2024-07-02

Source: https://www.modelscope.cn/datasets/OmniData/QuAC

---

displayName: QuAC (Question Answering in Context)
labelTypes:
- Text
license:
- CC BY-SA 4.0
mediaTypes:
- Text
paperUrl: https://aclanthology.org/D18-1241.pdf
publishDate: "2018"
publishUrl: https://quac.ai/
publisher:
- Stanford University
- University of Washington
- Allen Institute for Artificial Intelligence
- University of Massachusetts Amherst
tags:
- Question And Answer
taskTypes:
- Visual Question Answering
- Multi-Turn Question Answering

---
# 数据集介绍
  ## 简介
  上下文问答是一个大规模的数据集，由大约 14K 众包问答对话和总共 98K 问答对组成。数据实例包括两个群众工作者之间的交互式对话：（1）提出一系列自由形式问题以尽可能多地了解隐藏的维基百科文本的学生，以及（2）通过提供简短摘录来回答问题的老师（跨越）来自文本。
  ## 引文
  ```
@article{choi2018quac,
title={QuAC: Question answering in context},
author={Choi, Eunsol and He, He and Iyyer, Mohit and Yatskar, Mark and Yih, Wen-tau and Choi, Yejin and Liang, Percy and Zettlemoyer, Luke},
journal={arXiv preprint arXiv:1808.07036},
year={2018}
}
```
  
## Download dataset
:modelscope-code[]{type="git"}