An Experimental Evaluation of Transformer-based Language Models in the Biomedical Domain
Paul Grouchy, Jain, Shobhit, Liu, Michael, Kuhan Wang, Max Tian, Nidhi R. Arora, Hillary Ngai, Faiza Khan Khattak, Elham Dolatabadi, Sedef Akinli Koçak · arXiv (Cornell University) · 2020
With the growing amount of text in health data, there have been rapid advances in large pre-trained models that can be applied to a wide variety of biomedical tasks with minimal task-specific modifications. Emphasizing the cost of these models, which renders technical replication challenging, this paper summarizes experiments conducted in replicating BioBERT and further pre-training and careful fine-tuning in the biomedical domain. We also investigate the effectiveness of domain-specific and domain-agnostic pre-trained models across downstream biomedical NLP tasks. Our finding confirms that pre-trained models can be impactful in some downstream NLP tasks (QA and NER) in the biomedical domain; however, this improvement may not justify the high cost of domain-specific pre-training.