Learning from structured data is a core machine learning task. Commonly, such data is represented as graphs, which normally only consider (typed) binary relationships between pairs of nodes. This is a substantial limitation for many domains with highly-structured data. One important such domain is source code, where hypergraph-based representations can better capture the semantically rich and structured nature of code. In this work, we present HEAT, a neural model capable of representing typed and qualified hypergraphs, where each hyperedge explicitly qualifies how participating nodes contribute. It can be viewed as a generalization of both message passing neural networks and Transformers. We evaluate HEAT on knowledge base completion and on bug detection and repair using a novel hypergraph representation of programs. In both settings, it outperforms strong baselines, indicating its power and generality.
翻译:从结构化数据中学习是一个核心的机器学习任务。 通常,这类数据以图表形式呈现,通常只考虑对结点对口之间( 类型) 的二进制关系。 这是对许多领域高度结构化数据的重大限制。 其中一个重要的领域是源代码, 高空代表可以更好地捕捉代码的精度丰富和结构化性质。 在这项工作中, 我们展示了能代表打字和合格高压的神经模型, 每个高端都明确限定了参与节点的作用。 它可以被视为信息传递神经网络和变异器之间的一般化。 我们用新的高空描述程序对知识基础完成和错误检测及修复进行评估。 在这两种情况下, 它都超越了强大的基线, 表明其力量和一般性 。