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

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
Main Authors: Portelli, Beatrice, Scaboro, Simone, Chersoni, Emmanuele, Santus, Enrico, Serra, Giuseppe
Format: Journal Article
Language:English
Published: 07-09-2022
Subjects:
Computer Science - Computation and Language
Computer Science - Learning
Online Access:Get full text
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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.
DOI:10.48550/arxiv.2209.03452