lmqg/qa_squadshifts_synthetic

---
license: cc-by-4.0
pretty_name: Synthetic QA dataset on SQuADShifts.
language: en
multilinguality: monolingual
size_categories: 10K<n<100K
source_datasets:
- extended|wikipedia
task_categories:
- question-answering
task_ids:
- extractive-qa
---

# Dataset Card for "lmqg/qa_squadshifts_synthetic"

## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)

### Dataset Summary
This is a synthetic QA dataset generated with fine-tuned QG models over [`lmqg/qa_squadshifts`](https://huggingface.co/datasets/lmqg/qa_squadshifts), made for question-answering based evaluation (QAE) for question generation model proposed by [Zhang and Bansal, 2019](https://aclanthology.org/D19-1253/).
The test split is the original validation set of [`lmqg/qa_squadshifts`](https://huggingface.co/datasets/lmqg/qa_squadshifts), where the model should be evaluate on.

### Supported Tasks and Leaderboards

* `question-answering`

### Languages
English (en)

## Dataset Structure

### Data Fields
The data fields are the same among all splits.

#### plain_text

- `id`: a `string` feature of id
- `title`: a `string` feature of title of the paragraph
- `context`: a `string` feature of paragraph
- `question`: a `string` feature of question
- `answers`: a `json` feature of answers

### Data Splits
TBA

## Citation Information

```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```

---
许可证：CC BY 4.0
规范名称：基于SQuADShifts的合成问答（QA）数据集
语言：英语
多语言属性：单语种
规模区间：1万至10万条数据
源数据集：
- 扩展维基百科数据集
任务类别：
- 问答（Question Answering）
任务子类型：
- 抽取式问答（Extractive QA）
---

# 「lmqg/qa_squadshifts_synthetic」数据集卡片

## 数据集描述
- **仓库地址**：[https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **论文地址**：[https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **联络人**：[Asahi Ushio](http://asahiushio.com/)

### 数据集概述
本数据集基于 [`lmqg/qa_squadshifts`](https://huggingface.co/datasets/lmqg/qa_squadshifts) 微调后的问答生成（Question Generation, QG）模型生成，专为Zhang与Bansal于2019年提出的问答生成模型的基于问答的评估（Question-Answering based Evaluation, QAE）任务打造。
该数据集的测试划分集即为 [`lmqg/qa_squadshifts`](https://huggingface.co/datasets/lmqg/qa_squadshifts) 的原始验证集，模型应在此划分集上完成评估。

### 支持任务与排行榜
* `问答（Question Answering）`

### 语言
英语（en）

## 数据集结构

### 数据字段
所有数据划分的字段格式均保持一致。

#### 纯文本字段
- `id`：字符串类型的数据集唯一标识
- `title`：字符串类型的段落标题
- `context`：字符串类型的段落正文
- `question`：字符串类型的问题文本
- `answers`：存储答案的JSON格式字段

### 数据划分
待公布

## 引用信息

@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}

原始信息汇总

数据集概述

基本信息

• 名称: Synthetic QA dataset on SQuADShifts
• 许可证: cc-by-4.0
• 语言: 英语 (en)
• 多语言性: 单语种
• 规模: 10K<n<100K

数据来源

• 源数据集: 扩展自wikipedia

任务类型

• 任务类别: 问答
• 任务ID: extractive-qa

数据集结构

• 数据字段:
  • id: 字符串类型，标识符
  • title: 字符串类型，段落标题
  • context: 字符串类型，段落内容
  • question: 字符串类型，问题
  • answers: JSON格式，答案

数据分割

• 分割详情: 待定 (TBA)

引用信息

@inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", }