  The primary goal of ad-hoc retrieval (document retrieval in the context of
question answering) is to find relevant documents satisfied the information
need posted in a natural language query. It requires a good understanding of
the query and all the documents in a corpus, which is difficult because the
meaning of natural language texts depends on the context, syntax,and semantics.
Recently deep neural networks have been used to rank search results in response
to a query. In this paper, we devise a multi-resolution neural network(MRNN) to
leverage the whole hierarchy of representations for document retrieval. The
proposed MRNN model is capable of deriving a representation that integrates
representations of different levels of abstraction from all the layers of the
learned hierarchical representation.Moreover, a duplex attention component is
designed to refinethe multi-resolution representation so that an optimal
contextfor matching the query and document can be determined. More
specifically, the first attention mechanism determines optimal context from the
learned multi-resolution representation for the query and document. The latter
attention mechanism aims to fine-tune the representation so that the query and
the relevant document are closer in proximity. The empirical study shows that
MRNN with the duplex attention is significantly superior to existing models
used for ad-hoc retrieval on benchmark datasets including SQuAD, WikiQA,
QUASAR, and TrecQA.
