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.
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020 |
| Publisher | Association for Computational Linguistics |
| Number of pages | 7 |
| DOIs | |
| State | Published - Dec 2020 |
