AILAB-Udine@SMM4H 22: Limits of Transformers and BERT Ensembles
This work addresses text processing challenges in social media for health informatics, but it is incremental as it applies existing methods to a shared task.
The paper explored the limits of Transformer-based models on text classification, entity extraction, and normalization tasks in the SMM4H 22 Shared Task, finding that ensemble learning with different architectures yields positive effects and generative models show great potential for term normalization.
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 take-aways 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.