MRNN: A Multi-resolution Neural Network With Duplex Attention For Document Retrieval In The Context Of Question Answering

Abstract

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 specific

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