Picture for Frederic Bergeron

Frederic Bergeron

A System for Worldwide COVID-19 Information Aggregation

Add code
Jul 28, 2020
Akiko Aizawa, Frederic Bergeron, Junjie Chen, Fei Cheng, Katsuhiko Hayashi, Kentaro Inui, Hiroyoshi Ito, Daisuke Kawahara, Masaru Kitsuregawa, Hirokazu Kiyomaru, Masaki Kobayashi, Takashi Kodama, Sadao Kurohashi, Qianying Liu, Masaki Matsubara, Yusuke Miyao, Atsuyuki Morishima, Yugo Murawaki, Kazumasa Omura, Haiyue Song, Eiichiro Sumita, Shinji Suzuki, Ribeka Tanaka, Yu Tanaka, Masashi Toyoda, Nobuhiro Ueda, Honai Ueoka, Masao Utiyama, Ying Zhong

Figure 1 for A System for Worldwide COVID-19 Information Aggregation
Figure 2 for A System for Worldwide COVID-19 Information Aggregation
Figure 3 for A System for Worldwide COVID-19 Information Aggregation
Figure 4 for A System for Worldwide COVID-19 Information Aggregation

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.

* Poster on NLP COVID-19 Workshop at ACL 2020, 4 pages, 3 figures, 7 tables 
Viaarxiv icon
Github IconAccess Paper or Ask Questions
Twitter IconFacebook IconLinkedin IconWhatsapp IconMessenger Icon