Fine‐Tuned Transformers for Detection and Classification of Persuasion Techniques in Slavic Languages
Year: 2025
Type: conference-paper
Abstract: This paper details a system developed for the SlavicNLP 2025 Shared Task on the Detection and Classification of Persuasion Techniques in Texts for Slavic Languages.The shared task comprises two subtasks: binary detection of persuasive content within text fragments and multi-class, multi-label identification of specific persuasion techniques at the token level.Our primary approach for both subtasks involved fine-tuning pre-trained multilingual Transformer models.The resulting systems reached F1 score of 0.92 in paragraph-level detection (ranked third on average).We present our system architecture, data handling, training procedures, and official results, alongside areas for future improvement.
Author: Ekaterina Loginova
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412889042