ARC-NLP at PAN 2023: Hierarchical Long Text Classification for Trigger Detection

Date

2023-01-01

Author

Sahin, Umitcan
Kucukkaya, Izzet Emre
Toraman, Çağrı

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Fanfiction, Long text classification, Multi-label classification, Transformer-based language models, Trigger detection

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85175618619&origin=inward
https://hdl.handle.net/11511/109681

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Department of Computer Engineering, Conference / Seminar