CIA_NITT at WNUT-2020 Task 2: Classification of COVID-19 Tweets Using Pre-trained Language Models

Yandrapati Prakash Babu, Rajagopal Eswari


Abstract
This paper presents our models for WNUT2020 shared task2. The shared task2 involves identification of COVID-19 related informative tweets. We treat this as binary text clas-sification problem and experiment with pre-trained language models. Our first model which is based on CT-BERT achieves F1-scoreof 88.7% and second model which is an ensemble of CT-BERT, RoBERTa and SVM achieves F1-score of 88.52%.
Anthology ID:
2020.wnut-1.70
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:
471–474
Language:
URL:
https://aclanthology.org/2020.wnut-1.70
DOI:
10.18653/v1/2020.wnut-1.70
Bibkey:
Cite (ACL):
Yandrapati Prakash Babu and Rajagopal Eswari. 2020. CIA_NITT at WNUT-2020 Task 2: Classification of COVID-19 Tweets Using Pre-trained Language Models. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 471–474, Online. Association for Computational Linguistics.
Cite (Informal):
CIA_NITT at WNUT-2020 Task 2: Classification of COVID-19 Tweets Using Pre-trained Language Models (Prakash Babu & Eswari, WNUT 2020)
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PDF:
https://aclanthology.org/2020.wnut-1.70.pdf