  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.
