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Convolutional Neural Networks for Soft-Matching N-Grams in Ad-hoc Search

Published: 02 February 2018 Publication History

Abstract

This paper presents \textttConv-KNRM, a Convolutional Kernel-based Neural Ranking Model that models n-gram soft matches for ad-hoc search. Instead of exact matching query and document n-grams, \textttConv-KNRM uses Convolutional Neural Networks to represent n-grams of various lengths and soft matches them in a unified embedding space. The n-gram soft matches are then utilized by the kernel pooling and learning-to-rank layers to generate the final ranking score. \textttConv-KNRM can be learned end-to-end and fully optimized from user feedback. The learned model»s generalizability is investigated by testing how well it performs in a related domain with small amounts of training data. Experiments on English search logs, Chinese search logs, and TREC Web track tasks demonstrated consistent advantages of \textttConv-KNRM over prior neural IR methods and feature-based methods.

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    cover image ACM Conferences
    WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining
    February 2018
    821 pages
    ISBN:9781450355810
    DOI:10.1145/3159652
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    Published: 02 February 2018

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    Author Tags

    1. n-gram soft match
    2. neural ir
    3. relevance ranking

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    WSDM '18 Paper Acceptance Rate 81 of 514 submissions, 16%;
    Overall Acceptance Rate 498 of 2,863 submissions, 17%

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    • (2024)A Reproducibility Study of PLAIDProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657856(1411-1419)Online publication date: 10-Jul-2024
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