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Existing KG-augmented models for commonsense question answering primarilyfocus on designing elaborate Graph Neural Networks (GNNs) to model knowledgegraphs (KGs). However, they ignore (i) the effectively fusing and reasoningover question context representations and the KG representations, and (ii)automatically selecting relevant nodes from the noisy KGs during reasoning. Inthis paper, we propose a novel model, JointLK, which solves the abovelimitations through the joint reasoning of LM and GNN and the dynamic KGspruning mechanism. Specifically, JointLK performs joint reasoning between LMand GNN through a novel dense bidirectional attention module, in which eachquestion token attends on KG nodes and each KG node attends on question tokens,and the two modal representations fuse and update mutually by multi-stepinteractions. Then, the dynamic pruning module uses the attention weightsgenerated by joint reasoning to prune irrelevant KG nodes recursively. Weevaluate JointLK on the CommonsenseQA and OpenBookQA datasets, and demonstrateits improvements to the existing LM and LM+KG models, as well as its capabilityto perform interpretable reasoning.

JointLK: Joint Reasoning with Language Models and Knowledge Graphs for Commonsense Question Answering