Abstract
This paper presents the model submitted by NIT COVID-19 team for identified informative COVID-19 English tweets at WNUT-2020 Task2. This shared task addresses the problem of automatically identifying whether an English tweet related to informative (novel coronavirus) or not. These informative tweets provide information about recovered, confirmed, suspected, and death cases as well as location or travel history of the cases. The proposed approach includes pre-processing techniques and pre-trained RoBERTa with suitable hyperparameters for English coronavirus tweet classification. The performance achieved by the proposed model for shared task WNUT 2020 Task2 is 89.14% in the F1-score metric.- Anthology ID:
- 2020.wnut-1.66
- 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:
- 450–454
- Language:
- URL:
- https://aclanthology.org/2020.wnut-1.66
- DOI:
- 10.18653/v1/2020.wnut-1.66
- Cite (ACL):
- Jagadeesh M S and Alphonse P J A. 2020. NIT_COVID-19 at WNUT-2020 Task 2: Deep Learning Model RoBERTa for Identify Informative COVID-19 English Tweets. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 450–454, Online. Association for Computational Linguistics.
- Cite (Informal):
- NIT_COVID-19 at WNUT-2020 Task 2: Deep Learning Model RoBERTa for Identify Informative COVID-19 English Tweets (M S & P J A, WNUT 2020)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2020.wnut-1.66.pdf
- Data
- WNUT-2020 Task 2