Authors:
Tolgahan Cakaloglu
1
;
2
;
Xiaowei Xu
2
and
Roshith Raghavan
3
Affiliations:
1
Walmart Labs, Dallas, Texas, U.S.A.
;
2
University of Arkansas, Little Rock, Arkansas, U.S.A.
;
3
CVS Health, Boston, Massachussets, U.S.A.
Keyword(s):
Deep Learning, Ad-Hoc Retrieval, Learning Representations, Ranking, Text Matching.
Abstract:
The primary goal of ad-hoc retrieval is to find relevant documents satisfying the information need posted by 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 ad-hoc 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 refine the multi-resolution representation so that an optimal context for matching the query and document can be determined. More specifically the first attention mechanism determines optima
l 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.
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