论文标题
Semeval-2020 Newsweeper任务11：宣传分类的上下文感知丰富的功能表示
newsSweeper at SemEval-2020 Task 11: Context-Aware Rich Feature Representations For Propaganda Classification
论文作者
论文摘要
本文介绍了我们对Semeval 2020任务的提交11：针对跨度识别和技术分类的两个子任务中的每个子任务中的每个新闻文章中的宣传技术检测。我们利用预先训练的BERT语言模型通过为指定实体识别（NER）的任务开发的标记技术增强了，以开发一个用于识别文本中宣传跨度的系统。对于第二个子任务，我们将上下文特征纳入了预先训练的罗伯塔模型中，以分类宣传技术。我们在宣传技术子任务中排名第五。
This paper describes our submissions to SemEval 2020 Task 11: Detection of Propaganda Techniques in News Articles for each of the two subtasks of Span Identification and Technique Classification. We make use of pre-trained BERT language model enhanced with tagging techniques developed for the task of Named Entity Recognition (NER), to develop a system for identifying propaganda spans in the text. For the second subtask, we incorporate contextual features in a pre-trained RoBERTa model for the classification of propaganda techniques. We were ranked 5th in the propaganda technique classification subtask.
