Modeling diverse relevance patterns in ad-hoc retrieval

Yixing Fan, Jiafeng Guo, Yanyan Lan, Jun Xu, Chengxiang Zhai, Xueqi Cheng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018
PublisherAssociation for Computing Machinery, Inc
Pages375-384
Number of pages10
ISBN (Electronic)9781450356572
DOIs
StatePublished - Jun 27 2018
Event41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018 - Ann Arbor, United States
Duration: Jul 8 2018Jul 12 2018

Publication series

Name41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018

Other

Other41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018
CountryUnited States
CityAnn Arbor
Period7/8/187/12/18

Keywords

  • Ad-hoc retrieval
  • Neural network
  • Relevance patterns

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design
  • Information Systems

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  • Cite this

    Fan, Y., Guo, J., Lan, Y., Xu, J., Zhai, C., & Cheng, X. (2018). Modeling diverse relevance patterns in ad-hoc retrieval. In 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018 (pp. 375-384). (41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018). Association for Computing Machinery, Inc. https://doi.org/10.1145/3209978.3209980