Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment

Sharon Levy, William Yang Wang


Abstract
The spread of COVID-19 has become a significant and troubling aspect of society in 2020. With millions of cases reported across countries, new outbreaks have occurred and followed patterns of previously affected areas. Many disease detection models do not incorporate the wealth of social media data that can be utilized for modeling and predicting its spread. It is useful to ask, can we utilize this knowledge in one country to model the outbreak in another? To answer this, we propose the task of cross-lingual transfer learning for epidemiological alignment. Utilizing both macro and micro text features, we train on Italy’s early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
Anthology ID:
2020.nlpcovid19-acl.15
Volume:
Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020
Month:
July
Year:
2020
Address:
Online
Editors:
Karin Verspoor, Kevin Bretonnel Cohen, Mark Dredze, Emilio Ferrara, Jonathan May, Robert Munro, Cecile Paris, Byron Wallace
Venue:
NLP-COVID19
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
Language:
URL:
https://aclanthology.org/2020.nlpcovid19-acl.15
DOI:
Bibkey:
Cite (ACL):
Sharon Levy and William Yang Wang. 2020. Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment. In Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020, Online. Association for Computational Linguistics.
Cite (Informal):
Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment (Levy & Wang, NLP-COVID19 2020)
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PDF:
https://aclanthology.org/2020.nlpcovid19-acl.15.pdf