论文标题
COVID-19-19
Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment
论文作者
论文摘要
1920年，Covid-19的传播已成为社会上一个重要且令人不安的方面。在各个国家发生了数百万个案件，发生了新的爆发，并遵循了以前受影响地区的模式。许多疾病检测模型并未结合可用于建模和预测其传播的大量社交媒体数据。在这种情况下，询问是有用的，我们可以在一个国家使用这些知识来模拟另一个国家的爆发吗？为了回答这一点，我们提出了跨语性转移学习以进行流行病学一致性的任务。利用宏观和微观文本功能，我们通过Twitter进行了意大利早期的Covid-19爆发，并转移到其他几个国家。我们的实验显示出强劲的结果，在越野预测中最多0.85 Spearman相关性。
The spread of COVID-19 has become a significant and troubling aspect of society in 2020. With millions of cases reported across countries, new outbreaks have occurred and followed patterns of previously affected areas. Many disease detection models do not incorporate the wealth of social media data that can be utilized for modeling and predicting its spread. In this case, it is useful to ask, can we utilize this knowledge in one country to model the outbreak in another? To answer this, we propose the task of cross-lingual transfer learning for epidemiological alignment. Utilizing both macro and micro text features, we train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
