ClinicalT5: A Generative Language Model for Clinical Text
Qiuhao Lu, Dejing Dou, Thien Huu Nguyen
Abstract
In the past few years, large pre-trained language models (PLMs) have been widely adopted in different areas and have made fundamental improvements over a variety of downstream tasks in natural language processing (NLP). Meanwhile, domain-specific variants of PLMs are being proposed to address the needs of domains that demonstrate a specific pattern of writing and vocabulary, e.g., BioBERT for the biomedical domain and ClinicalBERT for the clinical domain. Recently, generative language models like BART and T5 are gaining popularity with their competitive performance on text generation as well as on tasks cast as generative problems. However, in the clinical domain, such domain-specific generative variants are still underexplored. To address this need, our work introduces a T5-based text-to-text transformer model pre-trained on clinical text, i.e., ClinicalT5. We evaluate the proposed model both intrinsically and extrinsically over a diverse set of tasks across multiple datasets, and show that ClinicalT5 dramatically outperforms T5 in the domain-specific tasks and compares favorably with its close baselines.