IRLab@IITBHU at WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets using BERT

Supriya Chanda, Eshita Nandy, Sukomal Pal


Abstract
This paper reports our submission to the shared Task 2: Identification of informative COVID-19 English tweets at W-NUT 2020. We attempted a few techniques, and we briefly explain here two models that showed promising results in tweet classification tasks: DistilBERT and FastText. DistilBERT achieves a F1 score of 0.7508 on the test set, which is the best of our submissions.
Anthology ID:
2020.wnut-1.56
Volume:
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
Month:
November
Year:
2020
Address:
Online
Editors:
Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
Venue:
WNUT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
399–403
Language:
URL:
https://aclanthology.org/2020.wnut-1.56
DOI:
10.18653/v1/2020.wnut-1.56
Bibkey:
Cite (ACL):
Supriya Chanda, Eshita Nandy, and Sukomal Pal. 2020. IRLab@IITBHU at WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets using BERT. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 399–403, Online. Association for Computational Linguistics.
Cite (Informal):
IRLab@IITBHU at WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets using BERT (Chanda et al., WNUT 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.wnut-1.56.pdf
Code
 VinAIResearch/COVID19Tweet