@inproceedings{ksiezniak-etal-2025-robust,
title = "Robust Detection of Persuasion Techniques in {S}lavic Languages via Multitask Debiasing and Walking Embeddings",
author = "Ksiezniak, Ewelina and
Wecel, Krzysztof and
Sawinski, Marcin",
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-1.25/",
doi = "10.18653/v1/2025.bsnlp-1.25",
pages = "224--230",
ISBN = "978-1-959429-57-9",
abstract = "We present our solution to Subtask 1 of the Shared Task on the Detection and Classification of Persuasion Techniques in Texts for Slavic Languages. Our approach integrates fine-tuned multilingual transformer models with two complementary robustness-oriented strategies: Walking Embeddings and Content-Debiasing. With the first, we tried to understand the change in embeddings when various manipulation techniques were applied. The latter leverages a supervised contrastive objective over semantically equivalent yet stylistically divergent text pairs, generated via GPT-4. We conduct extensive experiments, including 5-fold cross-validation and out-of-domain evaluation, and explore the impact of contrastive loss weighting."
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<abstract>We present our solution to Subtask 1 of the Shared Task on the Detection and Classification of Persuasion Techniques in Texts for Slavic Languages. Our approach integrates fine-tuned multilingual transformer models with two complementary robustness-oriented strategies: Walking Embeddings and Content-Debiasing. With the first, we tried to understand the change in embeddings when various manipulation techniques were applied. The latter leverages a supervised contrastive objective over semantically equivalent yet stylistically divergent text pairs, generated via GPT-4. We conduct extensive experiments, including 5-fold cross-validation and out-of-domain evaluation, and explore the impact of contrastive loss weighting.</abstract>
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%0 Conference Proceedings
%T Robust Detection of Persuasion Techniques in Slavic Languages via Multitask Debiasing and Walking Embeddings
%A Ksiezniak, Ewelina
%A Wecel, Krzysztof
%A Sawinski, Marcin
%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 ksiezniak-etal-2025-robust
%X We present our solution to Subtask 1 of the Shared Task on the Detection and Classification of Persuasion Techniques in Texts for Slavic Languages. Our approach integrates fine-tuned multilingual transformer models with two complementary robustness-oriented strategies: Walking Embeddings and Content-Debiasing. With the first, we tried to understand the change in embeddings when various manipulation techniques were applied. The latter leverages a supervised contrastive objective over semantically equivalent yet stylistically divergent text pairs, generated via GPT-4. We conduct extensive experiments, including 5-fold cross-validation and out-of-domain evaluation, and explore the impact of contrastive loss weighting.
%R 10.18653/v1/2025.bsnlp-1.25
%U https://aclanthology.org/2025.bsnlp-1.25/
%U https://doi.org/10.18653/v1/2025.bsnlp-1.25
%P 224-230
Markdown (Informal)
[Robust Detection of Persuasion Techniques in Slavic Languages via Multitask Debiasing and Walking Embeddings](https://aclanthology.org/2025.bsnlp-1.25/) (Ksiezniak et al., BSNLP 2025)
ACL