Abstract
This paper proposes an improved custom model for WNUT task 2: Identification of Informative COVID-19 English Tweet. We improve experiment with the effectiveness of fine-tuning methodologies for state-of-the-art language model RoBERTa. We make a preliminary instantiation of this formal model for the text classification approaches. With appropriate training techniques, our model is able to achieve 0.9218 F1-score on public validation set and the ensemble version settles at top 9 F1-score (0.9005) and top 2 Recall (0.9301) on private test set.- Anthology ID:
- 2020.wnut-1.73
- 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:
- 485–490
- Language:
- URL:
- https://aclanthology.org/2020.wnut-1.73
- DOI:
- 10.18653/v1/2020.wnut-1.73
- Cite (ACL):
- Linh Doan Bao, Viet Anh Nguyen, and Quang Pham Huu. 2020. SunBear at WNUT-2020 Task 2: Improving BERT-Based Noisy Text Classification with Knowledge of the Data domain. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 485–490, Online. Association for Computational Linguistics.
- Cite (Informal):
- SunBear at WNUT-2020 Task 2: Improving BERT-Based Noisy Text Classification with Knowledge of the Data domain (Doan Bao et al., WNUT 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.wnut-1.73.pdf
- Data
- WNUT-2020 Task 2