@inproceedings{miao-etal-2020-twitter,
title = "{T}witter Data Augmentation for Monitoring Public Opinion on {COVID}-19 Intervention Measures",
author = "Miao, Lin and
Last, Mark and
Litvak, Marina",
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.nlpcovid19-2.19",
doi = "10.18653/v1/2020.nlpcovid19-2.19",
abstract = "The COVID-19 outbreak is an ongoing worldwide pandemic that was announced as a global health crisis in March 2020. Due to the enormous challenges and high stakes of this pandemic, governments have implemented a wide range of policies aimed at containing the spread of the virus and its negative effect on multiple aspects of our life. Public responses to various intervention measures imposed over time can be explored by analyzing the social media. Due to the shortage of available labeled data for this new and evolving domain, we apply data distillation methodology to labeled datasets from related tasks and a very small manually labeled dataset. Our experimental results show that data distillation outperforms other data augmentation methods on our task.",
}
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<abstract>The COVID-19 outbreak is an ongoing worldwide pandemic that was announced as a global health crisis in March 2020. Due to the enormous challenges and high stakes of this pandemic, governments have implemented a wide range of policies aimed at containing the spread of the virus and its negative effect on multiple aspects of our life. Public responses to various intervention measures imposed over time can be explored by analyzing the social media. Due to the shortage of available labeled data for this new and evolving domain, we apply data distillation methodology to labeled datasets from related tasks and a very small manually labeled dataset. Our experimental results show that data distillation outperforms other data augmentation methods on our task.</abstract>
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%0 Conference Proceedings
%T Twitter Data Augmentation for Monitoring Public Opinion on COVID-19 Intervention Measures
%A Miao, Lin
%A Last, Mark
%A Litvak, Marina
%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 miao-etal-2020-twitter
%X The COVID-19 outbreak is an ongoing worldwide pandemic that was announced as a global health crisis in March 2020. Due to the enormous challenges and high stakes of this pandemic, governments have implemented a wide range of policies aimed at containing the spread of the virus and its negative effect on multiple aspects of our life. Public responses to various intervention measures imposed over time can be explored by analyzing the social media. Due to the shortage of available labeled data for this new and evolving domain, we apply data distillation methodology to labeled datasets from related tasks and a very small manually labeled dataset. Our experimental results show that data distillation outperforms other data augmentation methods on our task.
%R 10.18653/v1/2020.nlpcovid19-2.19
%U https://aclanthology.org/2020.nlpcovid19-2.19
%U https://doi.org/10.18653/v1/2020.nlpcovid19-2.19
Markdown (Informal)
[Twitter Data Augmentation for Monitoring Public Opinion on COVID-19 Intervention Measures](https://aclanthology.org/2020.nlpcovid19-2.19) (Miao et al., NLP-COVID19 2020)
ACL