论文标题
Inno在Semeval-2020任务11：利用纯变压器进行多级宣传检测
Inno at SemEval-2020 Task 11: Leveraging Pure Transformer for Multi-Class Propaganda Detection
论文作者
论文摘要
本文为“ Inno”团队的解决方案介绍了2020年2020任务11“新闻文章中宣传技术的检测”。第二个子任务的目的是对与新闻文章数据集中的18个给定宣传技术之一相对应的文本段进行分类。我们通过优化的学习方案测试了一个基于变压器的模型，该方案可以区分彼此之间的宣传技术。我们的模型在相应的验证集和测试集上显示了0.6和0.58的总F1分数，并且在两组上的每个类别上都表现出非零的F1分数。
The paper presents the solution of team "Inno" to a SEMEVAL 2020 task 11 "Detection of propaganda techniques in news articles". The goal of the second subtask is to classify textual segments that correspond to one of the 18 given propaganda techniques in news articles dataset. We tested a pure Transformer-based model with an optimized learning scheme on the ability to distinguish propaganda techniques between each other. Our model showed 0.6 and 0.58 overall F1 score on validation set and test set accordingly and non-zero F1 score on each class on both sets.
