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    <abstract>In this paper, we investigate ensemble methods for fine-tuning transformer-based pretrained models for clinical natural language processing tasks, specifically temporal relation extraction from the clinical narrative. Our experimental results on the THYME data show that ensembling as a fine-tuning strategy can further boost model performance over single learners optimized for hyperparameters. Dynamic snapshot ensembling is particularly beneficial as it fine-tunes a wide array of parameters and results in a 2.8% absolute improvement in F1 over the base single learner.</abstract>
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%0 Conference Proceedings
%T Ensemble-based Fine-Tuning Strategy for Temporal Relation Extraction from the Clinical Narrative
%A Wang, Lijing
%A Miller, Timothy
%A Bethard, Steven
%A Savova, Guergana
%Y Naumann, Tristan
%Y Bethard, Steven
%Y Roberts, Kirk
%Y Rumshisky, Anna
%S Proceedings of the 4th Clinical Natural Language Processing Workshop
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, WA
%F wang-etal-2022-ensemble
%X In this paper, we investigate ensemble methods for fine-tuning transformer-based pretrained models for clinical natural language processing tasks, specifically temporal relation extraction from the clinical narrative. Our experimental results on the THYME data show that ensembling as a fine-tuning strategy can further boost model performance over single learners optimized for hyperparameters. Dynamic snapshot ensembling is particularly beneficial as it fine-tunes a wide array of parameters and results in a 2.8% absolute improvement in F1 over the base single learner.
%R 10.18653/v1/2022.clinicalnlp-1.11
%U https://aclanthology.org/2022.clinicalnlp-1.11
%U https://doi.org/10.18653/v1/2022.clinicalnlp-1.11
%P 103-108

Markdown (Informal)

[Ensemble-based Fine-Tuning Strategy for Temporal Relation Extraction from the Clinical Narrative](https://aclanthology.org/2022.clinicalnlp-1.11) (Wang et al., ClinicalNLP 2022)

• Ensemble-based Fine-Tuning Strategy for Temporal Relation Extraction from the Clinical Narrative (Wang et al., ClinicalNLP 2022)

ACL

• Lijing Wang, Timothy Miller, Steven Bethard, and Guergana Savova. 2022. Ensemble-based Fine-Tuning Strategy for Temporal Relation Extraction from the Clinical Narrative. In Proceedings of the 4th Clinical Natural Language Processing Workshop, pages 103–108, Seattle, WA. Association for Computational Linguistics.