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arXiv · 2209.03452

AILAB-Udine@SMM4H 22: Limits of Transformers and BERT Ensembles

Beatrice Portelli·Simone Scaboro·Emmanuele Chersoni·Enrico Santus·Giuseppe Serra
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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 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.

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Beatrice Portelli, Simone Scaboro, Emmanuele Chersoni, Enrico Santus, Giuseppe Serra. 2022-09-07. AILAB-Udine@SMM4H 22: Limits of Transformers and BERT Ensembles. https://arxiv.org/abs/2209.03452

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