Abstract
The paper describes the system that team CompLx developed for sub-task 1a of the Social Media Mining for Health 2022 (#SMM4H) Shared Task. We finetune a RoBERTa model, a pretrained, transformer-based language model, on a provided dataset to classify English tweets for mentions of Adverse Drug Reactions (ADRs), i.e. negative side effects related to medication intake. With only a simple finetuning, our approach achieves competitive results, significantly outperforming the average score across submitted systems. We make the model checkpoints and code publicly available. We also create a web application to provide a user-friendly, readily accessible interface for anyone interested in exploring the model’s capabilities.- Anthology ID:
- 2022.smm4h-1.47
- Volume:
- Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Graciela Gonzalez-Hernandez, Davy Weissenbacher
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 176–181
- Language:
- URL:
- https://aclanthology.org/2022.smm4h-1.47
- DOI:
- Cite (ACL):
- Orest Xherija and Hojoon Choi. 2022. CompLx@SMM4H’22: In-domain pretrained language models for detection of adverse drug reaction mentions in English tweets. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 176–181, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- CompLx@SMM4H’22: In-domain pretrained language models for detection of adverse drug reaction mentions in English tweets (Xherija & Choi, SMM4H 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.smm4h-1.47.pdf
- Data
- GLUE