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

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 di...

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Bibliographic Details
Published inarXiv.org
Main Authors Portelli, Beatrice, Scaboro, Simone, Chersoni, Emmanuele, Santus, Enrico, Serra, Giuseppe
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 07.09.2022
Subjects
Transformers
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Summary: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.
ISSN:2331-8422