论文标题
多任务模型，用于文本中的监督抗议检测
Multitask Models for Supervised Protests Detection in Texts
论文作者
论文摘要
CLEF 2019抗议News实验室任务参与者，以确定较大新闻数据中与政治抗议的文本有关。三个任务包括文章分类，句子检测和事件提取。我应用多任务神经网络，能够同时对其中的两个和三个任务产生预测。多任务框架允许模型从所有三个任务的培训数据中学习相关功能。本文表明，尽管在研究设计上的差异引起的差异使直接比较变得困难，但据报道的自动化事件编码的最新表现附近或更高。
The CLEF 2019 ProtestNews Lab tasks participants to identify text relating to political protests within larger corpora of news data. Three tasks include article classification, sentence detection, and event extraction. I apply multitask neural networks capable of producing predictions for two and three of these tasks simultaneously. The multitask framework allows the model to learn relevant features from the training data of all three tasks. This paper demonstrates performance near or above the reported state-of-the-art for automated political event coding though noted differences in research design make direct comparisons difficult.
