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

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 transformerbased 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. https://doi.org/10.18653/v1/2023.semeval-1.86

Cite: Hromadka, T., Smoleň, T., Remis, T., Pecher, B., & Srba, I. (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) (pp. 629-637).