Integration of Heterogeneous Knowledge Sources for Biomedical Text Processing

Recently, research into bringing outside knowledge sources into current neural NLP models has been increasing. Most approaches that leverage external knowledge sources require laborious and non-trivial designs, as well as tailoring the system through intensive ablation of different knowledge sources, an effort that discourages users to use quality ontological resources. In this paper, we show that multiple large heterogeneous KSs can be easily integrated using a decoupled approach, allowing for an automatic ablation of irrelevant KSs, while keeping the overall parameter space tractable. We experiment with BERT and pre-trained graph embeddings, and show that they interoperate well without performance degradation, even when some do not contribute to the task.