@inproceedings{zou-etal-2025-empowering,
title = "Empowering Persuasion Detection in {S}lavic Texts through Two-Stage Generative Reasoning",
author = "Zou, Xin and
Wang, Chuhan and
Li, Dailin and
Wang, Yanan and
Wang, Jian and
Lin, Hongfei",
editor = "Piskorski, Jakub and
P{\v{r}}ib{\'a}{\v{n}}, Pavel and
Nakov, Preslav and
Yangarber, Roman and
Marcinczuk, Michal",
booktitle = "Proceedings of the 10th Workshop on Slavic Natural Language Processing (Slavic NLP 2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.bsnlp-1.20/",
doi = "10.18653/v1/2025.bsnlp-1.20",
pages = "177--182",
ISBN = "978-1-959429-57-9",
abstract = "This paper presents our submission to Subtask 2 (multi-label classification of persuasion techniques) of the Shared Task on Detection and Classification of Persuasion Techniques in Slavic Languages at SlavNLP 2025. Our method leverages a teacher{--}student framework based on large language models (LLMs): a Qwen3 32B teacher model generates natural language explanations for annotated persuasion techniques, and a Qwen2.5 32B student model is fine-tuned to replicate both the teacher{'}s rationales and the final label predictions. We train our models on the official shared task dataset, supplemented by annotated resources from SemEval 2023 Task 3 and CLEF 2024 Task 3 covering English, Russian, and Polish to improve cross-lingual robustness. Our final system ranks 4th on BG, SI, and HR, and 5th on PL in terms of micro-F1 score among all participating teams."
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<abstract>This paper presents our submission to Subtask 2 (multi-label classification of persuasion techniques) of the Shared Task on Detection and Classification of Persuasion Techniques in Slavic Languages at SlavNLP 2025. Our method leverages a teacher–student framework based on large language models (LLMs): a Qwen3 32B teacher model generates natural language explanations for annotated persuasion techniques, and a Qwen2.5 32B student model is fine-tuned to replicate both the teacher’s rationales and the final label predictions. We train our models on the official shared task dataset, supplemented by annotated resources from SemEval 2023 Task 3 and CLEF 2024 Task 3 covering English, Russian, and Polish to improve cross-lingual robustness. Our final system ranks 4th on BG, SI, and HR, and 5th on PL in terms of micro-F1 score among all participating teams.</abstract>
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%0 Conference Proceedings
%T Empowering Persuasion Detection in Slavic Texts through Two-Stage Generative Reasoning
%A Zou, Xin
%A Wang, Chuhan
%A Li, Dailin
%A Wang, Yanan
%A Wang, Jian
%A Lin, Hongfei
%Y Piskorski, Jakub
%Y Přibáň, Pavel
%Y Nakov, Preslav
%Y Yangarber, Roman
%Y Marcinczuk, Michal
%S Proceedings of the 10th Workshop on Slavic Natural Language Processing (Slavic NLP 2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 978-1-959429-57-9
%F zou-etal-2025-empowering
%X This paper presents our submission to Subtask 2 (multi-label classification of persuasion techniques) of the Shared Task on Detection and Classification of Persuasion Techniques in Slavic Languages at SlavNLP 2025. Our method leverages a teacher–student framework based on large language models (LLMs): a Qwen3 32B teacher model generates natural language explanations for annotated persuasion techniques, and a Qwen2.5 32B student model is fine-tuned to replicate both the teacher’s rationales and the final label predictions. We train our models on the official shared task dataset, supplemented by annotated resources from SemEval 2023 Task 3 and CLEF 2024 Task 3 covering English, Russian, and Polish to improve cross-lingual robustness. Our final system ranks 4th on BG, SI, and HR, and 5th on PL in terms of micro-F1 score among all participating teams.
%R 10.18653/v1/2025.bsnlp-1.20
%U https://aclanthology.org/2025.bsnlp-1.20/
%U https://doi.org/10.18653/v1/2025.bsnlp-1.20
%P 177-182
Markdown (Informal)
[Empowering Persuasion Detection in Slavic Texts through Two-Stage Generative Reasoning](https://aclanthology.org/2025.bsnlp-1.20/) (Zou et al., BSNLP 2025)
ACL