ARC-NLP at PAN 2023: Hierarchical Long Text Classification for Trigger
  Detection
Umitcan Sahin Izzet Emre Kucukkaya Cagri Toraman
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.