An Instruction Inference Graph Optimal Transport Network Model For Biomedical Commonsense Question Answering

Biomedical commonsense question answering is a challenging learning task that aims to give correct answers to biomedical commonsense questions. Many works have used rule-based or deep learning approachs to accomplish this task. Recently, an extensive research path is that pre-trained language models combined with graph neural networks (GNNs) to improve the accuracy of biomedical commonsense question answering. However, GNN is prone to the over-smoothing problem, causing the models to lose the ability to reason. In order to alleviate the over-smoothing problem and improve the inference ability for biomedical commonsense question answering, we propose a new end-to-end model named BiomGIN. In BiomGIN, we introduce the Graph Optimal Transport Networks (GOTNet) to use node-centroid attention to capture non-local messages in the knowledge graph, which alleviates the model over-smoothing problem. In addition, we design a question parsing module based on Transformer to generate linguistic instructions, which enhances the inference capability of the GNN. Finally, we evaluated our model on MedQA-USMLE dataset to compare with other baseline models. The experimental results demonstrate the method proposed in this paper achieves state-of-the-art results.