A System for Worldwide COVID-19 Information Aggregation
This system addresses the need for accessible and organized COVID-19 news for citizens worldwide, though it is incremental as it applies existing methods to new data.
The researchers tackled the problem of aggregating global COVID-19 information by building a system that collects reliable articles from 10 regions in 7 languages, using neural machine translation and a BERT-based topic classifier to organize content for users.
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.