  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.
