论文标题
Nopropaganda在Semeval-2020任务11：借来的序列标记和文本分类的方法
NoPropaganda at SemEval-2020 Task 11: A Borrowed Approach to Sequence Tagging and Text Classification
论文作者
论文摘要
本文介绍了我们对2020年Semeval-2020任务的贡献11：新闻文章中宣传技术的检测。我们从简单的LSTM基准开始，然后转到自回归的变压器解码器，以预测第一个子任务的长期连续宣传跨度。我们还通过将上述跨度的跨度与特殊令牌包裹在宣传技术分类的第二个子任务中，采用一种方法。我们的模型报告的F-评分为44.6％，相应地完成这些任务的小平均F-评分为58.2％。
This paper describes our contribution to SemEval-2020 Task 11: Detection Of Propaganda Techniques In News Articles. We start with simple LSTM baselines and move to an autoregressive transformer decoder to predict long continuous propaganda spans for the first subtask. We also adopt an approach from relation extraction by enveloping spans mentioned above with special tokens for the second subtask of propaganda technique classification. Our models report an F-score of 44.6% and a micro-averaged F-score of 58.2% for those tasks accordingly.
