arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新，休息一下吧，周末愉快~~
arXiv 2008.01523cs.CL
收藏

全球COVID-19信息聚合系统

A System for Worldwide COVID-19 Information Aggregation

Kyoto University（京都大学）；NII（日本信息学研究所）；Tohoku University（东北大学）；The University of Tokyo（东京大学）；The University of Tsukuba（筑波大学）；Waseda University（早稻田大学）；NICT（日本信息通信研究机构）；Institute of Industrial Science, the University of Tokyo（东京大学工学研究所）
Akiko Aizawa, Frederic Bergeron, Junjie Chen, Fei Cheng, Katsuhiko Hayashi, Kentaro Inui, Hiroyoshi Ito, Daisuke Kawahara, Masaru Kitsuregawa, Hirokazu Kiyomaru… 展开作者

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

备注：
Accepted to EMNLP 2020 Workshop NLP-COVID
阅读 PDF
arXiv 页面 ↗HTML 原文 ↗

摘要：

COVID-19全球大流行令公众密切关注相关新闻，涵盖卫生、治疗及对教育的影响等多个领域。与此同时，各国的COVID-19状况差异很大（例如，政策和流行病的发展），因此公民会对外国的新闻感兴趣。我们建立了一个全球COVID-19信息聚合系统，包含按主题排序的来自10个地区的7种语言的可靠文章。我们通过众包收集的可靠的COVID-19相关网站数据集确保了文章的质量。神经机器翻译模块将其他语言的文章翻译成日语和英语。在我们的文章主题对数据集上训练的基于BERT的主题分类器可以帮助用户通过将文章放入不同的类别来有效地找到他们感兴趣的信息。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

↑