@inproceedings{aizawa-etal-2020-system,
    title = "A System for Worldwide {COVID}-19 Information Aggregation",
    author = "Aizawa, Akiko  and
      Bergeron, Frederic  and
      Chen, Junjie  and
      Cheng, Fei  and
      Hayashi, Katsuhiko  and
      Inui, Kentaro  and
      Ito, Hiroyoshi  and
      Kawahara, Daisuke  and
      Kitsuregawa, Masaru  and
      Kiyomaru, Hirokazu  and
      Kobayashi, Masaki  and
      Kodama, Takashi  and
      Kurohashi, Sadao  and
      Liu, Qianying  and
      Matsubara, Masaki  and
      Miyao, Yusuke  and
      Morishima, Atsuyuki  and
      Murawaki, Yugo  and
      Omura, Kazumasa  and
      Song, Haiyue  and
      Sumita, Eiichiro  and
      Suzuki, Shinji  and
      Tanaka, Ribeka  and
      Tanaka, Yu  and
      Toyoda, Masashi  and
      Ueda, Nobuhiro  and
      Ueoka, Honai  and
      Utiyama, Masao  and
      Zhong, Ying",
    editor = "Verspoor, Karin  and
      Cohen, Kevin Bretonnel  and
      Conway, Michael  and
      de Bruijn, Berry  and
      Dredze, Mark  and
      Mihalcea, Rada  and
      Wallace, Byron",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.nlpcovid19-2.13",
    doi = "10.18653/v1/2020.nlpcovid19-2.13",
    abstract = "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.",
}