metadata
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
@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.",
}