<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="arora-2026-copol">
    <titleInfo>
        <title>CoPol at SemEval-2026 Task 9: Modeling Polarization Type Co-occurrence with Label Correlation Networks</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Pushkar</namePart>
        <namePart type="family">Arora</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 20th International Workshop on Semantic Evaluation (2026)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Kochmar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Debanjan</namePart>
            <namePart type="family">Ghosh</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kai</namePart>
            <namePart type="family">North</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mamoru</namePart>
            <namePart type="family">Komachi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">San Diego, California, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-414-9</identifier>
    </relatedItem>
    <abstract>POLAR-LDA is a label-dependency–aware system for SemEval-2026 Task 9 (multi-label polarization type classification) that augments an mDeBERTa-v3-base encoder with a Label Correlation Network (language-specific directed co-occurrence matrices + GAT), Asymmetric Loss tuned for extreme positive scarcity, and a language-grouped ensemble. The system scores 0.567 macro F1 across 22 languages (range 0.784 Hindi — 0.256 Italian) and shows clear ablation gains (ASL +0.041, LCN +0.030, ensemble +0.018). Key findings: absolute data voids (0–1 positive examples) form an unrecoverable floor for supervised learning; label co-occurrence is culturally situated (e.g., political\ensuremathłeftrightarrowreligious in Indic vs. political\ensuremathłeftrightarrowracial in some Western languages) and benefits from language-specific graphs; and per-label training volume predicts cross-lingual performance better than linguistic family. Limitations are honest and important: noisy AL estimates under scarcity, an incoherent residual “other” category, and domain mismatch between pretraining corpora and polarization discourse. Overall, the paper offers a strong shared-task system and useful empirical diagnostics—practical and well-executed, but incrementally novel methodologicall</abstract>
    <identifier type="citekey">arora-2026-copol</identifier>
    <location>
        <url>https://aclanthology.org/2026.semeval-1.443/</url>
    </location>
    <part>
        <date>2026-07</date>
        <extent unit="page">
            <start>3621</start>
            <end>3627</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T CoPol at SemEval-2026 Task 9: Modeling Polarization Type Co-occurrence with Label Correlation Networks
%A Arora, Pushkar
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F arora-2026-copol
%X POLAR-LDA is a label-dependency–aware system for SemEval-2026 Task 9 (multi-label polarization type classification) that augments an mDeBERTa-v3-base encoder with a Label Correlation Network (language-specific directed co-occurrence matrices + GAT), Asymmetric Loss tuned for extreme positive scarcity, and a language-grouped ensemble. The system scores 0.567 macro F1 across 22 languages (range 0.784 Hindi — 0.256 Italian) and shows clear ablation gains (ASL +0.041, LCN +0.030, ensemble +0.018). Key findings: absolute data voids (0–1 positive examples) form an unrecoverable floor for supervised learning; label co-occurrence is culturally situated (e.g., political\ensuremathłeftrightarrowreligious in Indic vs. political\ensuremathłeftrightarrowracial in some Western languages) and benefits from language-specific graphs; and per-label training volume predicts cross-lingual performance better than linguistic family. Limitations are honest and important: noisy AL estimates under scarcity, an incoherent residual “other” category, and domain mismatch between pretraining corpora and polarization discourse. Overall, the paper offers a strong shared-task system and useful empirical diagnostics—practical and well-executed, but incrementally novel methodologicall
%U https://aclanthology.org/2026.semeval-1.443/
%P 3621-3627

Markdown (Informal)

[CoPol at SemEval-2026 Task 9: Modeling Polarization Type Co-occurrence with Label Correlation Networks](https://aclanthology.org/2026.semeval-1.443/) (Arora, SemEval 2026)

• CoPol at SemEval-2026 Task 9: Modeling Polarization Type Co-occurrence with Label Correlation Networks (Arora, SemEval 2026)

ACL

• Pushkar Arora. 2026. CoPol at SemEval-2026 Task 9: Modeling Polarization Type Co-occurrence with Label Correlation Networks. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 3621–3627, San Diego, California, USA. Association for Computational Linguistics.