在SemEval-2019清除谣言任务7：ConvoLving ELMo反对谣言
CLEARumor at SemEval-2019 Task 7: ConvoLving ELMo Against Rumors
摘要：
本文介绍了我们提交的SemEval-2019任务7：谣言评估：确定谣言的真实性和谣言的支持。我们参与了这两个子任务。子任务A的目标是将谣言社交媒体帖子和回复帖子之间的交互类型分类为支持，质疑，否认或评论。子任务B的目标是预测给定谣言的真实性。对于子任务A，我们使用帖子文本的ELMo嵌入结合辅助特征实现了基于CNN的神经架构，并获得了44.6%的F1分数。对于子任务B，我们采用了MLP神经网络，利用我们对子任务A的估计，并获得了30.1%的F1分数（比赛第二名）。我们提供了我们的系统性能和本消融实验的结果和分析。
英文摘要：
This paper describes our submission to SemEval-2019 Task 7: RumourEval: Determining Rumor Veracity and Support for Rumors. We participated in both subtasks. The goal of subtask A is to classify the type of interaction between a rumorous social media post and a reply post as support, query, deny, or comment. The goal of subtask B is to predict the veracity of a given rumor. For subtask A, we implement a CNN-based neural architecture using ELMo embeddings of post text combined with auxiliary features and achieve a F1-score of 44.6%. For subtask B, we employ a MLP neural network leveraging our estimates for subtask A and achieve a F1-score of 30.1% (second place in the competition). We provide results and analysis of our system performance and present ablation experiments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
