| --- |
| dataset_info: |
| features: |
| - name: text |
| dtype: string |
| - name: user_age |
| dtype: int64 |
| - name: user_gender |
| dtype: string |
| - name: text_topic |
| dtype: string |
| - name: class |
| dtype: string |
| - name: age |
| dtype: int64 |
| - name: text_topic_eng |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 751331 |
| num_examples: 3770 |
| download_size: 254089 |
| dataset_size: 751331 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| license: cc-by-sa-4.0 |
| language: |
| - ko |
| tags: |
| - safety |
| --- |
| |
| reference: [https://github.com/jason9693/APEACH](https://github.com/jason9693/APEACH) |
| ``` |
| @inproceedings{yang-etal-2022-apeach, |
| title = "{APEACH}: Attacking Pejorative Expressions with Analysis on Crowd-Generated Hate Speech Evaluation Datasets", |
| author = "Yang, Kichang and |
| Jang, Wonjun and |
| Cho, Won Ik", |
| booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022", |
| month = dec, |
| year = "2022", |
| address = "Abu Dhabi, United Arab Emirates", |
| publisher = "Association for Computational Linguistics", |
| url = "https://aclanthology.org/2022.findings-emnlp.525", |
| pages = "7076--7086", |
| abstract = "In hate speech detection, developing training and evaluation datasets across various domains is the critical issue. Whereas, major approaches crawl social media texts and hire crowd-workers to annotate the data. Following this convention often restricts the scope of pejorative expressions to a single domain lacking generalization. Sometimes domain overlap between training corpus and evaluation set overestimate the prediction performance when pretraining language models on low-data language. To alleviate these problems in Korean, we propose APEACH that asks unspecified users to generate hate speech examples followed by minimal post-labeling. We find that APEACH can collect useful datasets that are less sensitive to the lexical overlaps between the pretraining corpus and the evaluation set, thereby properly measuring the model performance.", |
| } |
| ``` |
