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        <title>Deep Relevance Ranking Using Enhanced Document-Query Interactions</title>
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    <name type="personal">
        <namePart type="given">Ryan</namePart>
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    <name type="personal">
        <namePart type="given">Ion</namePart>
        <namePart type="family">Androutsopoulos</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
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            <namePart type="given">David</namePart>
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    <abstract>We explore several new models for document relevance ranking, building upon the Deep Relevance Matching Model (DRMM) of Guo et al. (2016). Unlike DRMM, which uses context-insensitive encodings of terms and query-document term interactions, we inject rich context-sensitive encodings throughout our models, inspired by PACRR‘s (Hui et al., 2017) convolutional n-gram matching features, but extended in several ways including multiple views of query and document inputs. We test our models on datasets from the BIOASQ question answering challenge (Tsatsaronis et al., 2015) and TREC ROBUST 2004 (Voorhees, 2005), showing they outperform BM25-based baselines, DRMM, and PACRR.</abstract>
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        <date>2018-oct-nov</date>
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            <start>1849</start>
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%0 Conference Proceedings
%T Deep Relevance Ranking Using Enhanced Document-Query Interactions
%A McDonald, Ryan
%A Brokos, George
%A Androutsopoulos, Ion
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F mcdonald-etal-2018-deep
%X We explore several new models for document relevance ranking, building upon the Deep Relevance Matching Model (DRMM) of Guo et al. (2016). Unlike DRMM, which uses context-insensitive encodings of terms and query-document term interactions, we inject rich context-sensitive encodings throughout our models, inspired by PACRR‘s (Hui et al., 2017) convolutional n-gram matching features, but extended in several ways including multiple views of query and document inputs. We test our models on datasets from the BIOASQ question answering challenge (Tsatsaronis et al., 2015) and TREC ROBUST 2004 (Voorhees, 2005), showing they outperform BM25-based baselines, DRMM, and PACRR.
%R 10.18653/v1/D18-1211
%U https://aclanthology.org/D18-1211/
%U https://doi.org/10.18653/v1/D18-1211
%P 1849-1860

Markdown (Informal)

[Deep Relevance Ranking Using Enhanced Document-Query Interactions](https://aclanthology.org/D18-1211/) (McDonald et al., EMNLP 2018)

• Deep Relevance Ranking Using Enhanced Document-Query Interactions (McDonald et al., EMNLP 2018)

ACL

• Ryan McDonald, George Brokos, and Ion Androutsopoulos. 2018. Deep Relevance Ranking Using Enhanced Document-Query Interactions. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1849–1860, Brussels, Belgium. Association for Computational Linguistics.