Online hate speech detection has become an important issue due to the growth
of online content, but resources in languages other than English are extremely
limited. We introduce K-MHaS, a new multi-label dataset for hate speech
detection that effectively handles Korean language patterns. The dataset
consists of 109k utterances from news comments and provides a multi-label
classification using 1 to 4 labels, and handles subjectivity and
intersectionality. We evaluate strong baseline experiments on K-MHaS using
Korean-BERT-based language models with six different metrics. KR-BERT with a
sub-character tokenizer outperforms others, recognizing decomposed characters
in each hate speech class.

介绍了适用于韩语模式的多标记数据集 K-MHaS，基于六种不同的指标使用韩语 BERT 模型进行评估，其中具有子字符令牌化器的 KR-BERT 优于其他模型。

K-MHaS：韩国在线新闻评论中的多标签仇恨言论检测数据集

K-MHaS: A Multi-label Hate Speech Detection Dataset in Korean Online News Comment

We introduce DAiSEE, the first multi-label video classification dataset
comprising of 9068 video snippets captured from 112 users for recognizing the
user affective states of boredom, confusion, engagement, and frustration in the
wild. The dataset has four levels of labels namely - very low, low, high, and
very high for each of the affective states, which are crowd annotated and
correlated with a gold standard annotation created using a team of expert
psychologists. We have also established benchmark results on this dataset using
state-of-the-art video classification methods that are available today. We
believe that DAiSEE will provide the research community with challenges in
feature extraction, context-based inference, and development of suitable
machine learning methods for related tasks, thus providing a springboard for
further research. The dataset is available for download at
this https URL

DAiSEE 是第一个包含 9068 个视频片段和 112 个用户的多标签视频分类数据集，用于识别烦恼、困惑、参与和挫败用户情感状态，具有四个级别的标签。提供了基准结果和用于相关任务的合适机器学习方法的发展。