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.
@article{arxiv.2209.03452,
title = {AILAB-Udine@SMM4H 22: Limits of Transformers and BERT Ensembles},
author = {Beatrice Portelli and Simone Scaboro and Emmanuele Chersoni and Enrico Santus and Giuseppe Serra},
journal= {arXiv preprint arXiv:2209.03452},
year = {2022}
}