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
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.wnut-1.70.pdf