wicho/kor_sae

---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ko
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
pretty_name: Structured Argument Extraction for Korean
dataset_info:
features:
- name: intent_pair1
dtype: string
- name: intent_pair2
dtype: string
- name: label
dtype:
class_label:
names:
'0': yes/no
'1': alternative
'2': wh- questions
'3': prohibitions
'4': requirements
'5': strong requirements
splits:
- name: train
num_bytes: 2885167
num_examples: 30837
download_size: 2545926
dataset_size: 2885167
---

# Dataset Card for Structured Argument Extraction for Korean

## 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:** [Structured Argument Extraction for Korean](https://github.com/warnikchow/sae4k)
- **Repository:** [Structured Argument Extraction for Korean](https://github.com/warnikchow/sae4k)
- **Paper:** [Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives](https://arxiv.org/abs/1912.00342)
- **Point of Contact:** [Won Ik Cho]([e-mail])

### Dataset Summary

The Structured Argument Extraction for Korean dataset is a set of question-argument and command-argument pairs with their respective question type label and negativeness label. Often times, agents like Alexa or Siri, encounter conversations without a clear objective from the user. The goal of this dataset is to extract the intent argument of a given utterance pair without a clear directive. This may yield a more robust agent capable of parsing more non-canonical forms of speech.

### Supported Tasks and Leaderboards

* `intent_classification`: The dataset can be trained with a Transformer like [BERT](https://huggingface.co/bert-base-uncased) to classify the intent argument or a question/command pair in Korean, and it's performance can be measured by it's BERTScore.

### Languages

The text in the dataset is in Korean and the associated is BCP-47 code is `ko-KR`.

## Dataset Structure

### Data Instances

An example data instance contains a question or command pair and its label:

```
{
"intent_pair1": "내일 오후 다섯시 조별과제 일정 추가해줘"
"intent_pair2": "내일 오후 다섯시 조별과제 일정 추가하기"
"label": 4
}
```

### Data Fields

* `intent_pair1`: a question/command pair
* `intent_pair2`: a corresponding question/command pair
* `label`: determines the intent argument of the pair and can be one of `yes/no` (0), `alternative` (1), `wh- questions` (2), `prohibitions` (3), `requirements` (4) and `strong requirements` (5)

### Data Splits

The corpus contains 30,837 examples.

## Dataset Creation

### Curation Rationale

The Structured Argument Extraction for Korean dataset was curated to help train models extract intent arguments from utterances without a clear objective or when the user uses non-canonical forms of speech. This is especially helpful in Korean because in English, the `Who, what, where, when and why` usually comes in the beginning, but this isn't necessarily the case in the Korean language. So for low-resource languages, this lack of data can be a bottleneck for comprehension performance.

### Source Data

#### Initial Data Collection and Normalization

The corpus was taken from the one constructed by [Cho et al.](https://arxiv.org/abs/1811.04231), a Korean single utterance corpus for identifying directives/non-directives that contains a wide variety of non-canonical directives.

#### Who are the source language producers?

Korean speakers are the source language producers.

### Annotations

#### Annotation process

Utterances were categorized as question or command arguments and then further classified according to their intent argument.

#### Who are the annotators?

The annotation was done by three Korean natives with a background in computational linguistics.

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

The dataset is curated by Won Ik Cho, Young Ki Moon, Sangwhan Moon, Seok Min Kim and Nam Soo Kim.

### Licensing Information

The dataset is licensed under the CC BY-SA-4.0.

### Citation Information

```
@article{cho2019machines,
title={Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives},
author={Cho, Won Ik and Moon, Young Ki and Moon, Sangwhan and Kim, Seok Min and Kim, Nam Soo},
journal={arXiv preprint arXiv:1912.00342},
year={2019}
}
```

### Contributions

Thanks to [@stevhliu](https://github.com/stevhliu) for adding this dataset.

annotations_creators:
- 专家生成（expert-generated）
language_creators:
- 专家生成（expert-generated）
language:
- 韩语（Korean）
license:
- CC BY-SA-4.0（知识共享署名-相同方式共享4.0协议）
multilinguality:
- 单语言（monolingual）
size_categories:
- 10000 < 样本数 < 100000
source_datasets:
- 原创数据集（original）
task_categories:
- 文本分类（text-classification）
task_ids:
- 意图分类（intent-classification）
pretty_name: 韩语结构化论点提取数据集
dataset_info:
features:
- name: intent_pair1
dtype: string
- name: intent_pair2
dtype: string
- name: label
dtype:
class_label:
names:
'0': 是/否型（yes/no）
'1': 选择型（alternative）
'2': 特殊疑问句（wh- questions）
'3': 禁令型（prohibitions）
'4': 请求型（requirements）
'5': 强请求型（strong requirements）
splits:
- name: train
num_bytes: 2885167
num_examples: 30837
download_size: 2545926
dataset_size: 2885167

---

# 韩语结构化论点提取数据集数据集卡片

## 目录
- [数据集描述](#dataset-description)
- [数据集概述](#dataset-summary)
- [支持任务与排行榜](#supported-tasks-and-leaderboards)
- [语言](#languages)
- [数据集结构](#dataset-structure)
- [数据实例](#data-instances)
- [数据字段](#data-fields)
- [数据划分](#data-splits)
- [数据集构建](#dataset-creation)
- [构建逻辑](#curation-rationale)
- [源数据](#source-data)
- [标注信息](#annotations)
- [个人与敏感信息](#personal-and-sensitive-information)
- [数据集使用注意事项](#considerations-for-using-the-data)
- [数据集的社会影响](#social-impact-of-dataset)
- [偏差讨论](#discussion-of-biases)
- [其他已知局限](#other-known-limitations)
- [附加信息](#additional-information)
- [数据集维护者](#dataset-curators)
- [许可信息](#licensing-information)
- [引用信息](#citation-information)
- [贡献致谢](#contributions)

## 数据集描述

- **主页:** [韩语结构化论点提取数据集](https://github.com/warnikchow/sae4k)
- **代码仓库:** [韩语结构化论点提取数据集](https://github.com/warnikchow/sae4k)
- **论文:** [《机器适配指令：理解非规范指令的意图论点》](https://arxiv.org/abs/1912.00342)
- **联络人:** [Won Ik Cho]([e-mail])

### 数据集概述

本韩语结构化论点提取数据集是一组带有对应问句类型标签与否定性标签的问答对与命令-问答对。在实际应用中，如Alexa、Siri这类AI智能体（AI Agent）时常会接收到用户意图不明确的对话。本数据集的目标是从无明确指令的给定语句对中提取意图论点，这将助力打造能够解析更多非规范口语表达的鲁棒性更强的AI智能体。

### 支持任务与排行榜

* `意图分类（intent_classification）`: 可使用Transformer（Transformer）类模型（如BERT）对该韩语语句对的意图论点或问句/命令对进行分类，模型性能可通过BERTScore进行评估。

### 语言

数据集文本为韩语，关联的BCP-47语言代码为`ko-KR`。

## 数据集结构

### 数据实例

一个典型的数据实例包含一个问句或命令对及其对应标签：

{
"intent_pair1": "明天下午五点帮忙添加小组作业的日程",
"intent_pair2": "明天下午五点添加小组作业的日程",
"label": 4
}

### 数据字段

* `intent_pair1`: 问句/命令对
* `intent_pair2`: 对应的问句/命令对
* `label`: 用于判定该语句对的意图论点，可选值包括`是/否型（yes/no）`（0）、`选择型（alternative）`（1）、`特殊疑问句（wh- questions）`（2）、`禁令型（prohibitions）`（3）、`请求型（requirements）`（4）与`强请求型（strong requirements）`（5）

### 数据划分

该语料库共包含30837条样本。

## 数据集构建

### 构建逻辑

本韩语结构化论点提取数据集的遴选，旨在助力模型从意图不明确或用户使用非规范口语表达的语句中提取意图论点。这对韩语尤为重要：英语中疑问词（who、what、where、when、why）通常置于句首，但韩语并非如此。对于低资源语言而言，这类数据的缺失会成为语言理解性能提升的瓶颈。

### 源数据

#### 初始数据收集与标准化

该语料库源自[Cho等人](https://arxiv.org/abs/1811.04231)构建的韩语单语句语料库，该语料库用于识别指令/非指令语句，包含大量非规范指令语句。

#### 文本生产者身份

该语料的文本生产者为韩语使用者。

### 标注信息

#### 标注流程

首先将语句归类为问句或命令论点，再根据其意图论点进行进一步分类。

#### 标注者身份

标注工作由三名具备计算语言学背景的韩语母语者完成。

### 个人与敏感信息

[需更多信息]

## 数据集使用注意事项

### 数据集的社会影响

[需更多信息]

### 偏差讨论

[需更多信息]

### 其他已知局限

[需更多信息]

## 附加信息

### 数据集维护者

本数据集由Won Ik Cho、Young Ki Moon、Sangwhan Moon、Seok Min Kim与Nam Soo Kim维护。

### 许可信息

本数据集采用CC BY-SA-4.0（知识共享署名-相同方式共享4.0）协议进行许可。

### 引用信息

@article{cho2019machines,
title={Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives},
author={Cho, Won Ik and Moon, Young Ki and Moon, Sangwhan and Kim, Seok Min and Kim, Nam Soo},
journal={arXiv preprint arXiv:1912.00342},
year={2019}
}

### 贡献致谢

感谢[@stevhliu](https://github.com/stevhliu)添加本数据集。

原始信息汇总

数据集概述

数据集名称

• 名称: Structured Argument Extraction for Korean
• 别名: SAE4K

数据集基本信息

• 语言: 韩语 (ko)
• 许可证: CC BY-SA-4.0
• 多语言性: 单语种
• 大小: 10K<n<100K
• 来源: 原始数据
• 任务类别: 文本分类
• 任务ID: 意图分类

数据集结构

• 特征:
  • intent_pair1: 字符串类型
  • intent_pair2: 字符串类型
  • label: 分类标签，包括 yes/no (0), alternative (1), wh- questions (2), prohibitions (3), requirements (4), strong requirements (5)
• 数据分割:
  • train: 30837个样本，数据大小2885167字节

数据集创建

• 注释创建者: 专家生成
• 语言创建者: 专家生成
• 数据收集: 来自Cho et al.构建的韩语单句语料库，用于识别指令/非指令
• 注释过程: 由三名具有计算语言学背景的韩语母语者进行分类和进一步的意图分类

使用考虑

• 许可证: 数据集根据CC BY-SA-4.0许可发布
• 引用信息: 引用时请参考Cho et al. (2019)的论文

数据集详细信息

数据实例

• 示例:

  { "intent_pair1": "내일 오후 다섯시 조별과제 일정 추가해줘", "intent_pair2": "내일 오후 다섯시 조별과제 일정 추가하기", "label": 4 }

数据字段

• intent_pair1: 问题/命令对
• intent_pair2: 对应的问题/命令对
• label: 意图分类标签，包括多种类型

数据分割

• train: 包含30837个样本，总数据大小为2885167字节