This paper describes our system (Solomon) details and results of participation in the SemEval 2020 Task 11 "Detection of Propaganda Techniques in News Articles"\cite{DaSanMartinoSemeval20task11}. We participated in Task "Technique Classification" (TC) which is a multi-class classification task. To address the TC task, we used RoBERTa based transformer architecture for fine-tuning on the propaganda dataset. The predictions of RoBERTa were further fine-tuned by class-dependent-minority-class classifiers. A special classifier, which employs dynamically adapted Least Common Sub-sequence algorithm, is used to adapt to the intricacies of repetition class. Compared to the other participating systems, our submission is ranked 4th on the leaderboard.

中文翻译：

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Solomon 在 SemEval-2020 任务 11：新闻文章中用于微调宣传检测的集成架构

本文描述了我们的系统 (Solomon) 详细信息和参与 SemEval 2020 任务 11“新闻文章中的宣传技术检测”\cite{DaSanMartinoSemeval20task11} 的结果。我们参与了任务“技术分类”（TC），这是一个多类分类任务。为了解决 TC 任务，我们使用基于 RoBERTa 的转换器架构对宣传数据集进行微调。RoBERTa 的预测由类相关的少数类分类器进一步微调。一个特殊的分类器，它采用动态适应的最小公共子序列算法，用于适应重复类的复杂性。与其他参与系统相比，我们的提交在排行榜上排名第 4。