论文标题
ailab-udine@smm4h 22：变压器和伯特合奏的限制
AILAB-Udine@SMM4H 22: Limits of Transformers and BERT Ensembles
论文作者
论文摘要
本文介绍了AILAB-UDINE团队为SMM4H 22共享任务开发的模型。我们探索了基于变压器的模型在文本分类，实体提取和实体归一化，处理任务1、2、5、6和10的限制。我们从参与不同任务中获得的主要外包是：使用合奏学习时组合不同架构的压倒性积极效果，以及用于期限正常化的生成模型的巨大潜力。
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.
