A System for Worldwide COVID-19 Information Aggregation
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
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.- Anthology ID:
- 2020.nlpcovid19-2.13
- Volume:
- Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020
- Month:
- December
- Year:
- 2020
- Address:
- Online
- Editors:
- Karin Verspoor, Kevin Bretonnel Cohen, Michael Conway, Berry de Bruijn, Mark Dredze, Rada Mihalcea, Byron Wallace
- Venue:
- NLP-COVID19
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- Language:
- URL:
- https://aclanthology.org/2020.nlpcovid19-2.13
- DOI:
- 10.18653/v1/2020.nlpcovid19-2.13
- Bibkey:
- Cite (ACL):
- 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, et al.. 2020. A System for Worldwide COVID-19 Information Aggregation. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Online. Association for Computational Linguistics.
- Cite (Informal):
- A System for Worldwide COVID-19 Information Aggregation (Aizawa et al., NLP-COVID19 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.nlpcovid19-2.13.pdf
- Video:
- https://slideslive.com/38939852
Export citation
@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.",
}
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%0 Conference Proceedings %T A System for Worldwide COVID-19 Information Aggregation %A Aizawa, Akiko %A Bergeron, Frederic %A Chen, Junjie %A Cheng, Fei %A Hayashi, Katsuhiko %A Inui, Kentaro %A Ito, Hiroyoshi %A Kawahara, Daisuke %A Kitsuregawa, Masaru %A Kiyomaru, Hirokazu %A Kobayashi, Masaki %A Kodama, Takashi %A Kurohashi, Sadao %A Liu, Qianying %A Matsubara, Masaki %A Miyao, Yusuke %A Morishima, Atsuyuki %A Murawaki, Yugo %A Omura, Kazumasa %A Song, Haiyue %A Sumita, Eiichiro %A Suzuki, Shinji %A Tanaka, Ribeka %A Tanaka, Yu %A Toyoda, Masashi %A Ueda, Nobuhiro %A Ueoka, Honai %A Utiyama, Masao %A Zhong, Ying %Y Verspoor, Karin %Y Cohen, Kevin Bretonnel %Y Conway, Michael %Y de Bruijn, Berry %Y Dredze, Mark %Y Mihalcea, Rada %Y Wallace, Byron %S Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020 %D 2020 %8 December %I Association for Computational Linguistics %C Online %F aizawa-etal-2020-system %X 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. %R 10.18653/v1/2020.nlpcovid19-2.13 %U https://aclanthology.org/2020.nlpcovid19-2.13 %U https://doi.org/10.18653/v1/2020.nlpcovid19-2.13
Markdown (Informal)
[A System for Worldwide COVID-19 Information Aggregation](https://aclanthology.org/2020.nlpcovid19-2.13) (Aizawa et al., NLP-COVID19 2020)
- A System for Worldwide COVID-19 Information Aggregation (Aizawa et al., NLP-COVID19 2020)
ACL
- 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, et al.. 2020. A System for Worldwide COVID-19 Information Aggregation. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Online. Association for Computational Linguistics.