论文标题
Donotdistribute在Semeval-2020任务11：在新闻文章中进行宣传检测的神经模型中的功能，填充和数据增强
Team DoNotDistribute at SemEval-2020 Task 11: Features, Finetuning, and Data Augmentation in Neural Models for Propaganda Detection in News Articles
论文作者
论文摘要
本文介绍了我们针对Semeval 2020共享任务的系统11：新闻文章中宣传技术的检测。我们参与了跨度标识和技术分类子任务，并使用基于BERT的模型以及手工制作的功能进行了有关实验的报告。我们的模型在这两个任务上都表现远高于基准，我们为消融研究和讨论我们的结果进行了讨论，以剖析不同特征和技术的有效性，目的是协助未来的研究进行宣传检测。
This paper presents our systems for SemEval 2020 Shared Task 11: Detection of Propaganda Techniques in News Articles. We participate in both the span identification and technique classification subtasks and report on experiments using different BERT-based models along with handcrafted features. Our models perform well above the baselines for both tasks, and we contribute ablation studies and discussion of our results to dissect the effectiveness of different features and techniques with the goal of aiding future studies in propaganda detection.
