WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets
Abstract
In this paper, we provide an overview of the WNUT-2020 shared task on the identification of informative COVID-19 English Tweets. We describe how we construct a corpus of 10K Tweets and organize the development and evaluation phases for this task. In addition, we also present a brief summary of results obtained from the final system evaluation submissions of 55 teams, finding that (i) many systems obtain very high performance, up to 0.91 F1 score, (ii) the majority of the submissions achieve substantially higher results than the baseline fastText (Joulin et al., 2017), and (iii) fine-tuning pre-trained language models on relevant language data followed by supervised training performs well in this task.
Keywords
Cite
@article{arxiv.2010.08232,
title = {WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets},
author = {Dat Quoc Nguyen and Thanh Vu and Afshin Rahimi and Mai Hoang Dao and Linh The Nguyen and Long Doan},
journal= {arXiv preprint arXiv:2010.08232},
year = {2020}
}
Comments
In Proceedings of the 6th Workshop on Noisy User-generated Text