Datasets:

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](wicho@hi.snu.ac.kr)

	### 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.