<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="figueras-etal-2023-dynamic">
    <titleInfo>
        <title>Dynamic Stance: Modeling Discussions by Labeling the Interactions</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Blanca</namePart>
        <namePart type="family">Figueras</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Irene</namePart>
        <namePart type="family">Baucells</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Tommaso</namePart>
        <namePart type="family">Caselli</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>Findings of the Association for Computational Linguistics: EMNLP 2023</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Houda</namePart>
            <namePart type="family">Bouamor</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Juan</namePart>
            <namePart type="family">Pino</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kalika</namePart>
            <namePart type="family">Bali</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>Stance detection is an increasingly popular task that has been mainly modeled as a static task, by assigning the expressed attitude of a text toward a given topic. Such a framing presents limitations, with trained systems showing poor generalization capabilities and being strongly topic-dependent. In this work, we propose modeling stance as a dynamic task, by focusing on the interactions between a message and their replies. For this purpose, we present a new annotation scheme that enables the categorization of all kinds of textual interactions. As a result, we have created a new corpus, the Dynamic Stance Corpus (DySC), consisting of three datasets in two middle-resourced languages: Catalan and Dutch. Our data analysis further supports our modeling decisions, empirically showing differences between the annotation of stance in static and dynamic contexts. We fine-tuned a series of monolingual and multilingual models on DySC, showing portability across topics and languages.</abstract>
    <identifier type="citekey">figueras-etal-2023-dynamic</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-emnlp.432</identifier>
    <location>
        <url>https://aclanthology.org/2023.findings-emnlp.432</url>
    </location>
    <part>
        <date>2023-12</date>
        <extent unit="page">
            <start>6503</start>
            <end>6515</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Dynamic Stance: Modeling Discussions by Labeling the Interactions
%A Figueras, Blanca
%A Baucells, Irene
%A Caselli, Tommaso
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F figueras-etal-2023-dynamic
%X Stance detection is an increasingly popular task that has been mainly modeled as a static task, by assigning the expressed attitude of a text toward a given topic. Such a framing presents limitations, with trained systems showing poor generalization capabilities and being strongly topic-dependent. In this work, we propose modeling stance as a dynamic task, by focusing on the interactions between a message and their replies. For this purpose, we present a new annotation scheme that enables the categorization of all kinds of textual interactions. As a result, we have created a new corpus, the Dynamic Stance Corpus (DySC), consisting of three datasets in two middle-resourced languages: Catalan and Dutch. Our data analysis further supports our modeling decisions, empirically showing differences between the annotation of stance in static and dynamic contexts. We fine-tuned a series of monolingual and multilingual models on DySC, showing portability across topics and languages.
%R 10.18653/v1/2023.findings-emnlp.432
%U https://aclanthology.org/2023.findings-emnlp.432
%U https://doi.org/10.18653/v1/2023.findings-emnlp.432
%P 6503-6515

Markdown (Informal)

[Dynamic Stance: Modeling Discussions by Labeling the Interactions](https://aclanthology.org/2023.findings-emnlp.432) (Figueras et al., Findings 2023)

• Dynamic Stance: Modeling Discussions by Labeling the Interactions (Figueras et al., Findings 2023)

ACL

• Blanca Figueras, Irene Baucells, and Tommaso Caselli. 2023. Dynamic Stance: Modeling Discussions by Labeling the Interactions. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6503–6515, Singapore. Association for Computational Linguistics.