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.- Anthology ID:
- 2022.findings-emnlp.525
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7076–7086
- Language:
- URL:
- https://preview.aclanthology.org/icon-24-ingestion/2022.findings-emnlp.525/
- DOI:
- 10.18653/v1/2022.findings-emnlp.525
- Cite (ACL):
- Kichang Yang, Wonjun Jang, and Won Ik Cho. 2022. APEACH: Attacking Pejorative Expressions with Analysis on Crowd-Generated Hate Speech Evaluation Datasets. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 7076–7086, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- APEACH: Attacking Pejorative Expressions with Analysis on Crowd-Generated Hate Speech Evaluation Datasets (Yang et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/icon-24-ingestion/2022.findings-emnlp.525.pdf