AILAB-Udine@SMM4H 22: Limits of Transformers and BERT Ensembles
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 7 Sep 2022.
Builds on
6 of 15 references.
References · 6
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
- 2020Domain-Specific Language Model Pretraining for Biomedical Natural Language Processingcited by 996
- 2019SpanBERT: Improving Pre-training by Representing and Predicting Spanscited by 832
- 2020TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classificationcited by 414
- 2021Improving Adverse Drug Event Extraction with SpanBERT on Different Text Typologiescited by 7
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Citing papers per year: 2024: 2.
2 papers.