Graph-based Hierarchical Relevance Matching Signals for Ad-hoc Retrieval
This work addresses the ad-hoc retrieval problem for information retrieval systems, offering an incremental improvement by incorporating graph structures to model document-level relationships.
The paper tackles the problem of ad-hoc retrieval by addressing the limitation of existing deep learning methods that ignore long-distance document-level word relationships, proposing a Graph-based Hierarchical Relevance Matching model (GHRM) that captures subtle and general hierarchical matching signals, and demonstrates its superiority over state-of-the-art methods on two benchmarks.
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.