论文标题
嘟！韩国语料库的在线新闻评论有毒语音检测
BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection
论文作者
论文摘要
在匿名斗篷下，在线平台上的有毒评论是不可避免的社会问题。仇恨言论的检测是针对英语，德语或意大利语等语言积极进行的，该语言已被释放。在这项工作中，我们首先介绍了9.4K手动标记的娱乐新闻评论，以识别从韩国广泛使用的在线新闻平台收集的韩国有毒演讲。关于社会偏见和仇恨言论的评论是指相关的，因为这两个方面都是相关的。通道间协议Krippendorff的Alpha得分分别为0.492和0.496。我们使用Charcnn，Bilstm和Bert提供基准，Bert在所有任务上都取得了最高分。由于仇恨语音检测是一个更主观的问题，因此模型通常在偏置识别上显示出更好的性能。此外，当Bert接受了偏见标签以进行仇恨言论检测时，预测评分会增加，这意味着偏见和仇恨是交织在一起的。我们将数据集公开提供，并通过语料库和基准进行开放竞赛。
Toxic comments in online platforms are an unavoidable social issue under the cloak of anonymity. Hate speech detection has been actively done for languages such as English, German, or Italian, where manually labeled corpus has been released. In this work, we first present 9.4K manually labeled entertainment news comments for identifying Korean toxic speech, collected from a widely used online news platform in Korea. The comments are annotated regarding social bias and hate speech since both aspects are correlated. The inter-annotator agreement Krippendorff's alpha score is 0.492 and 0.496, respectively. We provide benchmarks using CharCNN, BiLSTM, and BERT, where BERT achieves the highest score on all tasks. The models generally display better performance on bias identification, since the hate speech detection is a more subjective issue. Additionally, when BERT is trained with bias label for hate speech detection, the prediction score increases, implying that bias and hate are intertwined. We make our dataset publicly available and open competitions with the corpus and benchmarks.
