Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art

Patrick Lewis, Myle Ott, Jingfei Du, Veselin Stoyanov


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.
Anthology ID:
2020.clinicalnlp-1.17
Volume:
Proceedings of the 3rd Clinical Natural Language Processing Workshop
Month:
November
Year:
2020
Address:
Online
Editors:
Anna Rumshisky, Kirk Roberts, Steven Bethard, Tristan Naumann
Venue:
ClinicalNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
146–157
Language:
URL:
https://aclanthology.org/2020.clinicalnlp-1.17
DOI:
10.18653/v1/2020.clinicalnlp-1.17
Bibkey:
Cite (ACL):
Patrick Lewis, Myle Ott, Jingfei Du, and Veselin Stoyanov. 2020. Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art. In Proceedings of the 3rd Clinical Natural Language Processing Workshop, pages 146–157, Online. Association for Computational Linguistics.
Cite (Informal):
Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art (Lewis et al., ClinicalNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.clinicalnlp-1.17.pdf
Optional supplementary material:
 2020.clinicalnlp-1.17.OptionalSupplementaryMaterial.pdf
Video:
 https://slideslive.com/38939822
Data
BC5CDRBLUEGLUEHOCNCBI DiseaseSemantic ScholarWebText