论文标题
知识吸引问题的动态相关图网络回答
Dynamic Relevance Graph Network for Knowledge-Aware Question Answering
论文作者
论文摘要
这项工作调查了以知识图（kg）形式的外部知识来源回答的学习和推理的挑战。我们提出了一种新型的图形神经网络体系结构，称为动态相关图形网络（DRGN）。 DRGN根据问题和答案实体在给定的KG子图上运行，并使用节点之间的相关得分来动态建立新的边缘，以用于图网络中的学习节点表示。相关性的这种明确用法作为图形具有以下优点，a）模型可以利用现有关系，重新缩放节点权重，并影响邻里节点的表示形式在kg子图中汇总的方式，b）它有可能在kg中恢复缺失的边缘，而kg中所需的kg则需要进行推理。此外，作为副产品，由于考虑了问题节点与图形实体之间的相关性，我们的模型改善了处理负面问题。与最新发布的结果相比，我们提出的方法在两个质量检查基准（CommonSenseqa和OpenBookQa）上显示了竞争性能。
This work investigates the challenge of learning and reasoning for Commonsense Question Answering given an external source of knowledge in the form of a knowledge graph (KG). We propose a novel graph neural network architecture, called Dynamic Relevance Graph Network (DRGN). DRGN operates on a given KG subgraph based on the question and answers entities and uses the relevance scores between the nodes to establish new edges dynamically for learning node representations in the graph network. This explicit usage of relevance as graph edges has the following advantages, a) the model can exploit the existing relationships, re-scale the node weights, and influence the way the neighborhood nodes' representations are aggregated in the KG subgraph, b) It potentially recovers the missing edges in KG that are needed for reasoning. Moreover, as a byproduct, our model improves handling the negative questions due to considering the relevance between the question node and the graph entities. Our proposed approach shows competitive performance on two QA benchmarks, CommonsenseQA and OpenbookQA, compared to the state-of-the-art published results.
