论文标题
Aschern在Semeval-2020任务11：探戈需要三个：Roberta，CRF和转移学习
aschern at SemEval-2020 Task 11: It Takes Three to Tango: RoBERTa, CRF, and Transfer Learning
论文作者
论文摘要
我们描述了我们针对新闻文章中检测宣传技术的Semeval-2020任务11系统。我们使用基于Roberta的神经体系结构，其他CRF层，两个子任务之间的转移学习以及高级后处理，以处理任务的多标签性质，嵌套跨度之间的一致性，重复和标签之间的一致性。我们比基线微调的罗伯塔模型取得了显着改进，官方评估在跨度识别子任务中的36个团队中的第三组（几乎与第二次并列）排名为0.491的0.491分数，第二个（几乎与第1局）（几乎与第1局）相关的技术分类中的31个团队在F1上获得了0.62 n.62。
We describe our system for SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles. We developed ensemble models using RoBERTa-based neural architectures, additional CRF layers, transfer learning between the two subtasks, and advanced post-processing to handle the multi-label nature of the task, the consistency between nested spans, repetitions, and labels from similar spans in training. We achieved sizable improvements over baseline fine-tuned RoBERTa models, and the official evaluation ranked our system 3rd (almost tied with the 2nd) out of 36 teams on the span identification subtask with an F1 score of 0.491, and 2nd (almost tied with the 1st) out of 31 teams on the technique classification subtask with an F1 score of 0.62.
