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Multi-paragraph reasoning is indispensable for open-domain question answering(OpenQA), which receives less attention in the current OpenQA systems. In thiswork, we propose a knowledge-enhanced graph neural network (KGNN), whichperforms reasoning over multiple paragraphs with entities. To explicitlycapture the entities' relatedness, KGNN utilizes relational facts in knowledgegraph to build the entity graph. The experimental results show that KGNNoutperforms in both distractor and full wiki settings than baselines methods onHotpotQA dataset. And our further analysis illustrates KGNN is effective androbust with more retrieved paragraphs.

Multi-Paragraph Reasoning with Knowledge-enhanced Graph Neural Network