Papers with self-supervised intermediate fine-tuning strategy

1 papers
Self-Supervised Intermediate Fine-Tuning of Biomedical Language Models for Interpreting Patient Case Descriptions (2022.coling-1)

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Challenge: Existing work has found that biomedical language models lack the knowledge needed for such tasks.
Approach: They propose to fine-tune biomedical language models on the task of predicting masked medical concepts from PubMed abstracts to improve their performance.
Outcome: The proposed strategy improves the performance of biomedical language models on the task of predicting masked medical concepts from patient case descriptions.

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