KInITVeraAI at SemEval-2023 Task 3: Simple yet Powerful Multilingual Fine-Tuning for Persuasion Techniques Detection

Timo Hromadka, Timotej Smolen, Tomas Remis, Branislav Pecher, Ivan Srba


Abstract
This paper presents the best-performing solution to the SemEval 2023 Task 3 on the subtask 3 dedicated to persuasion techniques detection. Due to a high multilingual character of the input data and a large number of 23 predicted labels (causing a lack of labelled data for some language-label combinations), we opted for fine-tuning pre-trained transformer-based language models. Conducting multiple experiments, we find the best configuration, which consists of large multilingual model (XLM-RoBERTa large) trained jointly on all input data, with carefully calibrated confidence thresholds for seen and surprise languages separately. Our final system performed the best on 6 out of 9 languages (including two surprise languages) and achieved highly competitive results on the remaining three languages.
Anthology ID:
2023.semeval-1.86
Volume:
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
Month:
July
Year:
2023
Address:
Toronto, Canada
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
629–637
Language:
URL:
https://aclanthology.org/2023.semeval-1.86
DOI:
10.18653/v1/2023.semeval-1.86
Bibkey:
Cite (ACL):
Timo Hromadka, Timotej Smolen, Tomas Remis, Branislav Pecher, and Ivan Srba. 2023. KInITVeraAI at SemEval-2023 Task 3: Simple yet Powerful Multilingual Fine-Tuning for Persuasion Techniques Detection. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 629–637, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
KInITVeraAI at SemEval-2023 Task 3: Simple yet Powerful Multilingual Fine-Tuning for Persuasion Techniques Detection (Hromadka et al., SemEval 2023)
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PDF:
https://aclanthology.org/2023.semeval-1.86.pdf