AILAB-Udine@SMM4H’22: Limits of Transformers and BERT Ensembles
Beatrice Portelli, Simone Scaboro, Emmanuele Chersoni, Enrico Santus, Giuseppe Serra
Abstract
This paper describes the models developed by the AILAB-Udine team for the SMM4H’22 Shared Task. We explored the limits of Transformer based models on text classification, entity extraction and entity normalization, tackling Tasks 1, 2, 5, 6 and 10. The main takeaways we got from participating in different tasks are: the overwhelming positive effects of combining different architectures when using ensemble learning, and the great potential of generative models for term normalization.- Anthology ID:
- 2022.smm4h-1.36
- 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:
- 130–134
- Language:
- URL:
- https://aclanthology.org/2022.smm4h-1.36
- DOI:
- Cite (ACL):
- Beatrice Portelli, Simone Scaboro, Emmanuele Chersoni, Enrico Santus, and Giuseppe Serra. 2022. AILAB-Udine@SMM4H’22: Limits of Transformers and BERT Ensembles. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 130–134, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- AILAB-Udine@SMM4H’22: Limits of Transformers and BERT Ensembles (Portelli et al., SMM4H 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.smm4h-1.36.pdf