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.

本文提出了在 SemEval 2023 任务 3 的子任务 3 中检测说服技巧的最佳解决方案，主要是通过细调预训练的基于 Transformer 的语言模型来处理多语言输入数据和多个预测标签，该方案使用大型的跨语言模型（XLM-RoBERTa 大型模型）在所有输入数据上联合训练，而且针对已知语言和未知语言分别设置合理的置信度阈值，最终在 9 种语言中的 6 种语言上（包括两个未知语言）表现最优秀并取得了高度竞争的结果。