NLP
Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art
November 03, 2020
Abstract
A large array of pretrained models are available to the biomedical NLP (BioNLP) community. Finding the best model for a particular task can be difficult and time-consuming. For many applications in the biomedical and clinical domains, it is crucial that models can be built quickly and are highly accurate. We present a large-scale study across 18 established biomedical and clinical NLP tasks to determine which of several popular open-source biomedical and clinical NLP models work well in different settings. Furthermore, we apply recent advances in pretraining to train new biomedical language models, and carefully investigate the effect of various design choices on downstream performance. Our best models perform well in all of our benchmarks, and set new State-of-the-Art in 9 tasks. We release these models in the hope that they can help the community to speed up and increase the accuracy of BioNLP and text mining applications.
AUTHORS
Written by
Ves Stoyanov
Jingfei Du
Myle Ott
Patrick Lewis
Publisher
ClinicalNLP2020@EMNLP2020
Research Topics
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