  The ad-hoc retrieval task is to rank related documents given a query and a
document collection. A series of deep learning based approaches have been
proposed to solve such problem and gained lots of attention. However, we argue
that they are inherently based on local word sequences, ignoring the subtle
long-distance document-level word relationships. To solve the problem, we
explicitly model the document-level word relationship through the graph
structure, capturing the subtle information via graph neural networks. In
addition, due to the complexity and scale of the document collections, it is
considerable to explore the different grain-sized hierarchical matching signals
at a more general level. Therefore, we propose a Graph-based Hierarchical
Relevance Matching model (GHRM) for ad-hoc retrieval, by which we can capture
the subtle and general hierarchical matching signals simultaneously. We
validate the effects of GHRM over two representative ad-hoc retrieval
benchmarks, the comprehensive experiments and results demonstrate its
superiority over state-of-the-art methods.
