论文标题
所罗门在2020 Semeval-2020任务11：新闻文章中微调宣传检测的合奏结构
Solomon at SemEval-2020 Task 11: Ensemble Architecture for Fine-Tuned Propaganda Detection in News Articles
论文作者
论文摘要
本文介绍了我们的系统（所罗门）的详细信息和参与结果的结果11任务11“对新闻文章中宣传技术的检测” \ cite {dasanmartinosememeval20task11}。我们参加了任务“技术分类”（TC），这是一个多类分类任务。为了解决TC任务，我们使用了基于罗伯塔的变压器体系结构进行宣传数据集进行微调。罗伯塔（Roberta）的预测是通过类依赖性的少数类别分类器进一步微调的。使用动态适应最不常见的子序列算法的特殊分类器可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四。
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.
