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    <titleInfo>
        <title>Image Retrieval for Arguments Using Stance-Aware Query Expansion</title>
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    <name type="personal">
        <namePart type="given">Johannes</namePart>
        <namePart type="family">Kiesel</namePart>
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    <name type="personal">
        <namePart type="given">Nico</namePart>
        <namePart type="family">Reichenbach</namePart>
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    <name type="personal">
        <namePart type="given">Benno</namePart>
        <namePart type="family">Stein</namePart>
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    <name type="personal">
        <namePart type="given">Martin</namePart>
        <namePart type="family">Potthast</namePart>
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            <title>Proceedings of the 8th Workshop on Argument Mining</title>
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        <name type="personal">
            <namePart type="given">Khalid</namePart>
            <namePart type="family">Al-Khatib</namePart>
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        <name type="personal">
            <namePart type="given">Yufang</namePart>
            <namePart type="family">Hou</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Manfred</namePart>
            <namePart type="family">Stede</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Many forms of argumentation employ images as persuasive means, but research in argument mining has been focused on verbal argumentation so far. This paper shows how to integrate images into argument mining research, specifically into argument retrieval. By exploiting the sophisticated image representations of keyword-based image search, we propose to use semantic query expansion for both the pro and the con stance to retrieve “argumentative images” for the respective stance. Our results indicate that even simple expansions provide a strong baseline, reaching a precision@10 of 0.49 for images being (1) on-topic, (2) argumentative, and (3) on-stance. An in-depth analysis reveals a high topic dependence of the retrieval performance and shows the need to further investigate on images providing contextual information.</abstract>
    <identifier type="citekey">kiesel-etal-2021-image</identifier>
    <identifier type="doi">10.18653/v1/2021.argmining-1.4</identifier>
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        <date>2021-11</date>
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            <start>36</start>
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%0 Conference Proceedings
%T Image Retrieval for Arguments Using Stance-Aware Query Expansion
%A Kiesel, Johannes
%A Reichenbach, Nico
%A Stein, Benno
%A Potthast, Martin
%Y Al-Khatib, Khalid
%Y Hou, Yufang
%Y Stede, Manfred
%S Proceedings of the 8th Workshop on Argument Mining
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F kiesel-etal-2021-image
%X Many forms of argumentation employ images as persuasive means, but research in argument mining has been focused on verbal argumentation so far. This paper shows how to integrate images into argument mining research, specifically into argument retrieval. By exploiting the sophisticated image representations of keyword-based image search, we propose to use semantic query expansion for both the pro and the con stance to retrieve “argumentative images” for the respective stance. Our results indicate that even simple expansions provide a strong baseline, reaching a precision@10 of 0.49 for images being (1) on-topic, (2) argumentative, and (3) on-stance. An in-depth analysis reveals a high topic dependence of the retrieval performance and shows the need to further investigate on images providing contextual information.
%R 10.18653/v1/2021.argmining-1.4
%U https://aclanthology.org/2021.argmining-1.4
%U https://doi.org/10.18653/v1/2021.argmining-1.4
%P 36-45

Markdown (Informal)

[Image Retrieval for Arguments Using Stance-Aware Query Expansion](https://aclanthology.org/2021.argmining-1.4) (Kiesel et al., ArgMining 2021)

• Image Retrieval for Arguments Using Stance-Aware Query Expansion (Kiesel et al., ArgMining 2021)

ACL

• Johannes Kiesel, Nico Reichenbach, Benno Stein, and Martin Potthast. 2021. Image Retrieval for Arguments Using Stance-Aware Query Expansion. In Proceedings of the 8th Workshop on Argument Mining, pages 36–45, Punta Cana, Dominican Republic. Association for Computational Linguistics.