论文标题
贬义：对人群生成的仇恨言论评估数据集的攻击贬值表达
APEACH: Attacking Pejorative Expressions with Analysis on Crowd-Generated Hate Speech Evaluation Datasets
论文作者
论文摘要
在仇恨言论检测中，开发跨各个领域的培训和评估数据集是关键问题。鉴于，主要方法抓取社交媒体文本并雇用众群落来注释数据。遵循这一惯例通常会限制贬义表达的范围，这些范围缺乏概括的单个领域。有时，培训语料库和评估之间的域重叠，设定了在低数据语言上预处理语言模型时的预测性能。为了减轻韩语中的这些问题，我们提出宣传，要求未指定的用户产生仇恨言论示例，然后是最少的后标签。我们发现，apeach可以收集有用的数据集，这些数据集对训练训练的语料库和评估集之间的词汇重叠不太敏感，从而正确测量了模型性能。
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.
