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.

本研究是第一次针对新定义的触发警告分配计算任务提供数据集和评估结果。该任务的数据来自 Archive of Our Own (AO3) 上托管的叙事作品的编译语料库，作者依靠 AO3 提供的警告标签为 Fanfiction 分配触发警告。本文主要讨论最常分配的触发类型（暴力），并定义了一个文档级的二进制分类任务，即是否为一个 Fanfiction 分配暴力触发警告。在我们编译的语料库上，训练的 SVM 和 BERT 模型在四个评估设置上得出的 $ F_1 $ 结果在 0.585 到 0.798 之间，证明了这项暴力触发警告分配任务是可行的，但并不是容易的任务。