  Assessing relevance between a query and a document is challenging in ad-hoc
retrieval due to its diverse patterns, i.e., a document could be relevant to a
query as a whole or partially as long as it provides sufficient information for
users' need. Such diverse relevance patterns require an ideal retrieval model
to be able to assess relevance in the right granularity adaptively.
Unfortunately, most existing retrieval models compute relevance at a single
granularity, either document-wide or passage-level, or use fixed combination
strategy, restricting their ability in capturing diverse relevance patterns. In
this work, we propose a data-driven method to allow relevance signals at
different granularities to compete with each other for final relevance
assessment. Specifically, we propose a HIerarchical Neural maTching model
(HiNT) which consists of two stacked components, namely local matching layer
and global decision layer. The local matching layer focuses on producing a set
of local relevance signals by modeling the semantic matching between a query
and each passage of a document. The global decision layer accumulates local
signals into different granularities and allows them to compete with each other
to decide the final relevance score. Experimental results demonstrate that our
HiNT model outperforms existing state-of-the-art retrieval models significantly
on benchmark ad-hoc retrieval datasets.
