Fanfiction, a popular form of creative writing set within established fictional universes, has gained a substantial online following. However, ensuring the well-being and safety of participants has become a critical concern in this community. The detection of triggering content, material that may cause emotional distress or trauma to readers, poses a significant challenge. In this paper, we describe our approach for the Trigger Detection shared task at PAN CLEF 2023, where we want to detect multiple triggering content in a given Fanfiction document. For this, we build a hierarchical model that uses recurrence over Transformer-based language models. In our approach, we first split long documents into smaller sized segments and use them to fine-tune a Transformer model. Then, we extract feature embeddings from the fine-tuned Transformer model, which are used as input in the training of multiple LSTM models for trigger detection in a multi-label setting. Our model achieves an F1-macro score of 0.372 and F1-micro score of 0.736 on the validation set, which are higher than the baseline results shared at PAN CLEF 2023.

翻译：同人小说作为在既有虚构宇宙中进行创意写作的流行形式，已积累了庞大的在线受众。然而，保障参与者的身心健康与安全已成为该社区的关键议题。检测可能引发读者情绪困扰或精神创伤的触发内容是一项重大挑战。本文阐述了我们在PAN CLEF 2023触发检测共享任务中的方法，旨在识别给定同人小说文档中的多种触发内容。为此，我们构建了一个基于Transformer语言模型并融入递归机制的分层模型。具体而言，首先将长文档分割为较小的段落并用于微调Transformer模型；继而从微调后的Transformer模型中提取特征嵌入，作为多标签场景下训练多个LSTM模型进行触发检测的输入。我们的模型在验证集上取得了F1宏平均0.372与F1微平均0.736的分数，均高于PAN CLEF 2023公布的基线结果。