BANANA at WNUT-2020 Task 2: Identifying COVID-19 Information on Twitter by Combining Deep Learning and Transfer Learning Models

Tin Huynh, Luan Thanh Luan, Son T. Luu


Abstract
The outbreak COVID-19 virus caused a significant impact on the health of people all over the world. Therefore, it is essential to have a piece of constant and accurate information about the disease with everyone. This paper describes our prediction system for WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets. The dataset for this task contains size 10,000 tweets in English labeled by humans. The ensemble model from our three transformer and deep learning models is used for the final prediction. The experimental result indicates that we have achieved F1 for the INFORMATIVE label on our systems at 88.81% on the test set.
Anthology ID:
2020.wnut-1.50
Volume:
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | WNUT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
366–370
Language:
URL:
https://aclanthology.org/2020.wnut-1.50
DOI:
10.18653/v1/2020.wnut-1.50
Bibkey:
Cite (ACL):
Tin Huynh, Luan Thanh Luan, and Son T. Luu. 2020. BANANA at WNUT-2020 Task 2: Identifying COVID-19 Information on Twitter by Combining Deep Learning and Transfer Learning Models. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 366–370, Online. Association for Computational Linguistics.
Cite (Informal):
BANANA at WNUT-2020 Task 2: Identifying COVID-19 Information on Twitter by Combining Deep Learning and Transfer Learning Models (Huynh et al., WNUT 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.wnut-1.50.pdf
Data
WNUT-2020 Task 2