CoS-E

displayName: CoS-E
license:
- BSD 3-Clause
mediaTypes:
- Text
paperUrl: https://arxiv.org/pdf/1906.02361.pdf
publishDate: "2019"
publishUrl: https://github.com/salesforce/cos-e
publisher:
- Salesforce Research
tags:
- Q&a Text
taskTypes:
- Reading Comprehension
- Visual Question Answering
- Commonsense Reasoning

---
# 数据集介绍
## 简介
Common Sense Explanations (CoS-E) allows for training language models to automatically generate explanations that can be used during training and inference in a novel Commonsense Auto-Generated Explanation (CAGE) framework.
## 引文
@inproceedings{rajani2019explain,
title = "Explain Yourself! Leveraging Language models for Commonsense Reasoning",
author = "Rajani, Nazneen Fatema and
McCann, Bryan and
Xiong, Caiming and
Socher, Richard",
year="2019",
booktitle = "Proceedings of the 2019 Conference of the Association for Computational Linguistics (ACL2019)",
url ="https://arxiv.org/abs/1906.02361"
}

## Download dataset
:modelscope-code[]{type="git"}

显示名称：CoS-E
许可证：
- BSD 3条款许可证
媒体类型：
- 文本
论文链接：https://arxiv.org/pdf/1906.02361.pdf
发布日期：2019年
发布地址：https://github.com/salesforce/cos-e
发布方：
- Salesforce研究院
标签：
- 问答文本（Q&A Text）
任务类型：
- 阅读理解（Reading Comprehension）
- 视觉问答（Visual Question Answering）
- 常识推理（Commonsense Reasoning）

---

# 数据集介绍
## 简介
常识解释（Common Sense Explanations，简称CoS-E）可用于训练语言模型，使其能够自动生成解释文本，该解释可在新型常识自动生成解释（Commonsense Auto-Generated Explanation，简称CAGE）框架的训练与推理阶段中使用。
## 引用文献
@inproceedings{rajani2019explain,
title = "阐释自我！借助语言模型开展常识推理",
author = "Rajani, Nazneen Fatema 与 McCann, Bryan 与 Xiong, Caiming 与 Socher, Richard",
year="2019",
booktitle = "2019年计算语言学协会年会论文集（ACL2019）",
url ="https://arxiv.org/abs/1906.02361"
}

## 数据集下载
:modelscope-code[]{type="git"}