Abstract
This study describes our proposed system design for the SMM4H 2022 Task 8. We fine-tune the BERT, RoBERTa, ALBERT, XLNet and ELECTRA transformers and their connecting classifiers. Each transformer model is regarded as a standalone method to detect tweets that self-reported chronic stress. The final output classification result is then combined using the majority voting ensemble mechanism. Experimental results indicate that our approach achieved a best F1-score of 0.73 over the positive class.- Anthology ID:
- 2022.smm4h-1.18
- 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:
- 62–64
- Language:
- URL:
- https://aclanthology.org/2022.smm4h-1.18
- DOI:
- Cite (ACL):
- Tzu-Mi Lin, Chao-Yi Chen, Yu-Wen Tzeng, and Lung-Hao Lee. 2022. NCUEE-NLP@SMM4H’22: Classification of Self-reported Chronic Stress on Twitter Using Ensemble Pre-trained Transformer Models. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 62–64, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- NCUEE-NLP@SMM4H’22: Classification of Self-reported Chronic Stress on Twitter Using Ensemble Pre-trained Transformer Models (Lin et al., SMM4H 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2022.smm4h-1.18.pdf