KOAS: Korean Text Offensiveness Analysis System
San-Hee Park, Kang-Min Kim, Seonhee Cho, Jun-Hyung Park, Hyuntae Park, Hyuna Kim, Seongwon Chung, SangKeun Lee
Abstract
Warning: This manuscript contains a certain level of offensive expression. As communication through social media platforms has grown immensely, the increasing prevalence of offensive language online has become a critical problem. Notably in Korea, one of the countries with the highest Internet usage, automatic detection of offensive expressions has recently been brought to attention. However, morphological richness and complex syntax of Korean causes difficulties in neural model training. Furthermore, most of previous studies mainly focus on the detection of abusive language, disregarding implicit offensiveness and underestimating a different degree of intensity. To tackle these problems, we present KOAS, a system that fully exploits both contextual and linguistic features and estimates an offensiveness score for a text. We carefully designed KOAS with a multi-task learning framework and constructed a Korean dataset for offensive analysis from various domains. Refer for a detailed demonstration.
BibTeX
@inproceedings{park-etal-2021-koas,
title = "{KOAS}: {K}orean Text Offensiveness Analysis System",
author = "Park, San-Hee and
Kim, Kang-Min and
Cho, Seonhee and
Park, Jun-Hyung and
Park, Hyuntae and
Kim, Hyuna and
Chung, Seongwon and
Lee, SangKeun",
editor = "Adel, Heike and
Shi, Shuming",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-demo.9/",
doi = "10.18653/v1/2021.emnlp-demo.9",
pages = "72--78"
}