@inproceedings{li-etal-2020-jennifer,
title = "Jennifer for {COVID-19}: An {NLP}-Powered Chatbot Built for the People and by the People to Combat Misinformation",
author = "Li, Yunyao and
Grandison, Tyrone and
Silveyra, Patricia and
Douraghy, Ali and
Guan, Xinyu and
Kieselbach, Thomas and
Li, Chengkai and
Zhang, Haiqi",
editor = "Verspoor, Karin and
Cohen, Kevin Bretonnel and
Dredze, Mark and
Ferrara, Emilio and
May, Jonathan and
Munro, Robert and
Paris, Cecile and
Wallace, Byron",
booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nlpcovid19-acl.9",
abstract = "Just as SARS-CoV-2, a new form of coronavirus continues to infect a growing number of people around the world, harmful misinformation about the outbreak also continues to spread. With the goal of combating misinformation, we designed and built Jennifer{--}a chatbot maintained by a global group of volunteers. With Jennifer, we hope to learn whether public information from reputable sources could be more effectively organized and shared in the wake of a crisis as well as to understand issues that the public were most immediately curious about. In this paper, we introduce Jennifer and describe the design of this proof-of-principle system. We also present lessons learned and discuss open challenges. Finally, to facilitate future research, we release COVID-19 Question Bank, a dataset of 3,924 COVID-19-related questions in 944 groups, gathered from our users and volunteers.",
}
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<abstract>Just as SARS-CoV-2, a new form of coronavirus continues to infect a growing number of people around the world, harmful misinformation about the outbreak also continues to spread. With the goal of combating misinformation, we designed and built Jennifer–a chatbot maintained by a global group of volunteers. With Jennifer, we hope to learn whether public information from reputable sources could be more effectively organized and shared in the wake of a crisis as well as to understand issues that the public were most immediately curious about. In this paper, we introduce Jennifer and describe the design of this proof-of-principle system. We also present lessons learned and discuss open challenges. Finally, to facilitate future research, we release COVID-19 Question Bank, a dataset of 3,924 COVID-19-related questions in 944 groups, gathered from our users and volunteers.</abstract>
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%0 Conference Proceedings
%T Jennifer for COVID-19: An NLP-Powered Chatbot Built for the People and by the People to Combat Misinformation
%A Li, Yunyao
%A Grandison, Tyrone
%A Silveyra, Patricia
%A Douraghy, Ali
%A Guan, Xinyu
%A Kieselbach, Thomas
%A Li, Chengkai
%A Zhang, Haiqi
%Y Verspoor, Karin
%Y Cohen, Kevin Bretonnel
%Y Dredze, Mark
%Y Ferrara, Emilio
%Y May, Jonathan
%Y Munro, Robert
%Y Paris, Cecile
%Y Wallace, Byron
%S Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F li-etal-2020-jennifer
%X Just as SARS-CoV-2, a new form of coronavirus continues to infect a growing number of people around the world, harmful misinformation about the outbreak also continues to spread. With the goal of combating misinformation, we designed and built Jennifer–a chatbot maintained by a global group of volunteers. With Jennifer, we hope to learn whether public information from reputable sources could be more effectively organized and shared in the wake of a crisis as well as to understand issues that the public were most immediately curious about. In this paper, we introduce Jennifer and describe the design of this proof-of-principle system. We also present lessons learned and discuss open challenges. Finally, to facilitate future research, we release COVID-19 Question Bank, a dataset of 3,924 COVID-19-related questions in 944 groups, gathered from our users and volunteers.
%U https://aclanthology.org/2020.nlpcovid19-acl.9
Markdown (Informal)
[Jennifer for COVID-19: An NLP-Powered Chatbot Built for the People and by the People to Combat Misinformation](https://aclanthology.org/2020.nlpcovid19-acl.9) (Li et al., NLP-COVID19 2020)
ACL