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--- |
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license: apache-2.0 |
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task_categories: |
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- text-classification |
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language: |
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- ko |
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dataset_info: |
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features: |
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- name: comment |
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dtype: string |
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- name: preference |
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dtype: int64 |
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- name: profanity |
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dtype: int64 |
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- name: gender |
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dtype: int64 |
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- name: politics |
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dtype: int64 |
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- name: nation |
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dtype: int64 |
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- name: race |
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dtype: int64 |
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- name: region |
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dtype: int64 |
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- name: generation |
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dtype: int64 |
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- name: social_hierarchy |
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dtype: int64 |
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- name: appearance |
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dtype: int64 |
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- name: others |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 7458552 |
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num_examples: 38361 |
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- name: test |
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num_bytes: 412144 |
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num_examples: 2000 |
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download_size: 2947880 |
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dataset_size: 7870696 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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size_categories: |
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- 1M<n<10M |
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--- |
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# KoMultiText: Korean Multi-task Dataset for Classifying Biased Speech |
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## Dataset Summary |
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**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. |
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### Key Features |
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- **Large-Scale Dataset**: Contains 150,000 comments, including labeled and unlabeled data. |
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- **Multi-task Annotations**: Covers Preference, Profanity, and nine distinct types of Bias. |
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- **Human-Labeled**: All labeled data is annotated by **five human experts** to ensure high-quality and unbiased annotations. |
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- **Real-world Relevance**: Collected from "Real-time Best Gallery" of [DC Inside](https://www.dcinside.com/), a popular online community in South Korea. |
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### Labels |
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<img src="https://raw.githubusercontent.com/Dasol-Choi/KoMultiText/main/resources/dataset_configuration.png" width="700"> |
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--- |
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# Dataset Creation |
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## Source Data |
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- **Origin**: Comments collected from "Real-time Best Gallery" on [DC Inside](https://www.dcinside.com/). |
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- **Annotation Process**: |
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- **Human Annotation**: Five human annotators independently labeled all comments in the dataset to ensure accuracy and minimize bias. |
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- **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. |
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- **Dataset Composition**: |
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- **Labeled Data**: 40,361 comments (train/test split). |
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- **Unlabeled Data**: 110,000 comments for potential pretraining or unsupervised learning. [Veiw Dataset](https://huggingface.co/datasets/Dasool/DC_inside_comments) |
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## How to Load the Dataset |
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```python |
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from datasets import load_dataset |
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# Load the dataset |
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dataset = load_dataset("Dasool/KoMultiText") |
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# Access train and test splits |
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train_dataset = dataset["train"] |
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test_dataset = dataset["test"] |
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``` |
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## Code |
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- Korean BERT-based fine-tuning code. [Github](https://github.com/Dasol-Choi/KoMultiText?tab=readme-ov-file) |
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## Citation |
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<pre> |
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@misc{choi2023largescale, |
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title={Large-Scale Korean Text Dataset for Classifying Biased Speech in Real-World Online Services}, |
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author={Dasol Choi and Jooyoung Song and Eunsun Lee and Jinwoo Seo and Heejune Park and Dongbin Na}, |
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year={2023}, |
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eprint={2310.04313}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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</pre> |
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## Contact |
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- [email protected] |
