论文标题
触发警告：引导小说的暴力探测器
Trigger Warnings: Bootstrapping a Violence Detector for FanFiction
论文作者
论文摘要
我们在新定义的触发警告分配的计算任务上介绍了第一个数据集和评估结果。标记的语料库数据是由著名的幻想网站（AO3）托管的叙事作品编制的。在本文中，我们专注于最常见的触发类型（暴力），并定义文档级二进制分类任务，即是否将暴力触发警告分配给幻想小说，利用AO3作者提供的警告标签。在我们编译的Corpora上进行了四个评估设置培训的SVM和BERT模型，收益$ f_1 $结果范围从0.585到0.798，证明暴力触发警告任务是可行的，这是一项不平凡的任务。
We present the first dataset and evaluation results on a newly defined computational task of trigger warning assignment. Labeled corpus data has been compiled from narrative works hosted on Archive of Our Own (AO3), a well-known fanfiction site. In this paper, we focus on the most frequently assigned trigger type--violence--and define a document-level binary classification task of whether or not to assign a violence trigger warning to a fanfiction, exploiting warning labels provided by AO3 authors. SVM and BERT models trained in four evaluation setups on the corpora we compiled yield $F_1$ results ranging from 0.585 to 0.798, proving the violence trigger warning assignment to be a doable, however, non-trivial task.
