metadata

license: apache-2.0
task_categories:
  - text-classification
language:
  - ko
dataset_info:
  features:
    - name: comment
      dtype: string
    - name: preference
      dtype: int64
    - name: profanity
      dtype: int64
    - name: gender
      dtype: int64
    - name: politics
      dtype: int64
    - name: nation
      dtype: int64
    - name: race
      dtype: int64
    - name: region
      dtype: int64
    - name: generation
      dtype: int64
    - name: social_hierarchy
      dtype: int64
    - name: appearance
      dtype: int64
    - name: others
      dtype: int64
  splits:
    - name: train
      num_bytes: 7458552
      num_examples: 38361
    - name: test
      num_bytes: 412144
      num_examples: 2000
  download_size: 2947880
  dataset_size: 7870696
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
size_categories:
  - 1M<n<10M

KoMultiText: Korean Multi-task Dataset for Classifying Biased Speech

Dataset Summary

KoMultiText is a comprehensive Korean multi-task text dataset designed for classifying biased and harmful speech in online platforms. The dataset focuses on tasks such as Preference Detection, Profanity Identification, and Bias Classification across multiple domains, enabling state-of-the-art language models to perform multi-task learning for socially responsible AI applications.

Key Features

Large-Scale Dataset: Contains 150,000 comments, including labeled and unlabeled data.
Multi-task Annotations: Covers Preference, Profanity, and nine distinct types of Bias.
Human-Labeled: All labeled data is annotated by five human experts to ensure high-quality and unbiased annotations.
Real-world Relevance: Collected from "Real-time Best Gallery" of DC Inside, a popular online community in South Korea.

Labels

Dataset Creation

Source Data

Origin: Comments collected from "Real-time Best Gallery" on DC Inside.
Annotation Process:
- Human Annotation: Five human annotators independently labeled all comments in the dataset to ensure accuracy and minimize bias.
- Labeling Process: Annotators followed strict guidelines to classify comments into Preference, Profanity, and nine types of Bias. Discrepancies were resolved through majority voting and discussion.
Dataset Composition:
- Labeled Data: 40,361 comments (train/test split).
- Unlabeled Data: 110,000 comments for potential pretraining or unsupervised learning. Veiw Dataset

How to Load the Dataset

from datasets import load_dataset

# Load the dataset 
dataset = load_dataset("Dasool/KoMultiText")

# Access train and test splits
train_dataset = dataset["train"]
test_dataset = dataset["test"]

Code

Korean BERT-based fine-tuning code. Github

Citation

@misc{choi2023largescale,
      title={Large-Scale Korean Text Dataset for Classifying Biased Speech in Real-World Online Services}, 
      author={Dasol Choi and Jooyoung Song and Eunsun Lee and Jinwoo Seo and Heejune Park and Dongbin Na},
      year={2023},
      eprint={2310.04313},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Contact

[email protected]