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.

翻译：我们介绍了关于新定义的触发警告任务计算任务的第一个数据集和评价结果,从我们Own档案馆(AO3)一个广为人知的粉信网站(AO3)的叙述性作品中汇编了标签材料数据,在本文中,我们侧重于最经常分配的触发暴力类型,并界定了文件层面的二进制任务,即是否将暴力引发对幻想的警告,利用AO3作者提供的警告标签。SVM和BERT模型经过我们汇编的Corpora的四个评价组合的培训,产生了0.585至0.798美元的1法郎结果,证明暴力触发警告任务是一项非三进制任务。