CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain

• URL: http://arxiv.org/abs/2112.08754v2
• Date: Fri, 17 Dec 2021 11:45:41 GMT
• Title: CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain
• Authors: Lukas Lange, Heike Adel, Jannik Str\"otgen, Dietrich Klakow
• Abstract summary: We introduce the pre-trained CLIN-X (Clinical XLM-R) language models and show how CLIN-X outperforms other pre-trained transformer models. Our studies reveal stable model performance despite a lack of annotated data with improvements of up to 47 F1 points when only 250 labeled sentences are available. Our results highlight the importance of specialized language models as CLIN-X for concept extraction in non-standard domains.
• Score: 22.846469609263416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The field of natural language processing (NLP) has recently seen a large change towards using pre-trained language models for solving almost any task. Despite showing great improvements in benchmark datasets for various tasks, these models often perform sub-optimal in non-standard domains like the clinical domain where a large gap between pre-training documents and target documents is observed. In this paper, we aim at closing this gap with domain-specific training of the language model and we investigate its effect on a diverse set of downstream tasks and settings. We introduce the pre-trained CLIN-X (Clinical XLM-R) language models and show how CLIN-X outperforms other pre-trained transformer models by a large margin for ten clinical concept extraction tasks from two languages. In addition, we demonstrate how the transformer model can be further improved with our proposed task- and language-agnostic model architecture based on ensembles over random splits and cross-sentence context. Our studies in low-resource and transfer settings reveal stable model performance despite a lack of annotated data with improvements of up to 47 F1 points when only 250 labeled sentences are available. Our results highlight the importance of specialized language models as CLIN-X for concept extraction in non-standard domains, but also show that our task-agnostic model architecture is robust across the tested tasks and languages so that domain- or task-specific adaptations are not required.

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