KOLD : K orean Offensive Language Dataset (2022)

Abstract: Recent directions for offensive language detection are hierarchical modeling, identifying the type and the target of offensive language, and interpretability with offensive span annotation and prediction. These improvements are focused on English and do not transfer well to other languages because of cultural and linguistic differences. In this paper, we present the Korean Offensive Language Dataset (KOLD) comprising 40,429 comments, which are annotated hierarchically with the type and the target of offensive language, accompanied by annotations of the corresponding text spans. We collect the comments from NAVER news and YouTube platform and provide the titles of the articles and videos as the context information for the annotation process. We use these annotated comments as training data for Korean BERT and RoBERTa models and find that they are effective at offensiveness detection, target classification, and target span detection while having room for improvement for target group classification and offensive span detection. We discover that the target group distribution differs drastically from the existing English datasets, and observe that providing the context information improves the model performance in offensiveness detection (+0.3), target classification (+1.5), and target group classification (+13.1). We publicly release the dataset and baseline models.

## introduction online offensive language is a growing societal problem. it propagates negative stereotypes about the targeted social groups, causing representational harm @xcite . among various research directions for offensive language detection, level a feminism is not about gender inequality, it is a mental disorder feminism is not about gender inequality, it is a mental disorder (ko) gs25, 브레이브걸스 포스터 또 젠더 이슈 논란 (en) gs25's poster of brave girls evokes dispute over gender issues yet again (ko) 페미는 젠더가 아니라 정신병이다 (en) feminism is not about gender inequality, it is a mental disorder comment offensive span? group individual others untargeted target group? ## annotation task design we use a hierarchical annotation framework based on the multi-layer annotation schema in olid @xcite . additionally, we identify the specific target group of the offensive language. we also annotate the spans that support the labeling decision if the comment is offensive and/or contains a target of offensiveness. figure 1 illustrates an overview of our annotation task, and ## experiments and results we experiment using three different model architectures: (1) sequence classification model for predicting offensiveness, target type, and target group categories, (2) token classification model for predicting the offensive and target span, and (3) multitask model for predicting both category and span at once. we report the score of single-task models using various sizes of korean bert and roberta @xcite , and compare the result against the multi-task model. we further conduct an ablation study by excluding the title from the input to discover how much it impacts the prediction performance of the model. we also compare the results of our model with translated versions of english data, and a multilingual span prediction model. for the experiments, we use an 80-10-10 split for each task, and report the best performances based on the f 1 score of the test set result with the tuned hyperparameters. training details are reported in the appendix b. ## conclusion we present kold, a dataset of 40,429 comments of news articles and video clips, annotated within context. it is the first to introduce a hierarchical taxonomy of offensive language in korean with textual spans of the offensiveness and the targets. we establish baseline performance for multi-task model that both detects the categories and the spans that support the classification. through analysis and experiments, we show that target terms are often omitted in offensive comments, and title information helps models predict the target of the offense. this finding can be applied to other syntactically nullsubject languages other than korean (e.g., arabic, chinese, modern greek) as well. by comparing the distribution of target groups with existing english data and showing the inadequacy of multilingual models, we demonstrate that offensive language corpus customized for the language and its corresponding culture is necessary. we acknowledge that our dataset does not cover all communities of korean social media whose offensive language patterns may differ from each other. despite this limitation, kold will serve as a stepping stone to developing more accurate and adaptive offensive language detection models in korean.