论文标题
测试基于Bilstm的结构故事分类器的鲁棒性
Testing the Robustness of a BiLSTM-based Structural Story Classifier
论文作者
论文摘要
互联网上假冒故事的普遍性日益普遍，引起了人们对机器学习社区中假新闻的快速和可扩展发现的重大兴趣。尽管已经出现了几种用于此目的的机器学习技术，但我们观察到有必要评估噪声对这些技术性能的影响，其中噪声构成新闻文章被错误地标记为假（或真实）。这项工作朝着这个方向迈出了一步，我们检查了噪声对假新闻检测的最先进的结构模型（双向长期术语模型），用于假新闻检测，层次结构的话语级结构，用于Karimi和Tang（参考编号9）。
The growing prevalence of counterfeit stories on the internet has fostered significant interest towards fast and scalable detection of fake news in the machine learning community. While several machine learning techniques for this purpose have emerged, we observe that there is a need to evaluate the impact of noise on these techniques' performance, where noise constitutes news articles being mistakenly labeled as fake (or real). This work takes a step in that direction, where we examine the impact of noise on a state-of-the-art, structural model based on BiLSTM (Bidirectional Long-Short Term Model) for fake news detection, Hierarchical Discourse-level Structure for Fake News Detection by Karimi and Tang (Reference no. 9).
