A System for Worldwide COVID-19 Information Aggregation

Aizawa, Akiko; Bergeron, Frederic; Chen, Junjie; Cheng, Fei; Hayashi, Katsuhiko; Inui, Kentaro; Ito, Hiroyoshi; Kawahara, Daisuke; Kitsuregawa, Masaru; Kiyomaru, Hirokazu; Kobayashi, Masaki; Kodama, Takashi; Kurohashi, Sadao; Liu, Qianying; Matsubara, Masaki; Miyao, Yusuke; Morishima, Atsuyuki; Murawaki, Yugo; Omura, Kazumasa; Song, Haiyue; Sumita, Eiichiro; Suzuki, Shinji; Tanaka, Ribeka; Tanaka, Yu; Toyoda, Masashi; Ueda, Nobuhiro; Ueoka, Honai; Utiyama, Masao; Zhong, Ying

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 (http://lotus.kuee.kyoto-u.ac.jp/NLPforCOVID-19 ) containing reliable articles from 10 regions in 7 languages sorted by topics for Japanese citizens. 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. A BERT-based topic-classifier trained on an article-topic pair dataset helps users find their interested information efficiently by putting articles into different categories.