A System for Worldwide COVID-19 Information Aggregation
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.
Cite
@article{arxiv.2008.01523,
title = {A System for Worldwide COVID-19 Information Aggregation},
author = {Akiko Aizawa and Frederic Bergeron and Junjie Chen and Fei Cheng and Katsuhiko Hayashi and Kentaro Inui and Hiroyoshi Ito and Daisuke Kawahara and Masaru Kitsuregawa and Hirokazu Kiyomaru and Masaki Kobayashi and Takashi Kodama and Sadao Kurohashi and Qianying Liu and Masaki Matsubara and Yusuke Miyao and Atsuyuki Morishima and Yugo Murawaki and Kazumasa Omura and Haiyue Song and Eiichiro Sumita and Shinji Suzuki and Ribeka Tanaka and Yu Tanaka and Masashi Toyoda and Nobuhiro Ueda and Honai Ueoka and Masao Utiyama and Ying Zhong},
journal= {arXiv preprint arXiv:2008.01523},
year = {2020}
}
Comments
Accepted to EMNLP 2020 Workshop NLP-COVID