README.md

metadata

pretty_name: KoBEST
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
configs:
  - config_name: boolq
    data_files: boolq/*
  - config_name: copa
    data_files: copa/*
  - config_name: hellaswag
    data_files: hellaswag/*
  - config_name: sentineg
    data_files: sentineg/*
  - config_name: wic
    data_files: wic/*

Dataset Card for KoBEST

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Repository: https://github.com/SKT-LSL/KoBEST_datarepo
• Paper:
• Point of Contact: https://github.com/SKT-LSL/KoBEST_datarepo/issues

Dataset Summary

KoBEST is a Korean benchmark suite consists of 5 natural language understanding tasks that requires advanced knowledge in Korean.

Supported Tasks and Leaderboards

Boolean Question Answering, Choice of Plausible Alternatives, Words-in-Context, HellaSwag, Sentiment Negation Recognition

Languages

ko-KR

Dataset Structure

Data Instances

KB-BoolQ

An example of a data point looks as follows.

{'paragraph': '두아 리파(Dua Lipa, 1995년 8월 22일 ~ )는 잉글랜드의 싱어송라이터, 모델이다. BBC 사운드 오브 2016 명단에 노미닛되었다. 싱글 "Be the One"가 영국 싱글 차트 9위까지 오르는 등 성과를 보여주었다.',
 'question': '두아 리파는 영국인인가?',
 'label': 1}

KB-COPA

An example of a data point looks as follows.

{'premise': '물을 오래 끓였다.',
 'question': '결과',
 'alternative_1': '물의 양이 늘어났다.',
 'alternative_2': '물의 양이 줄어들었다.',
 'label': 1}

KB-WiC

An example of a data point looks as follows.

{'word': '양분',
 'context_1': '토양에 [양분]이 풍부하여 나무가 잘 자란다.	',
 'context_2': '태아는 모체로부터 [양분]과 산소를 공급받게 된다.',
 'label': 1}

KB-HellaSwag

An example of a data point looks as follows.

{'context': '모자를 쓴 투수가 타자에게 온 힘을 다해 공을 던진다. 공이 타자에게 빠른 속도로 다가온다. 타자가 공을 배트로 친다. 배트에서 깡 소리가 난다. 공이 하늘 위로 날아간다.',
 'ending_1': '외야수가 떨어지는 공을 글러브로 잡는다.',
 'ending_2': '외야수가 공이 떨어질 위치에 자리를 잡는다.',
 'ending_3': '심판이 아웃을 외친다.',
 'ending_4': '외야수가 공을 따라 뛰기 시작한다.',
 'label': 3}

KB-SentiNeg

An example of a data point looks as follows.

{'sentence': '택배사 정말 마음에 듬',
 'label': 1}

Data Fields

KB-BoolQ

• paragraph: a string feature
• question: a string feature
• label: a classification label, with possible values False(0) and True(1)

KB-COPA

• premise: a string feature
• question: a string feature
• alternative_1: a string feature
• alternative_2: a string feature
• label: an answer candidate label, with possible values alternative_1(0) and alternative_2(1)

KB-WiC

• target_word: a string feature
• context_1: a string feature
• context_2: a string feature
• label: a classification label, with possible values False(0) and True(1)

KB-HellaSwag

• target_word: a string feature
• context_1: a string feature
• context_2: a string feature
• label: a classification label, with possible values False(0) and True(1)

KB-SentiNeg

• sentence: a string feature
• label: a classification label, with possible values Negative(0) and Positive(1)

Data Splits

KB-BoolQ

• train: 3,665
• dev: 700
• test: 1,404

KB-COPA

• train: 3,076
• dev: 1,000
• test: 1,000

KB-WiC

• train: 3,318
• dev: 1,260
• test: 1,260

KB-HellaSwag

• train: 3,665
• dev: 700
• test: 1,404

KB-SentiNeg

• train: 3,649
• dev: 400
• test: 397
• test_originated: 397 (Corresponding training data where the test set is originated from.)

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

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

[More Information Needed]

Licensing Information

@misc{https://doi.org/10.48550/arxiv.2204.04541,
  doi = {10.48550/ARXIV.2204.04541},
  url = {https://arxiv.org/abs/2204.04541},
  author = {Kim, Dohyeong and Jang, Myeongjun and Kwon, Deuk Sin and Davis, Eric},
  title = {KOBEST: Korean Balanced Evaluation of Significant Tasks},
  publisher = {arXiv},
  year = {2022},
}

[More Information Needed]

Contributions

Thanks to @MJ-Jang for adding this dataset.