<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="reimer-etal-2023-stance">
    <titleInfo>
        <title>Stance-Aware Re-Ranking for Non-factual Comparative Queries</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Jan</namePart>
        <namePart type="given">Heinrich</namePart>
        <namePart type="family">Reimer</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Alexander</namePart>
        <namePart type="family">Bondarenko</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Maik</namePart>
        <namePart type="family">Fröbe</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Matthias</namePart>
        <namePart type="family">Hagen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 10th Workshop on Argument Mining</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Milad</namePart>
            <namePart type="family">Alshomary</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Chung-Chi</namePart>
            <namePart type="family">Chen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Smaranda</namePart>
            <namePart type="family">Muresan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Joonsuk</namePart>
            <namePart type="family">Park</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Julia</namePart>
            <namePart type="family">Romberg</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Singapore</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We propose a re-ranking approach to improve the retrieval effectiveness for non-factual comparative queries like ‘Which city is better, London or Paris?’ based on whether the results express a stance towards the comparison objects (London vs. Paris) or not. Applied to the 26 runs submitted to the Touché 2022 task on comparative argument retrieval, our stance-aware re-ranking significantly improves the retrieval effectiveness for all runs when perfect oracle-style stance labels are available. With our most effective practical stance detector based on GPT-3.5 (F₁ of 0.49 on four stance classes), our re-ranking still improves the effectiveness for all runs but only six improvements are significant. Artificially “deteriorating” the oracle-style labels, we further find that an F₁ of 0.90 for stance detection is necessary to significantly improve the retrieval effectiveness for the best run via stance-aware re-ranking.</abstract>
    <identifier type="citekey">reimer-etal-2023-stance</identifier>
    <identifier type="doi">10.18653/v1/2023.argmining-1.5</identifier>
    <location>
        <url>https://aclanthology.org/2023.argmining-1.5</url>
    </location>
    <part>
        <date>2023-12</date>
        <extent unit="page">
            <start>45</start>
            <end>51</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Stance-Aware Re-Ranking for Non-factual Comparative Queries
%A Reimer, Jan Heinrich
%A Bondarenko, Alexander
%A Fröbe, Maik
%A Hagen, Matthias
%Y Alshomary, Milad
%Y Chen, Chung-Chi
%Y Muresan, Smaranda
%Y Park, Joonsuk
%Y Romberg, Julia
%S Proceedings of the 10th Workshop on Argument Mining
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F reimer-etal-2023-stance
%X We propose a re-ranking approach to improve the retrieval effectiveness for non-factual comparative queries like ‘Which city is better, London or Paris?’ based on whether the results express a stance towards the comparison objects (London vs. Paris) or not. Applied to the 26 runs submitted to the Touché 2022 task on comparative argument retrieval, our stance-aware re-ranking significantly improves the retrieval effectiveness for all runs when perfect oracle-style stance labels are available. With our most effective practical stance detector based on GPT-3.5 (F₁ of 0.49 on four stance classes), our re-ranking still improves the effectiveness for all runs but only six improvements are significant. Artificially “deteriorating” the oracle-style labels, we further find that an F₁ of 0.90 for stance detection is necessary to significantly improve the retrieval effectiveness for the best run via stance-aware re-ranking.
%R 10.18653/v1/2023.argmining-1.5
%U https://aclanthology.org/2023.argmining-1.5
%U https://doi.org/10.18653/v1/2023.argmining-1.5
%P 45-51

Markdown (Informal)

[Stance-Aware Re-Ranking for Non-factual Comparative Queries](https://aclanthology.org/2023.argmining-1.5) (Reimer et al., ArgMining-WS 2023)

• Stance-Aware Re-Ranking for Non-factual Comparative Queries (Reimer et al., ArgMining-WS 2023)

ACL

• Jan Heinrich Reimer, Alexander Bondarenko, Maik Fröbe, and Matthias Hagen. 2023. Stance-Aware Re-Ranking for Non-factual Comparative Queries. In Proceedings of the 10th Workshop on Argument Mining, pages 45–51, Singapore. Association for Computational Linguistics.