论文标题
YNU-HPCC在Semeval-2020任务11：新闻文章中宣传技术检测的LSTM网络
YNU-HPCC at SemEval-2020 Task 11: LSTM Network for Detection of Propaganda Techniques in News Articles
论文作者
论文摘要
本文总结了我们对Semeval-2020任务中新闻文章的宣传检测技术的研究11。该任务分为SI和TC子任务。我们实施了手套单词表示，BERT预处理模型和LSTM模型体系结构来完成此任务。我们的方法为SI和TC子任务都取得了良好的效果。 SI子任务的宏F1得分为0.406，TC子任务的Micro-F1得分为0.505。我们的方法明显优于正式发布的基线方法，而SI和TC子任务分别对测试集排名第17和22。本文还将不同深度学习模型体系结构（例如BI-LSTM，LSTM，BERT和XGBOOST模型）与新闻推广技术的检测进行了比较。本文的代码可在以下网址提供：https：//github.com/daojiaxu/semeval_11。
This paper summarizes our studies on propaganda detection techniques for news articles in the SemEval-2020 task 11. This task is divided into the SI and TC subtasks. We implemented the GloVe word representation, the BERT pretraining model, and the LSTM model architecture to accomplish this task. Our approach achieved good results for both the SI and TC subtasks. The macro-F1-score for the SI subtask is 0.406, and the micro-F1-score for the TC subtask is 0.505. Our method significantly outperforms the officially released baseline method, and the SI and TC subtasks rank 17th and 22nd, respectively, for the test set. This paper also compares the performances of different deep learning model architectures, such as the Bi-LSTM, LSTM, BERT, and XGBoost models, on the detection of news promotion techniques. The code of this paper is availabled at: https://github.com/daojiaxu/semeval_11.
