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    <titleInfo>
        <title>Learning Dense Representations for Entity Retrieval</title>
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    <name type="personal">
        <namePart type="given">Daniel</namePart>
        <namePart type="family">Gillick</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Sayali</namePart>
        <namePart type="family">Kulkarni</namePart>
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    <name type="personal">
        <namePart type="given">Larry</namePart>
        <namePart type="family">Lansing</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Alessandro</namePart>
        <namePart type="family">Presta</namePart>
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    <name type="personal">
        <namePart type="given">Jason</namePart>
        <namePart type="family">Baldridge</namePart>
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    <name type="personal">
        <namePart type="given">Eugene</namePart>
        <namePart type="family">Ie</namePart>
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    <name type="personal">
        <namePart type="given">Diego</namePart>
        <namePart type="family">Garcia-Olano</namePart>
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        <dateIssued>2019-nov</dateIssued>
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            <title>Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)</title>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>We show that it is feasible to perform entity linking by training a dual encoder (two-tower) model that encodes mentions and entities in the same dense vector space, where candidate entities are retrieved by approximate nearest neighbor search. Unlike prior work, this setup does not rely on an alias table followed by a re-ranker, and is thus the first fully learned entity retrieval model. We show that our dual encoder, trained using only anchor-text links in Wikipedia, outperforms discrete alias table and BM25 baselines, and is competitive with the best comparable results on the standard TACKBP-2010 dataset. In addition, it can retrieve candidates extremely fast, and generalizes well to a new dataset derived from Wikinews. On the modeling side, we demonstrate the dramatic value of an unsupervised negative mining algorithm for this task.</abstract>
    <identifier type="citekey">gillick-etal-2019-learning</identifier>
    <identifier type="doi">10.18653/v1/K19-1049</identifier>
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        <url>https://aclanthology.org/K19-1049</url>
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        <date>2019-nov</date>
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            <start>528</start>
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%0 Conference Proceedings
%T Learning Dense Representations for Entity Retrieval
%A Gillick, Daniel
%A Kulkarni, Sayali
%A Lansing, Larry
%A Presta, Alessandro
%A Baldridge, Jason
%A Ie, Eugene
%A Garcia-Olano, Diego
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 nov
%I Association for Computational Linguistics
%C Hong Kong, China
%F gillick-etal-2019-learning
%X We show that it is feasible to perform entity linking by training a dual encoder (two-tower) model that encodes mentions and entities in the same dense vector space, where candidate entities are retrieved by approximate nearest neighbor search. Unlike prior work, this setup does not rely on an alias table followed by a re-ranker, and is thus the first fully learned entity retrieval model. We show that our dual encoder, trained using only anchor-text links in Wikipedia, outperforms discrete alias table and BM25 baselines, and is competitive with the best comparable results on the standard TACKBP-2010 dataset. In addition, it can retrieve candidates extremely fast, and generalizes well to a new dataset derived from Wikinews. On the modeling side, we demonstrate the dramatic value of an unsupervised negative mining algorithm for this task.
%R 10.18653/v1/K19-1049
%U https://aclanthology.org/K19-1049
%U https://doi.org/10.18653/v1/K19-1049
%P 528-537

Markdown (Informal)

[Learning Dense Representations for Entity Retrieval](https://aclanthology.org/K19-1049) (Gillick et al., CoNLL 2019)

• Learning Dense Representations for Entity Retrieval (Gillick et al., CoNLL 2019)

ACL

• Daniel Gillick, Sayali Kulkarni, Larry Lansing, Alessandro Presta, Jason Baldridge, Eugene Ie, and Diego Garcia-Olano. 2019. Learning Dense Representations for Entity Retrieval. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 528–537, Hong Kong, China. Association for Computational Linguistics.