论文标题
世界范围内Covid-19信息汇总的系统
A System for Worldwide COVID-19 Information Aggregation
论文作者
论文摘要
Covid-19的全球大流行使公众密切关注相关新闻，涵盖了各种领域，例如卫生，治疗和对教育的影响。同时，在国家之间的COVID-19条件（例如，流行病的政策和发展）非常不同，因此公民对国外的新闻感兴趣。我们为全球Covid-19信息构建了一个系统，其中包含来自10种语言的10个区域的可靠文章，并按主题排序。通过众包收集的我们可靠的Covid-19相关网站数据集可确保文章的质量。神经机器翻译模块将其他语言的文章翻译成日语和英语。基于BERT的主题分类室对我们的文章主题对数据集进行了培训，可帮助用户通过将文章列入不同的类别来有效地找到其感兴趣的信息。
The global pandemic of COVID-19 has made the public pay close attention to related news, covering various domains, such as sanitation, treatment, and effects on education. Meanwhile, the COVID-19 condition is very different among the countries (e.g., policies and development of the epidemic), and thus citizens would be interested in news in foreign countries. We build a system for worldwide COVID-19 information aggregation containing reliable articles from 10 regions in 7 languages sorted by topics. Our reliable COVID-19 related website dataset collected through crowdsourcing ensures the quality of the articles. A neural machine translation module translates articles in other languages into Japanese and English. A BERT-based topic-classifier trained on our article-topic pair dataset helps users find their interested information efficiently by putting articles into different categories.
